This study aims to demonstrate how agent-based simulation (ABS) may provide a computational testbed for mechanism design using concepts of bounded rationality (BR). ABS can be used to systematically derive and formalize different models of BR. This allows us to identify the cognitive preconditions for behavior intended by the mechanism and thereby to derive implications for the design of mechanisms.
Based on an analysis of the requirements of the decision context, the authors describe a systematic way of incorporating different BR concepts into an agent learning model. The approach is illustrated by analyzing an incentive scheme suggested for truthful reporting in budgeting contexts, which is an adapted Groves mechanism scheme.
The study describes systematic ways in which to derive BR agents for research questions where behavioral aspects might matter. The authors show that BR concepts may lead to other outcomes than the intended truth-inducing effect. A modification of the mechanism to more distinguishable levels of payments improves the results in terms of the intended effect.
The presented BR concepts as simulated by agent models cannot model human behavior in its full complexity. The simplification of complex human behavior is a useful analytical construct for the controlled analysis of a few aspects and an understanding of the potential consequences of those aspects of human behavior for mechanism design.
The paper specifies the idea of a computational testbed for mechanism design based on BR concepts. Beyond this, a systematic and stepwise approach is shown to formalize bounded rational behavior by agents based on a requirements analysis, including benchmark models for the comparison and evaluation of BR concepts.
Lorscheid, I. and Meyer, M. (2017), "Agent-based mechanism design – investigating bounded rationality concepts in a budgeting context", Team Performance Management, Vol. 23 No. 1/2, pp. 13-27. https://doi.org/10.1108/TPM-10-2015-0048Download as .RIS
Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited