On the Efficiency of Social Learning

Econometrica ◽  
2019 ◽  
Vol 87 (6) ◽  
pp. 2141-2168 ◽  
Author(s):  
Dinah Rosenberg ◽  
Nicolas Vieille
Keyword(s):  
Social Learning ◽  
Simple Condition ◽  
Learning Models ◽  
Binary Decision ◽  
Learning Efficiency ◽  
Private Signals ◽  
Entire Sequence ◽  
Necessary And Sufficient

We revisit prominent learning models in which a sequence of agents make a binary decision on the basis of both a private signal and information related to past choices. We analyze the efficiency of learning in these models, measured in terms of the expected welfare. We show that, irrespective of the distribution of private signals, learning efficiency is the same whether each agent observes the entire sequence of earlier decisions or only the previous decision. In addition, we provide a simple condition on the signal distributions that is necessary and sufficient for learning efficiency. This condition fails to hold in many cases of interest. We discuss a number of extensions and variants.

A General Analysis of Sequential Social Learning

2021 ◽  
Author(s):  
Itai Arieli ◽  
Manuel Mueller-Frank
Keyword(s):  
Social Learning ◽  
Utility Function ◽  
Posterior Probability ◽  
General Analysis ◽  
Sufficient Condition ◽  
Important Class ◽  
General Utility ◽  
Learning Game ◽  
Necessary And Sufficient ◽  
The Given

This paper analyzes a sequential social learning game with a general utility function, state, and action space. We show that asymptotic learning holds for every utility function if and only if signals are totally unbounded, that is, the support of the private posterior probability of every event contains both zero and one. For the case of finitely many actions, we provide a sufficient condition for asymptotic learning depending on the given utility function. Finally, we establish that for the important class of simple utility functions with finitely many actions and states, pairwise unbounded signals, which generally are a strictly weaker notion than unbounded signals, are necessary and sufficient for asymptotic learning.

Learning in Social Networks

2016 ◽  
pp. 503-542 ◽  
Author(s):  
Benjamin Golub ◽  
Evan Sadler
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Learning Theories ◽  
Social Connections ◽  
Network Centrality ◽  
Learning Models ◽  
Network Learning ◽  
Aggregation Of Information

This survey covers models of how agents update behaviors and beliefs using information conveyed through social connections. The chapter begins with sequential social learning models, in which each agent makes a decision once and for all after observing a subset of prior decisions; the discussion is organized around the concepts of diffusion and aggregation of information. Next, the chapter presents the DeGroot framework of average-based repeated updating, whose long- and medium-run dynamics can be completely characterized in terms of measures of network centrality and segregation. Finally, the chapter turns to various models of repeated updating that feature richer optimizing behavior, and concludes by urging the development of network learning theories that can deal adequately with the observed phenomenon of persistent disagreement.

scholarly journals Power Indices in the Context of Social Learning Behaviour in Social Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Ruili Shi ◽  
Chunxiang Guo ◽  
Xin Gu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Learning ◽  
Social Impact ◽  
Power Index ◽  
Decision Makers ◽  
Power Indices ◽  
Learning Efficiency ◽  
Relationship Closeness ◽  
The Social

This paper puts forward the concept of integrated power, synthetically measures the voters’ ability to influence the results of decision-making by influencing others through social learning, considering the interactions between decision-makers in social networks, and offers a method for measuring integrated power. Based on the theory and model of social learning, we analyze the influence of social learning on the voting process and power indices from the perspective of individuals’ professional level, position within the social network structure, relationship closeness, and learning efficiency. A measurement model of integrated power is constructed, and the variation in integrated power compared with that of the Banzhaf index is analyzed by numerical simulation. The results show that when the individual’s professional level is higher and closeness with neighboring decision-makers is greater, then the integrated power index is higher. An individual’s integrated power index may decrease when he/she changes from an isolated node to a nonisolated node, and then his/her integrated power will increase with the increases of neighbor nodes. Social learning efficiency can promote the integrated power of individuals with lower social impact and relationship closeness, but it is not beneficial for the core and influential members of the social network.

Social Learning and Macroeconomic Policy in Britain

2001 ◽  
Vol 21 (2) ◽  
pp. 133-152 ◽  
Author(s):  
IAN GREENER
Keyword(s):  
Social Learning ◽  
Policy Change ◽  
Policy Instruments ◽  
Macroeconomic Policy ◽  
Social Constructionist ◽  
Learning Models ◽  
The Social ◽  
Constructionist Approach ◽  
The 1960S

This paper examines the social learning models of policy of Hall and May attempting to create a synthesis of the best elements of each. We then apply the revised model to three specific instances of macroeconomic policy in Britain; the introduction of ‘Keynesian-plus’ policy in the 1960s, the movement from Keynesianism to monetarism, and the experiment with monetarism in the 1980s. In each case study, the degree of policy change is assessed, and possible reasons for that level of change explored. We conclude that a more social constructionist approach is required to understand the link between policy instruments, indicators, and paradigms, and, alongside this, a greater need to understand the implications of the assumptions underlying policy.

scholarly journals Peran Model Social Learning Dalam Meningkatkan Kompetensi Petani Garam di Kabupaten Pamekasan

Simulacra ◽  
2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Umi Hanik ◽  
Mutmainah Mutmainah
Keyword(s):  
Qualitative Research ◽  
Social Learning ◽  
Personal Characteristics ◽  
Self Concept ◽  
Research Approach ◽  
Learning Models ◽  
The Social ◽  
Helping Others ◽  
Depth Interviews

<em>This study aims to determine the role of social learning models in improving the competency of salt farmers in Pamekasan Regency. The research approach used is qualitative research with grounded theory. Data collection techniques using depth interviews, observation and documentation studies. The results of the study showed that increasing the competency of salt farmers through social learning models was carried out by presenting examples of behavior from aspects: 1) knowledge (knowledge); 2) skills (skills); 3) self concept; 4) personal characteristics (traits); and 5) motives (motives). The role of the social learning model for increasing salt farmers in Pamekasan Regency is: 1) to increase knowledge so that farmers have several alternative ways to make salt to produce quality; 2) developing the competency of salt farmers through the delivery of information; 3) foster an attitude of helping others; and 4) fostering a cooperative attitude towards outside parties who wish to establish cooperation.</em>

scholarly journals Misinterpreting Others and the Fragility of Social Learning

Econometrica ◽  
2020 ◽  
Vol 88 (6) ◽  
pp. 2281-2328 ◽  
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.

scholarly journals Social Learning Equilibria

Econometrica ◽  
2020 ◽  
Vol 88 (3) ◽  
pp. 1235-1267 ◽  
Author(s):  
Elchanan Mossel ◽  
Manuel Mueller-Frank ◽  
Allan Sly ◽  
Omer Tamuz
Keyword(s):  
Social Learning ◽  
Information Aggregation ◽  
Equilibrium Concept ◽  
Extensive Form ◽  
Equilibrium Behavior ◽  
Dynamic Setting ◽  
Private Signals ◽  
The Given ◽  
Asymptotic Equilibrium ◽  
General Dynamic

We consider a large class of social learning models in which a group of agents face uncertainty regarding a state of the world, share the same utility function, observe private signals, and interact in a general dynamic setting. We introduce social learning equilibria, a static equilibrium concept that abstracts away from the details of the given extensive form, but nevertheless captures the corresponding asymptotic equilibrium behavior. We establish general conditions for agreement, herding, and information aggregation in equilibrium, highlighting a connection between agreement and information aggregation.

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