Our method automatically generates models of dynamic decision- making that both have strong predictive power and are interpretable in human terms. We use an efficient model representation and a genetic algorithm-based estimation process to generate simple deterministic approximations that explain most of the structure of complex stochastic processes. We have applied the software to empirical data, and demonstrated it's ability to recover known data- generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity.
|Author||Nay John J. [aut], Gilligan Jonathan M. [cre, aut]|
|Date of publication||2017-10-20 06:26:09 UTC|
|Maintainer||Gilligan Jonathan M. <[email protected].edu>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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