Individual Learning in Normal Form Games: Some Laboratory Results

Cheung, Yin‐Wong; Friedman, Daniel

doi:10.1006/game.1997.0544

Cited by 283 publications

(227 citation statements)

References 13 publications

(3 reference statements)

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“…In the fitness learning model, we set the value of the parameter β at 0.95 and ψ at 0.25. This choice may seem arbitrary but it is consistent with empirical evidence suggesting that, in the short run, agents expect the past change to be almost entirely extrapolated into the future, while they believe that, in the longer run, the market rate will return to the fundamental value 7 . The value of ψ = 0.25 for quarterly observations suggests that the agents expect the market rate to return to the fundamental value in one year 8 .…”

Section: Low Frequency Observationsmentioning

confidence: 67%

“…He found that agents make use of 13-14 years of data (γ = 0.0183). In Section 7, we set a higher value of the gain, 7 For the detailed description of the traders' forecasts see Cheung and Chinn (2001) and Cheung et al (2004). 8 The empirical evidence suggests that the convergence of market rate to the fundamental rate may take longer than one year.…”

Section: Low Frequency Observationsmentioning

confidence: 99%

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Learning to forecast the exchange rate: Two competing approaches

Markiewicz

2013

Journal of International Money and Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. In this paper, we investigate the behavior of the exchange rate within the framework of an asset pricing model. We assume boundedly rational agents who use simple rules to forecast the future exchange rate. They test these rules continuously using two learning mechanisms. The first one, the fitness method, assumes that agents evaluate forecasts by computing their past profitability. In the second mechanism, agents learn to improve these rules using statistical methods. First, we find that both learning mechanisms reveal the fundamental value of the exchange rate in the steady state. Second, both mechanisms mimic regularities observed in the foreign exchange markets, namely exchange rate disconnect and excess volatility. Fitness learning rule generates the disconnection at different frequencies, while the statistical method has this ability only at the high frequencies. Statistical learning can produce excess volatility of magnitude closer to reality than fitness learning but can also lead to explosive solutions. Terms of use: Documents inJEL Code: C32, F31.

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Section: Low Frequency Observationsmentioning

confidence: 67%

Section: Low Frequency Observationsmentioning

confidence: 99%

Learning to forecast the exchange rate: Two competing approaches

Markiewicz

2013

Journal of International Money and Finance

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“…We chose to specifyB a i (t) as γ-weighted "empirical" beliefs (Cheung and Friedman, 1997) where the belief held by player i about the probability that player j will play action a in round t + 1 is given by:…”

Section: Player's Beliefsmentioning

confidence: 99%

“…Roth and Erev, 1995;and Arthur, 1991) focus on reinforcement learning where players determine their actions according to their success in bringing high payoffs in the previous periods of the game. In belief-based learning models (Cheung and Friedman, 1997;Boylan and El-Gamal, 1993;Sopher, 1994, 1997;Rankin et al, 2000;and Fudenberg and Levine, 1998) players use the past history of the game to update their beliefs about what their opponents may play. Finally, other studies (e.g.…”

Section: Introduction and Motivationsmentioning

confidence: 99%

“…To this end, we estimate the difference between players' actual (or "true") beliefs, elicited using an appropriate scoring rule, and γ-weighted beliefs (Cheung and Friedman, 1997) where players only consider the past history of their opponents' actions. We then examine whether this difference can be explained by players' own past actions, i.e.…”

Section: Introduction and Motivationsmentioning

confidence: 99%

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Dumbing down rational players: Learning and teaching in an experimental game

Terracol

Vaksmann

2009

Journal of Economic Behavior & Organization

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This paper uses experimental data to examine the existence of a teaching strategy among bounded rational players. If players realize that their own actions modify their opponents' beliefs and actions, they might play certain actions to this specific end; and forego immediate payoffs if the expected payoff gain from a teaching strategy is high enough. Our results support the existence of a teaching strategy in several ways: First they show that players update their beliefs in order to take account of the reaction of their opponents to their own action. Second, we examine if players actually use a teaching strategy by playing an action that induces a poor immediate payoff but is likely to modify the opponent's behavior so that a preferable outcome might emerge in the future. We find strong evidence of such a strategy in the data and confirm this finding within a logistic model which suggests that the future expected payoff that could arise from a teaching strategy has indeed a significant impact on choice probabilities. Finally, we investigate the effective impact of a teaching strategy on achieved outcomes and find that efficient teachers can successfully use teaching in order to reach their favorite outcome at the expense of their opponents. JEL codes: C72, C91, D83

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Behavioral Economics And Game Theory

Costa‐Gomes

2011

Wiley Encyclopedia of Operations Research and Management Science

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Game theory is a tool used to predict behavior in situations involving several decision makers. However, when people play games their behavior often shows systematic deviations from the theory's predictions. This article provides brief summaries of the different modeling approaches that attempt to account for such deviations. A common theme of these approaches is its close attention to a variety of empirically based phenomena that have been documented by psychologists. The result is a synthesis of theory and empirical evidence that is known as behavioral game theory.

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Individual Learning in Normal Form Games: Some Laboratory Results

Cited by 283 publications

References 13 publications

Learning to forecast the exchange rate: Two competing approaches

Learning to forecast the exchange rate: Two competing approaches

Dumbing down rational players: Learning and teaching in an experimental game

Behavioral Economics And Game Theory

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