Information Cascades and Social Learning

Bikhchandani, Sushil; Hirshleifer, David; Tamuz, Omer; Welch, Ivo

doi:10.3386/w28887

Cited by 30 publications

(10 citation statements)

References 152 publications

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“…Extensive literature surveys on social learning are in Bikhchandani et al (2021). Early researchers have noticed that markets do not operate as desired because agents do not take actions concurrently once a market opens.…”

Section: Literature Reviewmentioning

confidence: 99%

Observational Learning with Competitive Prices

Xu¹

2022

Preprint

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Will people eventually learn the value of an asset through observable information? This paper studies observational learning in a market with competitive prices. Comparing a market with public signals and a market with private signals in a sequential trading model, we find that Pairwise Informativeness (PI) is the sufficient and necessary learning condition for a market with public signals; and Avery and Zemsky Condition (AZC) is the sufficient and necessary learning condition for a market with private signals. Moreover, when the number of states is 2 or 3, PI and AZC are equivalent. And when the number of states is greater than 3, PI and Monotonic Likelihood Ratio Property (MLRP) together imply asymptotic learning in the private signal case.

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Section: Literature Reviewmentioning

confidence: 99%

Observational Learning with Competitive Prices

Xu¹

2022

Preprint

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“…3 See, e.g., Banerjee (1992), Bikhchandani et al (1992), Glazer and Rubinstein (1998), Dekel and Piccione (2000), Ottaviani and Sorensen (2001), Levy (2004), Levy (2007), Visser and Swank (2007), Gershkov and Szentes (2009), among many others. For a recent extensive survey, see Bikhchandani et al (2021).…”

Section: Relation To Relevant Literaturementioning

confidence: 99%

“…In our model, every signal provides more information about the actual value, yet as was exemplified in Section 1.1, the signal might still be detrimental. These stark differences are best exemplified by Bikhchandani et al (2021), who state that "In purely individual decision making, an extra signal always makes an agent weakly better off" (see Section 2.6 therein). Though this statement is completely true for the relevant models of information aggregation and social learning, one of our main results proves the opposite.…”

Section: Relation To Relevant Literaturementioning

confidence: 99%

Dynamic screening

Lagziel¹,

Lehrer²

2022

Preprint

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We study dynamic screening problems in which elements are subjected to noisy evaluations and, at every stage, some of the elements are rejected, whereas those remaining are independently re-evaluated in subsequent stages. We prove that, ceteris paribus, the quality of a screening process may not improve when the number of stages increases. Specifically, we examine the resulting elements' values and show that adding a single stage to a screening process may produce inferior results in terms of stochastic dominance, whereas increasing the number of stages substantially leads to a first-best outcome.

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“…Kindleberger cycles may defeat Cardwell's Law by mobilizing offsetting heuristics: fascination with novelty, success emulation, and comfort in following the herd. Novelty activates the brain's dopamine system: intermittent success, repeated or observed, elicits more repetition, optimism, and thus bubbles (Hirshleifer 2015;Bikhchandani et al 2021). Early movers' highly visible disequilibrium profits cue investors and CEOs into financing cascades of additional investment in similar things.…”

Section: Suspending Cardwell's Lawmentioning

confidence: 99%

“…A "disposition to admire, and consequently to imitate, the rich and the great" (Smith 1759) also has plausible survival value (Gibson and Hoglund 1992;Blackmore 1999, pp. 74 -81;Bikhchandani et al 2021) and may explain uninformed investors imitating successful investors (Bikhchandani et al 2006). A "fear of missing out" may reinforce this (Janeway 2018;McGinnis 2020).…”

Section: Suspending Cardwell's Lawmentioning

confidence: 99%

Kindleberger Cycles: Method in the madness of crowds?

Mørck¹

2021

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This article summarizes the 2012 Heckscher Lecture at the Stockholm School of Economics and the Ratio Institute. Thanks to David Cho for superb research assistance. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Information Cascades and Social Learning

Cited by 30 publications

References 152 publications

Observational Learning with Competitive Prices

Observational Learning with Competitive Prices

Dynamic screening

Kindleberger Cycles: Method in the madness of crowds?

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