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.

翻译：行为改变是许多可观察集体现象的核心，例如传染病的传播与控制、公共卫生政策的采纳以及动物向新栖息地的迁徙。在计算机模拟中准确表征个体行为改变过程仍是一个开放挑战。现有计算模型常采用现象学实现方法，缺乏行为数据的充分支持。由于与可观测量的关联薄弱，此类模型在模拟已观察及反事实涌现现象场景时效用有限，因其无法被有效验证或校准。本文提出一种简单的基于个体的随机逆转学习模型，该模型捕捉了个体行为改变的两个基本特性：基于累积奖励信号的学习能力，以及奖励移除或改变后习得行为的暂态持续性。模型仅包含两个参数，我们采用近似贝叶斯计算方法证明这些参数可从经验性逆转学习时间序列数据中完全识别。最后，我们展示了如何扩展该模型以解释更长时间尺度上涉及波动刺激的复杂行为动力学。本工作为开发可验证的个体层面行为改变模型迈出重要一步，此类模型可作为集体行为改变复杂模拟中经过验证的子模块。