Abstract
The reinforcement learning paradigm is ideally suited for dealing with the requirements posed by a recent approach to economic modeling called Agent-based Computational Economics (ACE): the application of Holland’s Complex Adaptive Systems (CAS) paradigm to economics. In this approach, economic phenomena emerge from the decentralized interactions among autonomous, heterogenous, boundedly rational, adaptive economic agents, rather than from idealized interactions among ‘representative agents’ or equilibrium analysis over the heads of the agents involved.
In this paper, we study an industrial goods market, where buyers need to decide between making and buying components. Traditionally, Transaction Cost Economics (TCE) has been used to analyze these types of situations. However, a number of criticisms of TCE have been raised, which the ACE approach allows us to resolve. Our resulting Agent-based Computational Transaction Cost Economics (ACTCE) approach allows us to study systems of interacting agents both at the level with which TCE deals (allowing comparison and verification), as well as at the level of individual agents, allowing extension of the theory’s predictive power.
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Klos, T., Nooteboom, B. (2006). Adaptive Learning in Complex Trade Networks. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_88
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DOI: https://doi.org/10.1007/11903697_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
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