Taking the 'P' out of STEEP - Forecasting Geopolitical data

Future prediction is a tricky business, especially when you don't have hard data to work with.  If you're forecaster in a domain that uses lots of numbers; like a meteorologist or a demographer, then you can use statistics to help you make predictions with increasing degrees of accuracy and assurance. However, for people in softer domains - i.e. subjects that don't really lend themselves that easily to 'hard' numbers, getting a consistent baseline to make predictions from is tricky.

This is especially the case for 'Geopolitics'  - which is in itself a hard subject to a get a handle on.  Put simply, this is the study of what motivates a particular 'nation state' and how it intends to achieve its aims.  Now, this is something that's very difficult to agree on (even my rough definition here could be contentious!).  For example, how do you define what China wants right now?  You can't really, you just have to make some assumptions based on what you know about the state in question and use this to come up with a rough idea of what 'China' as a functional entity wants.

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So, often when we're trying to think long-term about the future, you not only have to form a rough classification for what constitutes a country (and what it's national interests are) but after you've solved this (!) you can start to think about what this state might do.

Using themes to understand the future

Using current futures analysis techniques to think about states can be a little tricky.  Firstly, we like to use scenarios to predict (very roughly) how a particular state will behave in a particular context.  This is kind of useful for giving a broad range of outcomes that could happen, but is highly subjective and speculative (great fun though!).  Secondly, futures analysis often tends to deal with 'themes' - i.e. trends (things that could happen in the future) tend to fit into catergories.  For example, in intelligence analysis there is a technique called 'STEEP'.  This is a way of splitting trends into a rough categories that could happen in the future.  For this, the trends are put into one of the following types.

'Social' - For example, there will be more people in the future.

'Social' - For example, there will be more people in the future.

'Technological' - New technologies will arise that drive social and economic change.

'Technological' - New technologies will arise that drive social and economic change.

Economic - The pursuit of economic opportunity will remain a significant force for progress.

Economic - The pursuit of economic opportunity will remain a significant force for progress.

Environmental - People and states will continue to need resources - food, water and energy.

Environmental - People and states will continue to need resources - food, water and energy.

Politics - State 'X' will be at 'Y' by 'Z'

Politics - State 'X' will be at 'Y' by 'Z'


As you'll see in the very brief, hypothetical examples I've given above, most of the classifications tend to discuss general concepts [Note - there are lots of alternatives and derivatives of STEEP that widen the system to think of other areas, such as military and law].  But, when you take Politics (or Geopolitics), you tend to be talking about something slightly different as you're not really talking about a particular driving force, instead you're talking about actions, choices and desired outcomes.  And when you're doing this how do you differentiate between the needs and actions of a state? Aren't they just ransom to all the other trends described in the social, technological, economic and environmental areas? 

This is a real challenge for forecasters.  Chiefly, it seems because, when you get into politics you're starting to talk about behaviour. When you deal with geopolitics you are as much thinking about likely motivations and actions that a country is going to take.  Additionally, you are also thinking about how a state acts and responds to the trends you've already described.

When doing geopolitical analysis on any country, you'll see that most of the events and trends occurring both at present and in the future for that country, relate to those points I listed above.  Being procedural about it - you could say that these are generic issues for any state and Geopolitics is really a discussion of this.

This then becomes a real challenge for your forecast, as if you're not careful you end up duplicating all the generic trends you've captured in the 'STEE' data capture and then re-drafted them in your 'Geopolitical analysis' where you have effectively duplicated your thematic analysis but in the context of individual nation states and regions.  The tell tale sign of this, is if you're ended up with a forecast that's as substantive for its geopolitical analysis as it is for its thematic, but is broadly saying the same thing you've already said but with a national focus!

So, what to do next? Is it time to take the 'P' out of STEEP?

P = Prioritization

What's the solution?  Well, it's a tricky one.  All that you can do really is be aware of your data and manage it carefully.  If you've collected all your trend data, you then need to bring it together in a way that enables you to get a clearer understanding of which countries are the most significant in the future.

To do this, you need to assign and prioritize trends and then, once all the thematic data has been collected, somehow weight the trends and potential contributors to the trends.  Doing this allows you to determine which countries are the most consistently contributing to trends in certain areas.

For example, if every trend you collect relating to rare minerals, mentions or attributes China. Then you have a key 'actor' with regard to that trend.  Similarly, if you keep learning about health trends and that the UK is the most significant source of anti-cancer research, you then have another marker to assign to that particular trend. 

You then get to a point, either in your drafting schedule or your analysis, where you are bringing together your trends and assigning meaning/ownership to them.  Doing this enables you to start thinking about who is shaping and driving these trends, and if the same names keep coming up, you can make a pretty solid recommendation that certain actors are going to be significant in the future.  For example, if the US is consistently associated with information technology development and that you collect 300 trends highlighting the role of Silicon Valley in the global knowledge based economy then you can probably say, with a degree of confidence, that the US is likely to be significant in information technology for a reasonable amount of time (perhaps 5-30 years?).

Geopolitics is important, it's just very challenging to predict and current futures methods don't really deal with human behaviour very well (to be fair, what models do?).  By being clear on the data you are collecting it, classifying it appropriately and analysing it through a measured, considered and auditable process will enable you to at least quantify some aspects of what states are doing, by looking at the outputs they produce.  This, at least, gives you some kind of rough quantitative basis for discussing the future behaviour of nation states.

And if else fails, you can always use scenarios.  Or even better, just get everyone to play 'RISK'!

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Does 'Your country need you?' Trends in Defence recruitment.

Back in 2007 I did some analysis on future trends in defence recruitment.  This was a fairly extensive and complex piece of work, but one of the main findings was:

'When an economy is struggling, Defence recruitment increases.'

This is a pretty established historical trend, generally, when things are good, less people 'enlist'.  Defence can, and does, look like a stable way of earning, with the potential to learn new skills.  However, at the same time, when the economy for a particular country is booming, the general trend is for recruitment to reduce and for retention to be harder as more people are less willing to take the risks that come with joining one of the Armed Forces.

I found this when looking at trends in defence recruitment 8 years ago.  Today, we've revisited this analysis, and using openly available data, we've produced a 'horizon scan' that outlines many of the strategic issues for Defence recruitment.  This paints a fairly nuanced (and evidence-based) picture of future recruitment and retention trends for Defence agencies.  We've used mostly UK data for this assessment, but there is probably broad read-across to other countries.  For the full map please visit here. 

Top 5 issues facing Defence recruitment out to 2045.

We've looked mostly at future strategic issues and, as the map shows, these are pretty complex, especially for a sector as unique and important as Defence.  We've assumed that national security will be prime issue in 2045 and that 'people' (as opposed to technological alternatives) will be the main means of delivering defence.  Duly caveated, our main findings are:

1.   Defence will be seen as a 'high risk' employment sector.

As warfare becomes increasingly centered around precision technology and unmanned systems, the desire to place humans in situations of conflict - where they can be harmed or killed, will probably decline.  Similarly with more and more employment opportunities available in an increasingly accessible global employment market place, the number of people willing to 'sign up' and sacrifice certain civilian freedoms for the sake of the state could decline.   Could these trends drive a future retraction in the size of scale of 'people-centered' Defence operations?  When soldiers are put in conflict situations, will they be deployed under the knowledge that they are high-risk assets and remunerated accordingly?  Or will the threats of the future still require humans to be in the center of the action, whilst conflicts are split along the lines of value and belief?

2.  'Traditional' leadership in Defence will change significantly - with a greater proportion of women leaders in place by 2045.

By 2045, there will be a significant number of senior military leaders who are female.  This will represent a significant change from the leadership of today and will probably alter policies regarding employment and support to family.  Will there be a female Chief of the Defence staff before 2045?   Will there be more recruitment for women in the armed forces in the next few years?

3.  Do people want a 'job for life' any more?

Defence is a vocation.  Many people enlist because they feel strongly that being a solider is who they are?  To many who join, Defence is a way of life and that is part of the unique experience that they value so much.  It provides meaning and kinship with people seeking to achieve excitement and 'do their bit' for the state.  But, arguably, as the state becomes less significant in people's identities, and more and more people think of employment in flexible terms will this model work for the future?  Will defence have to adapt employ people from different ages and different career stages?  Interestingly, in the UK we've already gone in that direction slightly by having a greater focus on the recruitment of reservists, who are generally made up of a broader and more diverse range of ages and skills sets.  Could this variety be increasingly prized by the Armed Forces?  At the same time, if defence budgets continue to retract moving from 2.7% of GDP today to around 2.0% by 2045, will it be possible to afford the technical experts required to support the defence systems in the future? 

 4.  How will the state provide the necessary 'duty of care' to those leaving the Armed Forces?

If someone is willing to surrender certain rights and place themselves at risk for the state, it is fair that they should receive remuneration for their sacrifices.  In the future, as we know more about the long-term consequences of such sacrifices, will the state structures be able to provide the support required for these people.  For example, in the UK, it's assumed that the National Health Service can provide all the necessary support veterans require on leaving active service.  But, with competing demands on government budgets in the future, is this an assumption too far?  If you think of the number of charities such as 'Help for Heroes' - is the welfare burden for former Defence staff really met by the state today, let alone in the future? 

5.  How does a nation state compete for the best and brightest people in a global economy?

This is quite a big issue for the future of defence recruitment.  We are all part of the global economy today and, we assume, will become even more integrated into it over the next 30 years.  For example, let's say the UK represents around 70 million people, in a global market place of around 8 billion.  So, as a 'national recruiter' the potential candidates available to you represent around 1% of the total global population (in reality of the proportion of eligible recruits within this band will be narrower still due to the recruitments of age and health).  So, as more people and more industries 'go global' where do national employers like the armed forces go for their talent?  Does this make it even more inevitable that the overall 'people' profile of the armed forces will decrease as the requirements for 'defence specific' people become more exacting with less and less people able to fill niche roles that only the state, due to its unique role of fulfilling national security, can provide?

So, to sum all of this up.  The five points above have been pulled out from our analysis to show the future issues that Defence could face in the future, specifically with regard to its people.  None of these trends are certainties, they all represent some of the big strategic issues that Defence, and many other employers will probably face over the next 30 years.



A data-driven forecast for the future of healthcare.

Back in November 2014, we produced a forecast that was entirely data derived. Unlike a lot of futures reports, this analysis is based entirely on openly available data and analysed and visualised in a manner that illustrates all of the available data used to derive judgements. We believe this is important as it means we can reduce bias in our assessments but also when we make predictions for the future we can produce quantified assessments to reflect our belief in whether they will happen or not.  We've now developed this analysis significantly and have used it to test ideas and trends in a wide variety of areas, but, if you're interested in the future of health, we've now made the analysis available here.

But, for those of you, too busy to read the actual report, the main 'Top 5' findings are detailed below [caveated appropriately!]

Top 5 trends in Healthcare 2015 - 2050.

1. Fee paying healthcare is likely to increase out to 2050.

Insight - Because of greater demand on health systems (ageing, obesity and disease), the rise of new healthcare markets and strategies (from emerging markets) and increasing technologies and medications to promote and prolong life, fully funded state-based healthcare is unlikely to be sustainable out to 2050.

Judgement - There is a probability of 0.8 that by 2050, countries like the UK will deliver a far greater proportion of their healthcare through private agencies. State-based provision is likely to become increasingly difficult because of the continued evolution of diverse healthcare demands and increasingly complex technical requirements of future treatments. By such a point, states are likely to focus on facilitating access to affordable healthcare and promoting healthier lifestyles.

2. Global obesity rates are likely to increase over the next 30 years, prompting significant initiatives to address them.

Insight - Without coordinated intervention global obesity rates are likely to increase out to 2050. Basic projections suggest that if global obesity continues at its current level, an estimated 2 billion people in a global population of 7 billion in 2013 (contrasting with 857 million from a global population of 4.5 billion in 1980), then by 2050, around 30-60% of the global population will be obese. In total numbers, if the global population reaches 9.5 billion by 2050, this will represent a range of 2.7-5.7 billion obese people.

Judgement - There is a 0.95 probability that the levels of obesity in the global population will increase from 2014-2050. This trend will be driven by higher calorie diets as lower activity levels become the global norm. However, the problem may become so significant, so quickly, that policy reforms, new technologies and medicines may provide the necessary interventions to mitigate this trend.

3. Out to 2050, states are more likely to occupy the role of facilitating healthcare access as opposed to direct provision.

Insight - Over the next 30 years, the rising cost of healthcare and the increasing diversity of technologies and medicines to promote health and prolong life will mean state-based care strategies will be increasingly costly to maintain. This is likely to lead to many countries developing less costly models to promote and facilitate access to healthcare, guaranteeing a level of access to the least well off citizens alone, whilst enabling access (through part funded and tax incentive schemes) to the majority of their citizens. However, due to the variability of national strategies and priorities, there will be considerable variation in the political attitude to toward state-based healthcare.

Judgment - There is a 0.65 Probability that governments will move to roles based on facilitating access to healthcare as opposed to being the direct provider.


4. The use of healthcare data will be increasingly important for healthcare treatments.

Insight - Out to 2050, improvements in sensor technology, data collection and increasingly available open data will drive metric collection and increasingly sophisticated trials and health strategies. Such developments will change many perceptions on the use/protection of health information and patient confidentiality.

Judgment - The use of healthcare data will increase out to 2050. It is a certainty that data (once it has been approved for confidentiality and legal consideration) will be collected and used to improve the quality of human healthcare.

5. Policies to encourage healthy behaviours and lifestyles are likely to become increasingly important.

Insight - To reduce long term health issues government and company policies are increasingly likely to promote healthy behaviors and lifestyles to reduce long term costs on industry and the state. Such strategies will be more cost effective to implement in the long term and reduce the treatment of symptoms rather than the causes. However, certain specific requirements such as the guarantee of basic security and emergency responses to save lives will remain key ‘duties of care’ that will need to be maintained.

Judgment - There is a 0.7 Probability that policies to encourage healthy behaviors will increase over the next 30 years.

Judgment - There is a 0.95 Probability that the duty of care of governments to maintain and protect the health and safety of their citizens will endure out to 2050.


As well as weighting our top five findings we've also collected some 'outliers'. These are the rare, and very low reported trends. When you get all these together they can make for interesting reading, just think, if we'd done this exercise in 2009 - where would the term 'healthcare metrics' appeared?

9 'Outlier' trends for the future of Health

1. The next pandemic may not be flu.

2. Both Japan and the EU may suffer from a shortage of trained healthcare providers in the future.

3. Long term chronic illness (such as diabetes or forms of cancer) could represent significant healthcare issues in the future.

4. Hypertension could be an increasingly significant healthcare issue.

5. The rise of counterfeit medicines and synthetic narcotics could be of potential significance to the future of human health and the pharmaceutical industry.

6. The increased use and sophistication of biomarkers could be significant for addressing future health challenges.

7. Cognitive systems that sense, act, think, feel, communicate and evolve, could be increasingly important in how we understand and improve the healthcare solutions at our disposal.

8. ‘Localisation’ and the local environment could be increasingly significant for how healthcare options are delivered to the surrounding populace.

9. A revolution in farming and agriculture could improve or alter health dynamics anywhere around the world.

Any questions?  Get in touch, at





OK.  This gets a bit technical, so it’s easiest to start with a couple of definitions first.

normative [ˈnɔːmətɪv]


  1. implying, creating, or prescribing a norm or standard, as in language normative grammar

  2. expressing value judgments or prescriptions as contrasted with stating facts normative economics

  3. of, relating to, or based on norms




  1. A slight change in position, direction, or tendency:shift in public opinion

  2. [mass noun] Astronomy the displacement of spectral lines. See also red shift.

  3. (also shift key) a key on a typewriter or computer keyboard used to switch between two sets of characters or functions, principally between lower- and upper-case letters.

  4. short for sound shift.

  5. North American the gear lever or gear-changing mechanism in a vehicle.

  6. [mass noun] Building the positioning of successive rows of bricks so that their ends do not coincide.

  7. Computing a movement of the digits of a word in a register one or more places to left or right, equivalent to multiplying or dividing the corresponding number by a power of whatever number is the base.

  8. American Football a change of position by two or more players before the ball is put into play.

So by combining these definitions and putting it very simply -  a ‘normative shift’ occurs when norms change.  So this leads to the next question…

What is, or what are, norms?

The word ‘norm’ is derived from the Latin ‘norma’ which loosely translated means ‘rule’.  Today though it’s generally used to describe a rule or belief that is generally held to be standard for a large group of people, or for the most common social attitude.  ‘Norm’s’ don’t tend to be specifically defined like rules or laws, instead they are upheld and maintained as a kind of loose understanding that everyone conforms to as acceptable.  For example, I’ve come up with the following list that could be considered modern norms for a Western society (which, reflecting my biases is probably mostly for the UK):

  1. The expectation that agents of the state (such as the civil service, police officers, politicans, military) will maintain levels of ‘good conduct’.
  2. Every person is entitled to their own ‘space’.
  3. Every person, regardless of their class, colour or creed is afforded the same level of rights.
  4. Children, ill, elderly or disabled people should receive a level of support in line with their needs to help them lead normal, healthy lives.
  5. Every person should be entitled to a healthy life.

You’ll see just listing a number of what I believe western norms to be, highlights how loose these things are. I believe these things are important, I think most other people in my rough locality would agree to them.  Say then, I widened my area – moved say from my town to the county of Oxfordshire.  Then its probably a fair estimate to say that around 80% of the population would agree with most of the statements I’ve made.  Then, if I widened things further and looked at the whole of the UK, then there would probably there would be slightly less agreement, but generally most people would hold to these, for the UK at least.

Who sets norms?

So the list above could be the sort of norms that are typical for a Western State like the UK.  Using the UK as an example, this has established institutions and a democratic system of governance.  This means that generally, there is a system of checks and balances through the rule of law and accountable government, through which the loose system of ‘norms’ are generally monitored.  This isn’t really by any kind of official analysis, more of a general awareness of what is generally held to be ‘right’.

Just how loose this is can be illustrated by looking at how norms can vary internationally.  If I took this list of agreed norms to another country, it would probably quickly expose a different logic, or at least highlight some of the assumptions behind loosely agreed ‘norms’.  For example, in India or China, norm 3, however simply it’s presented, could be an issue.  Family bonds and arguments over the protection of traditions and lifestyles could take prority in different contexts where class, caste or ethnicity and culture could be perceived to be more important to the bulk of the populace.

Internationally, norms can vary.  But, if they are loose, if they are not fully defined and specific – like a law for example, how can they be agreed?  Put simply, they can’t.  They reflect the general consensus of belief and they can change; they can change slowly or they can change quickly.  Historically there are many instances of this – for example, issues of equality – attitudes to gender or homosexuality or the acknowledgement that smoking is bad for your health, are all examples of how dominant norms have changed.  In the West, they are perhaps subject to the most scrutiny during an election.  It is at this point, where politicians spend most of their time and effort, fathoming what it actually is that people believe and what they ascribe to.  This is often when things change at there quickest, because it is at that time when the future leaders assess what the most pertiinent norms are of their people and the national mood to help their party win.

No one really sets what a norm is, we all kind of agree on something and this is where it gets really interesting.  Although it is during elections that the knowledge of norms are tested – authorities and people with power generally have to assess what is the common belief – what the common norms are.  This is generally referred to as being ‘in touch’ and this is quite an important thing for any organisation.  Increasingly, it seems that the values and norms of the leadership should generally reflect those of their workforce and vice versa.  This could account for why companies often invest a lot of time and money in developing a culture that enables every employee to understand and feel like they fit into the main vision and that they can believe in what they are part of.  Generally, if companies, or political parties even, don’t do this, or if they becoming very badly out of touch with the collective norms of their workforce change can happen.  And, for those in power, its not generally a pleasant experience.

‘Normative shifts’

A normative shift occurs when the dominant group view of something changes.  A sudden ‘watershed’ moment occurs and practices are suddenly exposed as being morally dubious or out of step with society.  Practices that yesterday were acceptable are now archaic, or worse, morally corrupt.  Usually, the conditions leading up to such an event take time to build, until they reach a kind of ‘critical mass’ or ‘tipping point’.  And often, this occurs around something that is generally a bit of a grey moral issue, which ticks along until it suddenly erupts into a real problem.

For example, consider the following situation.  The average wage in a country is, say, 20,000 Euros.  An elected representative of the state earns 80,000 Euros.  As a result, it is felt that politicans earn too much.  Sensing the mood of the populace, the ruling party caps the pay of politicians at 80,000 Euros with no pay rises for five years.  This is acknowledged and MP’s are compensated by having an expenses system that means they are afforded certain benefits; as are many other state servants.  Essentially, they are allowed support and renumeration for living around the centre of government.  This system starts out and people make their claims for travel, accomodation and food.  Over the next ten years, parliament votes again to not increase pay.  So gradually, it becomes an unwritten rule, or a norm, that instead of being well paid – (say, for example, at this point a politician’s peer in the financial sector, earns 160,000 Euros) – politicians have a generous benefits system.  People start to understand that this is where extra money is made available to them and they spend it.  And gradually, year by year the claims get more extreme and outrageous.  Then suddenly, almost overnight, there is a recession.  MP’s are still earning 80,000 Euros but the average wage is now 15,000 Euros.  Suddenly, what a politician held as being cheap is a lot of money and what they are claiming as a benefit becomes a source of outrage to their electorate.  Everyone claiming expenses is now a subject of scrutiny.  It is at that point that the context has changed – a normative shift has occurred and the leadership is now exposed to scrutiny.

In the past, before the days of open governance, the standards of norms would have been enforced and maintained behind closed doors.  Often, if society became too unhappy, there would usually be a crack down, and this would generally be done at the expense of two or three of the worst offending individuals in the group, who would be ‘made an example’ of.  So, back in the day (and let’s say the day is up to around 2005), when a ‘normative shift’ occurred, the response was generally concieved behind closed doors with power structures generally policing themselves.

So what?

What I’ve described above isn’t new.  It’s just a description of how change happens.  But, if you look at the current trends of big data and the increasing importance of open data, coupled with the increased speed with which people access and demand information it presents some significant challenges for many institutions.  The expectation for institutions to remain ‘in touch’ will remain – presenting a number of difficulties especially if norms become increasingly diffuse and complex and the public expectation continues to demand quicker and quicker resolutions.  It will also be increasingly difficult for power structures to ‘self-police’ as the public demand and expectation for pertinent information around accountability rises.

In the future it will be increasingly important for institutions to illustrate both how they ‘police’ themselves morally and to keep pace with public norms.  Private companies will be held to task by share price – which is likely to be affected by adverse publicity if bad practices are highlighted.  Government decision making is likely to continue to come under constantly increasingly political and public scrutiny.   Thankfully, systems of democracy, will generally have the robustness to cope with this greater demand for transparency and accountability.  However, it is likely that closed decisions made within the depths of power structures – either for security or commercial reasons – will be increasingly contested.





‘Wolf Hall’ started on the BBC back in January. For those of you that don’t know about it (especially those not from the UK), it’s a book written by Hillary Mantel about a man called Thomas Cromwell. Often seen as a shadowy character in history, Mantell’s book brings life to a complex man during his period as an adviser to Henry VIII. This was a dangerous, uncertain period between 1530-1540 that saw England change significantly, breaking away from the Catholic Church and resulted in many Lords and Ladies being sent to the Tower of London. During such times, people around Henry VIII could literally end up with their heads on ‘the block’.

To survive as long as he did, Cromwell demonstrated two key characteristics; firstly he was a keen analyst of constitutional law, the functioning of the church and the state. He would do his research and he valued reason and evidence. But, at the same time, his other skill, which was perhaps more important for keeping him alive, was his quality as an advocate. (Please note – when I write about advocacy here, its in Cromwell’s context, which was when the role of an advocate, wasn’t really about achieving positive change, which it is often associated with today)



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Advocacy, tearing at the teeth of evidence

Both analysis and advocacy are valued into today’s modern working environment (which, thankfully, isn’t quite as ruthless as it was in Cromwell’s time). Whatever your role, or workplace, you may often find yourself having to balance these two skills, which can be a challenge as they can sometimes feel like forces in direct competition with each other.

For example, think the following scenario. Your company CEO is a white, 65 year old man. He has some ideas about the current marketing campaign to target 15-20 year olds. He wants to put an advert in ‘Smash hits’ magazine, because that’s what he remembers his grown up daughter reading when she was a teenager. Now you are pretty certain, that both A) smash hits isn’t around anymore and B) print magazines might not be the best way of reaching the core demographic.

So what do you do next? Do you:

A) Collect the evidence to present to the CEO that his initial ideas might be a little out of whack with market demographics, whilst highlighting an alternative strategy to ‘Smash Hits’ as print-based advertising source.


B) Research print publications similar to smash hits and present the CEO with a list of options based on his initial direction.

The answer to this depends both on what you value as an individual and the characteristics of the CEO. If the CEO is a reasoned, measured person, you’d probably be happy to go with A. However, if the CEO is more of a Henry VIII character i.e. a tyrant, you may find yourself wanting to go with B. In such a circumstance, it’s probably less risky to play the role of the committed 15th century advocate, delivering calmly and efficiently on what the CEO wanted (despite your possible reservations on the task).


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The value of ‘playing the advocate’

At its simplest sense, advocacy is the process through which an individual or group influences how decisions are made within an institution. As institutions can be highly complex, contained systems in their own right, an individual who can chart and understand all the people, processes and rules required to push a particular policy, or action, through to completion can be extremely valuable.

Think about your performance reviews – how much has your ability to deliver what senior management wanted been one of the key things you’ve been assessed on? A lot of the time, in our lives we will play the role of advocate and we can often make some ‘high stakes’ gains because of it. This was the case with Cromwell, despite being ‘common-born’ his advocacy meant he was able to deliver the particular things, his ‘master’ Henry VIII wanted, in doing so he rose to be one of the wealthiest and influential of kings advisers (SPOILER ALERT – This worked until he introduced Henry to Anne of Cleaves!)

But….here’s the rub. The conditions that enabled skilled advocates to thrive in the Middle Ages weren’t exactly brilliant. Today, thankfully, transparency is valued more than tyranny and as a result; the scrutiny for how an advocate delivers a particular outcome is getting greater and greater. How accountable are the processes an advocate follows? This is where, we are increasingly looking to ‘evidence-based’ decision making to help, and this is exhibited by the rational, analytical side of Cromwell’s personality.

The importance of evidence

Evidence is information that supports or challenges an argument. As a lawyer, this is something Cromwell was skilled at. By collecting and using evidence around issues he highlighted the value of analysis. Today, evidence based analysis is becoming more and more important as both the range of data at our disposal increases and the tools for using it to inform decisions improve. Once gathered, evidence can be used to illustrate our knowledge of a particular issue and to highlight the logic for making our decisions. By providing our evidence, by showing our working, we increase out accountability. If someone is asked on what basis they have made a decision or formed an idea, they can point to their evidence and the person scrutinising it can use this a basis to form their own view.

To go back to the ‘Smash hits’ scenario, if asked why the CEO had picked that particular magazine, and he had given the answer, ‘My Daughter read it in the 90′s’ might have provoked him to gather a bit more data, or at least give a bit more thought about his initial idea.

Yet this is generally the challenge for the advocate. How do they deliver the change a senior leader wants, when it flies in the face of the evidence at their disposal?

In Cromwell’s time, being an advocate involved a high level of personal loyalty and tenacity to deliver exactly what his master wanted. A lot his time and energy went into trying to read and predict what Henry VIII actually did want! He trod this tightrope every day, using his extensive knowledge and personal contacts to make sure his promises to the King were delivered successfully. But, today, both the values of wider society and the greater availability and public expectation for data makes it harder to simply deliver what ‘the master’ wants, especially if it isn’t based on some form of evidence or reasoned decision making.

To that end, what, or who, will we value in the future – the loyalty of the advocate who gets things done at all cost, or the analyst who values the use of evidence to make an informed decision?

I can’t answer this, but as we understand and value openness more and more – it’s an interesting tension to experience. Plus, it’s nice to know that it isn’t treasonous to discuss such things anymore; the tower’s just for tourists these days, isn’t it?





When I worked on the Global Strategic Trends programme one of the things that got the analysts most excited (aside from the prospect of free sandwiches in a meeting) was the ‘Scan of scans’.  In many ways, this is the holy grail for Futures analysis, as such a thing enables an analyst or decision maker to capture the strategic high ground.  In a nutshell, understanding what everyone else is saying about the future, gives you a more powerful starting point to make your own predictions about what may or may not happen in the future.  That’s the theory in a nutshell, but bear with me while I unpack it a little more in a brief technical history.

The ‘scan of scans’ is, effectively, an aggregate forecast.  This means its derived from all the hard work and analysis that has gone into other assessments.   Such techniques were pioneered by analysts like Phil Tetlock, who from 1989 collected around 27,500 forecasts and focused on using these to improve expert judgements (there is loads more on his work documented here in this excellent piece by Tim Hartnett in the Financial Times).  Aggregate forecasts are often used in many areas of quantifiable prediction, such as meteorology and earthquake prediction and, statistically, they generally produce more accurate forecasts.


How are aggregate forecasts used in the futures industry?

The strange thing is, probably, because of the difficulty of making specific/accurate predictions about future trends that relate to human behaviour, we don’t at present, tend to produce aggregate forecasts for long term futures analysis/horizon scanning.  There are a few reasons for this:  Firstly, we tend to focus on scenario-planning and ideation.  A lot of the tools and techniques that are used in the futures industry today are about generating ideas.  In a way these work really well – we have techniques that enable us to get interesting insights from large groups of experts.  But, at best, they represent methods of assessing the crowd consensus of  small groups of distinguished experts and peers.  Such a practice isn’t wrong, but, it has a place – generally perhaps at the beginning of the project, when you wish to sample expert opinion in order to build the research for your forecast (i.e. when you want to fully understand the problem/question your analysis is trying to address).

Secondly, there is the practical aspect.  Once you’ve amassed a lot of data, what do you do with it? For example, an archive of 27,500 forecasts will contain datasets describing expert judgements, predictions scenarios and models.  How do you standardise such varied data?  How do you derive some kind of meaning from such rich and varied datasets?

These are questions that myself and many forecasters have grappled with and the truth of it is to address them, you need to know what your context is?  What is the idea that you’re testing, what is your hypothesis, what outcome or risk are you betting on? Once you know this, you can go to your dataset and start to extract what you want to know? Knowing this you then need to derive some kind of meaning from all the previous scans and assessments that you’ve made. Back in the 1990’s (when Phil Tetlock first started to amass his archive) this would have been tricky.  But today it’s slightly easier thanks to the abundance of low cost computing and vast data storage.  Now, thanks to data science, cheap data storage and more and more data visualisation tools, you can work through all these many sources, and make assessments from them.

How do you produce a scan of scans?

At Simplexity Analysis we’ve developed a range of techniques to produce aggregate forecasts by combining machine processes with human analysis.  We essentially take forecasts, chop them up into strings and then look at the most frequently occurring instances of words – these are then put into ‘knowledge maps’, which, through iterations of systems analysis (balancing levels of machine and human interpretation), are tidied to gradually give us a clearer picture of the knowledge represented in these forecasts.  Put simply, we produce a sample of what is being reported and use the metrics derived from this processes as a basis for our predictions.  For these we give specific probabilities to reflect, categorically, our belief in the likelihood of a specific trend or outcome occurring.  Then, using a host of potential data visualisation tools we can display the data and the metrics we have used to produce our assessment in complex but explorable formats.


This form of sampling is a way of mining the rich and varied data contained in the abundance of forecasts published.  What’s really cool is that by sampling this way we can tell two things:  Firstly, what everyone else is saying – which is a useful place to start any assessment.  Secondly, we can pick up new ‘signals’ that are being reported.  For example, by selecting for the least reported trends – we can get an idea of potential outliers, new low frequency ideas that could represent the big idea of tomorrow.  For example, how many futures reports from 2009 mentioned the term ‘big data’?

Producing aggregate forecasts offers a new ways of accessing and predicting future trends.  For such forecasts to work most effectively, they need large data sets. The larger the dataset we sample from, the greater the confidence we can have in the insights we generate.  The great thing is, at present the range of data available, through online archives, social media, academic papers and open data means ‘big data’ is getting bigger every day, further increasing the scale and magnitude and therefore, hopefully the accuracy of the ‘scan of scans.’

Evolving ‘Futurology’

We’re not saying our processes our perfect.  They evolve and are tested as our predictions are either shown to be accurate or otherwise.  But, we believe, implementing clear, testable systems of prediction, are an important step in the evolution of ‘futures’ as a discipline.  In order to move away from non-specific, ideation techniques that don’t really produce clear, accountable predictions, we need to develop processes that quantify qualitative data and give clear, testable insights.  Because if we as futurists/futurologists/futures analysts are not accountable to our predictions, then what’s the point of making them?





Do we still value taking the time to write our findings succinctly and accurately? Current trends suggest you shouldn’t worry about this too much. You should just get an idea out there.

And there’s a lot to be said about this – posting an idea can be rewarding and it can a notion or a meme to evolve into something that is genuinely interesting/perfect/popular. But, is this always the outcome and, should things be left sometimes before you share them?  What happened to the notion of making something as good as it can possible be before you shared it?  Are there too many great pieces of analysis or writing, laboring in the bottom of the drawer or filing cabinet somewhere that means, it is just too dangerous not to share.  Everything we write is right, right?

At present, we tend to focus on output – keep sharing, keep generating.  The blog scene and the memesphere, means that the rapidity of spreading ideas leads people to rapid fire more ideas…this means we are growing constantly, we are flooding ourselves with pictures of cats, tweets and vlogs.  Text, when written, is generally becoming shorter and more rapid.  When we have an idea, we want it out there, just in the hope that it will be so compelling, so brilliant, it will be virulent.

The amount you produce, if not key, is certainly seen as important in social media and the big data world in general.  The number of comments, the number of followers, the number of likes – all these things determine popularity and these in turn convey impact or influence.  But do they mean quality?

Influencing, or achieving, quality generally takes time.  What I’ve learnt in drafting is that there are reasons why we often write sub-optimal products:

1. The writer is confused about what they’re trying to achieve.

2. The writer is lazy and not really thought about what they’re trying to achieve yet writes it anyway.

3. The writer is impatient – they can’t wait to get their product ‘out there’ so publishes what should have been a draft.

What does ‘shipping it’ mean for the civil service?

Getting ideas out there, perhaps with lower standards of quality makes organizations nervous.  When you have developed and maintained a reputation, how do you work at this pace?  Do you want work at the this pace?  The reality is, you probably can’t, nor do you want to.  Take for example, a function like the civil service, which in the UK has the following purpose:

The Civil Service helps the government of the day develop and implement its policies as effectively as possible.

And it does this, in a variety of different activities.  But, traditionally, it has undertaken one very important role in helping the government of the day and that is as serving as a secretariat.  In this function it serves to administrate and record everything that is discussed and determined in government and has developed accomplished processes for doing so.  For example, going back as far as 1942, Sir Edward Bridges (as Cabinet Secretary) described that the following instructions documenting Cabinet meetings and recording them in a format that was:

(a)  Brief

(b)  Self-contained

(c)   In the main, unpersonal

(d)  To the full extent discussion allows – decisive.

This kind of style was encouraged (and possibly is still encouraged) in the civil service.  The discipline of taking notes means that you’re supposed to produce outputs that aren’t overlong, windy or personal.  Such practices do convey a culture that safeguards and policies a degree of quality.  Traditionally ‘draftsmanship’ was valued as a skill; the ability to produce well-written, crisp, succinct prose still is much in demand.

But, in helping the government to function as ‘effectively of possible’ internal drafting takes on a certain style, both in terms of how its written, but also in what its saying.  The art of drafting internal reports can be a challenge and often requires a kind of pragmatic diplomacy that’s needed to move things forward.  This is something that secretariat function can do well. It holds and produces records and outputs.  The note takers are often the only way of determining what was actually agreed upon in a meeting.  This can even lead to a form of internal coding that means a full-on blazing argument is recorded in the official notes as a ‘full and frank discussion.’  In such circumstances the scribe, has a challenging task – they have to make sense of what happened for the official record, but also, perhaps protect their own skin by not actually writing what was said, but what was meant to have been said…

‘And so while the great ones depart to their dinner,

The secretary stays growing thinner and thinner,

Racking his brains to recall and report,

What he thinks that they think they ought to have thought.’

[Taken from Masters and Commanders by Andrew Roberts]

Such ‘Draftsmanship’ clearly encompasses many skills and values, but is it still prized by the civil service?  To still maintain such a ‘transformative function’ in the face of increasing demands to open up, is perhaps one of the biggest challenges the civil service faces in the future.  Can it maintain a stewardship role and ensure government records are kept honestly and disclosed openly yet smooth over situations where ‘full and frank’ debates have occurred to such an extent that there is no clear way to proceed?  Is it possible to be fully ‘open’ whilst it is working hard to overcome both personal and political frictions it has to address in order to keep the country functioning?

In the quest to be open, would it be a risk for an organisation that has been founded to help government function to publish or share early drafts or real accounts of meetings?  It probably would be.  Such an act could be seen as irresponsible, perhaps even dangerous and it would probably upset the smooth running of things.  In such a culture ‘shipping’ nascent ideas, or draft notions, just represents too much of a risk.  But will it always be so?

The Future is Open

There are signs that things are changing, the UK government is trying to open up and classification is being simplified.  With advances in big data, there are increasing initiatives to share public data, and the notion of what can be shared is improving.  Pioneering ideas like thinkl and the policy design lab are establishing places where large amounts of data can be shared and dialogues can be had in public.  Also, and perhaps most importantly, they are leading practices that encourage the release of data and trying to understand the implications such practices have for our present values.  By setting up such research spaces with large amounts of government data, such initiatives can show the utility and benefit of sharing information and enable policy ideas to be tested in public.

Such initiatives are crucial if they are to address the perceptions of a shadowy culture of ‘draftsmanship’ that is seen to haunt many systems of power.  Those who hold the pen, control the word, but is this changing?  Is policy making becoming increasingly ‘live’ and subject to the opinions of more and more people as it formed?  This is something we’re exploring through our work at Simplexity.  We’re looking forward to the debates such issues will have in the future as we try to understand what should and shouldn’t be shared.  Such debate will allow us to have real ‘full and frank’ discussions, as opposed to arguments behind closed doors.






‘Open data’ and how we use it, could be one of the most important debates of our time.  How much should a government or a company be ‘open’?  Is transparency a good thing?  What data does an organisation have and what should it share?   What is the value of my old data and should I just give it away?  These are the sort of questions a lot of people, in all sectors are asking themselves right now.

What is open data?

As a concept, Open data is related to big data.  Professor Nigel Shadbolt of the Open Data Institute, has written about its transformative power and recently defined it as ‘Information that is available for anyone to use, for any purpose at no cost and licensed as open data’.

Historically, there are examples of where information has been released (for free) to the general public, and its free release has, generally, driven positive technological, societal and political developments.  For example, back in the 19th century Florence Nightingales research on medical care for injured soldiers from the Crimean War was openly published and analysed, illustrating how a large number of instances of mortality were due to preventable diseases as opposed to deaths from their wounds.  In this example, official data was published and it achieved positive change – existing policy was challenged, using a quantitative model of openly available data, and it was altered as a result, resulting in better medical practices.

Today, there are many new examples of online initiaitives that are using open data for driving positive change.  For example, prescribinganlaytics.comcollects and collates data on NHS drug usage and makes prescription costs available online.  Data made available through this initiative showed that the prices of statins (the medication used to control cholesterol levels) varied considerably between different NHS authorities, because of different policies on procuring either licensed or generic versions of the same drug.  This analysis alone identified £200 million of savings by highlighting how different regional policies are set around spending.  It was estimated that around £1.4bn prescription efficiency could be found in the UK NHS using this data.

At the same time, initiatives like #bluelighthack work to promote a greater sharing and publication of police data; every month different police authorities publish data on particular types of crime types.  The collection and analysis of this data is already beginning to influence insurance premiums and can shape community discussions with local law enforcement.  Different police forces are using applications and systems developed outside ‘official’ government procured systems and are experiencing immediate effects in assessing where crimes occur and working to prevent them.

Such initiatives are strong positive examples of how open data can help achieve positive change.  But, it is worth noting that all of the examples above focus on government data and are generally from departments with a strong social remit (such as health, or policing).  Such areas have close contact with people and they can practice and implement policies that quickly impact on individuals and communities.  But, in other areas of government, and also, within industry,  how much of an ideal is ‘open data’ and what are the challenges it faces – do all organisations wish to disclose their data, will it benefit everyone?  If you’re a ‘closed system’, is it your interest to open up, or is it a naive, perhaps even dangerous, precident that could ruin your company?

‘Closed systems’

The scientific definition of a ‘closed system’ is a physical system that does not allow physical transfers (such as mass or energy) in or out.  These are useful in chemistry or physics because they provide sealed, self-contained systems that can be used to test theories and are important in the development in the development of theories like thermodynamics and chemistry.  They’re also seen in biology, where a closed system that has the right balance of plants, water, light and animal life produces a microcosm, that can function and sustain itself in a kind of self-contained system of balance.

Another definition of ‘closed’ comes from computing, where ‘closed source’ software is applied to computer code that is produced and, as an opposite to ‘open source’ computing, is kept secret and protected from ‘public release’.

Many businesses and government departments, are is in some senses ‘closed systems’.   Due to the tasks they perform, or the business they conduct, they keep their data contained, sealed within systems of classification and intellectual property protection. Historically, there are a number of reasons why such systems have evolved to protect and safeguard data.

  • Security.  Governments and some companies classify their data based on security.  The history of our times has driven this; in the past 50 years as we’ve gone through the Cold War and subsequently, the Global War on Terror, we’ve maintained strong cultures for being ‘closed’ because of the importance of national security.   National security extends across the full range of activities, from the protection of the ‘Nation State’, through to an individual’s safety and their rights.  With regard to security, if somehow all the data a government had, was suddenly released real, physical harm would be caused.
  • Value.  A lot of data and information is extremely valuable.  Software code is an example of this but we can all think of many others – blue prints, recipes,  unpublished manuscripts etc.  These are all instances of pieces of data and information that are commercially valuable.  Increasingly, where value actually resides, is not actually in produced products, but in the ideas that enable the formation of these products.  This means the release of such data unofficially or its theft is, quite simply, a criminal act that devalues someone’s product.
  • Reputation.  The reality of history and changing societal values means that sometimes, institutions hold data and information that could, perhaps reflect badly on our values today. This is really an issue for institutions over a certain age.  Governments for example, declassify documents after a certain amount of time – say 25 years, these are often heavily censored as well (arguments for censorship in the ‘national interest’ are remain an on-going source of controversy).  Older businesses also seek to protect their histories – for example, how does a company maintain its behaviours when, fifty years ago, it was trying to reflect the beliefs of the average ‘person’ in the street?  Through today’s lens, such beliefs will probably appear sexist, rascist and homophobic.  How does a company handle having data that reflects such values, does it disclose or keep it locked up tight so no-one can ever find out?

Does the notion of open data represent a challenge to such closed systems?  In a way, yes, it does.  Releasing data openly, means that governments and companies need to give up a level of control.  And this is a debate is driving a real and present trend to release information, especially in western democracies and many companies.  In the UK, since the Freedom of Information act in 2001, and through other initiatives around the world – the state is being increasingly expected to disclose information, in some ways, the notion of open data is the next evolution of this.  At the same time organisations that traditionally consist of large, closed systems like Glaxosmithkline for example, have been altering their disclosure policies and publishing their data on clinical trials openly since 2012. Such initiatives, are perhaps forward leaning – for many organisations and government departments there is probably apetite for opening up but a lot of people, especially those who traditionally ascribe to the reasons described above for protecting information, probably question the value, especially with the associated risks of ‘showing dirty washing’ in public, regardless of how old the washing is.

4 reasons why closed systems should ‘open up’?

1.  Openness encourages discussion.  Full disclosure of data means that everything comes out at once.  This could be uncomfortable, but for an old department, is it better to be open and honest about the past, rather than treat it with nervousness and attempt to conceal it?  Such attempts generally cause suspicion and accusations of conspiracy.  They also call for leadership that is strong enough to acknowledge the values of the past and discuss them in an honest manner that acknowledges openly that ‘things were different’ then. In doing so the delivery of such data should prompt sympathetic discussions and debates – are things as simple as an ‘apology’ on one hand, or ‘denial’ or the another?  Can everything an organisation has faced be boiled down to a basic, binary ‘black or white’, ‘right or wrong’ argument, or is more complex than that?  Going back to our history, being open on the things we have learned and, the mistakes we’ve made, is perhaps uncomfortable, but it does enable both us as people and the organisations we work for to develop.

2.  It can be difficult to ‘open up’, but any data helps.  Full disclosure may be naive, but it’s important to remember and support organisations that are disclosing any data and also reflect on the fact that a lack of data, doesn’t always equate to conspiracy.  For example, a young department with a positive remit like the Department of Energy and Climate Change (established in 2008) can realise a lot of data quickly, and this will have a lot of positive social impacts.  An older department like the Ministry of Defence, faces a lot more issues in disclosure.  As a department it holds vast archives of secure information often going back over hundreds of years.  The Army for instance has a ‘corporate memory’ that’s almost 500 years old!  This data is also contained in a myriad of forms in a bewildering range of locations.  How such departments can actually ‘know’ what data they have is a significant challenge, let alone how they could convert it into a format that they could disclose.  Such a difficulty is not an excuse for non-disclosure, but it is a challenge.  In such circumstances, acknowledging where information is and perhaps promoting it to others who can make it available for public analysis does offer a way of opening up such old records and get the data flowing.

3.  Open data increases the association with the ‘real world’.  Security is paramount and it remains one of the strongest arguments for keeping certain systems closed – ‘national security’ is one of historic reasons why our governments formed in the first place.  But, we, ‘the people’ are generally singular in our concerns and see security in a different way to governments.  We worry about human issues – like our personal security, health and our families and, generally we can cope with the idea of ‘one big threat’.  Perhaps this is something we’ve grown accustomed to since World War II.  But a large organisation or government has to function on a number of layers and cope with a multitude of demands from a plethora of departments and sources.  Generally, such demands have made it logical to form closed systems with large, well-organised hierarchies sustaining silos and systems of classification.  But, as voters ask for more and more details from their leaders (generally relating to their interests or their rights) and more and more organisations seek to share data, could openess actually improve things? By being clearer on what needs to be classified and secure and what can be open, would things be easier?   Perhaps in the future we will work to a simpler ‘security’, ‘commercial interest’ ‘everything else’ method of classification which means people can more easily request data from governments, but also at the same time governments and organisations could be clearer on what they need to keep in-house and what they can reliably take from the open source marketplace.

4. Openness promotes rational debate.  An often quoted goal for policy makers and politicians is that policy should be ‘evidence based’.  This is admirable and hopefully making data more freely available and analysing it rationally could actually improve things for decision makers.  As was the case with Florence Nightingale’s data – if you collect, group and analyse real information, it can show what policies are working and what policies need changing.  If we went in this direction, could it lead to us judge our leaders and decision makers not on their appearances or TV performances, but instead on the real outputs of their choices and decisions?  Additionally, if our leaders continue to be judged predominantly on their personalities and other emotionally-focused measures as opposed to quantifiable metrics, won’t it be inevitable that we wish to keep our systems closed in order to protect our collective reputation?

What we should share and what we should classify will continue to be debated.  In the meantime, if we recognise that there are a variety of positive reasons for closed systems to ‘open up’ and considerable benefits for them doing so, then perhaps we’ll start to form policies and make decisions based on what’s rational and quantifiable.  Does it really matter what someone looks like when they are eating a bacon sandwich?





I’ve worked as a futures analyst for almost ten years now and six years of that time was spent on the Global Strategic Trends Programme for the UK Ministry of Defence.  During this time, futures analysis (or horizon scanning as its sometimes called in the UK) has seen a lot of change and it’s probably fair to say that as a discipline, we know more today, than back when I started working on it for the first time in 2005.

I’ve focused this blog on mistakes I’ve made; which is perhaps a little crude.  In reality, these are learning points.  At the time we didn’t know these things weren’t necessarily the best way of doing things, it was how it was.  Now, with advances like big data and the growing sophistication of analysis tools and techniques futures analysis is improving.  But, to improve you have to make mistakes; trial and error is difficult sometimes, but worthwhile, as long as you learn from it.  So here are more top six mistakes (believe me, I’ve got more but it seemed wise to stop at six!) but also some thoughts for what it means for the future of futures analysis.

1.    Not being an organized ‘fox’

Experts are important for making predictions.  Experts have a lot of knowledge about certain things (that’s why they’re called experts!).  The use of experts is covered extensively in the ‘Signal and the Noise’ by Nate Silver, who works on the established analogy of ‘hedgehogs’ and ‘foxes’.  In this, experts are seen as hedgehogs; they have one approach to an issue (like the spikes of a hedgehog, represent its one tactic – a ‘prickly’ defence).  Foxes on the other hand are generalists, in contrast to hedgehogs, the analogy states that the fox doesn’t really go for one deep specialisation, instead it has a lot of little bits of knowledge about things.

Remembering this analogy is useful and it helps futures analysts frame how they should engage with experts and what they actually do themselves.  Futures analysts need to keep track of lots of small pieces of information about what could happen in the future.  This sounds simple but in reality it can be a daunting task.  With the bewildering array of information sources and experts out there in the world, a fox needs to be more and more organized, keeping tabs on these things so that they can be referenced in a meaningful way in order to make a balanced assessment.  One thing a futures analyst doesn’t need to be is a deep expert, or hedgehog.  They need to be aware of all the little things, all the very many threads of possibilities and then be able to quantify them and bring them together into assessment.  (I’m tempted to write, ‘spin them into gold’ but that would be wrong for a whole host other reasons, which I’ll get to later.)

2.  Confusing power with wisdom

Futures analysts will often find themselves rating ideas, or hypotheses on the future.  Such rating exercises can relate to sources (is a peer reviewed academic paper more reliable than a fringe discussion site?) but, more delicately, they can also relate to people.  If you’re making an assessment on a completely new area then how do you determine who the experts are when you are a complete novice to the field?  For this you need to look the data institutions generate and rate their reliability and accuracy.  Similarly for experts you need to study their outputs and assess the arguments they’ve made on particular issues.

When assessing experts you need to be clear on what you’re looking for.  Fame and status of an individual or an institution that has a lot of public influence doesn’t not mean that they will be better, occasionally it can prove the opposite.  This is especially the case for experts from large, powerful organisations.  An individual who holds a lot of weight strategically, or in the day to day operations of business or government department, generally doesn’t have a greater knowledge about what could happen in the future even if they do have a higher rank or position.  If anything they are less likely to know about what’s happening as they are unlikely to need to keep lots of little pieces of data and instead, have people to do this for them – be they a General or a University Professor, this tends to hold.  If assessments are tailored to the beliefs of such groups, (or worse the what a group believes these people want to hear) then they quickly becomes skewed and biased.    

3. Fear of reputational damage

Forecasts can take you into some strange areas.  I was once cautioned for not taking my job seriously because I came up with a hypothesis relating Micheal Jackson’s death to a break down of critical infrastructure (my hypothesis centred on the impacts a shock social event, like the death of a cultural icon, could have on communication structures).  Even discussing such an issue was seen as ‘ridiculous’ and by labeling it as so, we started to limit ourselves.  This was at a time, when an individual reputation was considered paramount to their future career; people were actively discouraged from to exploring ‘blue sky ideas’ for fear of committing career suicide or because of the perception that everyone around them was some kind of arbiter of standards.  In the civil service especially, for every person with ideas, there are another three sucking their teeth and saying ‘hmmm…I can just see the Daily Mail headline now.’

Such overriding concerns can mean people become pre-occupied with ‘face’.  Nobody wants to be the one that looks stupid.  No-one is prepared to say something that is outside the norm, for fear that they look like the ‘departmental jester’.   Such fear plays out constantly and I suspect most futures analysts have fallen foul of it at some point (they find themselves questioning their assessment – ‘should I write that, can I say that?’).  If an organization gets it wrong, the assessment, or worse the author is labelled as outlandish and they can be marginalized, or worse ridiculed, for their ideas.  This is very wrong; if the analyst has an idea, that they can illustrate with a hypothesis, data and an outline prediction for how it can happen then, at the very least, their ideas deserve to be heard.  To dismiss it unheard, or worse, use such assessments as ‘evidence’ that the futures assessment is nonsense is very bad but often a sad reality of how strategy formulation works.

When it’s even worse, is when futures analysis as a discipline is treated with the same level of ridicule. As a discipline, futures analysis or ‘horizon scanning’ is often seen as the poorer relative of intelligence analysis.  People often dismiss ‘futurology’ as non-quantitative hand-waving and such an attitude means many powerful individuals see it as nonsense or a waste of time.  The attitude of ‘crystal ball gazing’ in the popular press further reiterates this.   Could such, reputational concern, be a driving force for why many government departments seek to keep such activity at ‘arms length’ from ‘proper’ policy?

4.  Avoiding probability and predictions

Sometimes, perhaps because of some of the other mistakes listed here, there is often a desire to avoid specific predictions and an overall reluctance to use probabilities.  The use of probabilities and the desire to avoid making a specific, ‘x will happen by y, with a likelihood of z’ prediction, is often avoided.  This is often because of the factors I described above – a prediction, in some senses is a benchmark to which a forecaster can be held accountable.  If people are worried that they might look stupid or they think the assessment might reflect badly on the institution they work for you can see why there is reticence to give specifics or quantifiable metrics.  But, if you don’t use probabilities or make specific assessments then how do test yourself?  How do use a consistent, non-subjective benchmark for arguments, ideas and predictions?

There is a mistrust of probability amongst many managers and decision makers.  In my experience, it comes out when someone quotes a number.  As soon as someone takes their belief in something and expresses it on a numerical scale it causes a series of questions to be raised; how subjective is this best guess or how quantifiable/reliable is this estimate?  And often, attitudes to probability can cause people to split into two camps –

  • Areas that can generate ‘hard’ quantifiable data that allows for rigorous modeling for future predictions – such as climate science for example.  Such disciplines are highlighted as being on the more ‘scientific’ end of forecasting.
  • Areas that deal with political or social trends.  In such areas data is harder to quantify, a lot of economic data fits into this as well, but mostly it covers social and political data, which is notoriously difficult to quantify and measure.  With such data, probabilities and predictions tend to be non-specific and without numbers.

A lot of people fear boiling down their belief into a number; perhaps it’s too blunt, or too direct, or perhaps people fear that it will be wrong, or such an exercise can’t be conducted without more analysis.  But, unless you start doing this, unless you start collecting and generating probabilities then you will never get better at prediction.  It has to start somewhere, and however inaccurate or imprecise these first ‘best guesses’ are, they will still to form the foundation for the better future assessments.  Without probabilities you have no benchmark to address or estimate what could happen in the future and this means you never a particularly firm basis to improve from and move forward.

5. Ignoring ‘old’ data.

‘Trying to predict the future is like trying to drive down a country road at night with no lights while looking out the back window’. Peter F Drucker

Often there has been some confusion over the reliability of historical data.  A lot of forecasts I worked on, started from ground zero and ignored the data contained in other forecasts or historical trends.  Hopefully, this isn’t because people take Peter Drucker’s words literally!  At its most simplest historical analysis represents a way of looking through the data to come up with ideas and assessments other people have made.  At its most complex; searching as widely as possible for research that may be seeking to address the same things as your analysis will increase the range of your data and also, perhaps, show you the limitations in your own data or challenge your interpretation of it.

Detailed, considered historical analysis means you have a knowledge-base to work from.  The worst I’ve seen of this was in a project where all data, any data, historical or otherwise, was consistently ignored because of trust issues in the team doing the analysis.  This led to a whole range of last minute panics (not to mention terrible morale as the issues were never openly voiced), in which assessments were made ad-hoc at the last minute using general internet searches to provide the data.  This isn’t to say internet searches are bad, its just when they’re your only source done at the last minute, your assessment is likely to be limited and feature a lot of Wikipedia references (again, not necessarily bad, but it does only represent a very small fraction of the available data for an assessment).

6.    Not stating your assumptions, bias, or logic.

Again, this relates to lots of the other things I’ve learnt, rather tortuously over the past 10 years.  It does sound naïve and perhaps obvious, but if you can’t establish a practice and a culture that allows you to state your assumptions and to present your biases (as much as you are consciously able) then any futures analysis you make will be difficult, if not flawed.

Of all the lessons I’ve learned this is perhaps the hardest.  The realization that you’ve produced a forecast that is completely biased to your own world view and has been unconsciously drafted to reflect the perception of what the target market wants to hear is not a pleasant one.  In my defence, back in the 2007-2011 we didn’t really know any different, but as futures analysis develops, we know more now.  We know that we can and do produce biased assessments.  We know that experts can be biased, we also know that events can be designed that work with experts who reflect these biases, so much so, that you can write whatever future you want.  This may achieve some resonation with the people you want to influence, or perhaps the people who have a particular policy requirement that they wish your analysis to fit, but it will not give a more reliable assessment.  (Another Peter Drucker quote is ‘The best way to predict the future is to create it’).

I came to this realization slowly and the simple activity of writing down your beliefs, biases and assumptions around an issue as the first activity in your analysis, has taught me a lot.  However, although a seemingly simple exercise, it has also been the most controversial most quickly.  In doing so, such a basic exercise can expose a wide variety of personal, behavioral and cultural issues that may be long standing – but, its better to have them and be aware of them at the outset, rather than trying to fight them, issue by issue as you try to compile a forecast.

The future of futures analysis

Reflecting on all these things, on the things I tried; the things that worked, the things that failed has led me to seek to develop futures analysis that:

  • Provide a simple to understand assessment of future outcomes that are expressed using numerically related probabilities.
  • Don’t ignore other forecasts.  Adopt principles from the fabled ‘scan of scans’ or producing an ‘aggregate forecast’ will allow you to draw from the material that other analysts and researchers have generated.  Whatever you’re doing, its likely that lots of other people have done work like this before.  It is worth thinking about this trying to get as much data as you can.
  • Provides a full, navigable means of showing why and how the probabilities and assessments have been generated NOTE – this is also not without controversy – senior leaders generally, don’t want to read such detail whereas analysts do – this can lead to outputs that read like ‘War and Peace’ without proper editing (another criticism that is sometimes levied against producing forecasts).
  • Be up front and open about the biases you have.  As a first exercise, diagnose your bias, as it will be this that drives the data you collect and you need to be aware and fully open about this.
  • Be clear on the value experts provide.  Expert opinion is certainly important but facilitated sessions with experts isn’t the only technique for thinking about the future.  A bunch of clever people thinking about the future can be tremendously useful as a creative exercise or as a means of providing a wider range of beliefs around certain issues.  But, if it isn’t properly facilitated such activity can easily be biased, as described in ‘Quiet: The Power of Introverts in a world that can’t stop talking‘ by Susan Cain (I’ve also blogged about my own experiences of this on the Messy Fringe).   If done improperly, type-A personalities can dominate, and often, their beliefs can reflect those held of the audience that invited them (especially if that’s why they invited them in the first place).
  • Don’t bore your target audience, but don’t pander to them either.  Producing a forecast is challenging for all the reasons I’ve described.  But, if you’re trying to do all the above to answer a specific policy demand, or what you think the chief executive wants to hear, then you’re going to doom yourself to inaccuracy.  You need to be aware of the pragmatic reality of who has requested your forecast and perhaps tailor your output so that senior people can read the key assessments quickly, but you still need to show your working.  To be accurate, to be honest, unfortunately, you do have to compile ‘War and Peace’ but, you just need to get your main ideas into the first page, which, as heart-breaking as it sounds, is all that your readers will read – if you’re lucky.

Currently, at Simplexity we are working to take all these things forward.  Through the Open Futures project we are building a on open source, fully searchable database that brings together trend data both from published foresight reports but also from social media feeds.  This is a lot of data, but using data visualisation tools and analysis techniques we are now better able to navigate and understand such large datasets and producing meaningful, unbiased groupings.  These groups can then be assessed and probabilities assigned to particular context-relevant outcomes.  Using big data, long data and the increasingly expanding range of open data sources means the technology for futures analysis is becoming more and more sophisticated, perhaps, hopefully the way we present our assessments will as well.





A brilliant timeline charting the rise of the organization and the importance its had in helping alcoholics is on the AA website and it’s a fascinating chronology.

The organization is important for a number of reasons, but perhaps it’s of considerable significance because:

1.  Its techniques (principally the 12 step program) has proven results for addressing most, if not all, of the aspects of alcohol addiction.

2.  Its network of members, and unfortunately, the sheer scale of alcoholism in modern society means that there is a constant source of people who need help.  Present estimates suggest there are around 12 million alcoholics in the US alone.

Other forms of addiction.

Alcohol is probably the most socially recognized form of addiction in Western society.   But, the range and extent of possible addictions are increasing.  A good summary of different forms, either substance or behavior related is available at and it classifies addiction into the following areas:

  • Drugs (including nicotine, opiates, caffeine and alcohol)
  • Food
  • Shopping
  • Gambling
  • Love
  • Exercise
  • Work
  • Other Impulse control disorders and addicitons including
  • The Internet
    -Stealing (kleptomania)
    -Setting fires (pyromania)
    -Video games
    -Rage (intermittent explosive disorder)
    -Body Image

Such a classification is interesting precisely because it deals with both substances and behaviours – which are often difficult to categorize as clearly as physical, substance addiction. Behaviour evolves and, in the future, its likely that the things that we can form disorders around or get addicted to, will increase, one hypothesis to put forward is the role that technology, specifically, the internet will have in creating these new forms of addiction.

Alternative reality and addiction

One area that could see the most growth in addiction is with ‘alternative reality’ – the gaming sphere.  The space that gamers inhabit when they are in the game – be it stand-alone games or online multiplayer communites, an alternative reality is when ‘you’re in the game’.

The games we play get better and better and more and more lifelike.  More and more people play them.  Currently, estimates suggest around 1.2 billion people play games worldwide and 700m of them do so online.   And of this group, it seems all ages play them, any time, anywhere.


So, we have the conditions where we have a lot of people gaming, any where, any time.   It’s logical, that such practices alter behaviours.  Everyone is gaming, being online is a community, access anywhere means we can do it anywhere, therefore it isn’t a problem…right?

Could Alternative Reality addiction be a problem in the future?

The reality is any form of addiction is probably bad.  Addictions to a gaming universe that is perhaps more compelling, more enticing than real life is probably going to a problem both individually and societally. Over the past 7 years, notable deaths have occurred in extreme cases in China, the US and South Korea, generally following excessive game playing.

There are lots of examples from Science fiction, where creative works have explored the impacts of ‘alternative reality’ and the challenges presented by living two lives simultaneously and preferring the ‘alternative’ life over ‘real life.’ From Star Trek to South Park, this is a compelling idea and one that does seem to affect to us and will probably do so more in the future.

Addressing Alternative Reality Addiction

As with other forms of addicition, gaming or internet addiction is being increasingly recognized.  Internet gaming disorder was added to the Diagnostic and statistical manual of mental disorders in January 2014. Programs based on the alcoholics anonymous 12 step programs have been established and are available online, the following is an example from

The Twelve Steps and Principles of OLGA

These twelve steps and principles (the principles can also be used by athiests and agnostics) are guidelines for members of On-Line Gamers Anonymous to live by. We can recognize and overcome excessive gaming issues by using the twelve steps and/or principles in our lives.  

1. We admitted we were powerless over gaming, and that our lives have become unmanageable.  Principles – Honesty and Acceptance

  2. Came to believe that a Power greater than ourselves could restore us to sanity. 
Principle – Hope

  3. Made a decision to turn our will and our lives over to the care of God as we understood God.  Principle – Faith

4. Made a searching and fearless moral inventory of ourselves.  Principles – Action and Courage  

5. Admitted to God, to ourselves and to another human being the exact nature of our wrongs. Principle – Integrity

  6. Were entirely ready to have God remove all these defects of character. 
Principle – Willingness  

7. Humbly asked God to remove our shortcomings.  Principle – Humility

8. Made a list of all persons we had harmed, and became willing to make amends to them all. Principle – Brotherly love

9. Made direct amends to such people wherever possible, except when to do so would injure them or others.  Principle – Justice

10. Continued to take personal inventory and when we were wrong promptly admitted it. Principle – Perseverance

11. Sought through prayer and meditation to improve our conscious contact with God as we understood God, praying only for knowledge of God’s will for us and the power to carry that out.  Principle – Spirituality

12. Having had a spiritual awakening as the result of these steps, we carried this message to others who game excessively and practiced these principles in all our affairs.  Principle – Service

Such programmes are important in helping forming strategies and groups for addressing addiction.  As with Alcoholics Anononmous such programmes use practices and values from the Christian faith to help form the habits required to combat religion.  And it raises an interesting question for the future as the links between the internet and religion are often complicated.  For example, internet usage doesn’t tend to follow religious affiliation, so will the current cornerstones of faith used by the programme be as applicable to a gaming addict?

However, almost regardless of individual attitudes to religion, the 12 stage program represents an important treatment model that does have a positive impact on people suffering from addiction.  In the future, the need for such treatments and programs to support people who prefer an alternative, probably unobtainable, life online will, its fair to assume, be increasingly important.





This vignette is an apology from a gaming addict to their family; as per stage 5 of the addiction program.  

15th April 2017


I am progressing through the program.  I’m at stage 5 now, which tells me to make an amends.  This feels hard.  It is an acknowledgement, a validation that after all that happened, it was me that was wrong.

I’m writing this to say sorry.  Sorry for all the times I was there but not ‘there’.  Sorry for all the times you had to pick things up and deal with the kids and take care of everything.  But I also want to show a little of what it’s like, what happened in my head to get me there.  This isn’t to excuse what I did; I just hope somehow it can help you understand what happened to me.

It started because I had time to fill.  I picked up the tablet out of idle curiosity, remembering a game I played when I was young.  When you played it you were a god.  Capable of anything – war, scientific advancement, mapping the evolution of your very own society; be it an egalitarian democracy or a totalitarian autocracy.  You could build it.  You can be anyone from history, Churchill, Alexander the Great or Gengis Kahn.

In my first game I won easily, so I upped the difficulty setting and got pasted.  I was Caesar and I got my ass handed back to me by Ghandi.  The irony.  Ghandi assembled his forces.  Catapults, mounted horse and bomber units smashed my fledgling empire back to the stone age from where it had come. That wouldn’t do at all.

The days stretched out blank.  I was still between jobs.  No work was coming.  No emails, no calls.  My friends were out at work and I looked for things to do.  You were at work; my fingers itched to do something, my mind reached out for something, anything to do.  I took the tablet into a corner and plugged in.

How would I beat them?  How would I beat them all?  I invested in democracy.  I became the Greeks.  Blonde and muscled Alexander.  I learnt to furrow the investment in earlier – get three cities established quickly – Athens, Delphi and Alexandria.  I hastened research into the stone age technologies – alphabet, bronze working, masonry, then literacy and currency.  All the things I needed to get established.

There wasn’t enough time.  There was never enough time.  I would put down the tablet.  Go to school, get the kids.  When you came home I would go to the study, look at an empty inbox for a while, we’d cook some food, do bed time and then I’d seek out the tablet again.  But you know this – you’d see me in my corner, buried in the game.  Fill the time that used to be productive, when before I was making things, now I dream of conquest.

I beat everyone, expanded my influence.  Built wonders of the world; the oracle of Delphi; the pyramids, the colossus.  My people grew cultured.  I moved to assert myself, learning how to make gunpowder and chivalry and used my knights and riflemen to contain my neighbours; the Aztecs, the Germans, the Egyptians and the Spanish.  As soon as they tried to spread, I oppressed them with the threats of my growing power.

Suddenly it was night.  The last time I remembered looking at the TV, there’d been a cooking program on; all I remember is the washed out blue filter making the food look grey.  But now it was off and I was alone in the dark with the tablet.  It was low on power so I had to twist around in the chair we’d bought when my wife was pregnant with out first.  I had a crick in my neck and it was gone 2am.  You were sleeping. I dreamt of a million different impulse decisions.  Move the galley right; build an aqueduct; save the money/spend the money.

The next day was a school day.  I didn’t make breakfast. I remember you all shouting goodbye. I got up at 11 and ate waffles straight out of the pack and drank instant coffee.  I panicked for a while.  I couldn’t find the tablet.  I unearthed all the things, the Saturday newspapers I hadn’t read.  The kids toys scattered around, the blocks, the lego, dolls and car all scattered around my outline.  I found it under a pile of unopened mail from the bank.

The game was advancing now.  I was growing.  I knew how to get the fastest amount of scientific advancement before any rival.  The Greeks had half price libraries and a democracy, meaning they had the most money and the most science.  Also, I’d learnt, if I configured the game in an increasingly tight manner I could influence the circumstances that most accelerated my success.  Was this cheating?  I didn’t care.  It showed to me a process of beating them all my competitors the virtual English, Americans, Russians and Chinese.  I could set it so I could learn everything configure everything and grow everything before all my rivals.  I would spend my money fast, then when I’d spent it; I’d send my spies to steal me more from my competitors.

It was one of those afternoons when you came home from work.  I still wasn’t dressed.   I remember the argument.

‘You’re supposed to be getting the kids?’

‘No I’m not, its Tuesday?’

‘It’s Wednesday!  Why do you think I’ve been at work!’  You looked down at the tablet – ‘Have you been on that all day?’

‘What?  No!’

‘Jesus, you’re not even dressed!’

‘I…I’ve not felt too good today.’

I remember you blue eyes misting.  ‘That’s because you didn’t go to bed till 2am!  Dammit Craig, you need to stop this!’  You shouted at me and took the tablet, my reflexes were slow. I tried to snatch it back but you ran into the kitchen.

‘Get yourself dressed and go and get the kids!’

The school.  The school.  Same as ever, but somehow muted.  Toned down, the enthusiastic mothers in happy gaggles; the tired, out of place fathers standing to the sides alone.  I took my place.  The wind stung my eyes.  Everything made me squint.  It was too bright, too noisy.  I looked around the schoolyard; the pictures of children, the climbing frame and the sandpit.  So much detail alive with life and promise, yet it was inert.  It was just pictures, it was just information that didn’t help me do what I wanted so it was irrelevant, it was something to get through as quickly as possible.  Why wouldn’t the teachers just hurry up and let the kids out already?  I had things to do.  I was poised to sack Constantinople.  Didn’t they understand; I was on the cusp of a full house – economic, cultural, scientific and cultural victory.

‘Hi Dad,’  It was James, ‘Miss Fitchett wants to see you.’

‘What did you do now?’  I sighed.

‘We just wanted to show you how well James was doing.’  The woman explained, her eyes were creased from years in the job, yet her smile seemed genuine.

She showed me a wall, they children all were in different zones, the boy was moving from bronze to silver.

‘If he keeps at his reading, he’ll be on Silver by the end of the week.  We’ve really noticed him trying harder this term.’

I smiled.  My wife was spending more time in their room.  Reading to them more and more as I stayed downstairs with the tablet.

‘Great’ I smiled and ruffled James’ hair in some vaguely patriarchal manner that I remembered my own father doing to me.  She was still standing there, expecting me to say something more; but I didn’t know what else – you normally handled these kind of things.

‘What are the rules for this?’  I asked, gesturing at the board.

Miss Fitchett explained.  ‘Each child does different challenges and earns different points based on their abilities – James is breaking into silver challenges, he’s…’

‘Are there gold challenges?’  I asked, walking to the gold section. There wasn’t anyone else’s name there yet.

‘Yes, but they’re generally very advanced.’

‘So how many silvers is a gold worth?’  I asked.

James blushed as I asked more and more questions.  In the end, Miss Fitchett stopped talking about the stickers and kept repeating how pleased they were with James.  ‘Yeah it’s great, but wouldn’t it be better if he did more golds, that way he’d win quicker.’

Miss Fitchett’s smile lost its warmth ‘That’s not really the point.’ She said.

James didn’t start crying until we’d met Daisy.  She asked him what was wrong and put her arm around him.  They walked ahead of me to the car.

‘You shouldn’t be so embarrassing Daddy…and you were late!’

‘I know sweetheart and Daddy’s sorry OK.’

I don’t know how long this went on for.  There seemed like there were many days where I was late and we argued.  But it’s like this, until the point that you left and you weren’t there for a week and that’s all I did, that’s how it happened.  I cycled, around and around in my head.  Trying, trying all the time to make it perfect – all the time questing for the perfect win and when I did it, when I had that perfect moment, I was happy.  I looked up I shouted.  It was 4 am and the house was empty.  The TV had gone onto standby and an empty pizza box was on the table.  Everything around me was a sea of chaos.  Nothing but chaos yet there was only silence in the house.

Everything was completely still, there was no life, no other contact.  No one to tell about my achievement.  But then, when I looked at it on the tablet – the score, my high score, the highest score of all time, was at the top.  I photographed it. Who could I show it to?  But in a fleet of instant it was gone, a teenager from Korea overtook me.  How could it be?  I was the best. How could it come and go so quickly…

And it had and no-one cared.  Not even the people who used to care. You were gone and it was only then that I saw that it was me that had made you go.

I know this won’t fix things, but I’m trying.  I admit to my problem and I’m getting help.  I hope this will help amend some of the times that I ignored you or sat there grunting in the darkness while you watched our lives pass by.

I love you






There is a lot of stock invested in the wisdom of experts.  There is a strong degree of faith that circulates in many aspects of government that ‘experts’ are the ones to solve problems.  And this is often the case – policy makers are busy people, they have to make decisions and stay tuned to politics; they can’t be expected to understand lots of complex information, not when there are probably around 10-10,000 people who are recognized in each particular field.  As a result, they have to use experts.

Experts are used in many forms.  We have expert witnesses in our courts; whose opinions and eruditions can literally change the course of a court case.  We use experts to scrutinize our leaders – when the Prime Minister announces a new policy an expert – either from academia, industry or a think tank are asked for an opinion on the subject.  Often, one expert is bought in with an opposite view to another and a ‘healthy debate’ ensues.

In futures analysis experts are used frequently.  People with deep specialisms are selected; by an analyst, policy maker, facilitator or futurist and asked to attend a meeting to discuss the future.  In such a meeting, a collection of experts are treated to a range of futures techniques.  For example, the following quote illustrates what sort of exercises they might undertake to think about the future (taken from Governing the future 2007)

“Let us assume you are standing on the bridge of a ship. You scan the horizon (Horizon Scanning) and see an iceberg and your supply ship. You work out the likely speeds and direction of the iceberg and supply ship (trend analysis) and put the information into the ship’s computer (modelling) and then plot a course (roadmapping) so that you meet with the supply ship and not the iceberg. While you are doing this you dream of eating some nice chocolate that you hope is on the supply ship (visioning).

You realise that the speeds and directions of the iceberg and the supply ship might change, so you work out the range of options to make sure you have the greatest chance of meeting the supply ship (scenarios). Even with all of this planning, you know there is a chance of the unexpected and hitting the iceberg so you get the crew to do an evacuation drill (gaming). While they are doing it, you work back from the most likely future position of the supply ship to work out the steps you need to get there (backcasting).”

So, say you were an expert in Particle Science, you could attend a session on the future of nano materials and use any of the extensive futures techniques to come up with a view of what the future could look like (without perhaps visioning eating chocolate whilst trying to avoid icebergs).  You would probably be amongst say 10-20 others, perhaps more in plenary, but most probably in small groups to enable a facilitator to undertake these exercises with you.  You would probably be amongst a few other experts in your discipline, but for effective cross-fertilization of ideas, you’d probably be mixed up with a few others – say a biologist, an industry expert and perhaps a computer tech person and then a few others.

This would give you a transect – a focus group of ideas.  And this is a great way to generate ideas.  You’ll get ‘spark off’; if properly facilitated – ideas will form as people take ideas, discuss them and share opinions.  There may be conflicts; but if properly handled, such ‘warm discussions’ will perhaps pull out further ideas or difficulties.  Creatively, this can be very important for a project and can stimulate a wide range of interesting ideas.

A focus group is just as the words describe – it is a group focused on a particular issue or project.  Such a group of experts does have, collectively, more knowledge about a particular subject area than a policy maker could possess on their own; but that doesn’t represent the truth – it’s just a slightly larger sample of more informed people.

Lets take particle science again – a futures session with 20 people as a sample size.  In the whole field there are probably be an estimated 500 million people in employment regarding particle science.  Of these probably around 50 million could probably be regarded as experts of some particular discipline.  Lets say, in the UK accounts for around 10% of that, so around, 50,000 people in the UK could be attributed an expert status in accordance of some aspect of particle science – be it quantum physics through to underwater wood welding.  So, as a basic analysis, your sample using a focus group of experts is 0.04% of the total population of subject matter experts in the UK.

Here I’ve been slightly unkind, there are other techniques you can use – you can interview, survey and correspond with a wider group of experts.  You can widen your lens significantly, probably out to around 1000 or perhaps even 10,000 which is an effective way of using experts because of the size of your sample.  But, if you don’t have a large pool to draw from expert opinion is still just that, it is still opinion.  An analysts opinion which has been formed from three months of scrutinizing data on a particular issue may be just as valid as an expert who has thirty years experience in the field, this is why as a technique, the use of expert focus groups needs to used with care.

As a means for generating ideas properly facilitated focus groups with experts are powerful.  But for being a reliable source of what will happen in the future, perhaps less so – unless the assumptions the group is making and the overall analysis of the ideas to produce the implications are presented.

The real challenge in using experts comes in how the experts are handled – through facilitation, and how what they say is recorded in the drafting.  The process of taking expert ideas and thinking and crafting them into meaningful, evidence based policy is a challenge, but it is something that new initiatives like open policy making can be used to improve both the power of futures analysis, but also the overall honesty and reliability of our assessments.





As part of developing the taxonomy for our trend hub, I’ve been trying to categorise some of the time scales we think and plan for.  For sake of finding a start point to discuss this, I’ve split how we think about time into four simple categories (I appreciate that these will actually feel quite long spans of time to some people, but to get to thinking about the long-term, I think these might work as a starter for ten):

  • Short term (1 day-1 month)
  • Medium term (1 month – 1 year)
  • Medium-long (1 year -5 years)
  • Long term  (5+ years)

Short term thinking (1 day – 1 month)

The systems we operate in tend to be very good at dealing with the short term.  And by the short term, I’m going to define this as a day to a month.  This is because the short term is immediate.  It is tacit.  The reality of gold in your pocket now, trumps the prospect of gold in your pocket in the future in a vast number of cases.  What you think about, what you worry about is, generally, what’s real and, to most of us, we can only speculate on what could be real today, tomorrow, perhaps, next week, perhaps next month; we tend not to think about next year as ‘too much can happen by then.’  As people we naturally think in days, weeks, months.  We get used to the seasons and these are features we understand and mark off on our annual calendars.

Dealing with things that happen from today, till a month from now, tends to monopolise our focus, especially as it is hard enough to get a full picture of what is happening right now, let alone what could happen tomorrow.  Understanding what is happening in the world right now, has long been the remit of intelligence agencies and even they will admit it’s hard enough to fully really know what is happening right now.  It is, at best, an estimate with varying degrees of bias and error.  As the world continues to grow and there is more and more data available, just trying to get a picture of what is being discussed about you or your brand ‘right now’ is complex enough.



As the image above shows – there is a considerable amount of data constantly generated and instantly accessible.  With such a massive amount of potential data – it is only with our capabilities to estimate things.  We have to estimate our reputations, our impact, our popularity – we can put scores to these things, which make us feel a bit better as they are a little more quantitative, but they are still estimates.  And during our day to day lives, just what is being said about us and what the overall opinion that dominates this ‘logosphere’ or all the currently valid information, that is what we devote most of our energy in responding to.  Especially in organisations; we spend most of our time trying to fathom what the situation is ‘today’ that the tomorrow, we plan for is just literally that – it’s tomorrow.

Medium term (1month-1 year)

We generally think in the medium term to plan our lives and up and coming events.  We check our calenders and generally think about events or milestones when they are about a month away (again, I’m generalising here; I’m talking about us collectively, not those project managers, event organisers, climate scientists etc who do work on longer time horizons).   There are natural reasons why our circadian rhythms and our cycles are daily (we sleep), monthly (female reproductive cycles), quarterly (we used to plan and live by the seasons of the year) and for years – well, these are the bench marks of our life-spans – we measure our progress through life by our birthdays.  So, naturally we tend to think on a year by year basis.  Perhaps our lives are too short to do other wise!

It’s not just our biological dispositions that favour the medium term.  Financially, month by month works, it is a significant way to bench marks sales and profit.  Month on month we can see our performance and set our strategies to address ‘year end’ to ensure, that at the end of our annual cycle, we have made a profit.  I’ll try and put a number on this, but I’d estimate that around 80-90% of the strategic planning in the UK, is working on a year horizon.  Because, for a business, this is where its crunch time – it has made a profit, or it hasn’t.  You quickly have a big metric, and lots of smaller metrics to measure your success and progress against.

Medium – Long (1 year -5 years)

Thinking in terms of 1-5 years tends to be less common, both in our lives and in our organizations.  In democratically elected governments, such a span of time provides the effective functioning window of a political party in power (which, in reality is probably more like 2.5 years, when you take into account the learning curve post arrival and the election preparation approaching the end of this period).

Other systems of governance have used five year plans to deliver programmes of change.  Communist systems, both in Russia and today in China split longer term projects into 5 year programmes.  China still does this; probably because it’s a meaningful amount of time to deliver significant technical, social and engineering based projects.   China is currently in its 12th five year plan, running from 2011-2015, which currently focuses on ‘sustainable growth, industrial upgrading and the promotion of domestic consumption’.

So, is it a fair assessment to say that around 5 years is the longest amount of time we plan for?  5 years is perhaps, the most significant amount of time that change can be benchmarked.  For a child, 5 years sees a newborn, go to toddler, go to infant.  Another 5 years, sees SATS preparation, another 5, GCSE’s.  With our children, as they age, the milestones we reflect upon can be benchmarked yearly, but are more starkly realized by us as parents every five years.  Other events, like the olympics, swing by infrequently enough, for us to witness the cultural changes that have occurred since the last games.


Long term thinking > 5 years

There are some areas where we think longer than five years.  Some sectors, such as insurance or risk management need to mitigate against events that occur outside of the timescales that we are most comfortable in thinking in.  Hurricanes, floods and earthquakes happen when they happen and, as they are entirely outside our control, insurance companies calculate and cost for the risks of them occurring, sell you a policy and make their profits according to the frequency and scale of these events happening. 

Climate scientists also, have to model and predict what’s happening in the weather.  To do so, they have to understand highly complex data sets and also be comfortable dealing not only with the short term (will it rain tomorrow?) but also the long term, (when will London be threatened by the Thames flooding?) as a result, organisations like the Met Office Hadley Centre have developed clear ways of both thinking about long term change, developing their ideas and models but also, crucially, communicating these to the public, to make it clear why they think about issues happening over a long term period and why they are relevant to people today.

There are other areas, for example, the defence and security industries both have long term assessment programmes.  These mainly focus on threats thatcould occur over the next 10-30 years.  The logic being that these areas need to be able to adapt and develop their military, technological but also their people policies to either take advantage of opportunities or mitigate threats.  Around the world, a lot of defence and security agencies develop such work; I’m from this sector myself, having worked on the Global Strategic Trends programme for seven years.

What all of these projects have in common is that they’re not really concerned about what’s happening, only about what could happen in the future.  And, if you take 5 years as the start point for your long-term planning, then you can free yourself up from the short term issues of the day and think in a slower (and less urgent) manner about processes of change that take longer for us to see and even longer for us to understand.

To conclude then, I’d offer the following definition for long term thinking.

‘Long term planning is any conceptual exercise in planning or analysis that deals with any potential change that could occur in five years time or greater.’

The interesting thing is that outside of the human species, >5 than five years isn’t really that long at all.  In geological and evolutionary terms, 5-50 years (the kind of timelines our long term projects tend to stretch to) are a fraction of an eye blink.  This is discussed in a really interesting article here on long data.  But the challenge comes from defining an effective period of time that is both meaningful for our planning and relevant to our highly volatile and increasingly rapid systems of decision making, which are naturally biased towards the gravity of today.  We tend to worry about the tiger that’s there now, not the tiger that could be there in ten years time.



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I’ve been trying to determine what ‘horizon scanning’ actually means. In doing so, I’ve dug out some definitions of the term over the years and come up with a shorter definition of the term for further discussion.  Doing such a basic analysis shows that what used to be a term for a form of futures analysis has now become a term for futures analysis.

But, before, I go into the definition, I thought it was a useful declare that I have some biases on this subject.  So I’ve done my best to declare them consciously, hopefully without letting them taint my subsequent analysis and the definition I’ve formed.

My Bias

I worked as an editor for a project called the Global Strategic Trends programme from 2007-2014.   This is a programme of long-term ‘futures analysis’ or ‘horizon scanning’, that has been undertaken by the UK Ministry of Defence since 2001.  So, as far as I’m aware, my conscious biases are:

  1. I worked on the GST programme from 2007-2014.  In my time I contributed and edited Global Strategic Trends Edition 4 and associated products, such as the South Asia survey.   In this role I used a range of futures analysis, of which ‘horizon scanning’ was one.
  2. I left the UK MOD to start ‘Simplexity_UK’.  This is an analysis company that uses open data, data visualization and creative techniques to think about the future in ways that are both more imaginative but also with a degree of quantitative analysis.
  3. At Simplexity, we are currently extracting all of the trend data contained in a wide variety of futures outputs from 2001.  This activity is allowing us to compile a rough history of futures analysis over the past 15 years. I’ll cover the detailed analysis in further posts as the data is added to the Open Futures project.

What is horizon scanning?

To go back to the main question, I’ve assembled a few definitions of horizon scanning is and arranged them chronologically.


The Department for Environment Food and Rural Affairs (DEFRA) defined horizon scanning as:

“The systematic examination of potential threats, opportunities and likely future developments which are at the margins of current thinking and planning. Futures research may explore novel and unexpected issues, as well as persistent problems or trends. Overall, futures research is intended to improve the robustness of Defra’s policies and evidence base.” (Defra, 2002)

The website elaborates further:

“Horizon scanning is a distinct futures methodology and is generally not used to describe the discipline of futures as a whole. Instead horizon scanning is the act of gathering new insights that may point us towards affirming or discrediting existing trends and developments as well as identify new and emerging trends and developments which are on the margins of our current thinking, but which will impact on our lives in the future.”


In 2009, the UK Defence Science and Technology Laboratory had an established Horizon scanning function.  This took a definition of Horizon scanning from the Office of Science and Innovation:

This is perhaps more generic than the specific technique but, in its context, it was used by DSTL for focusing on science and technology.


Following the Jon Day review of cross-government horizon scanning, horizon scanning was defined as:

“A systematic examination of information to identify potential threats, risks, emerging issues and opportunities, beyond the Parliamentary term, allowing for better preparedness and the incorporation of mitigation and exploitation into the policy making process.”

In contrast to the 2002 Defra definition – this was when horizon scanning, started to have a wider application than a distinct futures methodology.

“Horizon scanning is used as an overall term for analysing the future: considering how emerging trends and developments might potentially affect current policy and practice. This helps policy makers in government to take a longer-term strategic approach, and makes present policy more resilient to future uncertainty. In developing policy, horizon scanning can help policy makers to develop new insights and to think ‘outside the box’.”


This definition of horizon scanning was recently taken further by the House of Commons Science and Technology Committee – which in April 2014, published ‘Government Horizon scanning: Ninth Report of Session 2013-2014′  defined it as:

“Horizon scanning, in its broadest sense, is an attempt to systematically imagine the future in order to better plan a response. In the absence of a crystal ball, it can help organisations to detect signals, identify trends and think more inventively about what the future might hold, enabling them to capitalise on opportunities and better mitigate threats. It is a crucial activity for any organisation tasked with long-term decision-making.”

It took the definition further to say:

‘The Government describes horizon scanning as “an overall term for analysing the future”.It states that it is used to consider “how emerging trends and developments might potentially affect current policy and practice”, so that policy-makers can “take a longer- term strategic approach” and develop policies that are “more resilient to future uncertainty.”’

Today, May 2014

Bringing things to the present, since the publication of Horizon scanning in government, the UK has a specific way of describing ‘Futures Analysis’ as ‘horizon scanning’.  This is a very basic analysis and my hypothesis is pretty ‘light’ at this stage.  But, it gives me something to start discussions. Taking all of the descriptions above could we simplify to produce the following, basic definition of what ‘horizon scanning’ means to the UK?

Horizon scanning = ‘A process for analyzing the future that determines and tests hypotheses on long-term* trends, risks and opportunities of potential strategic** significance.

*Long term = anything more than 5 years away.

**Strategic = Board/Cabinet Level

I’ve added the *’s for long term and strategic because, without a description of what these terms relate to, it is difficult, especially in a system as a complex as Whitehall to determine who is a ‘decision maker’.  Clear, distinct definitions, can help with this as, with being clear on the timelines that you are looking to.  Producing a simple definition of horizon scanning, for me at least, makes it easier to understand what everyone is saying when they describe horizon scanning, at least in the UK?  But, if you go anywhere else in the world, they’ll probably mean something different by it, and it will probably be a bit easier to understand.

If this works as a definition, it then provides a good start point to look at how the subject has evolved (looking at its history) and where it could go in the future?

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When I started work on the Global Strategic Trends Programme back in 2007, we spent most of our time and energy producing compelling narratives about the future.  Back then, we prioritized this as the best way to go – in our defence, this was because we knew no different.  We genuinely thought it was of primary importance to keep our audience engaged and make our product relevant.

Open, honest and clear editing. 

Today, we know more about the ‘futures’ as a discipline and that telling a good story, is not the most important thing for a forecast to do; it is far better for a forecast to open and honest with its logic and assumptions clearly articulated.  This means, rather than being ‘compelling’ it has to be ‘clear’.  To do this, we spent a lot of time honing editorial techniques that helped us gather and aggregate trend data.

As well as developing techniques for grouping and clustering large datasets we developed editorial practices that also followed the principles of being open, honest and clear.  It should be a reasonably easy thing to do to develop an editorial process that is open and unbiased but in reality it is very difficult to implement.  There are a number of factors that can influence the editing process.

The Editor/Author relationship

At its most basic the editorial hierarchy is short.  For a lot of fictional works and journalistic pieces, there is often one link.

Writer – Editor

This short link can be highly effective and for many areas of production and often such simple writer/editor relationships are crucial, perhaps sacrosanct to a good product.  When it works its symbiotic; with both parties working to make the product as good, or as clear, as it can possibly be.

But, if the relationship doesn’t work then it can quickly become toxic to all components; writer, editor and the final product. Often, for creative projects, – if the relationship doesn’t work things tend to break down way before the product is finished.  The project is shelved, generally because, ‘creative differences’ are cited.  Other times, provided editor and writer can have some sort of functioning relationship, something will generally be made.

The relationship between an editor and an author is important.  Key in fact, if it isn’t treated as such then the relationship will probably struggle and so will the outputs.  This is where openness is key, both parties – the editor and author need to be honest with each other for the relationship to work to its fullest potential.  Weak editing, can lead to the editor not protecting the ideas or the vision of the writer.  Weak writers, can be too sensitive to receive or act on the criticism of the Editor.  Either way, there is a degree of protection and trust that each party needs to invest, when this fails, the product suffers, but worse, so do relations. The worst case of this is when a biased editor elicits feedback on a writers work and doesn’t pass this on to the writer, that is when protection is failing.

Feedback and bias.

In most cases to improve a product creative endeavours or ideas need feedback. Independent people, or groups could be asked for their opinions on the early product and this process generally managed by the editor who looks for groups to provide comment.  Such feedback can be seductive, and the responses from those asked for reviews can be subjected to personality politics and bias from each party. For example, a bad editor could simply go to a group of trusted friends who are known to give good feedback or reviews, similarly the writer may only court people who like them or their ideas and from whom they know they’ll get get good feedback.  People who don’t give the feedback they want are ignored.  And that’s bad editing and will probably lead to a weaker draft; at such a time its crucial that the acts as an honest advocate of opinion and criticism.

A hierarchy of gatekeepers – editing consensus.

Issues with the author or the editor will both cause significant problems for making a clear, honest product.  Another thing that can cause problems is when things rather than having a short, simple editorial chain, there is a large chain of decision makers that a product has to go through.  Things are tricky when everyone is a gatekeeper who needs to be coaxed, or convinced on the path to production.  And the question is, is the final product improved if it is simply reflecting the consensus of a crowded platform of concerned decisions makers?

For most projects, a short editing hierarchy is desireable and is attainable for certain projects like a blog, a magazine article or perhaps a novel.  In this post, a lot of what I’ve written applies to creative projects, but there are some fundamentals that, I believe, be applied to most areas of production that involve forming and communicating an idea, especially in areas that we can get lost in the wheels-within-wheels of a hierarchy.

The challenges for open editing in policy production.

The lessons learned in observing editorial practices can be applied to the production of all outputs, perhaps most significantly – policy.  The development ofopen governance has the potential to impact on how we make and produce government outputs and policy.

For a government policy isn’t fiction, it’s real.  And what’s real about it is the power it conveys – both for the change it can cause but also for the person wielding the capacity to deliver that change.  But often the manner through which policy is generated is no different to how we produce reports or even fictional works.  Policy generation relies on data gathering, idea generation, aggregation, assessment, written production and critiques/peer review.

Whatever the exact means of generation, when a policy product is formed it is usually in the form of a written report.  Such whitepapers, think pieces or assessments are generally drafted by people who take the ideas and evidence from analysts, scientists and specialists.  Such people are policymakers and they take these ideas, draft them using some of the techniques above and then produce a version of policy which they can then share.

The policy maker as an editor and an author.

This is where things are again, different with policy.  The policy maker starts to take on a role, similar to both editor and author.  To get a policy ‘published’ it has to go on a journey through a complex and unpredictable range of decision makers and influencers.  Such a journey tests not only the policymaker’s drafting skills but also their political aptitude.

This is one of the key challenges open policy making faces and its caused by the very nature of what power is.  With policy, ideas and words equate to change and, while they are being generated, they can be influenced.  The things required to product both the quality and integrity of the first version, are tested in a myriad of ways, by a range of people, all of whom probably have an interest in shaping what goes into the final product.

Being open.  Sharing ideas from start to finish, letting daylight show the cracks, is the ideal.  But the ability to  illuminate the shadows that power casts is the perhaps one of the greatest challenges the open ideal faces.  But, by being mindful of good editorial practices and clear on what the roles of all producing parties are – be they, the editor, the author or the policy maker, we could perhaps be better able to make products that are both clearer to read and genuinely open – not only on what they contain, but on how they are made.





The General preferred it when times had been simpler.  When men had been men and, come to think of it, so had the women.  He’d been called to attend some kind of ‘brain storm’ and they were making him sit at the head of a series of tables laid out in a horseshoe shape.  Twenty people sat around him, the closer they got, the more important they were.  He could see now that three of them were women.  Three of them, actually sitting at the main table. He sighed as he waited for a scruffy civilian to wheel out a whiteboard.

‘Good morning General, ladies and gentlemen.  My name is Keith, I’m from the analysis section and I’ve been asked to facilitate today’s brainstorm.’

The General looked at the man, he wasn’t even forty.  He was wearing a polyster tie and a sports jacket.  What was the world coming to?  He reached for his mobile phone in his pocket.

‘OK.’ Keith continued. ‘As we only have the General joining us for this first morning session, I thought it might be good for us to quickly go around the table to introduce ourselves.’

The General snapped to attention. ‘I haven’t got time for that! Pick up the pen, or whatever it is you’re paid to do, and listen. We are here to decide what the future threats to Queen and country are.  Now, you make sure you get this.  The biggest threat to our country is and will be from terrorists.  Quite simple.’  He looked around the horseshoe table, all the people in green nodded at his words.  Those in dark blue shook their heads and those in light blue looked skywards,  but no-one said anything.

‘But what even is a terrorist?’  This was from someone at the end of the table, a young woman Major.

Keith smiled and wrote down ‘What is a terrorist?’ on his white board.

The General made a fist under the table.  ‘What kind of nonsense is this?  I’ve just come back from the cabinet – a terrorist is the biggest threat the country is facing today?  You go to London.  You sit with the people who know – did you know that there are 11,000 terrorists operating in England today?’

‘But,’ Keith volunteered ‘That’s today, we’re talking about thirty years from now.’

The General reddened and went into his jacket pocket for his blood pressure pill while everyone looked the other way.  As he poured himself a glass of water, his Chief of Staff, a Commodore from the Navy, spoke.

‘I think what the General is saying is that we need to think about how the threats of today will translate into the threats of tomorrow.  We need to address the real security issues presented by terrorist groups, whilst being mindful of the longer term threats they could present – such as to the supply chain, the sea and such like.’

Keith wrote ‘Supply chain’ on the board.

At this point, an Air Commodore on the General’s other side said, ‘I think he also meant that to achieve these things tomorrow we’ll also need to maintain a credible technical ability and deter future threats.’

Keith wrote ‘technical ability’ and ‘deterrence’ on his board.

The General gestured for his Military Assistant and whispered something to him.  At which point everyone started talking at once and no-one paid the slightest bit of attention to Keith.  It wasn’t until the young woman Major at the front slapped the table and said with the loudest voice in the room.

‘Oh come on really, is this how its going to happen again?  Are we really just going to fall about arguing about mass, the air-craft carriers and deterrence?  This is all we ever do – how are we ever going to move forward if we don’t think about the future honestly and openly?’

The General stared at her. ‘And how would you suggest we did things, young lady.’

The Major didn’t react like the General had wanted to her to.  To his annoyance she answered rationally, which women weren’t supposed to do. ‘Well, sir, I’d start by not asking the command chain the questions about the future as they’re the most biased by the views of today.  I’d probably go to a younger, more general and ,dare I say it, more diverse group for a wider range of opinions.’

The General took out his phone. ‘OK.  Well, Major – lets put that on the pile of all the things we should do and, in the interest of diversity, why don’t you tell us what we should be thinking about the future.’

‘Well, if I was in charge of the Army, I’d be worried about the internet and cyber warfare and what role the Armed forces will have to society in the future.’

The General’s colour went from puce to beetroot.  He squeezed his phone in his hand.  For a moment, he remembered a meeting, an operational brief back in Iraq – a REMY Captain had something almost as stupid and he’d thrown a radio at him.  Oh, he longed for those days again.  Back when his power had been real. To his relief the phone the started ringing.  He quickly covered the number and got up from his chair. ‘Yes fine, this is all very good.’ I’ve got to get this, it’s the Defence Chief.’

‘Well thank you General for your time,’ Keith said ‘was their anything you wanted to add quickly about what the Major said.’

‘Yes, yes, all very good.’  The General said, waving his hand. ‘Just remember terrorism is the most important thing and if you really need to do something about the internet, get that Brit down here – you know the one the that invented it – what’s his name.  “Tim Brook-Taylor” or whatever it is.  Now if you’ll please excuse me.’  And he was gone, through the special door that only he was allowed to use.

The void he left lasted for three heartbeats.  Everyone looked around the room to see who was nearest to the General’s seat.  The Commodore, the Air Commodore and an Army Colonel all looked at each other, then they all started speaking at once.

‘OK!’  Keith repeated, over and over, getting louder and louder until people looked at him, he was after all, the only man standing at the front of the room and he had the pen.

‘Let’s just go back to what the General said before he left.  We are all, of course, grateful for his time and his direction.’  Keith said, looking at the clock.  The General had arrived at 9.05, and he’d gone by 9.15.  He’d given ten minutes of his time to being ‘open minded’ and that, it seemed, had been enough.

He worked through the bullet points written on the board.

‘So, the General was concerned about possible terrorist threats.  We’ll put that down as one possible threat for the future.’

At this point the Army Colonel, who had very short hair, cleared his throat.

‘No.  The General didn’t say it was one possible threat, he said it was the threat for the future.  One that ‘only boots on the ground’ can be used to fix the ‘hearts and minds’ of the people.’

As one, the men in dark blue uniforms sucked in their breath.  The Commodore turned to face the colonel.

‘I think you’ll find that he said protection of supply chains was as important!’  His chins jostled under his white beard as he spoke. But then the thin Air Commodore chose this moment to get up from his chair and walk to the white board.

‘No, this is what I think he said.  May I?’

Keith had no choice but to hand his pen over as the Air Commodore turned over the white board and started drawing a process diagram about equipment procurement that described how drones would make boots on the ground irrelevant in the next five years. Halfway through his sermon the woman Major put up her hand.

‘I don’t really know what the General meant to say, but I’m pretty sure he didn’t say that!’

The Colonel at the far end of the table looked at her with fury. ‘I think we’ve all had enough of your input for today thanks Julie.’

They then spent the rest of the morning arguing. After coffee, the only people left were the woman Major, a retired Navigation officer, the Army colonel, Keith and his two scribes, young analysts from the graduate scheme.  It didn’t take long for them to go through their notes.  As they did one of the scribes said.‘I think it was Tim Berners Lee that invented the internet, not…’ before he could finish, the Colonel interrupted.

‘That’s what the General’ said.  And they all wrote it down.





There has been a long established global trend for the average family size to decrease.  Since 1950, the global average has gone from around 5 children per woman, to 2.5 by 2010.

Projections from the UN population prospects database suggest that this trend will continue.  All regions will see a decline in family size, with the global average currently expected to reach around 2.24 children per woman by 2050.  For some regions, the decline has been quite marked, with places like Latin America and Asia, seeing the average drop below 2 children per woman – a significant drop over a 100 year period.  Other regions, such as Europe and North America, will see either a slight increase or the maintenance of existing fertility levels.

Why has family size decreased?

There are a variety of reasons for why family size is decreasing.  Some of the most commonly referenced are:

  1. Declining child mortality.  Child birth is, generally, becoming safer and more children are surviving into adulthood.  This is combined with a general trend for parents to also support their children for longer through life, as opposed to having children to support the ‘family livelihood’.
  2. Changing social norms and employment.  Parenting roles and types of employment have changed significantly over the past 100 years and the increased diversity of different family types reflects this.

The pro’s and con’s of smaller families.

When family sizes decrease rapidly a national economy can actually benefit.  For a while, something called a ‘demographic dividend‘ can occur.  This happens when the next generation has family sizes considerably lower than its parents (for example, if you had five siblings and you and each of your siblings went on to have 1-2 children).  If child mortality stays low and life expectancy increases, then the parents of these smaller families could, theoretically, be economically productive for longer and have less dependents to support.  This can be a tremendous boom for an economy and has helped many countries to advance rapidly – China, as a result of the one child policy, benefited from a long period of productive economic growth.

However, there is a downside.  This increase in economic productivity only lasts while the individuals themselves are productive.  When they start to age and need social support, this dividend can turn into a liability.  Instead of having 5 children to care for them in their old age, parents now only have one or two, both of whom could themselves be in continuos employment with 1-2 children to support.  When this occurs, who ensures social support for the elderly – the state or the individual?  See below for a projected demographic profile for China in 2050.

Demographic and social policies are likely to remain delicate and challenging issues for most states.  Over the next forty years, states are likely to balance the need to provide health and pension provision to their ageing populations by attempting to keep their populations at relatively ‘healthy’ levels.  This means, that for most states, as well as ensuring the provision of care, will probably look to keep people working longer, whilst implementing the policies to support working parents to attempt to keep future population growth at around replacement levels, which is generaly about 2.1 children per woman.





This is a story about machines, AI and accountability.  In the future just what will we want our machines to understand and what will we understand ourselves?


Weaver looked up at the sky filled with matt black metal pigeons carrying goods and papers in and out of the Global Centre For Government Accountability.  He wondered if somehow he could convey to all the small whistling machines just how much power he had over them.  To an AI, someone like him -  a creator, a programmer – was a god.  Surely he was?   He had total power.  He could tweak them, control them, get them to do whatever he wanted.  But they couldn’t know this.  No one else could either.  That was the point.  Katie, his wife ,thought he did something with ‘IT processes’, something important and well paid that took him away from home often and she was right, in a way.

Everything that he did was secret, except to him and Bill Lacey, the head of accountability at LifeTech.  Only Bill knew what he’d done at InnovateInc when he’d posed as a programmer in its trading section and rewired the master AI with a sub-routine that made it develop obsessive-compulsive tendencies.  After he’d finished, it behaved so erratically, it would only make purchases on Thursday mornings at 11.15 am and it would only trade shares that contained the letters PMX or J.

Then there was Japan.  Now, that was fun.  Weaver had worked with some ex-special forces guy that Bill had bought from the states.  Together they’d broken into DASCHI HQ- the biggest supplier for manufacturing robots and Footoo’s – the most popular children’s virtual friends.  Once the spook had got him inside, Weaver located the master code for over 10,000 manufacturing droids and tweaked it to contain ‘Footoo’ personality codes.  Three weeks later when the droids had been installed on production lines around the world, everything went bug-house crazy as the two tonne machines tried to laugh, tickle and spin themselves whilst holding car doors, computer chips or nano-wires.  After that, around 30% of the machine production companies around the world went back to Life Tech.

He thought about that night, donning night vision goggles and a stealth suit and he looked up at the vast grey complex of the Global Centre for Government Accountability and sighed.  He hated government work.  This was going to be very different to Japan.


For this assignment Weaver’s name would be James Allenbrooke.  He was a middle manager with a career in analysis and was on secondment from ‘Life Tech’.  His (bogus) report described him as a solid performer keen to be at the ‘coal face’ for global policy formation.  As James Allenbrooke, he was supposed to be keen to be at the beating heart of governance – where the public met policy.

‘Course it ain’t nothing about policy.’  Bill had said when he took the assignment. ‘The place is just an excuse to house all the surplus bean counters and bureaucrats who exist exclusively to give us people with proper jobs – another pain in the ass.’

‘But I’m guessing this isn’t a people problem, though.’  Weaver said.  They’d been sat in the middle of the ‘quiet wood’ on the Life Tech campus.  This was a vast pine wood designed to conceal certain conversations from sound recorders and satellites.

Bill nodded.  ‘Yeah.  Looks like they’ve got some kind of coding protocol running that’s blocking approval for Pigeon6.  Our submission testing alogirthim thinks they’re probably holding it because the AI is scoring too high for emotional intelligence.  Looks like the machine could be too “caring”.’  He made speech marks with his fingers as he said this.

‘Too caring.’  Weaver repeated the phrase.  Caring wasn’t a concept he thought about much.  Especially in an unreported conversation in the quiet wood.  He leant back on his bench and looked up at the sky through the tree canopy.  Pigeons weren’t allowed over the LifeTech campus so he enjoyed seeing the sky, clear blue – like a vast expanse of ozone ocean.  For a second, something made him think of Katie and his daughter, Emma.  What would she be doing now? Taking out the books for her lessons, sitting cross legged in assembly?  He put the thought away and looked back at Bill. But Bill was done, he got up from his bench and grunted ‘Fix it,’ as he walked away.


The Centre for Government Accountability was huge.  It was arranged around a vast central building in the shape of an octagon.  This was the control centre.  From this ran, orbited eight square buildings, each corresponding roughly to a region of the earth.  Everything was interconnected via glass and steel walkways.  Every building had a region and a purpose in this burgeoning hub of global governance. Weaver was seconded to building 3.0 – Europe.

He followed his induction pilot programme on his smartphone to his desk.  For an age, it seemed, he schlepped around the vast building complex until he reached 3.4.45J, where he was assigned. He looked around, assessing the mixture of government spads, civil servants, industry thrusters and academic wonks and wondered how they all fitted together.  What could all these people do?  Did each of them really have a part to play in this living, breathing system of accountability?

In theory, they all helped the Centre of Accountability make the world a better place.  Anyone, anywhere who had a complaint, an issue or a request about any form of government – be it their council, their parliament, tribal elder or UN official – they’d put it through the Centre.  Everything and anything came there – email, phone call, tweet, letter, message in a bottle even – it all came to the central registry.  There it got processed by an AI and put into one of the various regional hubs for processing.

The whole idea of it made Weaver angry.  How could everyone really have equal say in things? Who cared what a plantation farmer from Timbucktoo thought about the trading price of bananas? Why they hell did everyone need to keep a track of everything these days?

It was, well, bananas.  All of these requests, from shepherds in the yemen or the director general of the BBC – they came through the registry, the main central building in the middle of the complex.

There were no other channels of enquiry for reasons of propriety and accountability.  The process stopped undue influence being paid by those in power circumventing this open system.  Because, everything that came into the centre could be seen by the global public at any time, unless it was classified as being a ‘commercial’ or a ‘special case’, which meant either national or global security was involved somehow in the information being disclosed.  Only if something had one of these two tags meant it couldn’t disclosed.

So this was why Weaver was here – to get in the middle of things.  Find out who’d or what had an issue with the ‘Pigeon 6’ patent and get the hell out of the Hague.  He hoped to be done by lunch – he fancied a beer and some mussels before he got the train back to England.  He had a suspicion about what was happening.  His theory was that a competitor, either InnovateInc or DASCHI or any number of the other companies he’d hit in the past four years, had a hand it it.  Proxy or code.  One way or the other.  Something wasn’t right.

He looked around the banks of pine desks and black plastic swivel chairs.  It was just like a call centre.  He used all the login-details that had been issued to him.  Breezed through all of the necessary clearances and in minutes he had the file open.

He could see it all.  The coding protocol.  The clearly communicated and worded request that LifeTech had sent.  It was so plain, anyone could understand it, even 8 year old Emma.  He noticed the point about encoding emotion.  How the latest drone could detect agitation in a demanders’ voice and respond by either increasing its speed or changing its direction, but it seemed that the LifeTech’s AI’s had been wrong.  That wasn’t why it was being held.  He sighed – it had been assigned to the ‘Human Referral Complaints Team record number #E1D67888.  This wasn’t going to be fixed by coding.  This was a wet-ware problem.


It was always human error.  Weaver remembered this and put his anger to one side.  He resolved to spend a few hours scoping and understanding the issue before handing it back to Bill to get a different person who could deal with people.  Someone, who could rattle the necessary cages or smooth the right feathers.  But, until then he had to be nice to people.

He went to ‘Human complaints team’ – a knot of 12 desks – opposite the server room that held row after row of black humming units that held the ‘Non-human complaints team’.  He almost pinned to be in there instead of having to do with call centre staff.  At least AI’s didn’t have a choice – they were rational.  They could be taken apart and understood.  This wasn’t something he could do with the array of fleshy, over dressed humanity that was gradually starting to notice him.  Two middle aged women sat at the two front desks of the cluster.

‘How can I help you my dear?’  The first woman spoke.  She introduced herself as Shirley.  She was stout and had thick, effective arms – the sort that should be dishing out porridge, or administering clips around the ear.  Before Weaver could answer, the other woman cut in.

‘Where are your clearances, ve do not know you?’  This was from woman who introduced herself as Maureen, who Weaver quickly determined was Dutch, middle class, and hyper-efficient.

He smiled and showed his pass. ‘I’m working down there for the next couple of weeks,’ He pointed to his nearby bay ‘I’m not really assigned to a team or anything so I just thought I’d wander around and see who I was sharing an office with.’

The lie came easy to Weaver.  He found that when he didn’t care about the people one little bit, and he’d say whatever he needed to say to get the hell away from this place.

These two women were steadfast lifers.  Loyal and committed to the organisation and the physical gatekeepers for the human complaints section.  He chatted to them for a while, making up a past for himself – company man, no family.  He asked them about the rest of the team. It could have been anyone blocking Pigeon6 – he needed as many names as possible.

Maureen coughed.  ‘I’ll look after him Shirley, you’ve got enough on.’  She turned to Weaver and gestured for him to follow as she glided around the cubicles.  He could have followed her with his eyes shut, tracking her by the lavender perfume she wore.

‘These are Rebecca, Mo, Fred and Stacey – they are current interns.’  She introduced him to a group of younger employees.  All fashionably disinterested. They were of an age that Weaver had little contact with and even littler interest and by their body language they felt the same.  One of them pointed at his trousers.

‘Nice chinos, dude.’  Said one of the boys with a mohawk, tattoos and metal jewellery stuck around his eyes and in his ears.

Weaver smiled ‘Nice piercings, must take you a while at customs.’

The intern snorted something but Maureen moved him on.

‘Don’t worry about them,’ Maureen said guiding him to another cluster of desks, ‘they never stay for long.  They’re on short term contracts and it’ll be good riddance to that bunch of good-for-nothings!  The others are this way.’

She led him to another cluster of desks.  One was empty, three were occupied – the nearest, spaced some distance away from the other two occupied desks was occupied by a tall woman called Pippa who was heavily pregnant.

‘How much longer?’  Weaver asked after they were introduced.

‘Three weeks’ She said, rubbing her bump.

‘Do you have kids?’

Weaver almost nodded then remembered stay in character.

‘No, but my sister has three.’ How easy the lies came. But he had to stop himself. Come back to the problem.  He went silent, smiling awkwardly as the burgeoning conversation died awkwardly between them until a man in a tweed jacket interrupted them by clearing his throat.

Weaver turned to the other two desks.  Two men were watching him, neither was smiling.  Next to the tweed man was a small Indian man with dark circles under his eyes.  He twitched when Weaver looked him in the eye.  He didn’t even introduce himself, he looked down and gestured toward the older man.  He was older.  White, balding and comfortable in tweed.  A single silk hanky poked out of the top of his jacket pocket.  He stared at Weaver of the top of his half-moon spectacles.

‘And you are?’

‘James Allenbrooke.’

‘Quite.  Well, James, I suggest you dispense with the small talk if you want to get along here.  The human complaints team has a quota and we don’t like distractions.’  He looked at the small Indian man as he said this and they both turned back to their display screens.

Maureen rolled her eyes and took him back to his desk. ‘Don’t worry about him, that’s Arnold.  He’s been here since the team was founded – thinks he runs the place but he’s only a grade 5.  We all stay out of his way.  A few of us go for lunch at 12.30, if you’d like to join us?’

Weaver said he would.  He went back to his desk and took out his notebook.  He wrote down everything he’d seen and the people he’d met.  He put:

  • Two loyal administrators.  Female.
  • Four disinterested interns.  High churn of temporary low paid staff.
  • One pregnant employee, expecting her first – waiting for maternity leave.
  • Two analysts.  One quiet.  One officious.

Finally, he scribbled  ‘Non-functioning team.  Needs management and restructure.  Pigeon 6 being held due to inefficiency, not the strength of the submission.’ With his notebook in hand he got up and left the Centre for Government Accountability.  He forgot all about his promise to Maureen and didn’t acknowledge her as he strode out of the building without looking back.  He was done.


LifeTech had made arrangements for him to say in an apartment overlooking the North Sea.  Weaver didn’t want to unpack his things.  He wanted to be back at the camper van at Lifetech.  He wanted a proper project for a man of his talents.  He wanted the excitement back.  He thought he wanted to see his family again and, in some subtle way, imply to them what a hero he was.  But that thought confused him, so he thought about something else.  He came back to issue in hand.

He left everything in his suitcase on top of his bed and sat down on his sofa with a coffee.  He looked out of his window at the urban development park.  Units of moulded plastic domiciles floated over the sea.  Each one looked like a shoebox filled with people like him.  Generic government or corporate contractors.  All in the same environment for a specific purpose or project.  There was nothing personal anywhere, nothing; no semblance of self.  Everything, grey, everything single-use, clean and sterile.  Everywhere there as someone like him, some small player, some cog in an ever expanding wheel.

He took out the paper he used to communicate with Bill, then he vt’d LifeTech’s classified comms team.

‘I need to get a classified package to Corporate communications.  It’s personal and urgent.  Could you send a pigeon, please?’

The comms manager looked into her display and bit her lip.

‘All of the fives are out.’

Weaver shook his head.  ‘That’s no good – this is business critical!’  He breathed deeply, holding back his temper.  Why was it always so difficult working with people?  Why did they always introduce so many messy complications and excuses?  There were never these problems with code.  Code was simple.  It worked or it didn’t.

‘I’m sorry sir…wait.  There is something we can do.’

She tapped away again and Weaver saw her face change as she bought up more details.

‘OK.  Sorry, Mr Allenbrooke.  I have your file now.  It seems I can have special permission to release Pigeon 6 – as long as we make it clear its for research purposes.  Would that be OK with you?’

‘That’s fine.  Send it now.’  Weaver grunted and clicked off the VT.



Pigeon6 arrived an hour later.  It landed on the balcony of the flat – at the special receiving post built for drones.  He intercepted it outside and was impressed by the latest design.  It was sleeker and smaller than the other drones and was about the size of small dog.  They’d made it more bird-like with a streamlined head and a body made out of a mixture of dull silver metal and photovoltaic cells that shone like plumage in the sunlight.  As the machine settled he stretched out one if its wings and saw how they’d even feathered the energy harvesting cells to maximise its exposure to the sun.

‘Clever’.  He said.  As soon as he spoke Pigeon6 twisted its curved head in his direction.  It didn’t really have a proper beak, but its face was narrowed into a point to cut through wind resistance and its eyes were one sleek band of black plastic.  The head, neck and other flexible parts were able to move by sections of a strong black mesh that would expand and contract in accordance with where the machine wanted, or needed to go.

He took out the message he’d hand written for Bill and sealed it using the company’s own biometric polymer seal.  This was a blob of vaguely liquid material, similar to the red wax seals used to officiate old correspondence.  Weaver folded up the paper, put it an envelope and placed his finger in the damp matter, which set instantly.  Now, only Bill could open it.  Only his finger prints, dna and a relaxed resting pulse rate would open the envelope.  Anyone else, or if he was deemed to be ‘under stress’ the seal would secret an enzyme that instantly dissolved the envelope and its contents.

Weaver took the envelope and told the pigeon to receive it.  It did nothing.  He remembered, that they hadn’t had time to configure it to his voice, so he took out his smartphone. 

‘Good thing I can code.’  He muttered, opening up the console in the birds chest and, using a USB from his phone, he encoded the delivery instructions.  A compartment clicked open in its side and he put in the message for Bill.  A minute later Pigeon6 was flying out to sea.  He stood there waiting for it to disappear into the horizon.  When he could see it no longer, he became aware of the phone in his hand.  For a moment he thought about phoning Katie.  There was only an hour between them and the UK.  It would be dinner time there.  If he phoned might get to speak to Emma before bed.  But he decide they were probably busy.  If they wanted him they’d have let him know.  That was generally how it worked.


Pigeon6 came back just as the sun was setting.  The orange light glistening on its solar cells as it arced gracefully over the complex and landed on Weaver’s balcony.  Weaver waited for a few moments, like he was letting the machine catch its breath before he approached it.  Once it appeared, he opened the compartment in its side and took out a similar envelope to the one he’d sent earlier.  He put his finger in the biometric seal, cracked it open and quickly read the contents.

A minute after he’d read the six words, the enzymes in the paper ate themselves and the only place the instructions resided were in Weavers head.

He left pigeon6 outside, ordered up a four pack of beer from reception and drank them all.  Later, he read through his notes again, his head in his hands.  A wet-ware problem dammit!  This really wasn’t his remit.  He didn’t know where to start!  He didn’t want to start!  He thought about phoning Katie to tell her the project would take longer than he expected.  But he didn’t. How long was he going to be staying for anyway?  He could be marooned here forever, lost in the bowels of the world’s deepest administrative blackhole.

He went to bed and slept poorly.  Paranoia swept over him like some creeping fever.  What if this was a sett up – a way of parking him far out of reach of the LifeTech? What if someone wanted him gone? After all, only Bill knew he was here! Such thoughts cycled over and over in his mind so at 4am he gave up trying to sleep and wrote them all down. But each time he did, the words came out as subroutines. Code for fixing a wayward machines. At 6am, he found himself on the balcony, watching the sun come up.  He picked up Pigeon6 and took it into his apartment.

He couldn’t arrive at why, but he found himself using his smartphone to transcribe his thoughts into Pigeon6’s command prompt.

He described them as variables as first.  He expressed each team member with a name and some simple characteristics.  Simple terms.

  • Maureen = strict, loyal.
  • Shirley = loyal, familiar
  • Intern 1 = disinterested, lazy
  • Intern 2 = disinterested, arrogant
  • Intern 3 = disinterested, fat
  • Intern 4 = lazy, understimulated 

He’d just finished Intern 4, when Pigeon6 turned its silver beak toward him and its command prompt interrupted him with blue, flashing letters.

> You shouldn’t call someone fat, even if they are fat.

Weaver sat back and looked at Pigeon6’s lifeless eyes.  He didn’t know what to write next.


 Despite feeling like he had grit in his eyes, Weaver made it into the Centre for Government Accountability by 9am. He spent the morning talking individually to the staff from the Human complaints team.  He reintroduced himself as having a specific role in auditing the team.

‘I’ve been sent to streamline the process for unresolved complaints.’  He explained to each of them, in turn.  He made out that the purpose of his first visit yesterday had been to meet them incognito.  With each team member he followed the same script.   He’d start by opening his notebook and showing them the number of unresolved complaints.

’10,000.’ He cleared his throat, ’10,000 requests or complaints that are going nowhere. I don’t need to tell you that each of these equates to a human being.  A person, somewhere in the world, is feeling like they are not relevant.  Ignored.  10,000 people, all silenced by our inefficiency.  Now, I need you to help me – why is this happening?’

And everyone said something different, and all that they said, Weaver wrote down.

‘It’s those bloody interns.’ Shirley said.  ‘They spend all their time mucking around, when they’re not too hung-over to find their desks.’

‘It is the management, they do not take enough interest.’ Maureen said.

‘It’s Bent’ said each of the interns.

‘I don’t think there is a problem.’ Said Baljeet, the man who worked opposite Bent.

Pippa, rolled her eyes and said ‘Where do you want me to start, everything is broken – no-one talks to each other and when, they do, no-one listens.’

‘Ah-huh’ Weaver said, writing ‘No-one listens’.

Then he spoke to Bent.

‘I don’t understand why you think things need improving.’  Bent said, his arms folded.

‘I don’t think things in general need improving; just the process for unresolved complaints.’ Weaver said.

‘Then you do think things need to be improved.’

‘No…’ Weaver sighed, he knew why he’d left Bent till last.  He didn’t get to continue.

‘Who do you think you are?’ Bent was on his feet, his cheeks red. ‘I’ve been here ten years, since the place was founded.  I’ve put all of my efforts – my life, even, to make sure everything works.  You’ve been here five minutes, you cast dispersions and you look for “efficiencies”.’ He made speech marks with his fingers. ‘Well, I tell you, whatever your name is, I’ve been here from the beginning and I’ll be here for a good few many years yet.  You won’t replace me!’

And he walked out.

‘Won’t replace Bent.’ Weaver wrote.  Then he went back to the apartment and slept.


 >Arnold Bent is lonely.

‘Tell me something I don’t know!’  Weaver said out loud, as he fished for the noodles at the bottom of his takeaway carton.  It was past 20.00.  He’d slept till 17.00, showered, then spent the rest of them time inputting the data he’d collected into Pigeon6’s console.

‘Why is ‘lonely’ relevant?’  Weaver communicated through the appropriate code.

>He seeks meaning through work.

‘Our boys definitely need to recode you.’  Weaver said, reaching for his beer.  No wonder the protocol wasn’t getting through, ‘Who coded you?’

Pigeon6 didn’t respond to audio – it was command prompt only, but sometimes, when he spoke out loud to it, it turned its head sideways a little, like an inquisitive dog. Weaver wondered if it was listening to him.

‘What does Bent’s loneliness have to do with a back log of 10,000 complaints?’

Pigeon6 moved its head.  It did nothing for a few moments and, not for the first time, Weaver questioned the wisdom of his plan.  But then, the text flashed up:

> Arnold Bent is lonely.  He seeks meaning through work.  He steps outside his role and tries to over perform.  This makes his line manager insecure and he steps back.  Arnold Bent doesn’t have sufficient authority to lead the rest of the team so they perform inadequately.  Without clear leadership, the team do what they want, and everyone ignores team related issues and just focuses on their individual quotas.

Weaver put down his noodles and looked into the machines, empty black eyes.

‘So what’s the solution.’

> Recognise Arnold Bent.

Weaver dispatched Pigeon6 with his secret message for Bill.  He explained how he’d fixed it – omitting the fact that he’d been given the solution by a mail drone.  He set Pigeon6 to return on delivery.  For some reason, he felt anxious to be parted with the machine – he wanted it close, at least while he was still on the project.

Things moved quickly after that.  On Weaver’s instructions, Bill used his contacts to get Bent promoted.  He was at his new desk, by lunchtime.  Weaver went to congratulate him.  He was surprised to see how pale the man was looking.

‘Well, all I can say is that I knew from the moment I saw you, you were in the wrong place.’  Weaver said, shaking Bent’s limp hand.

‘Thanks.’ Bent said, sitting alone in his office.  He was in charge of an administrative team in sector 42.7G, handling routine telephone enquiries – he had to manage a team of 7 middle aged women. Weaver smiled.  By that afternoon, the human complaints team had a meeting, chaired by a new, officially appointed manager who prioritised the backlog of unresolved issues and after that everything started moving again. A day later, as Weaver packed his things – the application for Pigeon6 was approved.

By then, it was Friday and Weaver stopped in at Life Tech on his way home.  For the first time, he didn’t go to Bill – instead he went to the AI labs.  Holding the Pigeon6 under his arm he spoke to a couple of developers, fresh from college.

‘Who coded this?’  He asked.

They looked at each other blankly, then one of them opened up the console and looked at the unique identifier.

‘Some contractor from the US, goes by the name of ‘Clinker’.  Why is there a problem?’

Weaver took Pigeon6 off him. ‘No, its fine.  But I’d like to keep this one for a little while longer.  For the main launch you may want to make a couple of tweaks.  I’ve done a full report for what I’ve seen while I’ve been using it, if it helps.’

The young developers were only too keen to help an executive.  They took his report and told him they’d make the changes.  He went home with Pigeon6 in his bag.

A week later Katie said he seemed different. ‘No, don’t get me wrong.’  She said.  ‘It’s nice.  I don’t know, you seem more present, I guess.  It’s nice for us both to have you around.’ Weaver smiled and listened.

A bit later, when Katie was reading Emma a bed time story, he went to his garden office. He picked up Pigeon6 and coded.

‘She says.  I’m more present, is this good?’

>Yes.  It is good.  She is happy.  You should be happy.


Pigeon6 twisted its head to one side and stared at him blankly.





Preamble – This is a short story about how change happens, and a moth.

Norman Shift

A white moth fluttered into a witch-hunt.  It settled on a window pane that spilled light into the emergency cabinet meeting.  Nobody noticed it.  All eyes were on the Prime Minister, who was the only person who wasn’t sweating.  As a billionaire, he didn’t need an expense account.

‘I’m sure you’ll all agree: we have to make an example of someone!’  The Prime Minster said.

There was a heartbeat of silence.  Eyes darted from person to person then everyone started speaking at once.

‘But I swear I only used the money to hire a designer…’ Said the Minister for the Interior.

‘And I only claimed money to pay someone to clear my guttering, I can’t go up a ladder at my age!’ Said the Minister for the Exterior.

‘And I need a place in the city.’ Said the Minister for Urbanisation.

‘Ladies.  Gentlemen.  Please, don’t worry…I have spoken to the chief whip and I’ve made my decision.’

‘But, Prime Minister, please I feel I must say something.’  The minister for Justice spoke and the Prime Minister frowned.  The Justice secretary was young, handsome and ambitious.  The qualities the Prime Minister admired in himself and hated in others.

‘This hasn’t happened over night’ The Justice Secretary continued, ‘I’m pretty certain no-one has broken any rules…the guidance is clear.’

The Prime Minister shook his head ‘Martin please, we’ve been over and over this.  The guidance may be open to interpretation.  Yes, people are entitled to claim for ‘reasonable living’.  But, as you well know.  There is a recession on.  The people.  The people don’t see it as fair and it jolly well isn’t.  And, we have an election to think of after all’

The Prime Minister said, staring him down.

‘As I said, an example must be made.’  He looked around the table, sensing any other dissenters.  Most looked away, except the man from the treasury, who merely nodded sagely as the Prime Minister looked at him.

‘The Chief Whip and I agree.  The biggest abuser is Norman Shift.  The press are already running an expose on him.  I will be telephoning him this afternoon.’

And it was done.  The name was out there.  Everyone nodded and talked to their immediate neighbours, agreeing explicitly with the direction.  An example needed to be made, and if you needed a good example of a free-loader Norman Shift was as good as you could get.  The fact that he wasn’t there as well, just made it easier.  As the excitement faded away, the white moth fluttered off and out of a small gap in the bomb-proofed Victorian window.

Norman Shift was a conservative back-bencher from the black-country.  He came into politics at a time when men were self-made and righteous and called to politics to make a difference.

‘I bloody told you…I’ll give it to ‘em.  I’ll give those fat cats what for’  That was what he’d said when he left Solihull.  He went to London to save the car industry.  But he didn’t.  He’d given that up as quickly as his accent.

‘I told, I fight for you.  I’m one of you.  You know it.’  That was his general line with the electorate, whom he engaged with as infrequently as possible.  But after the headlines, he couldn’t avoid it.  He’d only been at the civic hall for five minutes and he was already sweating heavily.

‘My friends, all I can tell you is that I’ve been treated badly by the system.  I get up at 5am every morning and I work till 11 every night.’

The crowd murmured angrily.

‘So do I?’ A man with curly black hair and faded denim shouted ‘I don’t get to claim for heated dog blankets!’

Norman watched the upset ripple across the crowd.  From the front to the back, People stood up and gestured at him.  Norman waved his fat hands; his sausage fingers wobbling like his chins.

‘No.  You don’t understand.  I know it looks bad.  But please, if you look at it from my point of view.  I haven’t had a pay rise since 2004.  The expense account, well it’s like my pay rise.  You want me to be well paid don’t you?  You want me to be rewarded for what I do for you?  I fight for you.  I’m one of you?’

‘You don’t look like us.’ A woman with a face creased from years of smoking shouted.  ‘You don’t sound like us neither.’

‘What do you earn?  How much, go on tell us?’

Norman.  Big Norm folded his arms across his large chest.

‘I’m not going to answer that and I’m offended that you should want to know.’

‘He earns £65,000 a year’ volunteered a young man from the Solihull Chronicle.  Norman left not long after, just before the first chair got thrown.


‘Who the bloody hell told them what I earned?’

‘Freedom of Information’ His Special Advisor told him ‘People are entitled to know what a public servant earns.’

‘No they’re not.  It’s rude.’  Norman put his cigar out on the bark of a poplar tree.  They were behind the civic centre, waiting for a taxi to drive up the long, muddy back lane.

‘How much longer are we going to wait here?  People saw me leaving, that awful man with the hair could come out here at any moment.’

The Special Advisor shrugged, lost in that day’s copy of the Daily Mail. Norman looked over the top of his glasses.

‘How bad is it?’

‘£5,000 for dog yoga?  Is that in the public interest?’  He was a young man with a ginger complexion and upset radical disposition ‘£2,000 to treat Mr Pickles’ Depression!’

Norman patted himself down.  He was certain he’d put a hip flask in his jacket before he’d left the flat in Westminister. ‘Mr Pickles has an electrolyte imbalance, he was very low for months.  It caused Julie no end of worry!’

The hip flask wasn’t there.  Norman grunted for himself and turned on the Special Advisor.

‘Listen.  I told you before, everyone was doing it.  It was fine.  The expenses office saw every single claim I made…’

He stopped.  Suddenly conscious of a weight in his jacket pocket.  For a brief second his mind returned to the hope of his hip flask.  But then it vibrated and he took out his phone.  He looked at his Special Adviser, mouthed the Letters ‘P’ and ‘M’ and pressed it to his ear, which was already sweaty.

‘I see.  But Prime Minister, I assure you, everyone was doing it.’  He changed the phone to another ear.  ‘But they were.  Oh.  Oh I see.  Well, I suppose I should thank you…an inquiry.  That will give me the chance to prove I’m in the right!’

When he put the phone back in his pocket his Special Advisor was gone.  Norman stood there alone.  Looking around the empty yard.  Everything was covered with mud and blackened from an old garage where they had converted old cars that ran on leaded petrol into unleaded.  It was a messy business.  All the red brick walls and the one, dying poplar tree were covered in soot.

Norman wiped his brow.  Putting his phone in his pocket.  He stopped, steadying himself on the tree.  As he watched, the white mouth fluttered down.  It stood out brightly.  Peppered white on sooty black bark.  Without a moments hesitation Norman squashed it with the palm of his hand.  Then he walked out through the mud to find a taxi.