Inspired and inspiring: Hervé Moulin and the discovery of the beauty contest game

Nagel, Rosemarie; Bühren, Christoph; Frank, Björn

doi:10.1016/j.mathsocsci.2016.09.001

Cited by 31 publications

(28 citation statements)

References 115 publications

(131 reference statements)

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“…In particular, the two-person version of the beauty contest has a dominant strategy of choosing 0 [although a recent paper has argued that subjects may be confusing this game with an alternative "distance payoff" game, where 0 is not a dominant strategy (37)]. Although many of our subjects discovered this dominant strategy, nearly half of them did not, even after many repetitions of the same game (up to 30).…”

Section: Discussionmentioning

confidence: 76%

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Computational modeling of epiphany learning

Chen

Krajbich

2017

Proc. Natl. Acad. Sci. U.S.A.

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Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of RL. In some cases learning seems to happen all at once. Limited prior research on these "epiphanies" has shown evidence of sudden changes in behavior, but it remains unclear how such epiphanies occur. We propose a sequential-sampling model of epiphany learning (EL) and test it using an eye-tracking experiment. In the experiment, subjects repeatedly play a strategic game that has an optimal strategy. Subjects can learn over time from feedback but are also allowed to commit to a strategy at any time, eliminating all other options and opportunities to learn. We find that the EL model is consistent with the choices, eye movements, and pupillary responses of subjects who commit to the optimal strategy (correct epiphany) but not always of those who commit to a suboptimal strategy or who do not commit at all. Our findings suggest that EL is driven by a latent evidence accumulation process that can be revealed with eye-tracking data.epiphany learning | eye tracking | pupil dilation | beauty contest | decision making H ow organisms learn is a central question in the behavioral sciences. Much of the literature has focused on reinforcement learning (RL) (1-3), where organisms gradually adjust their behavior in response to the outcomes of prior actions. However, there are other situations where organisms seem to suddenly and dramatically alter their behavior (4-7), often without any external input (8). This "epiphany" learning (EL) is characterized by an unexpected moment of insight, often portrayed in cartoons by a light bulb appearing over a person's head, and was first introduced to economics in ref. 9.The sudden, unexpected, and irreversible nature of EL makes it inherently difficult to predict or to study. Unlike with standard RL, where the decision maker's choices provide insight into the underlying mechanism, here decisions provide little to no insight into the mechanism underlying the generation of epiphanies, other than to establish that they have occurred ex post (9, 10). An understanding of the EL process thus remains elusive.Here, we sought to tackle this challenging problem with a combination of computational modeling and measures of the choice process. In particular, we use a model from the class of sequential-sampling models (SSMs) (11,12) to capture the unconscious accumulation of evidence toward the epiphany and eye-tracking technology to study this process (13)(14)(15)(16)(17)(18)(19)(20).SSMs have received much attention in the decision sciences for their ability to capture choice outcomes, response times (21-23), and, more recently, eye-tracking and neural activity in perceptual and value-based decision making (24-28). These models assume that at the time of choice the decision maker evaluates the available options and accumulates evidence for each one until there is sufficient relative evidence for one option over the others....

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Section: Discussionmentioning

confidence: 76%

“…In the following trial, subjects were matched with a new random trial/opponent from the database. By doing so, we were able to prevent subjects from noticing that their opponents' choices tended to decrease over time, which might have promoted a mimicking strategy (37).…”

Section: Resultsmentioning

confidence: 99%

Computational modeling of epiphany learning

Chen

Krajbich

2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Since its inception, this model has been used throughout the economics literature to describe the behavior of rational agents in strategic environments (c.f. [1,4,5,9,17,18]), particularly in macroeconomic settings.…”

Section: The Keynesian Beauty Contestmentioning

confidence: 99%

Price of Privacy in the Keynesian Beauty Contest

Elzayn

Schutzman

2019

Proceedings of the 2019 ACM Conference on Economics and Computation

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The Keynesian Beauty Contest is a classical game in which strategic agents seek to both accurately guess the true state of the world as well as the average action of all agents. We study an augmentation of this game where agents are concerned about revealing their private information and additionally suffer a loss based on how well an observer can infer their private signals. We solve for an equilibrium of this augmented game and quantify the loss of social welfare as a result of agents acting to obscure their private information, which we call the 'price of privacy'. We analyze two versions of this this price: one from the perspective of the agents measuring their diminished ability to coordinate due to acting to obscure their information and another from the perspective of an aggregator whose statistical estimate of the true state of the world is of lower precision due to the agents adding random noise to their actions. We show that these quantities are high when agents care very strongly about protecting their personal information and low when the quality of the signals the agents receive is poor.

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“…In the extreme case of the Runway Game, participants self-organize the same behavior regardless of whether or not the game even has a Nash solution. This insensitivity of players to basic game properties challenges the relevance of elementary gametheoretic concepts to more complex games [56].…”

Section: An Emergent Mental Modelmentioning

confidence: 99%

Cognitive mechanisms for human flocking dynamics

Frey

Goldstone

2018

J Comput Soc Sc

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Low-level "adaptive" and higher-level "sophisticated" human reasoning processes have been proposed to play opposing roles in the emergence of unpredictable collective behaviors such as crowd panics, traffic jams, and market bubbles. While adaptive processes are widely recognized drivers of emergent social complexity, complementary theories of sophistication predict that incentives, education, and other inducements to rationality will suppress it. We show in a series of multiplayer laboratory experiments that, rather than suppressing complex social dynamics, sophisticated reasoning processes can drive them. Our experiments elicit an endogenous collective behavior and show that it is driven by the human ability to recursively anticipate the reasoning of others. We identify this behavior, "sophisticated flocking", across three games, the Beauty Contest and the "Mod Game" and "Runway Game". In supporting our argument, we also present evidence for mental models and social norms constraining how players express their higher-level reasoning abilities. By implicating sophisticated recursive reasoning in the kind of complex dynamic that it has been predicted to suppress, we support interdisciplinary perspectives that emergent complexity is typical of even the most intelligent populations and carefully designed social systems.

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Inspired and inspiring: Hervé Moulin and the discovery of the beauty contest game

Cited by 31 publications

References 115 publications

Computational modeling of epiphany learning

Computational modeling of epiphany learning

Price of Privacy in the Keynesian Beauty Contest

Cognitive mechanisms for human flocking dynamics

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