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Agents in Economic Markets and Games
Published in Mariam Kiran, X-Machines for Agent-Based Modeling, 2017
Economic agent-based modeling is a separate research area used to explain the inner workings of economics. Tesfatsion [193] defines agent-based computational economics (ACE) as “the computational study of economic processes modeled as dynamic systems of interacting agents. Here ‘agent’ refers broadly to a bundle of data and behavioral methods representing an entity constituting part of a computationally constructed world.”
Can multiple contractors self-regulate their joint service delivery? A serious gaming experiment on road maintenance planning
Published in Construction Management and Economics, 2021
Joris Scharpff, Daan Schraven, Leentje Volker, Matthijs T. J. Spaan, Mathijs M. de Weerdt
The analysis of incentives and changes in agent behaviour is performed within the framework of (agent-based) computational economics (see for instance (Tesfatsion and Judd 2006)), a field that lies at the interface of computer science, economics and management science. The approach in this paper follows the game-theoretical model of agent behaviour and decision making (Nisan et al.2007), a model advocated by Parkhe (1993) for studies on agent behaviour and inter-firm cooperation or agency theory models (Eisenhardt 1985), and used in related studies by e.g. Roth (2002), Javed et al. (2014), Gao and Liu (2019). In essence, game theory models human decision makers as agents that play a game by performing actions that result in associated utilities. As a rule, it is assumed that agents are rational, or at least boundedly rational, and hence always choose actions that maximise their utility to the best of their knowledge and capability (Gigerenzer and Selten 2002, Kahneman 2003). This model can clearly illustrate the potential of monetary incentives: they change the utilities in the game, thereby steering agents to different action choices. How to design incentives to steer towards favourable outcomes is the main topic of mechanism design, see for example the work by Dash et al. (2003), and studied by Gupta et al. (2011) and Scharpff et al. (2013), but is not addressed here.