This dissertation consists of three essays that use lab and online experiments to investigate how individuals make decisions under uncertainty, in social contexts, and when forming beliefs about others. Each essay introduces a distinct setting, but all share a common goal, which is to improve our understanding of human decision-making.
\r\n \r\nChapter 1 examines commitment contracts. Their high rates of failure raise concerns since individuals may end up worse off than if they had never committed. We investigate whether some of these failures are actually anticipated, with individuals recognizing that future uncertainty might make failing the contract the best option upon some realizations of uncertainty. We refer to this behavior as planning for the possibility of failure. This approach is different from the usual interpretation of failures, which we call failing to plan, as it attributes failures to take-up mistakes. To study whether individuals plan for the possibility of failure, we conducted a controlled lab experiment designed to detect patterns of such planning. Our findings indicate that about one-third of all commitment choices can be attributed to this kind of foresight. This suggests that planning for failure is common, and that high failure rates are not necessarily driven by mistaken commitments. Thus, they do not by themselves call into question the value of commitment contracts.
\r\n\r\nThe second essay studies the decision to ask for help\u2014a behavior that can be critical in addressing information asymmetries but is often avoided. In an online experiment, we find that making potential helpers even minimally identifiable (e.g., through an uninformative ID number) significantly increases the likelihood of asking. Belief data suggest that this effect stems from shifts in how individuals weigh expected payoffs and other factors (particularly social ones) when deciding whether to ask.
\r\n\r\nThe third essay explores how people expect others to update their beliefs upon receiving new information. We find that when two individuals have different priors, people expect others\u2019 beliefs to move toward their own prior upon receiving new information. Although this result is consistent with the theoretical predictions for Bayesian agents, we find no support for the precision of information affecting the magnitude of the shift in the way the theory predicts. We find that this effect occurs not only due to under-updating of one's own beliefs but also due to recognition of under-updating by others.
", "doi": "10.7907/p5yr-yn70", "publication_date": "2025", "thesis_type": "phd", "thesis_year": "2025" }, { "id": "thesis:16198", "collection": "thesis", "collection_id": "16198", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:09292023-062110859", "primary_object_url": { "basename": "Thesis_Meng-Jhang Fong.pdf", "content": "final", "filesize": 3317541, "license": "other", "mime_type": "application/pdf", "url": "/16198/1/Thesis_Meng-Jhang Fong.pdf", "version": "v2.0.0" }, "type": "thesis", "title": "Essays in Behavioral Economics and Game Theory", "author": [ { "family_name": "Fong", "given_name": "Meng-Jhang", "orcid": "0009-0008-8832-3985", "clpid": "Fong-Meng-Jhang" } ], "thesis_advisor": [ { "family_name": "Agranov", "given_name": "Marina", "orcid": "0000-0002-0642-7975", "clpid": "Agranov-M" } ], "thesis_committee": [ { "family_name": "Palfrey", "given_name": "Thomas R.", "orcid": "0000-0003-0769-8109", "clpid": "Palfrey-T-R" }, { "family_name": "Pomatto", "given_name": "Luciano", "orcid": "0000-0002-4331-8436", "clpid": "Pomatto-L" }, { "family_name": "Sprenger", "given_name": "Charles David", "orcid": "0000-0001-6540-8814", "clpid": "Sprenger-C-D" }, { "family_name": "Agranov", "given_name": "Marina", "orcid": "0000-0002-0642-7975", "clpid": "Agranov-M" } ], "local_group": [ { "literal": "div_hss" } ], "abstract": "This thesis consists of three papers. Chapter 1 conducts experimental research on individual bounded rationality in games, Chapter 2 introduces a novel equilibrium solution concept in behavioral game theory, and Chapter 3 investigates confirmation bias within the framework of game theory.
\r\n\r\nIn Chapter 1 (joint with Wei James Chen and Po-Hsuan Lin), we investigate individual strategic reasoning depths by matching human subjects with fully rational computer players in a lab, allowing for the isolation of limited reasoning ability from beliefs about opponent players and social preferences. Our findings reveal that when matched with robots, subjects demonstrate higher stability in their strategic thinking depths across games, in contrast to when matched with humans.
\r\n\r\nIn Chapter 2 (joint with Po-Hsuan Lin and Thomas R. Palfrey), we investigate how players\u2019 misunderstanding about the relationship between opponents\u2019 private information and strategies influence their equilibrium behavior in dynamic environments. This theoretical study introduces a framework that extends the analysis of cursed equilibrium from the strategic form to multi-stage games and applies it to various applications in economics and political science.
\r\n\r\nIn Chapter 3, I employ a game-theoretic framework to model how decision makers strategically interpret signals, particularly when they face a utility loss from holding beliefs that differ from their partners. The study reveals that the emergence of confirmation bias is positively associated with the strength of prior beliefs about a state, while the impact of signal accuracy remains ambiguous.
", "doi": "10.7907/5xh7-yw15", "publication_date": "2024", "thesis_type": "phd", "thesis_year": "2024" }, { "id": "thesis:16369", "collection": "thesis", "collection_id": "16369", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:05042024-221411474", "primary_object_url": { "basename": "LUCIA_ALDO_2024.pdf", "content": "final", "filesize": 1133182, "license": "other", "mime_type": "application/pdf", "url": "/16369/1/LUCIA_ALDO_2024.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "An Experimental and Theoretical Investigation of Decision-Making Under Risk", "author": [ { "family_name": "Lucia", "given_name": "Aldo", "orcid": "0000-0002-4833-5948", "clpid": "Lucia-Aldo" } ], "thesis_advisor": [ { "family_name": "Sprenger", "given_name": "Charles D.", "clpid": "Sprenger-C-D" }, { "family_name": "Agranov", "given_name": "Marina", "orcid": "0000-0002-0642-7975", "clpid": "Agranov-M" }, { "family_name": "Pomatto", "given_name": "Luciano", "orcid": "0000-0002-4331-8436", "clpid": "Pomatto-L" } ], "thesis_committee": [ { "family_name": "Agranov", "given_name": "Marina", "orcid": "0000-0002-0642-7975", "clpid": "Agranov-M" }, { "family_name": "Sprenger", "given_name": "Charles D.", "clpid": "Sprenger-C-D" }, { "family_name": "Pomatto", "given_name": "Luciano", "orcid": "0000-0002-4331-8436", "clpid": "Pomatto-L" }, { "family_name": "Caradonna", "given_name": "Peter", "orcid": "0000-0002-4197-4739", "clpid": "Caradonna-Peter-P" } ], "local_group": [ { "literal": "div_hss" } ], "abstract": "This dissertation comprises three chapters related to the fields of decision theory, game theory, and experimental economics. Chapters 1 and 2 use experimental and structural methods to study individual decision-making in the domain of risk, while Chapter 3 examines decision-making under risk in settings of strategic interaction.
\r\n\r\nIn Chapter 1, co-authored with Shunto Kobayashi, we conduct the first experiment that studies two classical behaviors under risk inconsistent with Expected Utility together: the common ratio effect and preferences for randomization. We show that these two behaviors are strongly positively correlated in a manner inconsistent with the predictions of leading economic models and machine learning algorithms. Motivated by this observation, we develop a novel empirical approach which, unlike machine learning algorithms, imposes some basic assumptions on preferences but does not rely on specific decision models. We further demonstrate that this approach provides more accurate predictions---both inside and outside laboratory settings---compared to leading economic models and machine learning algorithms.
\r\n\r\nIn Chapter 2, I design an experiment testing Expected Utility's central independence axiom and contemporaneously eliciting measures of decision confidence. Recent theoretical work implicates decision confidence as a central component of decision-making under risk, attributing failures of Expected Utility to a lack of confidence. I find that choices characterized by high self-reported levels of decision confidence and low response times are more likely to comply with the independence axiom. Contrary to the common certainty effect rationale for independence violations, I show that subjects predominantly violate Expected Utility by choosing risky lotteries over certain amounts when they are unconfident in their choices.
\r\n\r\nIn Chapter 3, co-authored with Marco Loseto, we study static games in which players have convex preferences. Under convexity, players' preferences admit a conservative multi-utility representation: each utility generates an evaluation for each action, and actions are ranked according to the lowest evaluation. We characterize the set of optimal actions for players with convex preferences and propose an efficiency criterion to rank them. Next, we derive a new class of mixed Nash equilibria that we call ``strict'' because players strictly prefer randomization. In general, convexity may lead to a multiplicity of mixed Nash equilibria. However, we show that when they exist, only strict equilibria ensure that all mixed actions are efficient.
", "doi": "10.7907/2sk6-j508", "publication_date": "2024", "thesis_type": "phd", "thesis_year": "2024" }, { "id": "thesis:16369", "collection": "thesis", "collection_id": "16369", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:05042024-221411474", "primary_object_url": { "basename": "LUCIA_ALDO_2024.pdf", "content": "final", "filesize": 1133182, "license": "other", "mime_type": "application/pdf", "url": "/16369/1/LUCIA_ALDO_2024.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "An Experimental and Theoretical Investigation of Decision-Making Under Risk", "author": [ { "family_name": "Lucia", "given_name": "Aldo", "orcid": "0000-0002-4833-5948", "clpid": "Lucia-Aldo" } ], "thesis_advisor": [ { "family_name": "Sprenger", "given_name": "Charles D.", "clpid": "Sprenger-C-D" }, { "family_name": "Agranov", "given_name": "Marina", "orcid": "0000-0002-0642-7975", "clpid": "Agranov-M" }, { "family_name": "Pomatto", "given_name": "Luciano", "orcid": "0000-0002-4331-8436", "clpid": "Pomatto-L" } ], "thesis_committee": [ { "family_name": "Agranov", "given_name": "Marina", "orcid": "0000-0002-0642-7975", "clpid": "Agranov-M" }, { "family_name": "Sprenger", "given_name": "Charles D.", "clpid": "Sprenger-C-D" }, { "family_name": "Pomatto", "given_name": "Luciano", "orcid": "0000-0002-4331-8436", "clpid": "Pomatto-L" }, { "family_name": "Caradonna", "given_name": "Peter", "orcid": "0000-0002-4197-4739", "clpid": "Caradonna-Peter-P" } ], "local_group": [ { "literal": "div_hss" } ], "abstract": "This dissertation comprises three chapters related to the fields of decision theory, game theory, and experimental economics. Chapters 1 and 2 use experimental and structural methods to study individual decision-making in the domain of risk, while Chapter 3 examines decision-making under risk in settings of strategic interaction.
\r\n\r\nIn Chapter 1, co-authored with Shunto Kobayashi, we conduct the first experiment that studies two classical behaviors under risk inconsistent with Expected Utility together: the common ratio effect and preferences for randomization. We show that these two behaviors are strongly positively correlated in a manner inconsistent with the predictions of leading economic models and machine learning algorithms. Motivated by this observation, we develop a novel empirical approach which, unlike machine learning algorithms, imposes some basic assumptions on preferences but does not rely on specific decision models. We further demonstrate that this approach provides more accurate predictions---both inside and outside laboratory settings---compared to leading economic models and machine learning algorithms.
\r\n\r\nIn Chapter 2, I design an experiment testing Expected Utility's central independence axiom and contemporaneously eliciting measures of decision confidence. Recent theoretical work implicates decision confidence as a central component of decision-making under risk, attributing failures of Expected Utility to a lack of confidence. I find that choices characterized by high self-reported levels of decision confidence and low response times are more likely to comply with the independence axiom. Contrary to the common certainty effect rationale for independence violations, I show that subjects predominantly violate Expected Utility by choosing risky lotteries over certain amounts when they are unconfident in their choices.
\r\n\r\nIn Chapter 3, co-authored with Marco Loseto, we study static games in which players have convex preferences. Under convexity, players' preferences admit a conservative multi-utility representation: each utility generates an evaluation for each action, and actions are ranked according to the lowest evaluation. We characterize the set of optimal actions for players with convex preferences and propose an efficiency criterion to rank them. Next, we derive a new class of mixed Nash equilibria that we call ``strict'' because players strictly prefer randomization. In general, convexity may lead to a multiplicity of mixed Nash equilibria. However, we show that when they exist, only strict equilibria ensure that all mixed actions are efficient.
", "doi": "10.7907/2sk6-j508", "publication_date": "2024", "thesis_type": "phd", "thesis_year": "2024" } ]