{"id":24129,"date":"2017-06-20T09:07:40","date_gmt":"2017-06-20T08:07:40","guid":{"rendered":"https:\/\/www.lse.ac.uk\/granthaminstitute\/?post_type=publication&#038;p=24129"},"modified":"2024-08-19T14:40:04","modified_gmt":"2024-08-19T13:40:04","slug":"ambiguity-aversion-under-maximum-likelihood-updating","status":"publish","type":"publication","link":"https:\/\/www.lse.ac.uk\/granthaminstitute\/publication\/ambiguity-aversion-under-maximum-likelihood-updating\/","title":{"rendered":"Ambiguity aversion under maximum-likelihood updating"},"content":{"rendered":"<!DOCTYPE html PUBLIC \"-\/\/W3C\/\/DTD HTML 4.0 Transitional\/\/EN\" \"http:\/\/www.w3.org\/TR\/REC-html40\/loose.dtd\">\n<?xml encoding=\"UTF-8\"><p><strong>Abstract<\/strong><\/p>\n<p>Maximum-likelihood updating (MLU) is a well-known approach for extending static ambiguity sensitive preferences to dynamic set-ups. This paper develops an example in which MLU induces an ambiguity averse maxmin expected utility (MEU) decision-maker to (1) prefer a bet on an ambiguous over a risky urn and (2) be more willing to bet on the ambiguous urn compared to an (ambiguity neutral) subjective expected utility (SEU) decision-maker. This is challenging, since prior to observing (symmetric) draws from the urns, the MEU decision-maker (in line with the usual notion of ambiguity aversion) actually preferred the risky over the ambiguous bet and was less willing to bet on the ambiguous urn than the SEU decision-maker. The identified switch in betting preferences is not due to a violation of dynamic consistency or consequentialism. Rather, it results from MLU&rsquo;s selection of extreme priors, causing a violation of the stability of set inclusion over the course of the updating process.<\/p>\n<p><strong>Heyen, D. Theory and Decision (2017). doi:10.1007\/s11238-017-9611-2<\/strong><\/p>\n\n","protected":false},"excerpt":{"rendered":"<!DOCTYPE html PUBLIC \"-\/\/W3C\/\/DTD HTML 4.0 Transitional\/\/EN\" \"http:\/\/www.w3.org\/TR\/REC-html40\/loose.dtd\">\n<?xml encoding=\"UTF-8\"><p>Abstract Maximum-likelihood updating (MLU) is a well-known approach for extending static ambiguity sensitive preferences to dynamic set-ups. This paper develops&hellip;<\/p>\n\n","protected":false},"author":12,"featured_media":0,"template":"","tags":[849,872],"publication-type":[436],"topic_area":[4698],"class_list":["post-24129","publication","type-publication","status-publish","hentry","tag-ambiguity-aversion","tag-preferences","publication-type-research-articles","topic_area-behavioural-responses"],"acf":{"exclude_from_sync":{"ref_value":"field_560538b0e7350","value":"0","type":false,"post_type":""},"downloads":{"ref_value":"field_52f16cc1a80f2","value":false,"type":"repeater","post_type":""},"rss_newsletter":{"ref_value":"field_54f5c2c1544d8","value":"a:1:{i:0;s:7:\"exclude\";}","type":"array","post_type":""},"profile_link":{"ref_value":"field_52f164b5189e9","value":["daniel-heyen"],"type":"relationship","post_type":"profile"},"article_link":{"ref_value":"field_52eee3c7f0586","value":"https:\/\/link.springer.com\/article\/10.1007\/s11238-017-9611-2","type":false,"post_type":""},"article_link_title":{"ref_value":"field_5322399d89d42","value":"External link to article","type":false,"post_type":""},"spotlight":{"ref_value":"field_52f80896506d3","value":"","type":false,"post_type":""}},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Ambiguity aversion under maximum-likelihood updating - Grantham Research Institute on climate change and the environment<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.lse.ac.uk\/granthaminstitute\/publication\/ambiguity-aversion-under-maximum-likelihood-updating\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ambiguity aversion under maximum-likelihood updating - Grantham Research Institute on climate change and the environment\" \/>\n<meta property=\"og:description\" content=\"Abstract Maximum-likelihood updating (MLU) is a well-known approach for extending static ambiguity sensitive preferences to dynamic set-ups. 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