A microsimulation model of behaviour change calibrated to reversal learning data

TL;DR

This paper introduces a simple, validated microsimulation model of individual behaviour change based on reversal learning data, enabling better simulation and understanding of collective phenomena involving behaviour dynamics.

Contribution

It presents a minimal stochastic model of reversal learning with parameters identifiable from empirical data, advancing the development of validated behaviour change models.

Findings

Model captures key properties of behaviour change, including learning and persistence.

Parameters are fully identifiable from empirical reversal learning data.

Model can be extended to simulate complex behavioural dynamics over time.

Abstract

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 microsimulation 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.