Distinguishing Informational Cascades from Herd Behavior in the Laboratory

Çelen, Boğaçhan; Kariv, Shachar

doi:10.1257/0002828041464461

Cited by 289 publications

(188 citation statements)

References 17 publications

(35 reference statements)

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“…A stream of research documents evidence of herding experimentally. Through laboratory experiments, Anderson andHolt (1997) andÇ elen andKariv (2004) directly observe the point in time when subjects join a herd. Through field experiments, Salganik, Dodds, and Watts (2006) find that music buyers seek frequently downloaded songs; Cai, Chen, and Fang (2009) find that restaurant customers prefer popular dishes; Tucker and Zhang (2011) find that displaying click count information attracts web visitors to popular vendors, especially those who serve niche tastes.…”

Section: Introductionmentioning

confidence: 99%

Rational Herding in Microloan Markets

2012

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Microloan markets allow individual borrowers to raise funding from multiple individual lenders. We use a unique panel dataset which tracks the funding dynamics of borrower listings on Prosper.com, the largest microloan market in the United States. We find evidence of rational herding among lenders. Well-funded borrower listings tend to attract more funding after we control for unobserved listing heterogeneity and payoff externalities. Moreover, instead of passively mimicking their peers (irrational herding), lenders engage in active observational learning (rational herding); they infer the creditworthiness of borrowers by observing peer lending decisions, and use publicly observable borrower characteristics to moderate their inferences.Counterintuitively, obvious defects (e.g., poor credit grades) amplify a listing's herding momentum, as lenders infer superior creditworthiness to justify the herd. Similarly, favorable borrower characteristics (e.g., friend endorsements) weaken the herding effect, as lenders attribute herding to these observable merits. Follow-up analysis shows that rational herding beats irrational herding in predicting loan performance.

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Section: Introductionmentioning

confidence: 99%

Rational Herding in Microloan Markets

2012

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“…Nöth and Weber (2003), for instance, using an ingenious design in which subjects observe signals of di¤erent precision, conclude that "participants put too much weight on their private signal compared to the public information which clearly indicates the existence of overcon…d-ence." Çelen and Kariv (2004), in a study aimed at distinguishing informational cascades from herd behavior, also …nd that "subjects give excessive weight to their private information relative to the public information revealed by the behavior of others." Goeree et al (2007), revisiting the original Anderson and Holt (1997) experimental design with longer sequences of decision makers, analyze the data through the lenses of the Quantal Response Equilibrium (QRE) and also conclude that there is strong evidence of overweighting of the private information.…”

Section: Introductionmentioning

confidence: 99%

Updating ambiguous beliefs in a social learning experiment

Filippis

Guarino

Jéhiel

et al. 2017

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We present a social learning experiment in which subjects predict the value of a good in sequence. We elicit each subject's belief twice: …rst ("…rst belief"), after he observes his predecessors' prediction; second, after he also observes a private signal. Our main result is that subjects update on their signal asymmetrically. They weigh the private signal as a Bayesian agent when it con…rms their …rst belief and overweight it when it contradicts their …rst belief. This way of updating, incompatible with Bayesianism, can be explained by ambiguous beliefs (multiple priors on the predecessor's rationality) and a generalization of the Maximum Likelihood Updating rule. Our experiment allows for a better understanding of the overweighting of private information documented in previous studies.

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“…Otherwise, the player gets 0. 9 Social learning is possible because at t 2 the players observe the predictions made by all previous players Celen andKariv, 2004, Guarino, Harmgart andHuck, 2007) and games where agents are replaced by groups (Fahr and Irlenbusch, 2008). Several papers also address how experimental participants play social learning games against automated opponents with exogenously …xed behavior (Huck and Oechssler, 1999, Grebe, Schmidt and Stiehler, 2006, Kraemer, Nöth and Weber 2004.…”

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confidence: 99%

Do We Follow Others When We Should? A Simple Test of Rational Expectations

Weizsäcker¹

2008

SSRN Journal

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The paper presents a new meta data set covering 13 experiments on the social learning games by Bikhchandani, Hirshleifer, and Welch (1992). The large amount of data makes it possible to estimate the empirically optimal action for a large variety of decision situations and ask about the economic signi…cance of suboptimal play. For example, one can ask how much of the possible payo¤s the players earn in situations where it is empirically optimal that they follow others and contradict their own information. The answer is 53% on average across all experiments -only slightly more than what they would earn by choosing at random.The players' own information carries much more weight in the choices than the information conveyed by other players' choices: the average player contradicts her own signal only if the empirical odds ratio of the own signal being wrong, conditional on all available information, is larger than 2:1, rather than 1:1 as would be implied by rational expectations. A regression analysis formulates a straightforward test of rational expectations, which rejects, and con…rms that the reluctance to follow others generates a large part of the observed variance in payo¤s, adding to the variance that is due to situational di¤erences.

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Distinguishing Informational Cascades from Herd Behavior in the Laboratory

Cited by 289 publications

References 17 publications

Rational Herding in Microloan Markets

Rational Herding in Microloan Markets

Updating ambiguous beliefs in a social learning experiment

Do We Follow Others When We Should? A Simple Test of Rational Expectations

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