Models as ‘interested amateurs’

Community member post by Pete Barbrook-Johnson

Pete Barbrook-Johnson (biography)

How can we improve the often poor interaction and lack of genuine discussions between policy makers, experts, and those affected by policy?

As a social scientist who makes and uses models, an idea from Daniel Dennett’s (2013) book ‘Intuition Pumps and Other Tools for Thinking’ struck a chord with me. Dennett introduces the idea of using lay audiences to aid and improve understanding between experts. Dennett suggests that including lay audiences (which he calls ‘curious nonexperts’) in discussions can entice experts to err on the side of over-explaining their thoughts and positions. When experts are talking only to other experts, Dennett suggests they under-explain, not wanting to insult others or look stupid by going over basic assumptions. This means they can fail to identify areas of disagreement, or to reach consensus, understanding, or conclusions that may be constructive.

For Dennett, the ‘curious nonexperts’ are undergraduate philosophy students, to be included in debates between professors. For me, the book sparked the idea that models could be ‘curious nonexperts’ in policy debates and processes. I prefer and use the term ‘interested amateurs’ over ‘curious nonexperts’, simply because the word ‘amateur’ seems slightly more insulting towards models!

In this role, models are not intended to provide answers to policy makers’ questions, or tell them what might happen in the future. Instead, models aim to help policy makers, experts, and those affected by policy – all ‘experts’ on the policy topic at hand – improve their interaction and discussion, as well as their own thinking.

Models therefore become not ‘truth’ for policy makers to accept and take on board, but objects for everyone involved in making or advising or affected by the policy to consider and ‘wrestle’ with – even to attack. Let me provide two examples.

An agent-based model of farmer decision making

A group of local and regional policy stakeholders working on natural resource management and agricultural policy in Ethiopia was presented with an agent-based model of farmers’ decision making related to investing in soil conservation measures. The model set out decision and interaction rules for farmers based on theory developed from empirical work with farmers in the developing world.

The simple but specific model was quickly attacked by the stakeholders – they called it out on what they thought was missing or what was represented incorrectly. However, on some points, others jumped to the model’s defence, and stated that they agreed with it. The model’s detail helped to structure and focus conversation on specific issues, providing discipline to the discussions. The willingness of participants to attack the (unfeeling) model also brought out sensitive topics for discussion that might otherwise have remained untouched.

A fuzzy cognitive map of a river catchment

A varied group of local stakeholders discussing management of a river in the north of England aimed to explore future options for the operation of a large and ageing barrage on the river. In this process, stakeholders developed a fuzzy cognitive map of the river and the surrounding area. Building the map made clear the sheer number of factors important in the area, and the complexity of their interaction.

Of most value perhaps, was the fact that building the map helped stakeholders to realise themselves, in a non-confrontational manner, what they didn’t know, and/or which of their assumptions may be wrong. This happened because the mapping process forced them to agree collaboratively the links between key factors in the river system. Participants concluded some gaps in their knowledge could be filled easily with better interaction with other stakeholders, whereas others required primary research on the processes of the river.


Both of these projects benefited from the use of intuitive and accessible modelling techniques, used not to give answers or forecasts, but to structure a discussion and ‘ask’ potentially ‘stupid’ questions – just like Dennett’s philosophy undergrads. Like much participatory modelling work, the real value was in the process, not the model itself. However, the models’ specificity, intuitive appeal, and neutrality aided discussions in a way other methods or facilitation may not have. I, for one, am looking forward to building more models in the future for policy stakeholders to attack with relish!

What do you think? Do you agree models can act as ‘interested amateurs’? What types of models might work best in this way? What types of policy processes might find most value in using models in this way?

To find out more:
Johnson, P. (2015). Agent-Based Models as “Interested Amateurs”. Land, 4, 2, 281- 299. Online (DOI): 10.3390/land4020281

Dennett, D. (2013). Intuition Pumps and Other Tools for Thinking. Allen Lane: London, United Kingdom.

Thanks to Dr Alexandra Penn (University of Surrey) who leads the second case study.

Biography: Pete Barbrook-Johnson is a Research Associate at the Policy Studies Institute at the University of Westminster, UK, and a Visiting Research Fellow at the Centre for Research in Social Simulation at the University of Surrey, UK. His research interests fall at the crossroads between environmental policy, social and behavioural science, and complexity science. He has experience in a range of research methods including agent-based modelling of social and policy systems, stakeholder causal mapping, and qualitative and quantitative social research methods. He is on Twitter @bapeterj.

Enabling co-creation: From learning cycles to aligning values, rules and knowledge

Community member post by Lorrae van Kerkhoff

Lorrae van Kerkhoff (biography)

How do we improve? In the context of sustainable development, we continually confront the question of how we can develop meaningful and positive actions towards a ‘better’ world (social, ecological, economic outcomes) despite inherent uncertainties about what the future holds.

Co-creation is one concept among several that seek to reorientate us from simplistic, largely linear ideas of progress towards more nuanced, subtle ideas that highlight that there are many different aspects of ‘progress’, and these can be deeply contested and challenging to reconcile. Enabling co-creation, then – or operationalizing it – means finding practical ways to work together, to deal with our different experiences, aspirations and expectations as well as the uncertainties of the future.

Co-creation sits within a learning paradigm that suggests engagement, social and mutual learning, adaptation and flexibility are key to enabling action in the face of uncertainty. But how do we think about learning?

Bear with me on a short philosophical detour—the operationalizing part will make more sense because of it! While there are many variations on the theme of learning across sustainability and social-ecological systems practice, most concepts are based either explicitly or implicitly on the idea of an (adult) learning cycle. Proposed around 100 years ago by American pragmatist philosophers such as Charles S. Peirce and John Dewey, the learning cycle of “plan-do-reflect-revise-plan-do…”etc., has a firm grip on the way we think about learning and progress towards a particular (social, ecological) goal.

The idea of course is that by treating our interventions and actions as experiments, rather than proven cause-effect chains of events, we become open to learning and continual improvement, rather seeking one-off silver bullets towards an agreed target outcome, such as conserving a particular species or maintaining the ecological health of a landscape.

This makes a lot of sense. The fields of adaptive management and adaptive governance are good illustrations. The figure below shows a typical adaptive management cycle, clearly demonstrating the plan-do-reflect process.


Unfortunately, while the adult learning cycle has a very well-established place in education (where it was first developed), in the context of addressing complex social-ecological challenges, the jury is out.

Basically, evidence suggests it is much easier said than done. While the cycle is a big improvement on the one-shot silver bullet approach, there is little guidance on what to do when the ideal cycle comes unstuck, for example when the money for the monitoring program gets diverted to on-ground activities; when there are inadequate resources for evaluation; when incentives in the broader institution mean that admitting to (perceived) failures means personal or professional denigration… the list is long and somewhat depressing. Yes, we may be able to ‘reflect and learn’, but if we can’t do anything about the conditions which have blocked us, the learning cycle can start to look (or feel!) like wheel-spinning.

So what does this all mean for operationalizing co-creation? Colleagues and I (Wyborn et al., 2016, drawing on Gorddard et al., 2016) have proposed that we may be better equipped to deal with crafting our way skilfully towards our long-term, uncertain futures if we think about the learning process as less like a cycle, and more like a process of continual alignment between the values that guide what we want our futures to look like (or what aspects we what to persist or grow), the knowledge we have to tell us how best to pursue this, and the rules that shape what we can and cannot do to get there. As we wrote in the paper:

  • Values represent the personal, cultural and ethical factors that lead to the preferences that people express when confronted with certain knowledge and rules.
  • Rules, or institutions, represent legislation, regulations, constitutions, guidelines, and other formal factors; also societal and personal norms and behaviours.
  • Knowledge refers to the evidence, beliefs and judgments about how the social-ecological system works, an understanding of future changes and the consequences of different decisions.

This approach, known as “VRK” (Values, Rules, Knowledge), differs from the learning cycle, first because it is fundamentally not sequential (although sometimes there may be better places to start than others). Second, the normative, value-laden goal of the process is not external, in the sense that while the ‘target’ (species, landscape) is often a given part of the context for adaptive management, in the “VRK” approach such goals are assumed to represent broader values that are up for debate and discussion.

These broader categories may not sound easier to operationalize! They are less a framework than a heuristic to facilitate targeted conversation in a co-creation process.

But we can start by proposing a few strategies. Any co-creation process needs to begin by developing a thorough understanding of all the stakeholders’ and participants’ relevant knowledge, values and rules in relation to the topic and task at hand.

Knowledge may be the easiest place to start. In working in the area of climate adaptation, we have learned that we needed to examine the existing scientific models and scenarios before we could move on to discuss the more socially and politically charged topics of rules and values.

Visions of the desired future can be constructed collaboratively, to explore similarities and differences in values—what do we really want? Scenario tools may help. These may incorporate technical information or be more conceptual.

Identifying existing rules, and revisiting them to identify roadblocks and pathways towards that vision (or visions), can reveal what needs to change at an institutional level.

What are the disconnects between what we know, what we value and what we can or cannot do? Finding ways to address these disconnects or conflicts by adjusting the rules, seeking new or different kinds of knowledge (not necessarily scientific knowledge), and expressing values becomes the trajectory of this approach. The overall assumption in this process is that new spaces for action and change can open up when “VRK” come into closer alignment, and when any one ‘fails’ (for example, where “we can’t change that rule”) we can turn to the others to identify alternative pathways.

While there are no easy recipes for dealing with complex, uncertain challenges posed by social-ecological systems and the changes that are underway, having some guideposts along the way can help us navigate our co-creation endeavours. The “VRK” heuristic can help us think about what it is we do when we are ‘co-creating solutions’, without imposing rigid frameworks.

Have you used approaches that are similar to “VRK”? How have you found them to work?

Gorddard, R., Colloff, M. J., Wise, R. M., Ware, D. and Dunlop, M. (2016). Values, rules and knowledge: Adaptation as change in the decision context. Environmental Science and Policy, 57: 60–69. Online (DOI): 10.1016/j.envsci.2015.12.004

Wyborn, C., van Kerkhoff, L., Dunlop, M., Dudley, N. and Guevara, O. (2016). Future oriented conservation: Knowledge governance, uncertainty and learning. Biodiversity and Conservation. 25, 7: 1401-1408. Online (DOI): 10.1007/s10531-016-1130-x

Biography: Lorrae van Kerkhoff (@ANUsustsci) is an Associate Professor in sustainability science at the Fenner School of Environment and Society, The Australian National University. Her research focuses on understanding the role of science in governance and decision-making for sustainability, with a special interest in cross-cultural settings. She teaches in areas related to the social and political dimensions of sustainable development, and practical approaches to tackling complex environmental problems. She is a member of the Co-Creative Capacity Pursuit funded by the National Socio-Environmental Synthesis Center (SESYNC).

This blog post is one of a series developed in preparation for the second meeting in January 2017 of the Co-Creative Capacity Pursuit. This pursuit is part of the theme Building Resources for Complex, Action-Oriented Team Science funded by the National Socio-Environmental Synthesis Center (SESYNC).

Research team performance

Community member post by Jennifer E. Cross and Hannah Love

Jennifer E. Cross (biography)

How can we improve the creativity and performance of research teams?

Recent studies on team performance have pointed out that the performance and creativity of teams has more to do with the social processes of interaction on teams, than on individual personality traits. Research on creativity and innovation in teams has found that there are three key predictors of team success:

  1. group membership,
  2. rules of engagement, and
  3. patterns of interaction.

Each of these three predictors can be influenced in order to improve the performance of teams, as the following examples show. Continue reading

Getting to a shared definition of a “good” solution in collaborative problem-solving

Community member post by Doug Easterling

Doug Easterling (biography)

How can collaborative groups move past their divisions and find solutions that advance their shared notions of what would be good for the community?

Complex problems – such as how to expand access to high-quality health care, how to reduce poverty, how to remedy racial disparities in educational attainment and economic opportunity, and how to promote economic development while at the same time protecting natural resources – can’t be solved with technical remedies or within a narrow mindset. They require the sort of multi-disciplinary, nuanced analysis that can only be achieved by engaging a variety of stakeholders in a co-creative process.

Bringing together stakeholders with diverse perspectives allows for a comprehensive analysis of complex problems, but this also raises the risk of a divisive process. Continue reading

Taboo triangles

Community member post by Charles M. Lines

Charles M. Lines (biography)

Occasionally, asking your collaborators about other people and organisations to involve in the joint work may make you aware of ‘taboo triangles’. These occur when currently collaborating people or organisations feel uncomfortable with or even unable to countenance a certain person, group or organisation being invited into their existing relationships.

It is worth exploring the reasons for and stories behind these warning signs or taboos. Are they valid? Are they erected by traditions that have become unquestioned rules? Are they in reality a barrier which seeks to restrict access to some form of power, influence or sought after resource? Are they based upon assumptions and preconceptions rather than reality?

Above all, are they worth taking the risk of challenging or even ignoring? Continue reading

Dealing with deep uncertainty: Scenarios

Laura Schmitt Olabisi (biography)

Community member post by Laura Schmitt Olabisi

What is deep uncertainty? And how can scenarios help deal with it?

Deep uncertainty refers to ‘unknown unknowns’, which simulation models are fundamentally unsuited to address. Any model is a representation of a system, based on what we know about that system. We can’t model something that nobody knows about—so the capabilities of any model (even a participatory model) are bounded by our collective knowledge.

One of the ways we handle unknown unknowns is by using scenarios. Scenarios are stories about the future, meant to guide our decision-making in the present. Continue reading