Multi-issue social learning

Gal Bahar; Itai Arieli; Rann Smorodinsky; Moshe Tennenholtz

doi:https://doi.org/10.1016/j.mathsocsci.2020.01.006

Multi-issue social learning

Gal Bahar, Itai Arieli, Rann Smorodinsky, Moshe Tennenholtz

Data and Decision Sciences

Technion - Israel Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

We consider social learning where agents can only observe part of the population (modeled as neighbors on an undirected graph), face many decision problems, and arrival order of the agents is unknown. The central question we pose is whether there is a natural observability graph that prevents the information cascade phenomenon. We introduce the ‘celebrities graph’ and prove that indeed it allows for proper information aggregation in large populations even when the order at which agents decide is random and even when different issues are decided in different orders.

Original language	English
Pages (from-to)	29-39
Number of pages	11
Journal	Mathematical Social Sciences
Volume	104
DOIs	https://doi.org/10.1016/j.mathsocsci.2020.01.006
State	Published - Mar 2020

Keywords

Information cascades
Networks
Social learning

All Science Journal Classification (ASJC) codes

Sociology and Political Science
Social Sciences(all)
Psychology(all)
Statistics, Probability and Uncertainty

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https://doi.org/10.1016/j.mathsocsci.2020.01.006

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title = "Multi-issue social learning",

abstract = "We consider social learning where agents can only observe part of the population (modeled as neighbors on an undirected graph), face many decision problems, and arrival order of the agents is unknown. The central question we pose is whether there is a natural observability graph that prevents the information cascade phenomenon. We introduce the {\textquoteleft}celebrities graph{\textquoteright} and prove that indeed it allows for proper information aggregation in large populations even when the order at which agents decide is random and even when different issues are decided in different orders.",

keywords = "Information cascades, Networks, Social learning",

author = "Gal Bahar and Itai Arieli and Rann Smorodinsky and Moshe Tennenholtz",

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year = "2020",

month = mar,

doi = "https://doi.org/10.1016/j.mathsocsci.2020.01.006",

language = "الإنجليزيّة",

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TY - JOUR

T1 - Multi-issue social learning

AU - Bahar, Gal

AU - Arieli, Itai

AU - Smorodinsky, Rann

AU - Tennenholtz, Moshe

PY - 2020/3

Y1 - 2020/3

N2 - We consider social learning where agents can only observe part of the population (modeled as neighbors on an undirected graph), face many decision problems, and arrival order of the agents is unknown. The central question we pose is whether there is a natural observability graph that prevents the information cascade phenomenon. We introduce the ‘celebrities graph’ and prove that indeed it allows for proper information aggregation in large populations even when the order at which agents decide is random and even when different issues are decided in different orders.

AB - We consider social learning where agents can only observe part of the population (modeled as neighbors on an undirected graph), face many decision problems, and arrival order of the agents is unknown. The central question we pose is whether there is a natural observability graph that prevents the information cascade phenomenon. We introduce the ‘celebrities graph’ and prove that indeed it allows for proper information aggregation in large populations even when the order at which agents decide is random and even when different issues are decided in different orders.

KW - Information cascades

KW - Networks

KW - Social learning

UR - http://www.scopus.com/inward/record.url?scp=85079001559&partnerID=8YFLogxK

U2 - https://doi.org/10.1016/j.mathsocsci.2020.01.006

DO - https://doi.org/10.1016/j.mathsocsci.2020.01.006

M3 - مقالة

SN - 0165-4896

VL - 104

SP - 29

EP - 39

JO - Mathematical Social Sciences

JF - Mathematical Social Sciences

ER -

Multi-issue social learning

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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