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Genetic programming for understanding cognitive biases that generate polarization in social networks...

by Chathika S Gunaratne, Robert M Patton
Publication Type
Conference Paper
Book Title
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Publication Date
Page Numbers
546 to 549
Publisher Location
Boston, Massachusetts, United States of America
Conference Name
GECCO 2022: The Genetic and Evolutionary Computation Conference
Conference Location
Boston, Massachusetts, United States of America
Conference Sponsor
Association of Computing Machinery
Conference Date

Recent studies have applied agent-based models to infer human-interpretable explanations of individual-scale behaviors that generate macro-scale patterns in complex social systems. Genetic programming has proven to be an ideal explainable AI tool for this purpose, where primitives may be expressed in an interpretable fashion and assembled into agent rules. Evolutionary model discovery (EMD) is a tool that combines genetic programming and random forest feature importance analysis, to infer individual-scale, human-interpretable explanations from agent-based models. We deploy EMD to investigate the cognitive biases behind the emergence of ideological polarization within a population. An agent-based model is developed to simulate a social network, where agents are able to create or sever links with one another, and update an internal ideological stance based on their neighbors' stances. Agent rules govern these actions and constitute of cognitive biases. A set of 7 cognitive biases are included as genetic program primitives in the search for rules that generate hyper-polarization among the population of agents. We find that heterogeneity in cognitive biases is more likely to generate polarized social networks. Highly polarized social networks are likely to emerge when individuals with confirmation bias are exposed to those with either attentional bias, egocentric bias, or cognitive dissonance.