Complementary Information and Learning Traps*

Abstract We develop a model of social learning from complementary information: short-lived agents sequentially choose from a large set of flexibly correlated information sources for prediction of an unknown state, and information is passed down across periods. Will the community collectively acquire the best kinds of information? Long-run outcomes fall into one of two cases: (i) efficient information aggregation, where the community eventually learns as fast as possible; (ii) “learning traps,” where the community gets stuck observing suboptimal sources and information aggregation is inefficient. Our main results identify a simple property of the underlying informational complementarities that determines which occurs. In both regimes, we characterize which sources are observed in the long run and how often.

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Misinterpreting Others and the Fragility of Social Learning

Econometrica ◽

10.3982/ecta16981 ◽

2020 ◽

Vol 88 (6) ◽

pp. 2281-2328 ◽

Cited By ~ 2

Author(s):

Mira Frick ◽

Ryota Iijima ◽

Yuhta Ishii

Keyword(s):

Social Learning ◽

Negative Impact ◽

Information Aggregation ◽

The State ◽

Type Distribution ◽

Long Run ◽

Private Signals ◽

The World ◽

True State ◽

Over Time

We exhibit a natural environment, social learning among heterogeneous agents, where even slight misperceptions can have a large negative impact on long‐run learning outcomes. We consider a population of agents who obtain information about the state of the world both from initial private signals and by observing a random sample of other agents' actions over time, where agents' actions depend not only on their beliefs about the state but also on their idiosyncratic types (e.g., tastes or risk attitudes). When agents are correct about the type distribution in the population, they learn the true state in the long run. By contrast, we show, first, that even arbitrarily small amounts of misperception about the type distribution can generate extreme breakdowns of information aggregation, where in the long run all agents incorrectly assign probability 1 to some fixed state of the world, regardless of the true underlying state. Second, any misperception of the type distribution leads long‐run beliefs and behavior to vary only coarsely with the state, and we provide systematic predictions for how the nature of misperception shapes these coarse long‐run outcomes. Third, we show that how fragile information aggregation is against misperception depends on the richness of agents' payoff‐relevant uncertainty; a design implication is that information aggregation can be improved by simplifying agents' learning environment. The key feature behind our findings is that agents' belief‐updating becomes “decoupled” from the true state over time. We point to other environments where this feature is present and leads to similar fragility results.

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Design of binary LDPC codes for Slepian-Wolf coding of correlated information sources

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP) ◽

10.1109/globalsip.2017.8309135 ◽

2017 ◽

Cited By ~ 1

Author(s):

Saeed Eleruja ◽

Umar-Faruk Abdu-Aguye ◽

Marcel Ambroze ◽

Martin Tomlinson ◽

Mohammed Zak

Keyword(s):

Information Sources ◽

Ldpc Codes ◽

Correlated Information

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The Effect of Public Capital on Aggregate Output

Review of Economics ◽

10.1515/roe-2013-0104 ◽

2013 ◽

Vol 64 (1) ◽

pp. 51-72

Author(s):

Jan-Erik Wesselhöft

Keyword(s):

Dynamic Effect ◽

High Growth ◽

Public Capital ◽

Oecd Countries ◽

Crowding Out ◽

Private Capital ◽

Large Set ◽

Public And Private ◽

Long Run ◽

Positive Effect

Abstract Based on new estimates of public and private capital stocks for 22 OECD countries we study the dynamic effect of public capital on the real gross domestic product using a vector autoregression approach. Whereas most former studies put effort on examining the effects of public capital in a single country, this paper covers a large set of OECD countries. The results show that public capital has a positive effect on output in the short-, medium- and long-run in most countries. In countries where the effect is negative, possible explanations as the different productivities of investments, crowding out or high growth rates of government debt are analyzed.

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NOMA for Correlated Information Sources in 5G Systems

IEEE Communications Letters ◽

10.1109/lcomm.2020.3027726 ◽

2020 ◽

pp. 1-1 ◽

Cited By ~ 1

Author(s):

Kyuhyuk Chung

Keyword(s):

Information Sources ◽

5G Systems ◽

Correlated Information

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The Structure of Prediction Market Research: Important Publications and Research Clusters

The Journal of Prediction Markets ◽

10.5750/jpm.v11i1.1294 ◽

2017 ◽

Vol 11 (1) ◽

pp. 51-65

Author(s):

Frank M. A. Klingert

Keyword(s):

Market Research ◽

A Priori ◽

Information Aggregation ◽

Research Paper ◽

Prediction Market ◽

Main Research ◽

Literature Reviews ◽

Efficient Information ◽

Number Of Publications ◽

Selection Of

This research paper identifies the most important publications and research clusters in the field of prediction markets. Two literature reviews in 2007 and 2014 have already shown a rising number of publications and classified them into several classes. However, the a priori selection of classes limited the analysis. Furthermore, it is still not quantitatively measured which publications have influenced prediction market research most. This research paper extends the existing literature based on the analysis of more than 18,000 citations. Thus, it identifies the most important publications and relevant research topics. It indicates that prediction market research relies primarily on publications within its own field. This paper concludes that some publications have already become “classic” and four main research clusters have emerged: Efficient information aggregation, manipulation, innovation markets and forecasting elections.

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