Automatic generation of finite state machine models of dynamic decisionmaking that both have strong predictive power and are interpretable in human terms. We use an efficient model representation and a genetic algorithmbased 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 datagenerating processes by simulating data with agentbased models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity.
Package details 


Author  Nay John J. [aut], Gilligan Jonathan M. [cre, aut] 
Date of publication  20180808 22:40:03 UTC 
Maintainer  Gilligan Jonathan M. <[email protected]> 
License  MIT + file LICENSE 
Version  0.2.2 
URL  https://github.com/jonathang/datafsm 
Package repository  View on CRAN 
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