Multi-Player Bayesian Learning with Misspecified Models

Murooka, Takeshi; Yamamoto, Yuichi

Multi-Player Bayesian Learning with Misspecified Models

Murooka, Takeshi; Yamamoto, Yuichi

OSIPP Discussion Paper, 2021, DP-2021-E-001, 1-112

Number of Access:452（2025-08-22 02:11 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/78357

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Title	Multi-Player Bayesian Learning with Misspecified Models
Creator	NRID 1000010796345 1000010796345 researchmap permalink takeshimurooka takeshimurooka Scopus Author ID 37079438400 37079438400 CreatorName Murooka, Takeshi
Creator	NRID 1000040855178 1000040855178 CreatorName Yamamoto, Yuichi
Subject	model misspecification
	learning
	convergence
	overconfidence
	bias transmission
Abstract	We consider strategic players who may have a misspecified view about an environment, and investigate their long-run behavior. Each period, players simultaneously take actions, observe a public outcome, and then update own belief about an uncertain economic state by using Bayes’ rule. We provide a condition under which players’ beliefs and actions converge to a steady state, and then characterize how one’s misspecification influences the long-run (steady-state) actions. When a player has a biased view about the physical environment (e.g., overconfidence on own capability or prejudice on an opponent’s capability), the presence of strategic interaction influences the size of the impact of misspecification, but not the direction. In particular, when the game is symmetric, the presence of strategic interaction amplifies the deviation of the long-run actions from those in the correctly specified model. When a player misspecifies the opponent’s view about the environment (e.g., the player is not aware of the opponent’s bias), the strategic interaction generates a directional impact for the long-run actions. We extensively discuss implications to a variety of applications, such as Cournot duopoly, team production, tournaments, and discrimination.
Publisher	Osaka School of International Public Policy, Osaka University
Source Title	OSIPP Discussion Paper
Volume (Issue)	DP-2021-E-001
Page	1-112
Date of Issued	2021-01-15
Language	English
Handle URL	https://hdl.handle.net/11094/78357
Relation.references	URI http://www.osipp.osaka-u.ac.jp/archives/DP/2021/DP2021E001.pdf
Access Rights	metadata only access
Category	ディスカッション／ワーキング・ペーパー DP/WP
Category	OSIPP Discussion Paper

Resource Type	technical report
Local Type	DP/WP
DCMI Type	Text
DCTERMS.bibliographicCitation	OSIPP Discussion Paper. 2021, DP-2021-E-001, p. 1-112
DC.title	Multi-Player Bayesian Learning with Misspecified Models
DC.creator	Murooka, Takeshi
DC.creator	Yamamoto, Yuichi
DC.publisher	Osaka School of International Public Policy, Osaka University
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2021-01-15
DC.relation' scheme='DCTERMS.URI	http://www.osipp.osaka-u.ac.jp/archives/DP/2021/DP2021E001.pdf
DC.subject	model misspecification
	learning
	convergence
	overconfidence
	bias transmission
DCTERMS.abstract	We consider strategic players who may have a misspecified view about an environment, and investigate their long-run behavior. Each period, players simultaneously take actions, observe a public outcome, and then update own belief about an uncertain economic state by using Bayes’ rule. We provide a condition under which players’ beliefs and actions converge to a steady state, and then characterize how one’s misspecification influences the long-run (steady-state) actions. When a player has a biased view about the physical environment (e.g., overconfidence on own capability or prejudice on an opponent’s capability), the presence of strategic interaction influences the size of the impact of misspecification, but not the direction. In particular, when the game is symmetric, the presence of strategic interaction amplifies the deviation of the long-run actions from those in the correctly specified model. When a player misspecifies the opponent’s view about the environment (e.g., the player is not aware of the opponent’s bias), the strategic interaction generates a directional impact for the long-run actions. We extensively discuss implications to a variety of applications, such as Cournot duopoly, team production, tournaments, and discrimination.
DC.format	78357
citation_title	Multi-Player Bayesian Learning with Misspecified Models
citation_author	Murooka, Takeshi
citation_author	Yamamoto, Yuichi
citation_publisher	Osaka School of International Public Policy, Osaka University
citation_language	English
citation_date	2021-01-15
citation_journal_title	OSIPP Discussion Paper
citation_volume	DP-2021-E-001
citation_firstpage	1
citation_lastpage	112
citation_public_url	https://hdl.handle.net/11094/78357
citation_abstract_html_url	http://www.osipp.osaka-u.ac.jp/archives/DP/2021/DP2021E001.pdf

Multi-Player Bayesian Learning with Misspecified Models

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