Why are ‘unintended consequences’ almost inevitable?

We so often hear politicians, managers and those in positions of authority wailing about the ‘unintended consequences’ of their actions – or worse, excusing damaging outcomes with a shrug of the shoulders and saying “But they were unintended consequences …” as if that lets them ‘off the hook’ of their responsibilities.

But were these consequences in the real world really unintended? Maybe they were actually inevitable, even ‘normal’ given the actions taken. Could it be that if appropriate approaches and mindsets had been employed then the damaging consequences could have been avoided?

To which the answer is and emphatic “Yes!”.

If one is bringing about change in the real world, in the complex and largely unpredictable inter-connected world in which we live then it is no good merely following the ‘best practice’ taught on business administration courses. The real world cannot be treated as if it is a supermarket chain.

Instead, complexity thinking offers a clear set of principles that should be followed when intervening in wider policy, commercial, social, community, medical and environmental matters.

Avoiding those unintended consequences

I am sure you are now thinking “OK, if we can’t predict how can we see what the consequences will be?”. Good point! The answer is that, yes, we can’t predict specific instances ( x will happen in this place at 1256 on the 27th March to Fred Bloggs and Xioa Lui), but we can anticipate the future if we are forward-looking.

Indeed, complexity thinking can identify that certain classes of outcome are likely, even inevitable, if we go about a particular task in a certain way – or if we organise ourselves inappropriately.

[The rest of this post will give examples]

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Applying Complexity Thinking to the Real World

PRINCIPLES FOR [BRINGING ABOUT] EFFECTIVE CHANGE IN [REAL-WORLD] PRACTICE

The eleven ‘Principles of Practice’ below have been derived from the experiences of practitioners (people who are responsible for bringing about real-world change) which are discussed in a companion book “Complexity Demystified – a Guide for Practitioners” (downloadable as a PDF from here).

An example of how these principles can be used is shown by the ‘Liveable Cities‘ project which, in 2017, published a series of short booklets illustrating complexity thinking at work.

The principles below indicate that, in practice, integrative and iterative approaches are essential to bring about desired changes – and so doing minimises ‘unintended consequences‘:

• Principle 1: Dynamic, ongoing change can be influenced purposefully. This is because: a) the underlying ‘drivers of complexity’ have been identified, and b) practical techniques are available to purposefully engage with and shape the underlying drivers.

• Principle 2: Context understanding and perceptions are diverse – there is no ‘single view of the truth’. The experienced complexities of stakeholders are necessarily different, as are their knowledge and information needs.

• Principle 3: Change is ongoing, dynamic and multi-level – there may be no end. This means that trends, flows, gradients, potentials and other ‘energy metrics’ are appropriate dynamic indicators of the progress of practice.

• Principle 4: There are many qualities of power and influence to accommodate. These affect people’s ability to adapt and may arise from individuals, their beliefs and vulnerabilities, or from community values, from gender issues, institutional structures and the political economy, and from the changing environment and so on.

• Principle 5: It is necessary to appreciate who is / what are best placed to bring about change. Given the inevitable natural complexity of practice, those tasked with achieving change may not be the ones best placed in a situation to be drivers of change. Given the time horizons, practitioners should work adaptively through those who are best placed.

• Principle 6: Interventions must have the necessary requisite variety, i.e. have appropriate complexity-worthiness given the desired changes. This insight arises from Ashby’s ‘Law of Requisite Variety’ (1957) which states, in essence, that to influence something your practical behaviours must be equivalent to, and preferably exceed, the repertoire of behaviours of that which you are trying to influence – i.e. to deal with innovation by innovating.

• Principle 7: Practice is not just about adapting, but is also about being able to adapt the adapting and learn. This is because people are in continual co-evolution with the environment and, as there will never be a ‘steady-state’ balance or equilibrium, anticipatory innovation will always be required. One cannot adapt once and then stop.

• Principle 8: Different decision-making and problem-solving styles are required for different situations. Because practice involves inevitable novelty and change over time, there can never be a ‘one-size-fits-all’ solution, nor can ‘optimized’ processes be used as ‘best practice’ in all situations. Indeed, many modelling tools are just not fit for purpose in the face of real-world complexity.

• Principle 9: When reasoning about change, past evidence does not guarantee future prediction. This means that, though we may have evidence of a past train of events, there is no guarantee that we can extrapolate a reliable prediction from this into the future. As there are limits to what we can know and observe, there will always be uncertainties and unknowns, and that we must accept this as a given. A key skill for practice and risk management is envisioning, and being prepared to act on possible, not probable, futures.

• Principle 10: When innovating, transition to new forms may be the only valid option. Because of the inevitable novelty already mentioned, the transformation from non-adaptive capabilities to being appropriately complexity-worthy will require purposeful, ongoing, innovation and adaptation. Gradual, superficial, incremental transition is just not an option in some unsettling circumstances.

• Principle 11: Change will be impeded unless appropriate degrees of freedom and ‘wiggle room’ are available. Being open to change means appreciating where the ‘spaces of possibilities’ are, and how to maximize and exploit them. A misplaced drive for control, repeatability and certainty may clamp down on the very space that is needed for adaptive behaviour to flourish.

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ISIS Ascendant – Because the West gave up its Winning Strategy in 2001?

A Winning Strategy Lost?

The UK’s ‘Bin Laden Dossier‘ of 2001 reports that Bin Laden’s motivation was to wage jihad against countries (such as the USA and UK) engaging in ‘un-Islamic behaviour’.

In his speeches, Bin Laden cited examples of this, such as: the westernised mode of dress of young people; their liking of pop music and consumer goods; and their lack of respect for their elders and leaders for example.
In other words, the West was (unwittingly) already winning a ‘hearts and minds’ campaign though the spread of consumer capitalism and western goods, media, film, entertainments and comfortable lifestyle.

After 9/11, it could be reasoned that the West’s best course of action was to continue, and intensify, these so-called ‘soft power’ activities – not to attack anyone with hard power.

But this is not what happened and the War on Terror (always unwinable by definition) went ahead. The situation in 2015 continues to ramp up conflict, into what now seems inevitable – World War Three (WWIII).

World War Three (WWIII) – The War of Ideologies

Conflict in the Middle East is not new. It goes back at least as far as Egyptian times (Ostler 2006).  And then, with Islam, it had the Shia / Sunni schism at its root. From the time of The Crusaders onwards, so-called Western forces and governments have had little success in changing outcomes (Stewart 2006,  Tanner 2002, Wallach 2005).

Recent experiences in the region (Iraq, Afghanistan – even for the Russians) are not encouraging for the coalition that the UK and US governments are hoping to put together in 2016.

Also, analysis of the capabilities of Western coalitions (Lwin 1997, Mackay and Tatham 2011, Smith 2005, Treverton 2003) show up a series of endemic weaknesses in mindset, approach, capabilities and strategy and tactics.

With these in mind it is worth while trying to analyse what possible futures we may face. With certainty an impossibility, a sound approach is to develop a range of hypotheses (as in this example high-level scenario involving tactical nuclear weapons) and, using judgement (not just computer modelling), evaluate their relative merits.

Of prime importance is being realistic about what can and can’t be achieved practically. Especially in a situation where hundreds of factions vie for power, where allegiences are endlessly changing and loyalty cannot be relied upon. At the heart of WWIII is a clash of unreconcilable ideologies which, as the Cold War showed, may take generations to resolve. Negotiation (Howard 1999) is not an option – yet.

Till then, as Lawrence noted following his experiences of the ‘Arab Revolt’ of 1916-18 – and of the subsequent negotiations (including the betrayal of the Sykes-Picot agreement, which still rankles in the Middle East today) – nothing and no one can be trusted and nothing can be relied upon.

In 2016, little has changed from his time. Money, mercenaries and mediocrity mean that the chances of ‘success’ for the West’s new coalition are slim. And in a situation where it is not even possible to say unequivocally what constitutes ‘winning’, then we are all in for a long haul.

What has changed is that this time the consequences are global – and fanaticism,  ruthless death and suicide are ISIS’ most potent weapons of coercion. No one is ‘safe’ and, currently, every possibility may happen.

We live in interesting times.

References:

– Lwin, M. R. (1997) ‘General Tzu’s Army: OPFOR of the Future’. JFQ, USA.
– Howard, N. (1999) ‘Confrontation Analysis: How to Win Operations Other than War. CCRP, DoD.
– Lawrence, T. E. (1922) ‘Seven Pillars of Wisdom’. Various editions.
– Mackay, A. Tatham, S. (2011) ‘Behavioural Conflict: Why understanding people and their motivations will prove decisive in future conflict’. Military Studies Press. Saffron Walden, UK.
– Ostler, N. (2006) ‘Empires of the Word: A Language History of the World’. Harper Collins.
– Smith, R. (2005) ‘The Utility of Force’, p323-331. Published by Allen Lane, London.
– Stewart, R. (2006) Occupational Hazards, pp360-361. Picador, London.
– Tanner, S. (2002) ‘Afghanistan: A Military History from Alexander the Great to the war against the Taliban.
– Treverton, G.F. (2003) ‘Reshaping National Intelligence for an Age of Information’. Cambridge University Press. New York.
– Wallach, J. (2005) ‘Desert Queen. The Extraordinary Life of Gertrude Bell: Adventurer, Adviser to Kings, Ally of Lawrence of Arabia’. Phoenix, London.

War on Terror – always unwinnable?

The so-called ‘War on Terror‘ has a rather silly name – as daft as a ‘war on democracy’ or a ‘war on happiness’.

Terrorism has been part of the human condition for millenia. As history shows, one person’s terrorist is another person’s freedom fighter (think of Israel, South Africa and Northern Ireland for example). One cannot destroy it, any more than you can destroy the human emotions.

So ‘terror’ can never be eradicated. There will always be those who dissent, feel oppressed or disenfranchised (often with good cause). Indeed, complexity science would say that dissent is a necessary condition for the stability of society – in that otherwise society would be totalitarian.

As our post on types of organisational form and the work by Michael Thompson on ‘organising and disorganising’ indicates, diversity in human society (and in nature in general) is very complicated and the differences of viewpoint and motivation are the source of the rich interactions seen in the world (at over fifty-eight levels from micro to macro and from instant to eons).

A trite, soundbite ‘war on terror’ is not, and was never, going to change that – something much deeper was required.

Prediction in the face of Deep Uncertainty

So what about prediction? What is the difference between a backward-looking approaches to decision-making and an anticipatory forward-looking ones (discussed here)? It’s partly about the difference between probability (and risk) and possibility (and deep uncertainty) and partly about people’s assumptions about what causes the future to come about. And it’s not just about the ability to model either – there are limits to that.

And, anyway, why is prediction such a challenge? I think it was Dan Quayle who is supposed to have said “The trouble with the future is that you don’t know what is going to happen!”. He’s so right …

It’s easy to assume that if we know the past, we can predict the future. But the natures of the past and of the future are not the same. The past is like a puzzle, things happen and provide facts which are like pieces of a jigsaw puzzle. If some are missing, we probably know they are the red ones with wiggly edges and we can go and look for them. What we have candidate pieces we can apply a matching algorithm against the known need. When the right piece comes up we have the solution. Certainty and predictability seem self-evident, but are they?

The diagram below illustrates this. If we have evidence (green circle) it implies an event and the presence of other evidence waiting to be discovered. Our assumptions can be based on what we know or might know. But the limits on prediction are set by the ‘Prediction Horizon’. With puzzling, we can only predict a short way ahead about, for example,  people’s intentions (shown by the top of the curve, probably only seconds); but we can predict quite a long way ahead about geographical matters (the lower part of the curve). Why is that?Well. when we try to look into the future we are actually considering, not puzzles, but mysteries. A mystery is ill-formed, it has no pieces, there is no right answer or final outcome and we cannot be sure we have solved it – this is the essence of deep uncertainty. What we can do is form hypotheses about possible futures and seek indicators that support or refute the various possibilities. By ‘competing’ the hypotheses we can anticipate futures and put in place adaptive contingencies. Succesful outcomes are, therefore, practical and feasible. Look at the next chart.

Some of the possible futures take us beyond the prediction horizon because we are not relying on past evidence – instead we are using our imaginations to ‘think the unthinkable’ – to think not what is probable, but what is possible. The trick, as the third chart shows, is the way that you analyse possibilities and compete the hypotheses. This requires leadership, professionalism, intellect, imagination and courage to buck the trends – and people are doing it effectively and succeeding where others struggle.

But I’m not going to give those secrets away here!

References: The discussion of puzzles and mysteries has been adapted from Treverton, Gregory F. “Reshaping National Intelligence for an Age of Information“. Cambridge University Press (2003).

 

Limits to modelling – Godel’s Incompleteness theorem

Agent-based modelling is often hailed as a way of modelling the future, predicting outcomes in social situations. But there are both hard limitations on what you can predict with modelling, and a lack of understanding of those limitations.This post examines those limitations and shows which models are appropriate for which tasks,

But first, the baselines. As Professor Michael Batty said in 2009 …

Possibility, Risk Asessments and dealing with Possible Futures

There is a fundamental flaw in most risk assessments – they are based on past data. That means that they are useless in the face of crises or deep uncertainty, in ambiguous situation or when faced with so-called ‘zero-day’ events.

[TBC]

Transitioning to Adaptive, Forward-looking Ways-of-Working

In previous posts we talked about the different Aspects of adaptation and what this means for the learning that organisations, communities or individuals might have to do. What follows from this is an obvious question – how do you go about transitioning to the more adaptive and forward-looking ways-of-working needed to harness dynamic change?

The first thing to note is that there are a number of different characteristics of adaptation – these are shown on the chart below and are matched to the Adaptation Aspects previously discussed. In each cell of the matrix is a pen-picture summarising the ways-of-working that apply. Note that the first two columns concern backward-looking behaviours and the last two columns characterise the kind of forward-looking behaviours needed to be adaptive in the face of change (especially given unexpected and unpredictable change).

So, if you want to transition from backward-looking to forward-looking ways of working can that be done in one go? Possibly. We like to use the steam engines to iPods analogy. The technology that was used to make steam engines and bicycles can’t be used to make iPods and enable Facebook social networking. The possibilities offered by steam engines and bicycles are completely different from those available with iPods and Facebook and can only be achieved by transformation. More importantly for practice, steam engineers (in a Facebook world) would have no way of understanding what was going on in a modern context.

Indeed, the consequence of this for practice goes further than just individual capabilities. If you specify and evaluate projects and people in steam-engineering terms, you are only going to get more steam engineers and steam engines – transformation will be impossible in this case. Also, even when people are open to change they still want step-by-step certainty, but often there is no ‘fully connected graph’ between here and there – there has to be a ‘jump’ via some other modality / dimension (eg, via the development of software in the iPods example above). That, IMHO, is what true adaptation is about – having the agility (see below) to make the jump.

So what are the options for the ways in which one might transition / transform? The chart below shows four basic options.

You can of course take Option A and do nothing – that might be appropriate to your context if it is stable and predictable. Or, in an organisation which is risk-averse, people might take Option B and just ‘tinker round the edges’ – but that will keep things within the current organisational behaviours.

To really start adapting Option C is needed. Here people are literally ‘pushing the boundaries’ and changing the organisational principles (eg, permissions, authorities, incentives and so on – see p117 of Complexity Demystified for more on this).

But sometimes, as in the steam-engines to iPods example, things just have to transform and Option D must be taken – really ‘thinking outside the box’. You might be able to make a proof-of-concept transition first which is then an enabler for the full transformation. What might this involve? What are the factors, tensions and modifiers that might need to be traded off? There is a full list from p145 onwards of the Complexity Demystified book.

Go on – make the jump – be different by choice!

By the way – concerning agility, here are a set of characteristics which together lead to overall ‘Agility‘ (the ability to sustain appropriate change – purposefully and dynamically in relation to some context – over time). They are all ‘inter-twingled’ enablers of each other and the adaptation we have been discussing here is just one of them:

1. Resilience: the ability to recover from or adjust to misfortune, damage, or a destabilising perturbations in the environment;
2. Robustness: the ability to maintain effectiveness across a range of tasks, situations, and conditions;
3. Responsiveness: the ability to react to a change in the environment in a timely manner (ie, in a relevant timescale, not just quickly);
4. Flexibility: the ability to employ multiple ways to succeed and having the capacity to move seamlessly between them;
5. Innovation: the ability to consider possible futures, do new things and the ability to do old things in new ways; and
6. Adaptation: the ability to change ways-of-working; the ability to change the ‘organisation’ / community; ability to change necessary capabilities and the ability to ‘morph’ as needed.

Actually, in the real world, as they are all inter-twingled it makes no sense to try to analytically separate them out into distinct categories in practice.

(c) 2012 The abaci Partnership LLP.

Organisation forms and Contextual Complexities

‘Contextual complexity’ provides an ‘objective’ perspective (as far as it can be) on the realities of the context and is the basis of exposing givens, realities and unspoken assumptions. When practitioners wish to establish their Contextual complexity (by undertaking ‘Symptom Sorting’) they will have to examine the types of phenomena manifested in the particular situation with which they are concerned and describe in objective terms, as far as possible, the nature of the context in which these phenomena are arising. There is then a range of organisational forms that can be matched to the contextual complexities. Four typical examples are described below.

Migration and transition between these four types of organisational forms (or types of ‘communities of interest’ (CoIs) as they might be more appropriately called) over time may occur as either institutional forms become firmer or as greater agility is required. From any transition or migration as a result of changing contexts follows the need to morph to the adapt and access the necessary capabilities required, including the kind of interoperability that is suitable. This continuum is illustrated below:

Organisational Form Type 1: Self-contained, pre-defined, ‘establishment’:
This type of organisational form is for systems in contexts that are well known with mostly linear relationships:
*    There is a single design authority responsible for design, development and implementation;
*    The subsystem capabilities and data regarding, for example, coordinate systems in use, spatial accuracy, data consistency, etc are determined at design-time and sharing is enabled through pre-determined mechanisms;
*    The relationships between subsystems are agreed in advance of any interaction;
*    Any exchanges between subsystems are point-to-point and have pre-defined information exchange requirements; External exchange with other systems (‘interoperability’) is minimal;
*    The systems and their subsystems remain linked over ‘long’ periods of time during which the configurations and overall relationships are largely static;
*    Certainty and efficiency of these systems are high, but flexibility and robustness are low, as the system relies on the whole system working as intended;
Such self-contained systems are good for stable, well-understood and physically and virtually protected environments, but not flexible in the face of unexpected change.

Organisational Form Type 2: System of Systems (SoSs), established institutionally over time, ‘enterprise’:
In this type of form, ‘systems’ work with a common, agreed context, though each sub-organisation has its own sub-intent. Each of these intents is pooled with the others, following the formulation of a common intent, into a standardised system-of-systems where user interactions and processes are streamlined as well. For these SoSs it can be noted that:
*    The systems ‘participating’ in SoSs are semi-independent entities who agree to share selected information via agreed mechanisms;
*    The design authority for the system is established by a committee of nominated design representatives;
*    The participating systems may not join and leave as they please, as the agreements established through the SoSs put obligations on them that they have agreed to at the outset;
*    Interoperability is obtained using pre-agreed standards that can be open source or proprietary, based on the ‘harmonization’ of data specifications regarding semantics, reference systems, and scale etc;
*    The overall relationships are firm and well-defined between the systems, and reliable collaboration can be achieved under the agreements;
*    The effectiveness, flexibility, certainty etc. can be modest because of collaboration overhead that can occur for individual systems; the robustness is medium as there are no common services involved;
Systems of Systems are good for partners working together in familiar, ongoing contexts over time.

Organisational Form Type 3: Federations – federates join and share services in a ‘plug and play’ manner (regularised coalitions, ‘community’):
In this case, events define the ‘context’ which are shaped by circumstances, the user needs are context-driven and fluid. Federations are established are largely agreed ‘on-the-fly’. In a federation, a number of collaborators have their own intent, parts of which are traded-off to achieve shared goals as circumstances require. This shared intent is formulated based on consensus and compromise between federates and owned by all federates. Shared federation resources are established and relied upon by the federates. Federations can be imagined as collaboration through and / or the provision of services in the ‘Cloud’. These ‘come-as-you-are’ communities-of-interest are characterised as follows:
*    Federations are context-driven groupings of pre-existing entities, with nested and overlapping memberships that have ‘porous’ boundaries between them;
*    The federates explicitly join the federation and, whilst remaining ‘independent’, accept obligations from, and subordinate themselves to some aspects of the federation;
*    The federates place obligations on the federation in return to provide common services;
*    The relationships between federates are determined in the moment, and can be transitory or long-term – all exchanges between the federates are negotiated dynamically;
*    The interoperability between the federates is based on open standards and relies on these enabling standards being implemented among federates upfront and evolved over time;
*    Overall, these federations are enduring and adaptive though they require a minimal ‘critical mass’ to be viable;
*    The dynamic run-time effectiveness is high. Overall, certainty is high, but local levels of service may be lowered as federates may drop in and out.
Federations are good for real-world situations of all types where diversity enables effective, successful operations, as in cross-agency and cross-disciplinary working where federates work in flexible, complementary ways.

Organisational Form Type 4: Independent ‘Ad-hoc’ ‘Social Networking‘ activity built on whatever is available (extreme coalitions, crowds and crowdmapping):
This organisational form is found in contexts that are ever-changing and unpredictable. As such there are no pre-established user requirements and built-in assumptions are minimal – actual needs emerge with the dynamics of the situation. Each entities’ individuality and integrity is retained and communities form on an ad-hoc basis in a self-organized manner, following a transient shared purpose. The characteristics of this kind of setup are as follows:
*    Independent entities share information and come together as they choose and / or as circumstances dictate;
*    There is no design authority, other than that pre-existing resources and infrastructure are used if available;
*    The interoperability is virtual and emergent – largely socio-technical. Interactions take place with whatever and ‘whoever’ is available, between ‘entities’ that offer technical and non-technical services;
*    People and entities join and leave as they please – there are no obligations on anybody;
*    The relationships that are formed are of a transitory nature and largely emerge by themselves;
*    The ad-hoc collaboration is enabled and achieved via wikis, social networking etc;
*    For such a setup, effectiveness, timeliness and flexibility can be high. These ad-hoc communities can also spontaneously disperse, so the robustness in this type of user scenarios as such is low, but resilience in the face of unexpected disruption is high, as there are no single points of failure;
In summary, ad-hoc collaboration is good for disasters and high-tempo activity in the face of uncertainty and dynamically changing circumstances in which flexibility and adaptation are required and / or essential.

For an example of these constructs applied in the Geoinformation context see also:

* Broenner C. (2012): Appropriate Interoperability: From technical to socio-technical. In: Behr, F.J.,et.al. (Editors): Geoinformation – Catalyst for planning, development and good governance. Applied Geoinformatics for Society and Environment 2012, pp 203-210.

(c) 2012. The abaci Partnership LLP

How do we define ‘Complexity’ – ways of talking about it

In our book, ‘Complexity Demystified – a Guide for Practitioners‘ (page 8) we differentiate four different ways of talking about complexity: as it is naturally; as academics see it generally in theory; as it is seen objectively when in some context; and as experienced subjectively by people. We think it is important to make it clear which type one is talking about as otherwise it is easy to get at cross purposes.

We describe these four types as follows – the first two are context-independent:

– ‘Natural complexity’ – to refer to complexity as it is in the real world – an expression of the phenomena that arise, unadorned by any particular set of perceptions, abstractions or terminology.

– ‘Academic complexity’ to refer to the descriptions and explanations of natural complexity that are provided by complexity scientists and others using the abstractions of scientific terminology (eg, emergence, co-evolution etc).

Then there are the two ways of talking about complexity so we can reflect sensibly on the activities of practitioners (those who work with dynamic change in their everyday lives) in some context as follows:

– ‘Contextual complexity’ provides an objective perspective on the realities of the context and is the basis of exposing givens, realities and unspoken assumptions. So, contextual complexity refers to the types of phenomena manifested in the particular situation with which practitioners are concerned, and describes in objective terms, as far as possible, the context in which they are arising.

– ‘Experienced complexity’ describes the context from the subjective view of practitioners themselves – how it is experienced subjectively by individuals, such as practitioners, or by communities or institutions in a context and which they described in terms that make sense to the subject given their abilities, experience and viewpoint. These descriptions can range from common-sense observations (sadly, an undervalued, yet powerful natural ability) of the phenomena to ones that can be highly complicated and contrived. Many are ‘co-constructed’ realities built up in a social context into a set of prejudices or ‘habits’ of thought.

These are of course no more than an ‘arbitrary’ way of labelling ways of talking about ‘complexity’ – most people use the academic complexity definitions. But the everyday, common sense, ways of talking about how ‘complexity’ is perceived or experienced are important too. ‘Complexity’ is not the same in all situations – from a practitioners’ point of view anyway (see p108).

Take this example of surfing. For all the observers that natural complexity is the same …

• But for the scientist, the phenomena are seen through the eyes of academic complexity.
• For the novice surfer, the experienced complexity is that this situation is a crisis, whereas for the experienced surfing instructor it is merely challenging.
• Back on the beach, during training, the novice and the instructor can talk calmly from the point of view of the contextual complexity, compare that with their experiences and work on how to improve their abilities to adapt.

So you see how much is gained from realising that there is not just the one ‘type’ of complexity that academics talk about. What practitioners think, experience and apply in practice is even more important, yet it is often discounted!

(c) 2012. The abaci Partnership LLP