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An Agent-Based Model of Behavior in “Beauty Contest” Games
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Recently, computer simulation, particularly agent-based modeling, has grown in popularity as a method to uncover macro patterns and developments that emerge from simple micro behavior. The present paper combines both techniques by using protocol analysis to uncover player strategies in an experiment and encoding those strategies in an agent-based computer simulation. In particular, Keynes’ (1936) beauty contest analogy is simulated in a number-guessing context. Several researchers have conducted experiments asking subjects to play “p-beauty contest games” in order to compare the experimental results with those predicted by the game-theoretic, deductive reasoning concept of iterated dominance. Our results are compared with those found experimentally in order to demonstrate the usefulness of a combining agent-based modeling with protocol analysis.
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... Agent-based models are essentially a tool to discover patterns in behaviors that emerges from simple rules -micro behavior. Agentbased modeling for guessing game is not very developed area of research, for example see paper by Nichols and Radzicki [24]. ...
This paper is about people's strategic behavior as observed through experiments. The research question posed in this paper is how players (primarily children) learn in complex strategic situations that they have never encountered before. We examine data from various games played during popular game theory lectures and present findings about players' strategic learning progress while competing with other players. Four ``pick a number'' games with similar-looking rules but very different properties were investigated. These games were presented to various groups of listeners. The collected data is available for replication and analysis in an open repository. In this paper, we analyze data and propose an agent-based model of a beauty pageant game to explain observed behavior. Finally, we discuss the findings, hypotheses to test, and open questions for future research.
Proceedings of the 9th Illia O. Teplytskyi Workshop on Computer Simulation in Education (CoSinE 2021) co-located with 17th International Conference on ICT in Education, Research, and Industrial Applications: Integration, Harmonization, and Knowledge Transfer (ICTERI 2021), Kherson, Ukraine, October 1, 2021
Many decisions are based on beliefs concerning the likelihood of uncertain events such as the outcome of an election, the guilt of a defendant, or the future value of the dollar. Occasionally, beliefs concerning uncertain events are expressed in numerical form as odds or subjective probabilities. In general, the heuristics are quite useful, but sometimes they lead to severe and systematic errors. The subjective assessment of probability resembles the subjective assessment of physical quantities such as distance or size. These judgments are all based on data of limited validity, which are processed according to heuristic rules. However, the reliance on this rule leads to systematic errors in the estimation of distance. This chapter describes three heuristics that are employed in making judgments under uncertainty. The first is representativeness, which is usually employed when people are asked to judge the probability that an object or event belongs to a class or event. The second is the availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development, and the third is adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available.
This chapter treats a variety of epistemological issues surrounding generative explanation in the social sciences, and discusses the role of agent-based computational models in generative social science.
The role of expectations in the empirical analysis of agricultural supply is examined under the assumption of separation of expectations and constraints in dynamic decision making. Extrapolative, adaptive, implicit, rational and quasi-rational, and futures-based models of expectation formation are discussed. Empirical and experimental evidence for and against various models of expectation is summarized.
The General Theory of Employment, Interest, and Money / John Maynard Keynes Note: The University of Adelaide Library eBooks @ Adelaide.
Agent-based computational modeling is changing the face of social science. In Generative Social Science , Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation. This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies . Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.
Advancing the state of the art of simulation in the social sciences requires appreciating the unique value of simulation as a third way of doing science, in contrast to both induction and deduction. Simulation can be an effective tool for discovering surprising consequences of simple assumptions. This essay offers advice for doing simulation research, focusing on the programming of a simulation model, analyzing the results sharing the results, and replicating other people's simulations. Finally, suggestions are offered for building of a community of social scientists who do simulation.
We combine Nagel's “step-k” model of boundedly rational players with a “law of effect” learning model. Players begin with a disposition to use one of the step-krules of behavior, and over time the players learn how the available rules perform and switch to better performing rules. We offer an econometric specification of this dynamic process and fit it to Nagel's experimental data. We find that the rule of learning model vastly outperforms other nested and nonnested learning models. We find strong evidence for diverse dispositions and reject the Bayesian rule-learning model.Journal of Economic LiteratureClassification Numbers: C70, C52, D83.