Towards an Inductive Game Theory: Extracting Strategies and Causal Networks from Time-series Data
Room:
WID room 3280B (Anyone without WID access can use special events elevator on the WID 1st floor (near Aldo cafe) to access 3rd floor (room 3280) Speaker: Jessica Flack
Co-Director of the Center for Complexity & Collective Computation, Wisconsin Institute for Discovery
University of Wisconsin-Madison
Presentation Title: Towards an Inductive Game Theory: Extracting Strategies and Causal Networks from Time-series Data
Abstract:
One weakness of game theory is that the strategies individuals play and the payoffs associated with those strategies are posited rather than derived from data. I will discuss an approach my collaborators and I have been developing for extracting strategies, or decision-making rules, from social event time series data. In social groups the collective implementation of the strategies by multiple individuals produces functionally important statistical features of social structure, like the distribution of power or distribution of fight sizes. Hence it would be useful to have a meso-scale description of how alternative aggregate social properties arise from different combinations of strategies at the individual level. This meso-scale description can be formalized in terms of causal networks the topology of which specifies how the strategies (and individuals) are combined.
I will discuss how these causal networks can be constructed from strategy data.
Event Date: