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 →
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 →
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 →
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 →
How can we capture the highly qualitative, subjective and rich nature of people’s thinking – their mental models – and translate it into formal quantitative data to be used in numerical models?
This cannot be addressed by a single method or software tool. We need multi-method approaches that have the capacity to take us through the learning journey of eliciting and representing people’s mental models, analysing them, and generating algorithms that can be incorporated into numerical models.
More importantly, this methodology should allow us to see in a transparent way the progression on this learning journey. Continue reading →