Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011
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Visiting Fellow, Brunel University Business School, Kingston Lane, Uxbridge, UB8 3PH, UK Averil@alpha2omega.co.uk
An exploration of recent work in complexity theory and neuroscience to explain why & how disruptive events happen in systems and how we could be better respond, particularly in the policy making arena.
Complexity Science demonstrates that disruptive events do not need an associated trigger and that they are a normal part of a complex system. This insight implies that if we are always looking for weak signals we will certainly be caught unawares.
The recently published neuroscience work of Iain McGilchrist shows how futures activities and applications are affected by the division of the brain into two hemispheres and why the resulting incompatible versions of our view of the (future) world, with quite different priorities and values, could be used to improve our ability to work with disruptive events.
The assumption, that disruptive events can be managed by planning & forecasting, is therefore not a workable option. Instead, policy making needs to assume that unexpected disruptive events will happen, even with the best horizon scanning system in place.
To date, futures techniques have developed out of need; they are highly pragmatic, were originally developed for use in commercial organisations, and are used because they deliver insights. However, other than some post-graduate futures programs (provided that futures is an interdisciplinary field grounded in a variety of social science), there has never been much underlying theory, or rationale, for either futures or its techniques.
In addition, there has been little published work, or understanding, on how to really apply
(implement) the results of futures studies & projects. Much futures work, whilst very interesting and great fun to discuss over dinner, goes unused, unappreciated, and makes little observable difference. Futures may, or may not be a discipline, or may perhaps be developing into one, but either way, complexity theory can provide a way to understand how techniques can work, work better, and most importantly lead to better decision-making (and perhaps contribute to use of theories of social change in futures).
So what is complexity theory and what is its relevance to futures and foresight? There are of course as many answers as people (Byrne, Mitchell, Goodwin, Strogatz, Waldrop) but like any developing area, there are many common themes. This paper will explore selected of the elements of complexity theory to help apply complexity theory to futures and thence to complexity-based futures to policy-making. There are five elements of complex systems of relevance.
T HEME : C ROSSCUTTING S ESSIONS H ORIZON SCANNING
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Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011
1.
A system cannot be explained by breaking it down into its component parts because the key element is the interaction between the parts. The system needs to be considered as a whole. As a result of these interactions complex systems exhibit emergence – (selforganised) behaviour that results from these interactions. For example, consider taxies in a city. The location and availability of taxies in cannot be explained by breaking the system down into its individual parts – drivers, cars, customers, fares, other taxies etc.
Rather it is an emergent property of the whole system resulting from the interaction between all the parts – the taxies, customers, the road system, the traffic (itself and emergent property of a city’s transport system), city cab rules, and even the weather (the system environment).
The implication is that futures techniques must be able to embrace emergence and to focus on the idea of interactions rather than constituent parts. Futures techniques need to enable a vision of a system’s emergent properties.
2.
All systems have component agents (taxies, customers) and each agent in a system acts on its own set of rules
, and can be thought of as trying to get the ‘best’ outcome for itself
(best fare for the driver, lowest fare or fastest ride for the customer). The rules (which can be very simple; for a taxi driver they may be simply 1) stop for any customer, 2) go wherever the customer wants ) can lead to the very unexpected results (‘why are there no cabs around here today’). But the rules do not have to be fixed – they can change according changes in the system too (for a driver a late night in a bad area may change the rules to ‘ don’t stop for anyone’
). Rules can be laws and policies, but also values & perspectives etc.
The implication is that futures techniques must be able to accommodate changes even in the basic rules. Futures techniques need to enable a vision of changes in the essential profile of a system.
3.
The interactions between the component parts of a complex system (which include positive and negative feedback loops – no cabs on the street –‘ phone for one’ or ‘ take the bus’ ) lead to non-linear relationships between ‘causes’ and ‘effects’. A ‘small’ cause can have ‘large’ effect, and a ‘large’ cause a ‘small’ (or no) effect. Systems are therefore not just very difficult to predict they are fundamentally impossible to predict . Systems can also be unexpectedly very stable – highly resistant to change by policy intervention or very unstable – such as where a policy intervention leads to stream of unexpected changes, perhaps in an unrelated area. For example a city’s policy to increase the number of cabs available to go to the suburbs may have no effect, or its policy to ensure clean cabs simply results in cab drivers carrying around instant cleaning kits to use when challenged - and as a result letting their cabs get even dirtier between times. One way of visualising such non-linear relationships is as tipping points or phase changes – such water freezing. Or using the taxi example, phase change could result in ‘ no cabs for half an hour and then six come around the corner at once ’. Not only does phase change happen very suddenly, and over the whole system, but there are no early warning signals. Phase changes can occur in rules, expectations, behaviours, etc. Such phase change is increasing recognised as common in public policy as organisation systems
(people) adapt to the new environmental parameters (policies); the system can change
T HEME : C ROSSCUTTING S ESSIONS H ORIZON SCANNING
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Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011 radically. Equally, there can be long periods of (apparent) stability, which are called
‘ attractors ’ as they are states that the system is ‘attracted’ to, and can which render policies utterly ineffective.
The implication is that futures techniques need to accommodate phase change situations, accepting that they will happen, and consider what ‘phase-changed’ worlds might look like. Futures techniques need to enable visions of phasechanged worlds – i.e. from very different perspectives, including ones that are not considered possible now. Futures techniques must also accept the likleyabsence of any early warning signals.
4.
Extreme sensitivity to initial conditions . You never start with a clean slate – extremely tiny errors in understanding where the system start from can send any ‘forecast’ off in totally the wrong direction. Using the taxi example, a driver’s entire day my be determined by the first turn, left or right, made out of the parking space. An incredible example from another area is an equation called the logistic equation. f(x) = R x (1 - x) is an apparently simple equation, but when iterated produces some incredible behaviour. The result diverges dramatically depending on the value of R – it bifurcates , the line of the value taking one value or the other depending on the initial value of R. But it is incredibly sensitive to the value of R; a difference in the 7th decimal place will determine which of two possible tracks it goes down.
The implication is that futures techniques need to recognise that a system has a critical history which can always influence the future; once any change has happened, a system cannot go back to where it was, as the initial conditions have now changed. Failed policy cannot be repealed and things started again from scratch – it has already had an irremovable effect; a re-examination of the situation anew is required. Futures techniques need to recognise that everything is part of a system, that there is no ‘new’ starting point, and that tiny, perhaps trivial actions can have a huge, irrevocable, impacts.
5.
Non-equilibrium . Systems are not at equilibrium (if they are they are dead) and are always changing. Systems evolve , as do the agents, their rules and interactions – and the system plays out in a ‘ fitness landscape’ . Imagine a landscape of mountains and valleys, where ‘high’ is good for an agent (a performance measure ) - an agent (taxi driver) aspires (has a strategy to) be on a high peak (making a big profit). But it is no good blindly climbing the nearest highest peak because it may not be a relatively high peak, and to get to a really high peak you may first need to descend into a valley. In other words, by blindly climbing the nearest highest peak (the current optimum) an agent may get stuck there while others climb higher peaks. And in any case the landscape is not static and will change over time (and will nev er ‘settle down’) – a peak may become worthless compared to others and the taxi driver may go out of business ( selection ).
Therefore agents need to optimise their actions - to move or slide as the landscape changes but also to explore the landscape far way to identify other options (objectives, strategies, policies) and not cut off future options. But some of these options may not make any sense now – they may only do so as the landscape evolves and changes
(using the taxies example customers may decide to share taxies to different destinations
T HEME : C ROSSCUTTING S ESSIONS H ORIZON SCANNING
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Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011 as the norm). Agents need to be able to see the adjacent deep valley a potential future peak, or recognise that a peak where they are now is in fact low compared to others.
Evolution-type strategies are often used in a fitness landscape, which means that a balance of optimisation and exploration is required.
The implication is that futures techniques must be provide both optimisation and exploration processes to help identify a range of potential future situations and options. They must also enable acceptance that some options will sound negative or ludicrous now (for example descending initially into a profit-free valley now in order to access higher peaks). Futures techniques need to enable reframing to see the landscape from different perspectives and to enable the generation of optimum and currently non-optimum alternative potential strategies and options.
Several authors have developed concise descriptions of complex systems incorporating most of these concepts including Glouberman, Cohen, and Axelrod, but from a futures perspective,
Axelrod’s is the most useful.
“Agents , of a variety of types , use their strategies , in patterned interaction , with each other and with artefacts . Performance measures on the resulting events drive the selection of agents and/or strategies through processes of error-prone copying and recombination , thus changing the frequencies of the types within the system.”
The taxi example is easy to understand complexity in these terms; a flock of birds flying is another.
Element Example - Taxies in a city
Agents... Taxi drivers (and their customers)…
...of a variety of types
...use their strategies (rules)...
Experienced, new, clean, not clean, night, cheating
1 Minimise time to find customer
2 Go where customer wants
Example - Flying flock of birds
Birds in a flock
Experienced, young, male, female, hungry
1 Avoid crowding neighbours
2 Steer towards the average heading of neighbours
3 Move toward the average position of neighbours
...in patterned interaction, with each other...
Customer directs drive, pays driver. Driver drives customer to location
Fly together to a certain location without crashing
...and with artefacts…
Money, cars, phones Wind, wings
Performance measures on the
Profit made, car undamaged Obstacle not hit, bird stays with the
T HEME : C ROSSCUTTING S ESSIONS H ORIZON SCANNING
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Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011 resultin g events…
...drive the selection of agents and/or strategies...
Drivers who don’t make enough profit go out of business
Taxies that are damaged reduce drivers’ profits flock
Die if hit an obstacle
Get eaten by predator if loose the flock
...through processes of errorprone copying and recombination...
Try out strategies of other successful drivers – jumping lights, cleaning taxi
… changing the frequencies of the types within the system
More successful drivers, fewer new drivers, more cleaner taxies
Try out strategies to get to best foodposition in flock – fly faster, slower
More faster flyers
In summary the implications of complexity theory for futures and foresight techniques is that they must:
1.
enable a vision of a system’s emergent properties
2.
embrace emergence rather than planning & forecasting
3.
focus on interactions rather than constituent parts
4.
recognise that even the basic rules and essential profile of a system can change (where rules can be laws and policies, but also values, perspectives etc);
5.
enable visioning of phase change situations (with no early warning signals) and the resulting changed world
6.
recognise that everything is part of a system , where tiny, trivial actions can have a huge, irrevocable impacts
7.
enable reframing to visualise systems from very different perspectives, including ones not possible now, and enable understanding of these different perspectives on potential policy options
8.
enable the generation of a range of future options and alternative potential strategies through both optimisation and exploration including some that sound negative, impossible or ludicrous now
One way to think of all of these ideas together is the concept of Reframing. In simple terms
Reframing is:

Changing the way you think

Changing the way you see things

Changing the way you understand things
T HEME : C ROSSCUTTING S ESSIONS H ORIZON SCANNING
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Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011

Not trying to solve problems using the same frame they emerged from

Generating new solutions to issues

Enabling alternative actions
An example of Reframing: After much searching in many different areas of the city, you have finally found a possible flat to buy and you go for a walk to explore the area a little more. You pass a newsagents and decide to drop in for the paper and some chocolate. You pay and take your purchases, but the newsagent gives you too much change. You realise, keep quiet and start to walk out of the shop; noone will know… But then you think, I may buy the flat. I may be coming here again. The owner will know who I am. You turn round and return the excess change.
By simply thinking about a possible future (purchase of the flat), you have changed your frame .
And you have changed your opinion and even changed your actions . Axelrod calls it the
‘ shadow of the future’ .
From a futures perspective the benefits of reframing are:

Realising that there are more choices than thought, so those possibilities need to be created – new frames are required

Being aware that you already have own frames and that our ways of seeing are driven and constrained by our values; unrecognized and unexamined, our preferences often pass for objective assessments of plausibility & probability. “ Impossible is merely an opinion
”.

Bringing something new into existence

Creating something outside the cone of possibilities

Deconstructing an existing frame to explain or demonstrate to others that they are stuck in one frame

Benefiting from an ability to switch frames
Some futures techniques do use Reframing; for example Causal Layered Analysis employs
Reframing at the deep myth/metaphor level.
Intriguingly the recent and somewhat controversial work of Wolfram may enable us to ground this complexity-derived insight in very fundamental theory, and demonstrate why the
‘unexpected’ can suddenly arise from a system that appeared to be quite simple and stable for a long time.
T HEME : C ROSSCUTTING S ESSIONS H ORIZON SCANNING
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Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011
Modern life and education trains us to think in terms of linear causality yet complexity theory shows us that we need to think differently, less linearly, with less continuity, and with embedded change. But thinking in such ways is difficult, disturbing, & different. One reason why is the way our brains work. Recent work by McGilchrist shows that the two halves of the brain, while they are always both required, work in very different ways and also inhibit the actions of each other.
This is a very important issue for futures techniques and for the activity of reframing.
Use of the left half of the brain is normal in ‘work’ - it has an ‘objective’ approach to assembling and analysing information, makes ‘logical’ decisions, defends such decisions, and generates supporting evidence etc. The left half of the brain is always engaged in a purpose – it has an end in view. It is about sharp boundaries, either/or (rather than ‘and’), language, abstraction, hypothesis, & analysis. Anyone using the left half of the brain can explain how and why, and argue with other views. McGilchrist considers that modern life is defined by the left half of the brain.
As a result the left half of the brain is particularly poor at foresight, especially complexity-based futures, not least because the left side of the brain also has no concept of time! In addition, the left hand side of the brain

does not work well in situations of time, change, & uncertainty or where simple cause and effect are not there, so its view is ...
if we do this, then of course that will happen

has a closed system of knowledge - it cannot break out to know anything new, so its view is .
..tomorrow is more of today

cannot change once a thing it is known, so its view is .
..nothing will really change

needs clarity to manipulate things that are known, fixed, static, isolated, de contextualised, explicit, general, disembodied, lifeless, and perfect, so its view is ...
we have a clear plan to achieve our objectives

is intolerant and inflexible, so its view is ...that is simply not a possible option
Essentially applying the left half of the brain to a problem generates linear, sequential analysis, makes the implicit explicit, and brings clarity, which is crucial in developing understanding; but in doing so the ‘whole’, and all the other ways of seeing, of reframing , are lost.
Can using more of the right hand side of the brain help?
The right half of the brain views the world ‘in the round’. It is individual, changing, evolving, interconnected, implicit, incarnate, living within the context of a live world, but never fully graspable & always imperfectly known. It is a
‘journey’, inattention, & ‘creative’, but as an unveiling creativity rather than as a wilful creativity. It is contextual with no sharp boundaries; one thing morphs into another. The right hand half of the brain is vigilant for what is with no agenda or purpose and as a result it enables a different way of seeing – of perceiving. So some futures methods have developed to enable us to do right hand side work, while ‘labelling’ it as left hand side work (or distracting the left hand side so that the right hand side gets a go!). Techniques such as scenarios – all about the holistic, implicit, interconnected, whole; scenarios are never quite fully graspable & always imperfectly known.
The future is intrinsically resistant to precision and clarification, the approach which the left half needs to take, but which the right half can manage very well without. What futures techniues
T HEME : C ROSSCUTTING S ESSIONS H ORIZON SCANNING
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Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011 need to provide is a method to think differently, to think in ways that the right hand side does, but not to do it too formally or analytically, or it will be too attentive and will revert to a left side approach instead. Futures needs methods to be suitably inattentive …. whilst being sure something of benefit will result.
But right half approaches however are often not acceptable in ‘work’.
Right half approaches are often seen as unfocussed, intuitive, not evidence based, not logical, never clear, subjective. So the right half approach is not usually allowable, although much required in poets, artists, musicians etc. The world of ‘work’, does not allow the right half approach, support it, or even accept its existen ce (‘ we need results, tomorrow, with full figures’ ). Therefore we won’t or can’t apply it, we feel very uncomfortable applying it (especially anyone who is highly analytical,
‘scientific’ etc), and we simply don’t know how to justify/support applying it if we did!
But it is not as simple as using the Left or the Right side of the brain (L or R). The brain uses, and must use, both. Futures, Foresight and re-framing activities must therefore use both. The relationship between L & R depends on mutual separation in the brain and the relationship is not symmetrical or reciprocal. R interacts with the world and can do so alone. But it can hugely enrich this interaction by using the capabilities of L. So the outputs of L (the benefits of language and logic and analysis) must be returned to R in order to ‘live’ (to go outside the brain). But the outputs of R, while enrichable by L, do not need to be unpacked and enriched by L. But for most people R does not dominate, as the power of L - Language, Linearity and Logic, is dominant in the western, modernist tradition. We have come to depend on these three L’s, which has enabled primacy of the L approach today.
Intriguingly the apparent sequence of events causing one another in time is, according to
McGilchrist, an artefact of the L way of viewing the world. This must have implications and also difficulties when operating in a complex, noncause & effect world. L probably can’t operate well in such a world, and probably only R can.
When operating in a fitness landscape and needing to combine both very random and highly focussed exploration, both the unfocussed exploration (the R approach) and focussed exploitation (the L approach) are required.
The implication for futures techniques is that, in order to take account of some of the complexity related issues described above, futures techniques require a more R type approach and understanding of the world in order to have an open receptiveness that allows things to grow and to avoid the L capacity to force things into certainty, clarity, and explicitness. Futures techniques need to take more of an R approach to provide the
‘inattention behind attention’ - things we can’t bring about by an effort of will (sleep, wisdom etc). Only R does metaphor; we need metaphor to understand the world - fundamentally and essentially and not as just an added extra; metaphor can help us find alternative views.
McGilchrist also shows that the relationship between L & R depends on their mutual inhibition.
The essential human dilemma is the need to attend openly (to not miss something) and yet to focus (to identify and understand). R needs not to know what L knows as this would destroy its ability to see the whole (which is the source of all new or alternative views that do not already exist within L). The mutual inhibition means that L and R can only say ‘no’ or ‘not say no’ to what the other presents to it. R is conscious of ‘Other’, L only of itself.
T HEME : C ROSSCUTTING S ESSIONS H ORIZON SCANNING
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Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011
The implication for futures techniques is that semi-transparency is required – to unite the visible and the invisible (i.e. to put new perceptions gained from reframing back into the old understanding); R then L then R. Futures techniques need not L or R, or simply both, but R then L then R.
From a more philosophical point of view the L view is always simple – lets say L thinks, ‘A’. If the
R view is ‘Not A’, then L can only have its own view (‘A’), but R can have its own and L’s view –
‘A and Not A’. Such an approach (enabled by the separation and mutual inhibition of the halves) enables brain to hold both views – very much a complexity type approach. Gilchrist sees the move from ‘A’ to ‘A and Not-A’ as the move from Enlightenment to Romanticism, but it could easily be seen as a move from the (merely) complicated to the complex.
The implication for f utures techniques is that with the R approach of ‘A and Not A’ there is no simple ‘fact of the mater’ we can hold two different ideas at the same time – we can tolerate ambiguity. Being able to hold both ‘A’ and ‘Not A’ at the same time may explain why we miss weak signals that there are? Futures techniques need to exploit this ability to enable the development of alternative views and alternative strategies (policies).
In summary the implications of McGilchrist’s work in neuroscience for futures and foresight techniques is that they must:
9.
take more of a right brain approach to provide the insights and alternative perspectives that can’t be brought about by an effort of will
10.
take more of a right brain approach to enable the use of metaphor to understand the world and to find alternative views
11.
not use just left or right brain approaches, or even both, but use the right, then the left, and then the right
12.
exploit the right brain’s ability to hold (believe) conflicting views at the same time.
We all have our favourite story of a failed or ludicrous policy where the lack of foresight becomes obvious - in retrospect. The lesson is often that the environment/time/space/area in which one works/lives in determines how one thinks, and not only is it difficult to get out of that ‘thought channelling’ process, but one is usually not even aware that thoughts are being channelled.
The key insight for policy making of complexity-based futures is that command and control approaches don’t work in complex systems. The key implication is that a system cannot be controlled from above – policy operating in a complex system cannot achieve a specific outcome directly. Instead, following Axelrod, policymaking needs to harness complexity – to deliberately change the structure of the system , and to do so by changing the way that a system is perceived
- reframing. Policy makers need to watch for the ‘emergent’ properties that arise as a system organises itself following a policy intervention, and use policy to preserve the conditions in which the best solutions arise.
T HEME : C ROSSCUTTING S ESSIONS H ORIZON SCANNING
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Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011
Several writers (Battram, Swanson and Bhadwal) have considered how complexity-based foresight can be applied to policy making, including; looking for shorter term, finer grained measure of success that can usefully stand in for longerrun, broad goals; ‘try it on and see what you get’; using social activity to support the growth and spread of value criteria; observing patterns, relationships, patterns, & rhythms, rather than events; building networks of reciprocal interaction that foster trust & co-operation; promoting effective neighbourhoods; and promoting variation.
Much as it might be interesting and intellectually challenging to re-invent the whole policy making process in the light of complexity-based futures, the current (and historic) general concept of policy making is likely to be the one in which application of complexity-based futures will take place, at least for a while. We will therefore use a simple, generic, policy-making concept (Bhimji) - direction, design, and delivery – to briefly explore one example complexitybased technique to deal with disruptive events in policy-making – Promoting Variation.
Policy Direction

Policy Design

Policy Delivery
Objectives Objectives Objectives
Discover new policy problems or opportunities.
Scope or define a policy area and determine a vision
Identify policy options
Test policy options
Implement policy
Monitor policy
Variation in policy making terms means using several options to achieve an intended outcome - implementing a variety of policies to address the same issue increases the likelihood of achieving desired outcomes. Variation can be viewed as several ‘parallel experiments’ being undertaken simultaneously with the aim of achieving a common objective, and as an embodiment of the ‘optimise and explore’ concept. Variation also constitute a risk management approach, whereby a policy is more able to work as its environment changes
– while many of the policy interventions will fail (and failures are a normal feature of complex systems), having several options increases the likelihood that at least one option will succeed. In addition, variations may be interpreted differently by different parts of a relevant community, who in turn may produce a range of different responses (variations).
There are three key ideas which can help policy makers to use variation

Promoting variation by designing and implementing a range of alternative policy options to meet the various needs of different stakeholders; using a mix of policy instruments, exploring synergies with other policies, providing opportunities for riskspreading, and undertaking cost-benefit analysis .

Create an enabling environment for variation facilitate conditions that enable societies to create alternative approaches to achieve a common objective or to respond to a common issue; identify influencing factors and remove barriers to facilitating variation.

Study from past and current experiences and adapt as needed for example reviewing what policy interventions would usefully create or destroy variety and
T HEME : C ROSSCUTTING S ESSIONS H ORIZON SCANNING
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Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011 considering if the variety that results ever offers potential value? Consider if there may be alternative sources of variety which could have greater promise? And arrange organisational routines to generate a good balance between exploration & exploitation options.
Variation has relevance at each stage of the simplified policy making process:
Policy Direction Policy Design Policy Delivery
Implement policy
Monitor policy
Discover new policy problems or opportunities.
Scope or define a policy area and determine a vision
1.
Understand the resources or skill-sets required for the deployment of each of these alternative strategies and to facilitate adoption and deployment of these strategies through appropriate policies to minimize risks
2.
Identify and characterise the probable conditions of risk
3.
Identify a set of alternative response strategies that can be undertaken to minimize the impacts from the any projected risks
4.
Scenario analysis and similar planning methods can be used
Identify policy options
Test policy options
1.
Create an enable environment for variation to occur
2.
Design and use a mix of policy instruments to achieve a single policy objective
3.
Provide a range of policy options
4.
Remove the barriers that hinder the adoption of these strategies
5.
See and make linkages with other policies that have similar intent
1.
Comparative analysis
2.
3.
of the costs of implementation and benefits accrued on implementation of each of the strategies needs to be undertaken in a regular manner to update on the efficiency of each of the strategies as newer conditions unfold and emerge
Monitor and evaluate the policy instruments deployed to promote variation, as well as to incorporate feedback from the grassroots level where variation needs to be promoted
Observe which policies work well and strengthening those policies
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Forth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
S EVILLE , 12-13 M AY 2011
In a complex system (which all societies are), futures techniques need to recognise that

It is all about relationships & interactions

Emergence & phase change not prediction & forecast

Impossible is only an opinion

It is good to be dreamy and think inattentively

You can hold two opposite opinions at once

Seeing differently is essential
The assumption that disruptive events can be managed by planning and forecasting is not a workable option. Instead, policies need to assume that disruptive events will happen, and will be unexpected, even with the best horizon scanning system in place. Methods for working with this assumption need to take account of the different ways in which the brain interprets the world.
Promoting Variation is one way of making policies more resilient in a complex system.
References
Robert Axelrod and Michael Cohen, Harnessing Complexity: Organizational Implications of a Scientific Frontier, 2001
Arthur Battram, Navigating Complexity: The Essential Guide to Complexity Theory in Business and Management,
2000
Wahid Bhimji, Guidance on the Use of Strategic Futures Analysis for Policy Development in Government, 2009
David Byrne, Complexity Theory and the Social Sciences: An Introduction, 1998
Sholom Glouberman & Brenda Zimmerman, Complicated and Complex Systems: What would successful reform of
Medicare look like? 2002
Brian Goodwin, How the Leopard Changed its Spots, 1994
Iain McGilchrist, The Master and His Emissary, The Divided Bran and the Making of the Western World, 2009
Melanie Mitchell, Complexity: A Guided Tour, 2009
Ilya Prigogine and Isabelle Stengers, Order out of Chaos, May 1989
Darren Swanson and Suruchi Bhadwal, Creating Adaptive Policies: A Guide for Policymaking in an Uncertain World,
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Steven Strogatz, Sync, The Emerging Science of Spontaneous Order, 2003
Mitchell Waldrop, Complexity, The Emerging Science at the Edge of Order and Chaos, 1992
Stephen Wolfram, A New Kind of Science, 2002
T HEME : C ROSSCUTTING S ESSIONS H ORIZON SCANNING
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