{"id":{"repo_id":"penn","oai_identifier":"oai:repository.upenn.edu:20.500.14332/62830"},"canonical_url":"https://search.dev.ndltd.org/etd/penn/oai:repository.upenn.edu:20.500.14332/62830","repository":{"repo_id":"penn","name":"University of Pennsylvania","base_url":"https://repository.upenn.edu/server/oai/request"},"display":{"title":"ESSAYS ON DECISION MAKING UNDER UNCERTAINTY","abstract":"Chapter 1 studies a decision maker who approaches a decision problem under uncertainty by formulating a set of plausible probabilistic models of the environment, while being aware that these models are only stylized and incomplete approximations. The decision maker faces two layers of uncertainty. Not only is she uncertain about which model in this set has the best fit (ambiguity), but she is also concerned that the best-fit model itself might be a poor description of the environment (model misspecification). We develop an axiomatic foundation for preferences that capture concerns about these two layers of uncertainty and allow us to compare individuals' degrees of aversion to model misspecification and to ambiguity independently of each other. In other words, these two conceptually distinct behavioral phenomena are captured by independent parameters in our representation and imply different choice patterns. Chapter 2 studies a decision maker who employs a set of probabilistic models to solve an infinite-horizon problem under uncertainty and learns over time. As in the previous chapter, the decision maker faces two layers of uncertainty: ambiguity, as she does not know which model has the best fit, and model misspecification, as she is concerned that none of the hypothesized models accurately describes the environment. We provide an axiomatic foundation for forward-looking preferences that exhibit flexible attitudes toward model misspecification and ambiguity. In a central specification of our model, we propose a new behavioral axiom that justifies updating beliefs over the set of models via Bayes' rule even when concerns about model misspecification and ambiguity are present. Chapter 3 is the result of joint work with Fabio Maccheroni and Massimo Marinacci. We study updating in decision-making under model misspecification. The lack of separation between tastes and beliefs leads us to study optimal beliefs. Since they incorporate misspecification concerns, they are taste-laden and no longer only reflect information. We show that they evolve according to an optimal updating rule and that under them the decision maker behaves in a traditional expected utility fashion. When concerns about misspecification are present but misplaced, the decision maker will eventually learn the correct model. When, instead, they are not misplaced, optimal beliefs concentrate asymptotically on models that best approximate the correct one according to a long-run entropic measure of fit.","abstract_html":"Chapter 1 studies a decision maker who approaches a decision problem under uncertainty by formulating a set of plausible probabilistic models of the environment, while being aware that these models are only stylized and incomplete approximations. The decision maker faces two layers of uncertainty. Not only is she uncertain about which model in this set has the best fit (ambiguity), but she is also concerned that the best-fit model itself might be a poor description of the environment (model misspecification). We develop an axiomatic foundation for preferences that capture concerns about these two layers of uncertainty and allow us to compare individuals' degrees of aversion to model misspecification and to ambiguity independently of each other. In other words, these two conceptually distinct behavioral phenomena are captured by independent parameters in our representation and imply different choice patterns. Chapter 2 studies a decision maker who employs a set of probabilistic models to solve an infinite-horizon problem under uncertainty and learns over time. As in the previous chapter, the decision maker faces two layers of uncertainty: ambiguity, as she does not know which model has the best fit, and model misspecification, as she is concerned that none of the hypothesized models accurately describes the environment. We provide an axiomatic foundation for forward-looking preferences that exhibit flexible attitudes toward model misspecification and ambiguity. In a central specification of our model, we propose a new behavioral axiom that justifies updating beliefs over the set of models via Bayes' rule even when concerns about model misspecification and ambiguity are present. Chapter 3 is the result of joint work with Fabio Maccheroni and Massimo Marinacci. We study updating in decision-making under model misspecification. The lack of separation between tastes and beliefs leads us to study optimal beliefs. Since they incorporate misspecification concerns, they are taste-laden and no longer only reflect information. We show that they evolve according to an optimal updating rule and that under them the decision maker behaves in a traditional expected utility fashion. When concerns about misspecification are present but misplaced, the decision maker will eventually learn the correct model. When, instead, they are not misplaced, optimal beliefs concentrate asymptotically on models that best approximate the correct one according to a long-run entropic measure of fit.","abstract_has_math":false,"creators":["Maselli, Alfonso"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Dillenberger, David"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-24T03:46:55Z","subjects":["Economics"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repository.upenn.edu/handle/20.500.14332/62830","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Dillenberger, David"]},{"key":"dc:creator","label":"Author","values":["Maselli, Alfonso"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-06-05T16:17:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-06-05T16:17:21Z"]},{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation/Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Economics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://repository.upenn.edu/handle/20.500.14332/62830"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["2026"]},{"key":"dc:description.abstract","label":"Abstract","values":["Chapter 1 studies a decision maker who approaches a decision problem under uncertainty by formulating a set of plausible probabilistic models of the environment, while being aware that these models are only stylized and incomplete approximations. The decision maker faces two layers of uncertainty. Not only is she uncertain about which model in this set has the best fit (ambiguity), but she is also concerned that the best-fit model itself might be a poor description of the environment (model misspecification). We develop an axiomatic foundation for preferences that capture concerns about these two layers of uncertainty and allow us to compare individuals' degrees of aversion to model misspecification and to ambiguity independently of each other. In other words, these two conceptually distinct behavioral phenomena are captured by independent parameters in our representation and imply different choice patterns. Chapter 2 studies a decision maker who employs a set of probabilistic models to solve an infinite-horizon problem under uncertainty and learns over time. As in the previous chapter, the decision maker faces two layers of uncertainty: ambiguity, as she does not know which model has the best fit, and model misspecification, as she is concerned that none of the hypothesized models accurately describes the environment. We provide an axiomatic foundation for forward-looking preferences that exhibit flexible attitudes toward model misspecification and ambiguity. In a central specification of our model, we propose a new behavioral axiom that justifies updating beliefs over the set of models via Bayes' rule even when concerns about model misspecification and ambiguity are present. Chapter 3 is the result of joint work with Fabio Maccheroni and Massimo Marinacci. We study updating in decision-making under model misspecification. The lack of separation between tastes and beliefs leads us to study optimal beliefs. Since they incorporate misspecification concerns, they are taste-laden and no longer only reflect information. We show that they evolve according to an optimal updating rule and that under them the decision maker behaves in a traditional expected utility fashion. When concerns about misspecification are present but misplaced, the decision maker will eventually learn the correct model. When, instead, they are not misplaced, optimal beliefs concentrate asymptotically on models that best approximate the correct one according to a long-run entropic measure of fit."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["PhD"]},{"key":"dc:title","label":"Title","values":["ESSAYS ON DECISION MAKING UNDER UNCERTAINTY"]}]}],"canonical_facts":{"dc:contributor.advisor":["Dillenberger, David"],"dc:creator":["Maselli, Alfonso"],"dc:date.accessioned":["2026-06-05T16:17:21Z"],"dc:date.available":["2026-06-05T16:17:21Z"],"dc:date.issued":["2026"],"dc:description":["2026"],"dc:description.abstract":["Chapter 1 studies a decision maker who approaches a decision problem under uncertainty by formulating a set of plausible probabilistic models of the environment, while being aware that these models are only stylized and incomplete approximations. The decision maker faces two layers of uncertainty. Not only is she uncertain about which model in this set has the best fit (ambiguity), but she is also concerned that the best-fit model itself might be a poor description of the environment (model misspecification). We develop an axiomatic foundation for preferences that capture concerns about these two layers of uncertainty and allow us to compare individuals' degrees of aversion to model misspecification and to ambiguity independently of each other. In other words, these two conceptually distinct behavioral phenomena are captured by independent parameters in our representation and imply different choice patterns. Chapter 2 studies a decision maker who employs a set of probabilistic models to solve an infinite-horizon problem under uncertainty and learns over time. As in the previous chapter, the decision maker faces two layers of uncertainty: ambiguity, as she does not know which model has the best fit, and model misspecification, as she is concerned that none of the hypothesized models accurately describes the environment. We provide an axiomatic foundation for forward-looking preferences that exhibit flexible attitudes toward model misspecification and ambiguity. In a central specification of our model, we propose a new behavioral axiom that justifies updating beliefs over the set of models via Bayes' rule even when concerns about model misspecification and ambiguity are present. Chapter 3 is the result of joint work with Fabio Maccheroni and Massimo Marinacci. We study updating in decision-making under model misspecification. The lack of separation between tastes and beliefs leads us to study optimal beliefs. Since they incorporate misspecification concerns, they are taste-laden and no longer only reflect information. We show that they evolve according to an optimal updating rule and that under them the decision maker behaves in a traditional expected utility fashion. When concerns about misspecification are present but misplaced, the decision maker will eventually learn the correct model. When, instead, they are not misplaced, optimal beliefs concentrate asymptotically on models that best approximate the correct one according to a long-run entropic measure of fit."],"dc:description.degree":["PhD"],"dc:identifier.uri":["https://repository.upenn.edu/handle/20.500.14332/62830"],"dc:language.iso":["en"],"dc:subject":["Economics"],"dc:title":["ESSAYS ON DECISION MAKING UNDER UNCERTAINTY"],"dc:type":["Dissertation/Thesis"]},"updated_at":"2026-07-24T03:46:55Z"}