Informational Externalities, Herding, and Incentives

Bru, Lluís; Vives, Xavier

doi:10.1628/0932456022975475

Cited by 12 publications

(22 citation statements)

References 10 publications

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“…Here the global decision maker's costsC(π, u) are defined in (17), (18), T π is the public belief Bayesian update (11), and the measure σ(π, a) is defined in (11).…”

Section: A Stochastic Dynamic Programming Formulationmentioning

confidence: 99%

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Quickest Detection POMDPs With Social Learning: Interaction of Local and Global Decision Makers

Krishnamurthy

2012

IEEE Trans. Inform. Theory

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We consider how local and global decision policies interact in stopping time problems such as quickest time change detection. Individual agents make myopic local decisions via social learning, that is, each agent records a private observation of a noisy underlying state process, selfishly optimizes its local utility and then broadcasts its local decision. Given these local decisions, how can a global decision maker achieve quickest time change detection when the underlying state changes according to a phase-type distribution? The paper presents four results. First, using Blackwell dominance of measures, it is shown that the optimal cost incurred in social learning based quickest detection is always larger than that of classical quickest detection. Second, it is shown that in general the optimal decision policy for social learning based quickest detection is characterized by multiple thresholds within the space of Bayesian distributions. Third, using lattice programming and stochastic dominance, sufficient conditions are given for the optimal decision policy to consist of a single linear hyperplane, or, more generally, a threshold curve. Estimation of the optimal linear approximation to this threshold curve is formulated as a simulation-based stochastic optimization problem. Finally, the paper shows that in multi-agent sensor management with quickest detection, where each agent views the world according to its prior, the optimal policy has a similar structure to social learning.

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“…Here the global decision maker's costsC(π, u) are defined in (17), (18), T π is the public belief Bayesian update (11), and the measure σ(π, a) is defined in (11).…”

Section: A Stochastic Dynamic Programming Formulationmentioning

confidence: 99%

“…Recall T (π, y) is the Hidden Markov Model Bayesian filter defined in (7). Thus the only difference between the classical and social learning quickest detection problems is the update of the belief state, namely (7) in the classical setup versus (11) in the social learning formulation.…”

Section: ) Notationmentioning

confidence: 99%

Quickest Detection POMDPs With Social Learning: Interaction of Local and Global Decision Makers

Krishnamurthy

2012

IEEE Trans. Inform. Theory

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“…8 Complementarities in the decision to produce information also arise due to other reasons in several other papers. For example see, Froot, Scharfstein, and Stein (1992); Hirshleifer, Subrahmanyam, and Titman (1994); Bru and Vives (2002);andVeldkamp (2006a and2006b). 9 Our paper can be linked to contexts that are even beyond financial markets.…”

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confidence: 99%

Learning and Complementarities in Speculative Attacks

Goldstein

Ozdenoren

Yuan

2011

The Review of Economic Studies

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We study a model where the aggregate trading of currency speculators reveals new information to the central bank and affects its policy decision. We show that the learning process gives rise to coordination motives among speculators leading to large currency attacks that cannot be explained by fundamentals and introducing nonfundamental volatility into exchange rates and policy decisions. We derive comparative statics regarding the nature of currency attacks and the effectiveness of the central bank's decision. We show that the central bank can improve the ex-ante effectiveness of its policy by committing to put a lower weight ex-post on the information coming from the market, and that transparency may either increase or decrease the effectiveness of learning from the market, depending on how it is implemented. Journal of Economic LiteratureClassification Codes: G14, G15, F31, C7.

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“…. , N , at which price the demand from k informed traders is met by suppliers: follows Bru and Vives [8]. Without the information aggregation by the auctioneer, the model becomes similar to that of Minehart and Scotchmer [30], who showed that the traders cannot agree to disagree in a rational expectations equilibrium, i.e., the equilibrium may not exist, or if it exists, it is a herding equilibrium where all the traders choose the same action.…”

Section: Model and Equilibriummentioning

confidence: 96%

“…In contrast, this paper shows that stochastic herding with a power law distribution emerges in the symmetric structure of information inference among traders. The model can be extended to incorporate the heterogeneous information structure that modifies the threshold (8). The analytical method to characterize the aggregate fluctuations by the fictitious tatonnement remains valid, and the resulting distribution will be affected by the heterogeneity in the traders' reference groups.…”

Section: Information Structurementioning

confidence: 99%

Beauty Contests and Fat Tails in Financial Markets

Nirei

2013

SSRN Journal

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This paper demonstrates that fat-tailed distributions of trade volume and stock returns emerge in a simultaneous-move herding model of rational traders who infer other traders' private information on the value of assets by observing aggregate actions.Without parametric assumptions on the private information, I analytically show that the traders' aggregate actions follow a power-law distribution with exponential truncation. Numerical simulations show that the model is able to generate the fat-tailed distributions of returns as observed empirically. I argue that the learning among a large number of traders leads to a criticality condition for the power-law clustering of actions.

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Informational Externalities, Herding, and Incentives

Cited by 12 publications

References 10 publications

Quickest Detection POMDPs With Social Learning: Interaction of Local and Global Decision Makers

Quickest Detection POMDPs With Social Learning: Interaction of Local and Global Decision Makers

Learning and Complementarities in Speculative Attacks

Beauty Contests and Fat Tails in Financial Markets

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