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Econometric Information Recovery in Behavioral Networks

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Document pages: 28 pages

Abstract: In this paper we suggest an approach to recovering behavior related, preference-choice network pathway information from observational data. We model the process as a self-organized behavior based random exponential network-graph system. As a behavior based optimizing criterion, we recognize the connection between adaptive intelligent behavior and causal entropy maximization. For information recovery, we use an information theoretic basis for estimation, inference, model evaluation, and prediction. To indicate the nature of the network information recovery problem, we identify simple applications of ordered-directed binary and weighted exponential networks. As a solution basis for recovering network pathway probabilities, we specify and work through an empirical problem. The objective is to recover the unknown network probabilities across the ensemble that can be computed analytically without sampling the configuration space.

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