Dealing with many of the world’s most pressing problems requires an ability to understand and reason about causal complexity. For example, understanding climate change involves reasoning about non-obvious causes, spatial gaps, temporal delays, cyclic causality, and distributed causality where the agency/intentionality of one’s actions are on a different level than those of the emergent outcome. In the Understandings of Consequence Lab, we study causal cognition and learning in a complex world.
We pursue the following kinds of questions:
- What are the inherent default patterns or assumptions that we make as human beings that influence how we reason about complexity in our world?
- What are the characteristics of human cognition that lead to these default patterns?
- In what ways do these patterns help or hurt us when reasoning about causal complexity?
- How can we help people to use their cognition well in reasoning about causal complexity?
- How can we educate tomorrow’s learners to reason well about a complex world and to be able to solve the difficult problems that they will face?
Our work has deep implications for policy and practice. We work with experts in the sciences and beyond to impact policy. We also collaborate with teachers to develop curriculum and approaches to teaching the next generation to reason well about causal complexity.