Social Learning with Coarse Inference

Guarino, Antonio; Jéhiel, Philippe

doi:10.1257/mic.5.1.147

Cited by 48 publications

(51 citation statements)

References 27 publications

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“…They do not discount, instead, more extreme actions. This behavior is in line with a model of subjective beliefs in which 18 We considered the medians of each session for the SL treatment and of each individual's decisions in the IDM treatment for  1 ; and the medians of each session for the SL treatment for  1 2 ; we reject the null hypothesis that they come from the same distribution (p-value = 0014). We repeated the same test considering only the IDM treatment for  1 ; again, we reject the null hypothesis (p-value = 0015).…”

Section: 13supporting

confidence: 59%

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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Section: 13supporting

confidence: 59%

Updating ambiguous beliefs in a social learning experiment

Filippis

Guarino

Jéhiel

et al. 2017

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“…17 While in the case of a con…rming signal the median subject puts only a slightly lower weight on the signal than a Bayesian agent would do, in the case of a contradicting signal, the weight is considerably higher, 170. 18 The di¤erent weight is observed also for the …rst and third quartiles. Essentially, subjects update in an asymmetric way, depending on whether the signal con…rms or not their prior beliefs: contradicting signals are overweighted with respect to Bayesian updating.…”

Section: How Do Subjects Weigh Their Signal Relative To Their Predecementioning

confidence: 94%

“…We will refer to it as the SL treatment. Drawing the precision from the tiny interval [07 071], instead of having the simpler set up with …xed precision equal to 07, was only due to a research question motivated by the theory of Guarino and Jehiel (2013), where the precision is indeed supposed to di¤er agent by agent; this research question, however, is not the object of this paper. Reducing the length of the sequence to 4 subjects was instead motivated by the opportuneness to collect more data for the …rst periods of the sequence.…”

Section: Experimental Designmentioning

confidence: 99%

Updating ambiguous beliefs in a social learning experiment

Filippis¹,

Guarino²,

Jéhiel³

et al. 2016

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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. We present a novel experimental design to study social learning in the laboratory. Subjects have to predict the value of a good in a sequential order. We elicit each subject's belief twice: …rst ("prior belief"), after he observes his predecessors' action; second ("posterior belief"), after he observes a private signal on the value of the good. We are therefore able to disentangle social learning from learning from a private signal. Our main result is that subjects update on their private signal in an asymmetric way. They weigh the private signal as a Bayesian agent would do when the signal con…rms their prior belief; they overweight the signal when it contradicts their prior belief. We show that 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. Terms of use: Documents in EconStor may

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“…Information redundancy neglect in social learning has been studied, for instance, by Eyster and Rabin (2010). In their work, while each agent uses his private information and learns from others, he is convinced that others only use their private information: as a result, he interprets a predecessor's action as if it simply reected the agent's private in- 1 Redundancy neglect was not the focus of a study by Guarino and Jehiel (2013); nevertheless, they also nd an overweighting of early signals in a model of social learning with a continuous action space. We will discuss this work in the next section.…”

Section: Introductionmentioning

confidence: 99%

“…Once again, the data are in contrast with the PBE, the BRTNI and the ABEE, but not with the OC model. 21 This model is never rejected, 20 In the case of constant k, the p-value is 0.08.…”

mentioning

confidence: 99%

Information redundancy neglect versus overconfidence: a social learning experiment

Angrisani¹,

Guarino²,

Jéhiel³

et al. 2017

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We study social learning in a continuous action space experiment. Subjects, acting in sequence, state their belief about the value of a good, after observing their predecessors' statements and a private signal. We compare the behavior in the laboratory with the Perfect Bayesian Equilibrium prediction and the predictions of bounded rationality models of decision making: the redundancy of information neglect model and the overcondence model. The results of our experiment are in line with the predictions of the overcondence model and at odds with the others'.

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Social Learning with Coarse Inference

Cited by 48 publications

References 27 publications

Updating ambiguous beliefs in a social learning experiment

Updating ambiguous beliefs in a social learning experiment

Updating ambiguous beliefs in a social learning experiment

Information redundancy neglect versus overconfidence: a social learning experiment

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