Chapter 19 Agent-Based Models and Human Subject Experiments

Duffy, John

doi:10.1016/s1574-0021(05)02019-8

Cited by 182 publications

(122 citation statements)

References 103 publications

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“…In other words, people are only boundedly rational. Recent research suggests that models with boundedly rational learning behavior fit observed outcomes better (see, for example, Duffy (2006) for a survey).…”

Section: Introductionmentioning

confidence: 99%

Efficiency of continuous double auctions under individual evolutionary learning with full or limited information

et al. 2011

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In this paper we explore how specific aspects of market transparency and agents' behavior affect the efficiency of the market outcome. In particular, we are interested whether learning behavior with and without information about actions of other participants improves market efficiency. We consider a simple market for a homogeneous good populated by buyers and sellers. The valuations of the buyers and the costs of the sellers are given exogenously. Agents are involved in consecutive trading sessions, which are organized as a continuous double auction with order book. Using Individual Evolutionary Learning agents submit price bids and offers, trying to learn the most profitable strategy by looking at their realized and counterfactual or "foregone" payoffs. We find that learning outcomes heavily depend on information treatments. Under full information about actions of others, agents' orders tend to be similar, while under limited information agents tend to submit their valuations/costs. This behavioral outcome results in higher price volatility for the latter treatment. We also find that learning improves allocative efficiency when compared to outcomes Zero-Intelligent traders.

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Section: Introductionmentioning

confidence: 99%

Efficiency of continuous double auctions under individual evolutionary learning with full or limited information

et al. 2011

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“…Stochastic components in agent-based models might be, for example, the initial state of the system, external shocks (e.g. the occurrence of a new competitor or radical innovations), the options which the agents discover in a certain period (remember that in agent-based models, decision-makers are not able to survey the entire search space at once and, thus, stepwise discover new solutions) or the agents' choice of options depending on propensities (rather than definite preferences) modeled via the probability of taking a certain option and, for example, subject to learning (Duffy 2006).…”

Section: Simulation Approaches In Abmmentioning

confidence: 99%

Agent-based modeling in managerial science: an illustrative survey and study

Wall

2014

Rev Manag Sci

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This article provides an overview of the current state of agent-based modeling in managerial science. In particular, the aim is to illustrate major lines of development in agent-based modeling in the field and to highlight the opportunities and limitations of this research approach. The article employs a twofold approach: First, a survey on research efforts employing agent-based simulation models related to domains of managerial science is given which have benefited considerably from this research method. Second, an illustrative study is conducted in the area of management accounting research, a domain which, so far, has rarely seen agentbased modeling efforts. In particular, we introduce an agent-based model that allows to investigate the relation between intra-firm interdependencies, performance measures used in incentive schemes, and accounting accuracy. We compare this model to a study which uses both, a principal-agent model and an empirical analysis. We find that the three approaches come to similar major findings but that they suffer from rather different limitations and also provide different perspectives on the subject. In particular, it becomes obvious that agent-based modeling allows us to capture complex organizational structures and provides insights into the processual features of the system under investigation.

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“…After each block of 50 days, the sets of decision rules used by the individual firms evolve using a genetic algorithm: some rules are eliminated, while others are reproduced, with selection based on the fitness of the rules, applying crossover, and mutation. See also Brenner (2005) and Duffy (2005) on reinforcement learning and genetic algorithms.…”

Section: 4mentioning

confidence: 99%

Chapter 21 ACE Models of Endogenous Interactions

Vriend

2006

Handbook of Computational Economics

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Chapter 19 Agent-Based Models and Human Subject Experiments

Cited by 182 publications

References 103 publications

Efficiency of continuous double auctions under individual evolutionary learning with full or limited information

Efficiency of continuous double auctions under individual evolutionary learning with full or limited information

Agent-based modeling in managerial science: an illustrative survey and study

Chapter 21 ACE Models of Endogenous Interactions

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