Complexity and Agent-based Modelling

Community member post by Richard Taylor and John Forrester

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Richard Taylor (biography)

Policy problems are complex and – while sometimes simple solutions can work – complexity tools and complexity thinking have a major part to play in planning effective policy responses. What is ‘complexity’ and what does ‘complexity science’ do? How can agent-based modelling help address the complexity of environment and development policy issues?

Complexity

At the most obvious level, one can take complexity to mean all systems that are not simple, by which we mean that they can be influenced but not controlled. Complexity can be examined through complexity science and complex system models. Continue reading

Models as ‘interested amateurs’

Community member post by Pete Barbrook-Johnson

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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! Continue reading

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

Community member post by Lorrae van Kerkhoff

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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? Continue reading

Complexity, diversity, modelling, power, trust, unknowns… Who is this blog for?

Community member post by Gabriele Bammer

Gabriele Bammer (biography)

This is the first annual “state of the blog” review.

This is a blog for researchers who:

  • want better concepts and methods for understanding and acting on complex real-world problems – problems like refugee crises, global climate change, and inequality.
  • are intrigued by the messiness of how components of a problem interact, how context can be all-important and how power can stymie or facilitate action.
  • understand that complex problems do not have perfect solutions; instead that “best possible” or “least worst” solutions are more realistic aims.
  • enjoy wrangling with unknowns to better manage, or even head-off, unintended adverse consequences and unpleasant surprises.
  • are keen to look across the boundaries of their own expertise to see what concepts and methods are on offer from those with different academic backgrounds grappling with other kinds of problems.
  • want to join forces to build a community which freely shares concepts and methods for dealing with complex problems, so that these become a stronger part of the mainstream of academic research and education.

November saw this blog’s first anniversary and this 100th blog post reviews what we are aiming for and how we are tracking. Continue reading

Two barriers to interdisciplinary thinking in the public sector and how time graphs can help

Community member post by Jane MacMaster

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Jane MacMaster (biography)

After one year or so delivering seminars that share practical techniques to help navigate complexity to public sector audiences, I’ve observed two simple and fundamental barriers to dealing more effectively with complex, interdisciplinary problems in the public sector.

First, is the lack of time to problem-solve – to pause and reflect on an issue, to build a deeper understanding of it, to think creatively about it from different angles, to think through some ideas, to test out some ideas. There is too much else going on.

Second, is that it’s often quite difficult to put one’s collective finger on what, exactly, the problem is. Continue reading

Uncertainty in participatory modeling – What can we learn from management research?

Community member post by Antonie Jetter

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Antonie Jetter (biography)

I frequently struggle to explain how participatory modeling deals with uncertainty. I found useful guidance in the management literature.

After all, participatory modeling projects and strategic business planning have one commonality – a group of stakeholders and decision-makers aims to understand and ultimately influence a complex system. They do so in the face of great uncertainty that frequently cannot be resolved – at least not within the required time frame. Businesses, for example, have precise data on customer behavior when their accountants report on annual sales. However, by this time, the very precise data is irrelevant because the opportunity to influence the system has passed.

Two key lessons from the management literature deal with the nature of uncertainty and responding to four major types of uncertainty. Continue reading