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Estimating Computational Models of Dynamic Decision Making from Transactional Data

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Group Decision and Negotiation. Theory, Empirical Evidence, and Application (GDN 2016)

Abstract

The goal of this work is to estimate and validate computational models of dynamic decision making against data on sequences of actual decisions made in naturalistic settings. While this paradigm has its roots in laboratory studies under controlled conditions, increasing instrumentation of operational environments is enabling parallel investigations in field settings. Here, decision processes associated with the dispatch of debris removal personnel and equipment are investigated using data from a series of tornado storms in the State of Alabama in 2011. A multi-faceted approach to model validation is presented, thereby illustrating how objective, operational data may be used to inform models of complete decision making processes.

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Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. 1313589.

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Correspondence to Xin Zhang .

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Brooks, J., Mendonça, D., Zhang, X., Grabowski, M. (2017). Estimating Computational Models of Dynamic Decision Making from Transactional Data. In: Bajwa, D., Koeszegi, S., Vetschera, R. (eds) Group Decision and Negotiation. Theory, Empirical Evidence, and Application. GDN 2016. Lecture Notes in Business Information Processing, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-319-52624-9_5

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