Treasure Hunt: Social Learning in the Field

Möbius, Markus; Phan, Tuan Q.; Szeidl, Ádám

doi:10.3386/w21014

Cited by 38 publications

(32 citation statements)

References 40 publications

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“…Also see Mueller-Frank and Neri (2013). Möbius et al (2015) test DeGroot learning models in a setting where individuals could communicate more than just their beliefs. They seeded signals in a real-life network of college students that they had measured in previous work.…”

Section: Social Learning and Diffusionmentioning

confidence: 99%

Field Experiments, Social Networks, and Development

Breza

2016

The Oxford Handbook of the Economics of Networks

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This chapter explores the use of field experiments as a tool to study the economics of social networks, with an emphasis on applications in development economics. Field experiments can be powerful vehicles to measure causal treatment effects. However, when treatments “spill over” onto others in the social network, many new considerations arise. The chapter begins with a discussion of methodological challenges involved in conducting social network experiments. The chapter then explores how field experiments have begun to shed light on five key issues in the economics of networks, including social learning and diffusion, other-regarding preferences, peer monitoring and enforcement, risk sharing, and network formation.

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Section: Social Learning and Diffusionmentioning

confidence: 99%

Field Experiments, Social Networks, and Development

Breza

2016

The Oxford Handbook of the Economics of Networks

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“…Choi, Gale, and Kariv (2005) demonstrated that, in networks of three nodes, the data are consistent with Bayesian behavior. Meanwhile, Mobius, Phan, and Szeidl (2015) conducted a field experiment to pit DeGroot learning against a Bayes-likethough decidedly non-Bayesian-alternative where agents may "tag" information (pass on information about the originator) to dampen double-counting. Meanwhile, Mobius, Phan, and Szeidl (2015) conducted a field experiment to pit DeGroot learning against a Bayes-likethough decidedly non-Bayesian-alternative where agents may "tag" information (pass on information about the originator) to dampen double-counting.…”

Section: Introductionmentioning

confidence: 99%

Testing Models of Social Learning on Networks: Evidence From Two Experiments

Chandrasekhar

Larreguy

Xandri

2020

ECTA

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We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors' previous guesses before guessing again. Agents are drawn from a mixture of learning types-Bayesian, who face incomplete information about others' types, and DeGroot, who average their neighbors' previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model;(2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reduced-form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian-villager and Mexican-student samples, respectively.A. G. CHANDRASEKHAR, H. LARREGUY, AND J. P. XANDRI information from their network neighbors when engaging in learning. This constraint may be for many reasons, including but not limited to operating in settings where learning is through observations of others' actions or the costs of communicating very complex information are too high and therefore only summaries are transmitted. 2 We study such a coarse communication environment. We focus on a setting in which individuals receive signals about an unknown binary state of the world in the first period and, in subsequent rounds, they communicate to their neighbors their best (binary) guess about the state of the world. 3 In this ubiquitous setting, if all agents are Bayesian, and there is common knowledge of this, under mild assumptions, learning will be asymptotically efficient in large networks (see Gale and Kariv (2003) and Mossel and Tamuz (2010) for a myopic learning environment and Mossel, Sly, and Tamuz (2015) for a strategic learning environment). However, if all agents update their guess as the majority of their neighbors' prior guesses-as modeled by a coarse DeGroot model of learning, also known as the majority voting model (Liggett (1985))-then it is possible that a non-trivial set of agents will end up stuck making the wrong guess. In practice, it might be that there is a mix of sophisticated (Bayesian) and naive (DeGroot) learners, and that Bayesians are aware of this and incorporate it in their calculations. Such an incomplete information model, its relevance, and implications for asymptotic learning have not been studied in our coarse communication environment.This paper develops an incomplete information model of social learning with coarse communication on a network in which agents can potentially be Bayesian or DeGroot, and agents have common knowledge of the distribution of Bayesian or DeGroot types in the population. Bayesian agents then learn in an environment of incomplete information. The model nests the two extreme cases-complete information all-Ba...

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“…They present an explanation of their results in the context of a generalization of the DeMarzo et al (2003) model, suggesting a more general way in which bounded rationality might influence information updating in a network. In particular, the more general model takes into account 2 Banerjee et al (2013) and Mobius et al (2015) contain some field evidence consistent with the DeGroot model. Our experiment also relates to a paper by Enke and Zimmermann (2013) on correlation neglect, which describes agents' tendency to overweight information, received from multiple sources, that is correlated due to its origin from the same source.…”

Section: Introductionmentioning

confidence: 99%

An experimental study of persuasion bias and social influence in networks

Brandts

Giritligil

Weber

2015

European Economic Review

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In many areas of social life, individuals receive information about a particular issue of interest from multiple sources. When these sources are connected through a network, then proper aggregation of this information by an individual involves taking into account the structure of this network. The inability to aggregate properly may lead to various types of distortions. In our experiment, four agents all want to find out the value of a particular parameter unknown to all. Agents receive private signals about the parameter and can communicate their estimates of the parameter repeatedly through a network, the structure of which is known by all players. We present results from experiments with three different networks. We find that the information of agents who have more outgoing links in a network gets more weight in the information aggregation of the other agents than under optimal updating. Our results are consistent with the model of "persuasion bias" of DeMarzo et al. (2003).

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Treasure Hunt: Social Learning in the Field

Cited by 38 publications

References 40 publications

Field Experiments, Social Networks, and Development

Field Experiments, Social Networks, and Development

Testing Models of Social Learning on Networks: Evidence From Two Experiments

An experimental study of persuasion bias and social influence in networks

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