  Instance-Based Learning Theory (IBLT) is a comprehensive account of how
humans make decisions from experience during dynamic tasks. Since it was first
proposed almost two decades ago, multiple computational models have been
constructed based on IBLT (i.e., IBL models). These models have been
demonstrated to be very successful in explaining and predicting human decisions
in multiple decision making contexts. However, as IBLT has evolved, the initial
description of the theory has become less precise, and it is unclear how its
demonstration can be expanded to more complex, dynamic, and multi-agent
environments. This paper presents an updated version of the current theoretical
components of IBLT in a comprehensive and precise form. It also provides an
advanced implementation of the full set of theoretical mechanisms, SpeedyIBL,
to unlock the capabilities of IBLT to handle a diverse taxonomy of individual
and multi-agent decision-making problems. SpeedyIBL addresses a practical
computational issue in past implementations of IBL models, the curse of
exponential growth, that emerges from memory-based tabular computations. When
more observations accumulate over time, there is an exponential growth of the
memory of instances that leads directly to an exponential slow down of the
computational time. Thus, SpeedyIBL leverages parallel computation with
vectorization to speed up the execution time of IBL models. We evaluate the
robustness of SpeedyIBL over an existing implementation of IBLT in decision
games of increased complexity. The results not only demonstrate the
applicability of IBLT through a wide range of decision making tasks, but also
highlight the improvement of SpeedyIBL over its prior implementation as the
complexity of decision features and number of agents increase. The library is
open sourced for the use of the broad research community.
