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Center for Nonlinear Studies |
There is significant interest in developing methods for predicting human behavior, for instance to enable the outcomes of unfolding events to be forecast or the nature of ongoing but “hidden” ac-tivities to be inferred, and machine learning (ML) has proven to be a useful approach to such problems. In this talk I propose that the performance of ML algorithms can often be improved by incorporating social science concepts and models into their development and implementation, and offer three examples illustrating ways this can be accomplished. First I consider the problem of predicting whether nascent social diffusion events will ultimately propagate widely or will instead quickly dissipate. “Complex contagion” models from sociology are used to identify novel features of the diffusion phenomenon that are predictive of the diffusion’s fate, and these fea-tures are used in an ML algorithm to generate the desired forecasts. Next the task of anticipating and defending future actions of opponents in adversarial settings is addressed, and it is shown that simple game-theoretic models from economics can be combined with ML to enable the de-sign of effective defenses. Finally I examine the problem of inferring the (unobserved) nature of relationships in social networks, and demonstrate that the social psychological theory of structur-al balance can be exploited to enhance ML solutions for this task. In all cases the social science-based learning algorithms are shown to outperform “gold-standard” methods in empirical tests.
Host: Joshua Neil, 665,2121, jneil@lanl.gov