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. They can be verbal, artistic, graphical, quantitative, or any combination of these. The beauty of scenarios in dealing with unknown unknowns is that they harness the power of speculation and imagination, whether performed by humans or by computers. You may not know which technology is going to make the biggest difference in shifting greenhouse gas emission trajectories, or even how to figure that out. But you can imagine several different possibilities, and considering that range of possibilities gives you some idea of where the future could go, helping you to structure your decisions accordingly.
Some scenario approaches involve running a computer model thousands of times, with thousands of different combinations of parameters and equation structures, including some that the human users wouldn’t have thought to run. The output from this type of exercise sketches out a scenario space, encompassing a wide range of outcomes, from ‘good’ to ‘bad’ to ‘ugly’ and everything in between.
Decision-makers may not know which of these outcomes is more or less likely than another, but this kind of exercise allows them to help identify which decisions could shift the outcome more strongly in the ‘good’ direction (or at least away from the ‘bad’).
One example of this type of deep-uncertainty analysis is Robust Decision Making (RDM) developed at the RAND Corporation. Robust decision making typically uses a simulation model — for example, a system dynamics model or (for water-related problems) a hydrological model — to develop the scenario space that informs decisions. It also has a participatory component, with stakeholder deliberations used to define desirable and undesirable outcomes, and to rule out implausible scenarios (those for which there simply isn’t a logical argument about how they could happen).
Questions at the frontier of scenario-based policy analysis include: How can different values and preferences be incorporated into the scenario approach (for example, how do we deal with a situation in which one person’s ‘bad, avoid’ scenario is another person’s ‘great, let’s do it’ scenario)? How can we use more than one model (sometimes called a model ‘ensemble’) to create a scenario space for decision-making? How do we use participatory modeling approaches to scope a scenario space?
Such ways of dealing with deep uncertainty complement other contributions to this blog about the benefits of models for supporting decision-making under uncertainty. For example, in her blog post, Antonie Jetter argued that participatory model-building can help to mitigate or address some other kinds of uncertainty associated with models. Because of their deliberative and collaborative nature, participatory modeling exercises can narrow the range of effect uncertainty, in which we don’t know how components of the model relate to one another. By sharing their experiences of the system from multiple perspectives, stakeholders can triangulate this uncertainty to some extent.
What methods have you used to deal with uncertainty, especially unknown unknowns? Share ideas in the comments!
For more information on improving decision-making under deep uncertainty, see materials provided by the Society for Decision Making Under Deep Uncertainty: www.deepuncertainty.org
Biography: Laura Schmitt Olabisi is an associate professor at Michigan State University, jointly appointed in the Environmental Science & Policy Program and the department of Community Sustainability. She uses system dynamics modeling and other systems methods to investigate the future of complex socio-ecological systems, often working directly with stakeholders by applying participatory research methods. Her research has addressed soil erosion, climate change, water sustainability, energy use, sustainable agriculture, food security, and human health in the U.S., the Philippines, Nigeria, Zambia, Malawi, and Burkina Faso. She is member of the Participatory Modeling Pursuit funded by the National Socio-Environmental Synthesis Center (SESYNC).
This blog post is one of a series resulting from the second meeting in October 2016 of the Participatory Modeling 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).