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Leveraging Socioeconomic Models For Prediction

Abstract: 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 socioeconomic 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 which are predictive of the diffusion’s fate, and these features 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 design of effective de-fenses. Finally I examine the problem of inferring the (unobserved) nature of relationships in so-cial networks, and demonstrate that the social psychological theory of structural balance can be exploited to enhance ML solutions for this task. In all cases the sociologically-grounded learning algorithms re shown to outperform existing “gold-standard” methods in empirical tests.

Bio: Rich Colbaugh received a Ph.D. in Engineering from The Pennsylvania State University in 1986, and is presently a Distinguished Member of the Technical Staff at Sandia National Laboratories. His research activities focus on modeling, analyzing, predicting, and influencing the evolution of dynamical systems of importance in nature and society, with an emphasis on large, complex networks of relevance to national security, social and economic systems, advanced technology, and biology. This research has been published in leading journals and conferences, supported by agencies such as the DoD, DHS, DOE, NASA, and NSF, and recognized through awards from NASA, DoD, DOE, IEEE, SIAM, ASME, AACC, and SCI. Dr. Colbaugh recently spent three years in Washington, DC advising DoD senior leadership on counterterrorism and counterproliferation efforts.