On designing economic agents that behave like human agents

Arthur, W. Brian

doi:10.1007/bf01199986

Cited by 157 publications

(118 citation statements)

References 11 publications

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“…This extremely complicated process is highly unrealistic and there is no evidence that real people use it (Holt, 1993). Rosenthal (1993) and others (e.g., Holland, 1976;Arthur, 1990Arthur, , 1993and Aumann, 1986) have proposed that players begin with a set of "rules-of-thumb" and that the propensity to employ a particular rule-of-thumb is reinforced as in the adaptive behavior model. In other words, the definition of "behavior" is broadened to include rules-of-thumb as well as simple actions.…”

Section: Figurementioning

confidence: 99%

Boundedly Rational Rule Learning in a Guessing Game

Stahl

1996

Games and Economic Behavior

176

146

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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-k rules 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 Literature Classification Numbers: C70, C52, D83.

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Section: Figurementioning

confidence: 99%

Boundedly Rational Rule Learning in a Guessing Game

Stahl

1996

Games and Economic Behavior

176

146

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“…and plugging the two heuristics (9) and (11) into the price equation (5). This leads to the following 7-dimensional system consisting of two equations describing the evolution of performance measures U 1,t−1 and U 2,t−1 , one describing the fraction n 1,t of the first forecasting heuristic, one equation describing the price dynamics and three more equations used to take lagged price deviations into account:…”

Section: Stability Analysis Of Fundamental Steady Statementioning

confidence: 99%

“…5 In the learning-to-forecast experiments subjects were paid by forecasting performance. Bao et al (2014) recently ran learning-to-forecast and learning-to-optimize experiments, where subjects not only forecast but also engage in trading activities and are (partly) paid by realized profits or utility.…”

Section: Two Type Heuristics Switching Modelmentioning

confidence: 99%

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Path dependent coordination of expectations in asset pricing experiments: A behavioral explanation

Agliari

Hommes

Pecora

2016

Journal of Economic Behavior & Organization

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In the learning-to-forecast laboratory experiments in Hommes et al. (2005), three different types of aggregate asset price behavior have been observed: monotonic convergence to the stable fundamental steady state, dampened price oscillations and permanent price oscillations. We present a simple behavioral 2-type heuristics switching model explaining individual as well as aggregate behavior in the experiment. Based on relative performance, agents switch between a simple trend-following and an anchor and adjustment heuristic that differ in how much weight is given to the long run average price level. The nonlinear switching model exhibits path dependence through co-existence of a locally stable fundamental steady state and a stable (quasi-)periodic orbit, created via a so-called Chenciner bifurcation. Depending on initial states, agents coordinate individual expectations either on a stable fundamental steady state path or on almost self-fulfilling persistent price fluctuations around the fundamental steady state.

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“…However, this heterogeneity in reinforcement learning has been largely unexploited in the literature. On the contrary, since the pioneering work done by Arthur (1991) and Arthur (1993), most empirical application of reinforcement learning assumes that agents are homogeneous in these parameters. Therefore, it leaves two issues unanswered in the literature.…”

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confidence: 99%

Reinforcement learning in auction experiments

陳樹衡

Hsieh²

2008

PsycEXTRA Dataset

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In this paper, we study the learning behavior possibly emerging in six series of prediction market experiments. We first find, from the experimental outcomes, that there is a general positive correlation between subjects' earning performance and their reliance on using limit order to trade. Given this connection, we, therefore, focus on subjects' learning behavior in terms of their use of limit order or market order. A 3-parameter Roth-Erev reinforcement learning (RL) model is empirically constructed for each subject. A numerical algorithm known as differential evolution is applied to estimate the maximum likelihood function derived from the RL model. The results of the estimated parameters show not just their great heterogeneity, but also the sharp contrasts among subjects. This great heterogeneity is, however, not caused by the experimental designs, such as the information distribution among agents. The statistical analysis shows that the heterogeneity comes from the subject themselves. In other words, they may be considered as personality traits of subjects. We then go further to test whether these personality traits have impact on subjects' earning performance, and some initial evidences suggest the significance of both attention and determination parameters. Hence, to be winners, not only does subjects need to learn, but also they need to learn with right personality traits (parameters).

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On designing economic agents that behave like human agents

Cited by 157 publications

References 11 publications

Boundedly Rational Rule Learning in a Guessing Game

Boundedly Rational Rule Learning in a Guessing Game

Path dependent coordination of expectations in asset pricing experiments: A behavioral explanation

Reinforcement learning in auction experiments

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