Our core research areas
Knowledge-brokered Modeling
Sometimes it's ok if you don't know what is going on inside your model. We use weather forecasts and ChatGPT all the time without worrying why they've told us what they have. But in many other cases - like when we want to understand how some particular forcing or event or policy might play out off into the future - it is really important that we understand the assumptions we've made.
We have invested most of our modeling efforts in recent years in participant-engaged, knowledge-brokered model development. This has meant bringing people who might use a model or model outputs together to first define shared questions, then key model processes, before moving to building a computational toolkit for all to use and consider.
While we wish we had more to share online at this time, these processes take a bit more time. Some intermediate results that may be of interest include:
-
This causal loop model of rural-urban mobility in Senegal, produced in this 2022 workshop, that led us to develop the capabilities and aspirations algorithm in MIDAS, as a way to better differentiate the near through long-term impacts of rural investments on rural resilience to climate shocks.
-
This causal loop model of state agency investments in managing forest cover, produced in this 2023 workshop in Rondônia, Brazil, from which we are developing a participatory policy lab model for land cover management.
-
This causal loop model of rural-rural mobility in Madagascar, produced in this 2024 workshop, that has led us to develop a place attachment module in MIDAS, to better capture the processes through which migrant farmers continue to invest in agriculture and entrepreneurial ventures in their home villages and regions.
If this process is of interest, please get in touch.
Games for research and learning
Games - as one slice of the bigger world of framed field experiments - can be wonderful vehicles 1) to learn about how people make or would make decisions, in ways that they can't or don't wish to simply tell you; 2) to explore the possible consequences of interventions that are impractical or too expensive to try out, in order to narrow things down and identify the things worth trying in the real world; as well as 3) to aid in experiential learning, and help people explore consequences of their decisions without the risk of many real-world consequences.
To function in any of these ways, games require careful and iterative design in order to isolate meaningful dilemmas and represent them with validity in a hypothetical instrument.
We've invested much of our effort over the last decade in developing games applied to particular natural resource and coordination dilemmas, and more recently have begun to develop more flexible games frameworks that interested researchers and practitioners can (relatively) easily adapt to their own interests and purposes.
If these are of interest, please be in touch, or look over our Netlogo games library and games training tools.
Pro-poor,
people-first development
Environmental stewardship is important as an outcome. But enabling people to choose to be good stewards is even more important as a process. Building a world in which we all share the same capacity to take risks, try new things, and take on shared responsibilities is hard, in large part because we rarely succeed in developing rules, institutions, or other encouragements that help lift up people in the back. Every instrument you can think of - welfare benefits, microfinance schemes, insurance programs - is easier for those in the middle to take advantage of.
So what do we do? In our lab, we 'think Mario Kart.'
Mario Kart has stayed fun for gamers, their siblings and their grandparents alike for decades, because it is based on a pro-equity design principle called rubberbanding. Karts at the back go faster and get better items, rubberbanding the back and the front of the race together so that no racer ever feels hopeless. Mario Kart does this by:
-
Knowing who is in the back and how to reach them
-
Generating benefits, and
-
Targeting those benefits at those in the back
These are easy tasks in a game, but hard in real, lower-resource contexts. Mobile phones have made it easier to connect to people all over, observe crises and dilemmas, and connect to benefits, but we still struggle to build out option sets and opportunity for those far in the back. Sometimes we have to work with what we have, and find ways in which those in the middle can't help but help those behind them when they help themselves.
Finding governance solutions to people and environment problems is challenging. Games, models, and participation can help. We hope you'll look through our materials and publications and find something of interest.