Playing with Ambiguity: An Agent Based Model of Vague Beliefs in Games

Playing with Ambiguity: An Agent Based Model of Vague Beliefs in Games

Konstantinos Georgalos (University of York, UK)
Copyright: © 2018 |Pages: 19
DOI: 10.4018/978-1-5225-2594-3.ch003
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This chapter discusses the way that three distinct fields, decision theory, game theory and computer science, can be successfully combined in order to optimally design economic experiments. Using an example of cooperative game theory (the Stag-Hunt game), the chapter presents how the introduction of ambiguous beliefs and attitudes towards ambiguity in the analysis can affect the predicted equilibrium. Based on agent-based simulation methods, the author is able to tackle similar theoretical problems and thus to design experiments in such a way that they will produce useful, unbiased and reliable data.
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In this chapter, our aim is to explore how Agent-Based Simulation techniques can act as a complement to a relatively new field of the experimental economics literature, that of preferences towards ambiguity. As experimental techniques in economics constitute an indispensable part of the applied and empirical research, with scholarly research getting published in the top journals of the profession, it is of paramount significance for the implemented experimental protocols to be carefully designed so as to provide by minimizing the number of possible flaws. Moreover, advances in the field of decision theory, combined with the numerous available datasets of experimental observations, pose a huge challenge to the ‘rational’ agent paradigm1. As a result, empirics have rendered the use of more realistic modelling of human behaviour as well as the interdisciplinary research to be more than necessary. Due to this, several new scientific fields have emerged such as the field of ‘Behavioral’ economics (where elements from psychology and biology are coalesced with the economic theory) or the field of neuroeconomics (where advances of neuroscience are applied) to name but a few. In addition to the latter, crucial improvements have been made to the literature of decision making under ambiguity, or stating it in a different way, improvements on how to model agents’ behaviour in situations where they lack useful information. This fact can be explained by the increased frequency of research papers published that either focus on similar theoretical issues or on the application of theory to real life. Consequently, every single field in economics now takes advantage of these advances augmenting the ability to explain data and behaviour in a more realistic way (e.g. macroeconomics, game theory, environmental economics). Similar applications can be found in the present volume by Trindade, Magessi, and Antunes (2014) and Arciero et al. (2014).

In this chapter, we show how three distinct fields can be brought together enabling us to design and conduct more effective experiments that will generate useful and unbiased data. Our aim is to use advances of the literature of decision making under ambiguity that centres on individual choice, in order to predict behaviour in strategic interaction environments. The commonest way to model interaction in economics is game theory. There are several other different ways to model social interaction such as using the public choice approach (for a similar approach, see Lucas and Payne (2014) and Trigo (2014) or principal- agent models to name but a few. Incorporating the theoretical advances into the game theoretical models enables us, on the one hand, to solve puzzles that the standard assumption of the rational choice produces and, on the other, to obtain better predictions of how agents will react in similar interactions. The next step, after having derived the theoretical predictions, is to test this theory in the lab. The role of agent-based simulation becomes apparent at the step before entering the lab. Thus, what we provide is an intuition of how decision theory, game theory and computer science can be combined for the optimum design of economic experiments. As this chapter is addressed to readers of multifarious scientific backgrounds, careful consideration has been taken as regards the fact that they may not be familiar with the tools and methods that are employed in economic analysis to model individual behaviour. Henceforth, effort has been made to keep mathematics and definitions to the lowest formal level possible. A mathematical as well as a technical appendix with the code for the simulation is attached at the end of this chapter.

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