Diverging Opinions

Andreoni, James; Mylovanov, Tymofiy

doi:10.1257/mic.4.1.209

Cited by 77 publications

(60 citation statements)

References 38 publications

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“…21 Misdirected learning also occurs in some other settings in which individuals have misspecified models of the world. 21 Andreoni and Mylovanov (2012) and Kondor (2012) develop closely related models within the commonprior paradigm. With more and more redundant actions accumulating, this mistake is amplified, preventing learning even in the long run.…”

Section: Learning With Misspecified Modelsmentioning

confidence: 99%

Unrealistic Expectations and Misguided Learning

Heidhues

Kőszegi²,

Strack

2018

Econometrica

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We explore the learning process and behavior of an individual with unrealistically high expectations (overconfidence) when outcomes also depend on an external fundamental that affects the optimal action. Moving beyond existing results in the literature, we show that the agent's beliefs regarding the fundamental converge under weak conditions. Furthermore, we identify a broad class of situations in which "learning" about the fundamental is self-defeating: it leads the individual systematically away from the correct belief and toward lower performance. Due to his overconfidence, the agent-even if initially correct-becomes too pessimistic about the fundamental. As he adjusts his behavior in response, he lowers outcomes and hence becomes even more pessimistic about the fundamental, perpetuating the misdirected learning. The greater is the loss from choosing a suboptimal action, the further the agent's action ends up from optimal. We partially characterize environments in which self-defeating learning occurs, and show that the decisionmaker learns to take the optimal action if, and in a sense only if, a specific non-identifiability condition is satisfied. In contrast to an overconfident agent, an underconfident agent's misdirected learning is self-limiting and therefore not very harmful. We argue that the decision situations in question are common in economic settings, including delegation, organizational, effort, and public-policy choices.

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Section: Learning With Misspecified Modelsmentioning

confidence: 99%

Unrealistic Expectations and Misguided Learning

Heidhues

Kőszegi²,

Strack

2018

Econometrica

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“…A second example is the experiment in Andreoni and Mylovanov (2012). In this experiment, some subjects are exposed to conflicting information causing a subset of them to change their beliefs towards the majority, while others do not.…”

Section: Resultsmentioning

confidence: 99%

The coevolution of beliefs and networks

Arifovic

Eaton

Walker

2015

Journal of Economic Behavior & Organization

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In this research, we set out some of the behavioral foundations of social learning. Social psychologists have shown that people experience cognitive dissonance when two or more of their cognitions diverge, and that they actively manage the dissonance. With this in mind we develop a model of social learning in networks to understand the coevolution of beliefs and networks. We focus on beliefs concerning an objective phenomenon. Initial beliefs are based on noisy, private and unbiased information. Because private information is noisy, initial beliefs differ, creating dissonance. Social behavior is motivated by a desire to minimize this dissonance. In many circumstances this behavior adversely affects the efficiency of social learning, such that in equilibrium the mean aggregate belief is biased and there is significant variation of beliefs across the population. The parameterizations of our model that result in the most inefficient learning produce a fractionalized network structure in which there are a number of distinct groups: within any group all beliefs are identical; beliefs differ from group to group, sometimes greatly; there is no intergroup interaction.Since dissonance minimizing behavior is apparently a deeply rooted feature of humans, one that cannot be changed, we are led to ask: What policies could improve the situation? Our results suggest that policies that improve the availability of objective information and/or increase the size of networks enhance efficiency of social learning. On the other hand, anything that makes changing networks more attractive as a dissonance minimizing strategy has the opposite effect.

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“…The proposed mechanisms governing disagreement include different initial beliefs or factors that affect perception of later incoming signals [27][28][29][30][31], different update rules [28], and ambiguity aversion [28,42]. These models and ours are different in three major ways.…”

Section: Discussionmentioning

confidence: 99%

“…Theoretical models have also been proposed in social sciences to explain disagreement in the context of polarization. For example, prior beliefs or initially received signals can cause disagreement between agents, even if they receive the same public signals from then on [27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Collective opinion formation model under Bayesian updating and confirmation bias

Nishi

Masuda

2013

Phys. Rev. E

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We propose a collective opinion formation model with a so-called confirmation bias. The confirmation bias is a psychological effect with which, in the context of opinion formation, an individual in favor of an opinion is prone to misperceive new incoming information as supporting the current belief of the individual. Our model modifies a Bayesian decision-making model for single individuals [M. Rabin and J. L. Schrag, Q. J. Econ. 114, 37 (1999)] for the case of a well-mixed population of interacting individuals in the absence of the external input. We numerically simulate the model to show that all the agents eventually agree on one of the two opinions only when the confirmation bias is weak. Otherwise, the stochastic population dynamics ends up creating a disagreement configuration (also called polarization), particularly for large system sizes. A strong confirmation bias allows various final disagreement configurations with different fractions of the individuals in favor of the opposite opinions.

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Diverging Opinions

Cited by 77 publications

References 38 publications

Unrealistic Expectations and Misguided Learning

Unrealistic Expectations and Misguided Learning

The coevolution of beliefs and networks

Collective opinion formation model under Bayesian updating and confirmation bias

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