Community member post by Richard Taylor and John Forrester
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?
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 →
Community member post by Rebecca Jordan and Steven Gray
As investigators who engage the public in both modeling and research endeavors we address two major questions: Does citizen science have a place within the participatory modeling research community? And does participatory modeling have a place in the citizen science research community?
Let us start with definitions. Citizen science has been defined in many ways, but we will keep the definition simple. Citizen science refers to endeavors where persons who do not consider themselves scientific experts work with those who do consider themselves experts (around a specific issue) to address an authentic research question. Continue reading →
It seems simple enough to say that community values and aspirations should be central to informing government decisions that affect them. But simple things can turn out to be complex.
In particular, when research to inform land and water policy was guided by what the community valued and aspired to rather than solely technical considerations, a much broader array of desirable outcomes was considered and the limitations of what science can measure and predict were usefully exposed. Continue reading →
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 →
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 →
How to give others your hard-won insights so that their work can be more informed, efficient, and effective? As I’ve gotten older, it is something that I think about more.
It is widely recognized that the environment is an integrated but also “open” system. As a result, when working with issues relating to the environment we are faced with the unsatisfying fact that we won’t know “truth”. We develop an understanding that is consistent with what we currently know and what we consider state-of-the-practice methods. But, we can never be sure that more observations or different methods would not result in different insights. Continue reading →