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Understanding Memory Mechanisms in in Socio-Technical Systems: the Case of an Agent-based Mobility Model

accepted for publication
  • This paper explores memory mechanisms in complex socio-technical systems, using a mobility demand model as an example case. We simplified a large-scale agent-based mobility model into a Markov process and discover 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. To make the process Markovian, we extend the state space by incorporating two history-dependent components. 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-dependent 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.
Author:Gesine Steudle, Stefanie WinkelmannORCiD, Steffen Fürst, Sarah Wolf
Document Type:Article
Parent Title (English):Advances in Complex Systems
Year of first publication:2024
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