Excessive Herding in the Laboratory: The Role of Intuitive Judgments

March, Christoph; Ziegelmeyer, Anthony

doi:10.2139/ssrn.3140365

Cited by 1 publication

(3 citation statements)

References 26 publications

(47 reference statements)

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“…In such environments, a fully rational agent who engages in observational learning should realize that other agents, whose actions he observed, are also engaging in observational learning and that, therefore, there is redundancy in their actions, which he should account for. Experimental evidence shows that redundancy neglect is common (Kübler and Weizsäcker 2004), that even mild redundancy neglect can be harmful (Eyster et al 2015), and that many subjects believe naïvely that each observable choice reveals a substantial amount of another person's private information (March and Ziegelmeyer 2018).…”

Section: Related Literaturementioning

confidence: 99%

“…22 See Kübler and Weizsäcker (2004), Eyster et al (2015), and March and Ziegelmeyer (2018) for evidence of redundancy neglect, and see Enke and Zimmerman (2019) for evidence of correlation neglect. …”

mentioning

confidence: 99%

“…See Kübler and Weizsäcker (2004), Eyster et al (2015), and March and Ziegelmeyer (2018) for evidence of redundancy neglect, and see Enke and Zimmerman (2019) for evidence of correlation neglect.…”

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confidence: 99%

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A general analysis of boundedly rational learning in social networks

Mueller‐Frank

Neri

2021

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We analyze boundedly rational learning in social networks within binary action environments. We establish how learning outcomes depend on the environment (i.e., informational structure, utility function), the axioms imposed on the updating behavior, and the network structure. In particular, we provide a normative foundation for quasi‐Bayesian updating, where a quasi‐Bayesian agent treats others' actions as if they were based only on their private signal. Quasi‐Bayesian updating induces learning (i.e., convergence to the optimal action for every agent in every connected network) only in highly asymmetric environments. In all other environments, learning fails in networks with a diameter larger than 4. Finally, we consider a richer class of updating behavior that allows for nonstationarity and differential treatment of neighbors' actions depending on their position in the network. We show that within this class there exist updating systems that induce learning for most networks.

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Section: Related Literaturementioning

confidence: 99%