https://doi.org/10.1140/epjst/e2020-900137-x
Regular Article
Two tales of complex system analysis: MaxEnt and agent-based modeling
1
Oxford Martin Programme on Technological and Economic Change, Oxford, UK
2
Institute for New Economic Thinking at the Oxford Martin School, Oxford, UK
3
Mathematical Institute, University of Oxford, Oxford, UK
a e-mail: jangho.yang@oxfordmartin.ox.ac.uk
Received:
13
July
2019
Received in final form:
9
September
2019
Published online:
7
July
2020
Over the recent four decades, agent-based modeling and maximum entropy modeling have provided some of the most notable contributions applying concepts from complexity science to a broad range of problems in economics. In this paper, we argue that these two seemingly unrelated approaches can actually complement each other, providing a powerful conceptual/empirical tool for the analysis of complex economic problems. The maximum entropy approach is particularly well suited for an analytically rigorous study of the qualitative properties of systems in quasi-equilibrium. Agent-based modeling, unconstrained by either equilibrium or analytical tractability considerations, can provide a richer picture of the system under study by allowing for a wider choice of behavioral assumptions. In order to demonstrate the complementarity of these approaches, we use here two simple economic models based on maximum entropy principles – a quantal response social interaction model and a market feedback model –, for which we develop agent-based equivalent models. On the one hand, this allows us to highlight the potential of maximum entropy models for guiding the development of well-grounded, first-approximation agent-based models. On the other hand, we are also able to demonstrate the capabilities of agent-based models for tracking irreversible and out-of-equilibrium dynamics as well as for exploring the consequences of agent heterogeneity, thus fundamentally improving on the original maximum entropy model and potentially guiding its further extension.
© The Author(s) 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
