Mirta Galesic

Professor




Human ability to adaptively organize in collectives with common interests is one of the key elements of our species’ success. We shape and reshape our friendships, teams, and societies on time scales from days to years and generations to cope with various problems we encounter. We adjust our beliefs about what problems are more important, change the way we use information from others, and adapt the pattern of our social connections to the problem at hand.

For example, as scientists we often work on several research projects at once, each requiring a different set of expertise and skills. Depending on the problems we are facing, we build and join teams, changing the frequency of communicating and the way we integrate different perspectives. Similarly, as citizens, we connect with others to cope with a variety of ever-changing societal problems. Depending on whether we think the most important problems are economic growth, curbing climate change, or averting outgroup threats, we join different communities and make decisions in different ways.

We are so good at moving between these different socio-cognitive constellations of networks and cognitions depending on the problems at hand, that we are not even aware of this constant collective adaptation – until it goes wrong. Sometimes our collectives seem stuck and unable to adapt to the problems they face, even though the solutions might seem obvious to outside observers. For example, scientific and other teams can become less productive after initial successes, and communities can seem unable to resolve urgent important problems even when the solution seems obvious.

In her work at the Santa Fe Institute and with her ERC-funded research group at the Complexity Science Hub Vienna, Mirta and her collaborators build theoretical frameworks needed to understand collective adaptation. By integrating domain knowledge from cognitive and social sciences with computational models adapted from statistical physics and applied mathematics, and by collecting data from group experiments, surveys, and large textual corpora, they aim to understand questions such as: How do collectives adapt their social learning strategies and their network structures in response to multiple problems that change over time? How do people form their beliefs about what the important problems are in the first place, and why are some of these beliefs more difficult to change? How do individual differences in beliefs about important problems affect collective adaptation?