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Instance-Based Learning Theory (IBLT) is a comprehensive account of howhumans make decisions from experience during dynamic tasks. Since it was firstproposed almost two decades ago, multiple computational models have beenconstructed based on IBLT (i.e., IBL models). These models have beendemonstrated to be very successful in explaining and predicting human decisionsin multiple decision making contexts. However, as IBLT has evolved, the initialdescription of the theory has become less precise, and it is unclear how itsdemonstration can be expanded to more complex, dynamic, and multi-agentenvironments. This paper presents an updated version of the current theoreticalcomponents of IBLT in a comprehensive and precise form. It also provides anadvanced implementation of the full set of theoretical mechanisms, SpeedyIBL,to unlock the capabilities of IBLT to handle a diverse taxonomy of individualand multi-agent decision-making problems. SpeedyIBL addresses a practicalcomputational issue in past implementations of IBL models, the curse ofexponential growth, that emerges from memory-based tabular computations. Whenmore observations accumulate over time, there is an exponential growth of thememory of instances that leads directly to an exponential slow down of thecomputational time. Thus, SpeedyIBL leverages parallel computation withvectorization to speed up the execution time of IBL models. We evaluate therobustness of SpeedyIBL over an existing implementation of IBLT in decisiongames of increased complexity. The results not only demonstrate theapplicability of IBLT through a wide range of decision making tasks, but alsohighlight the improvement of SpeedyIBL over its prior implementation as thecomplexity of decision features and number of agents increase. The library isopen sourced for the use of the broad research community.

SpeedyIBL: A Comprehensive, Precise, and Fast Implementation of Instance-Based Learning Theory