  Behaviour change lies at the heart of many observable collective phenomena
such as the transmission and control of infectious diseases, adoption of public
health policies, and migration of animals to new habitats. Representing the
process of individual behaviour change in computer simulations of these
phenomena remains an open challenge. Often, computational models use
phenomenological implementations with limited support from behavioural data.
Without a strong connection to observable quantities, such models have limited
utility for simulating observed and counterfactual scenarios of emergent
phenomena because they cannot be validated or calibrated. Here, we present a
simple stochastic individual-based model of reversal learning that captures
fundamental properties of individual behaviour change, namely, the capacity to
learn based on accumulated reward signals, and the transient persistence of
learned behaviour after rewards are removed or altered. The model has only two
parameters, and we use approximate Bayesian computation to demonstrate that
they are fully identifiable from empirical reversal learning time series data.
Finally, we demonstrate how the model can be extended to account for the
increased complexity of behavioural dynamics over longer time scales involving
fluctuating stimuli. This work is a step towards the development and evaluation
of fully identifiable individual-level behaviour change models that can
function as validated submodels for complex simulations of collective behaviour
change.
