UNDERSTANDING MEMORY MECHANISMS IN SOCIO-TECHNICAL SYSTEMS: THE CASE OF AN AGENT-BASED MOBILITY MODEL

Abstract

This paper explores memory mechanisms in complex socio-technical systems, using a mobility demand model as an example case. We simplify a large-scale agent-based mobility model, formulate the corresponding stochastic process, and observe that the mobility decision process is non-Markovian. This is due to its dependence on the system’s history, including social structure and local infrastructure, which evolve based on prior mobility decisions. Complementing the mobility process with two history-determined components leads to an extended mobility process that is Markovian. Although our model is a very much reduced version of the original one, it remains too complex for the application of usual analytic methods. Instead, we employ simulations to examine the functionalities of the two history-determined components. We think that the structure of the analyzed stochastic process is exemplary for many socio-technical, -economic, -ecological systems. Additionally, it exhibits analogies with the framework of extended evolution, which has previously been used to study cultural evolution.