Bayesian Learning When Players Are Misspecified about Others

Murooka, Takeshi; Yamamoto, Yuichi

Bayesian Learning When Players Are Misspecified about Others

Murooka, Takeshi; Yamamoto, Yuichi

Institute of Social and Economic Research Discussion Papers, 2025, 1284, 1-37

アクセス数:12件（2025-04-29 15:26 集計）

固定URL: https://hdl.handle.net/11094/101068

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タイトル	Bayesian Learning When Players Are Misspecified about Others
著者	NRID 1000010796345 1000010796345 researchmap permalink takeshimurooka takeshimurooka Scopus Author ID 37079438400 37079438400 著者名 Murooka, Takeshi
著者	NRID 1000040855178 1000040855178 著者名 Yamamoto, Yuichi
内容	Online Appendix : 46 p.
抄録	This paper considers Bayesian learning when players are biased about the data-generating process, and are biased about the opponent’s bias about the data-generating process. Specifically, we assume that each player’s bias about others takes the form of interpersonal projection, which is a tendency to overestimate the extent to which others share the player’s own view. We show that even an arbitrarily small amount of bias can destroy correct learning of an unknown state, i.e., there is zero probability of the posterior belief staying in a neighborhood of the true state.
出版者	The Institute of Social and Economic Research, Osaka University
収録物名	Institute of Social and Economic Research Discussion Papers
巻(号)	1284
ページ	1-37
刊行年月	2025-04
言語	英語
ハンドルURL	https://hdl.handle.net/11094/101068
PISSN	0473453X
eISSN	24350982
関連情報 (references)	URI https://www.iser.osaka-u.ac.jp/static/resources/docs/dp/DP1284.pdf
アクセス権	metadata only access
カテゴリ	ディスカッション／ワーキング・ペーパー DP/WP
カテゴリ	Institute of Social and Economic Research Discussion Papers

資源タイプ	テクニカルレポート
ローカル資源タイプ	ディスカッション／ワーキング・ペーパー
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Institute of Social and Economic Research Discussion Papers. 2025, 1284, p. 1-37
DC.title	Bayesian Learning When Players Are Misspecified about Others
DC.creator	Murooka, Takeshi
DC.creator	Yamamoto, Yuichi
DC.publisher	The Institute of Social and Economic Research, Osaka University
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-04
DC.relation' scheme='DCTERMS.URI	https://www.iser.osaka-u.ac.jp/static/resources/docs/dp/DP1284.pdf
DC.description	Online Appendix : 46 p.
DCTERMS.abstract	This paper considers Bayesian learning when players are biased about the data-generating process, and are biased about the opponent’s bias about the data-generating process. Specifically, we assume that each player’s bias about others takes the form of interpersonal projection, which is a tendency to overestimate the extent to which others share the player’s own view. We show that even an arbitrarily small amount of bias can destroy correct learning of an unknown state, i.e., there is zero probability of the posterior belief staying in a neighborhood of the true state.
DC.format	101068
citation_title	Bayesian Learning When Players Are Misspecified about Others
citation_author	Murooka, Takeshi
citation_author	Yamamoto, Yuichi
citation_publisher	The Institute of Social and Economic Research, Osaka University
citation_language	英語
citation_date	2025-04
citation_journal_title	Institute of Social and Economic Research Discussion Papers
citation_volume	1284
citation_firstpage	1
citation_lastpage	37
citation_public_url	https://hdl.handle.net/11094/101068
citation_abstract_html_url	https://www.iser.osaka-u.ac.jp/static/resources/docs/dp/DP1284.pdf

Bayesian Learning When Players Are Misspecified about Others

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