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 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 data generating processes by simulating data with agentbased models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity.
Package details 


Maintainer  
License  MIT + file LICENSE 
Version  0.1.0 
URL  http://johnnay.github.io/datafsm/ 
Package repository  View on GitHub 
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