東華大學圖書館 |

Three Essays in Behavioral and Experimental Economics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Three Essays in Behavioral and Experimental Economics // Jing Zhou.
Author:	Zhou, Jing,
Description:	1 electronic resource (211 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Experimental psychology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31332077
ISBN:	9798384033547

Three Essays in Behavioral and Experimental Economics /
Zhou, Jing,

Three Essays in Behavioral and Experimental Economics /Jing Zhou. - 1 electronic resource (211 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

This dissertation consists of three chapters that explore why individuals make seemingly suboptimal decisions in risk management, why they fail to use information in a Bayesian manner when updating their beliefs, and how novel methodological tools can be developed to advance modelling and inference about subjective beliefs or perceptions, considering cognitive limitations.Chapter 1 studies the underlying mechanisms behind a classical behavioral puzzle in risk management, called Probability Matching. Probability matching refers to people's tendency to randomize between different risky options, or even match their choice frequency to the outcome probability, when choosing over binary lotteries that differ only in their probabilities. Why? I present an experiment designed to distinguish between three broad classes of explanations: models of Correlation-Invariant Stochastic Choice (mixing due to factors orthogonal to how outcomes are jointly determined, such as non-standard preferences or errors), models of Correlation-Sensitive Stochastic Choice (e.g., deliberately mixing due to misperceived hedging opportunity), and Framing Effects (indecisiveness due to frame-sensitive heuristics e.g., similarity heuristic: attending to dissimilar but irrelevant attributes (outcomes), while ignoring relevant attributes (probabilities)). My experimental design uses a diagnostic approach, differentiating between their testable predictions over a series of treatments. The results suggest that a substantial proportion of mixing behavior aligns with models of Correlation-Sensitive Stochastic Choice, while the other classes have limited explanatory power.In Chapter 2, a joint work with Menglong Guan, ChienHsun Lin, and Ravi Vora, we experimentally investigate how people value and utilize different statistical characteristics of a set of realized binary signals, referred to as sample features, to understand why individuals deviation from the Bayesian benchmark when updating beliefs. We find that, subjects systematically under-infer the information contained in each sample feature. Furthermore, the magnitude of under-inference significantly varies across sample features. Specifically, under-inference is least severe with Sample Proportion (the relative frequency of different outcomes in the realized signals), compared to more informative features such as Sample Count (the absolute number of different outcomes in the realized signals). We also find that the standard measure of informativeness used in information theory does not fully explain subjects' preferences for sample features. Subjects demonstrate a strict preference for the information contained in the Sample Proportion over those without it and undervalue the usefulness of sample size. Combining preference and belief updating behaviors, we find that subjects deviate less from the Bayesian benchmark when provided with a more-preferred feature than a less-preferred one. These results suggest that some biases in signal usage is more likely an intentional deviation rather than a result of inattentive heuristics. In Chapter 3, a joint work with Xin Jiang, we introduce a novel elicitation method, called the Dynamic Binary Method (DBM), designed to address the common challenge individuals face in pinpointing the best point estimate of their beliefs, particularly when their beliefs are imprecise. Unlike Classical Methods (CM), which require respondents to make absolute judgments and form a point estimate of their true beliefs, DBM guides them through a series of binary relative judgments, enabling them to express interval beliefs by exiting the process at any step. To assess the empirical validity of DBM, we conduct both within-subject and between-subject experiments using a diverse range of perception tasks drawn from previous literature and CM as a benchmark of performances in each task. We find that DBM does not perform significantly differently from CM at the aggregate level, regardless of whether the perception questions use artificial/laboratory settings or real-life settings, and irrespective of the measurement used. Notably, DBM outperforms CM when the objective truth is extreme. Furthermore, we find a negative correlation between the length of stated beliefs in tasks using DBM and their accuracy. Additionally, we find that the length stated in DBM can predict respondents' performance in CM tasks at the aggregate level, albeit not strictly in a monotonic manner. Finally, we explore methods to use DBM-collected data for predicting stated point beliefs in DBM, offering insights into potential applications of the method beyond its immediate implementation.

English

ISBN: 9798384033547Subjects--Topical Terms:

2144733
Experimental psychology.
Subjects--Index Terms:

Bayesian manner

Three Essays in Behavioral and Experimental Economics /
LDR:06136nmm a22004453i 4500 001 2429718
005 20250430100858.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798384033547
035 $a (MiAaPQD)AAI31332077
035 $a AAI31332077
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Zhou, Jing, $e author. $3 3817707
245 1 0 $a Three Essays in Behavioral and Experimental Economics / $c Jing Zhou.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (211 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
500 $a Advisors: Oprea, Ryan Committee members: Eyster, Erik; Yuksel, Sevgi.
502 $b Ph.D. $c University of California, Santa Barbara $d 2024.
520 $a This dissertation consists of three chapters that explore why individuals make seemingly suboptimal decisions in risk management, why they fail to use information in a Bayesian manner when updating their beliefs, and how novel methodological tools can be developed to advance modelling and inference about subjective beliefs or perceptions, considering cognitive limitations.Chapter 1 studies the underlying mechanisms behind a classical behavioral puzzle in risk management, called Probability Matching. Probability matching refers to people's tendency to randomize between different risky options, or even match their choice frequency to the outcome probability, when choosing over binary lotteries that differ only in their probabilities. Why? I present an experiment designed to distinguish between three broad classes of explanations: models of Correlation-Invariant Stochastic Choice (mixing due to factors orthogonal to how outcomes are jointly determined, such as non-standard preferences or errors), models of Correlation-Sensitive Stochastic Choice (e.g., deliberately mixing due to misperceived hedging opportunity), and Framing Effects (indecisiveness due to frame-sensitive heuristics e.g., similarity heuristic: attending to dissimilar but irrelevant attributes (outcomes), while ignoring relevant attributes (probabilities)). My experimental design uses a diagnostic approach, differentiating between their testable predictions over a series of treatments. The results suggest that a substantial proportion of mixing behavior aligns with models of Correlation-Sensitive Stochastic Choice, while the other classes have limited explanatory power.In Chapter 2, a joint work with Menglong Guan, ChienHsun Lin, and Ravi Vora, we experimentally investigate how people value and utilize different statistical characteristics of a set of realized binary signals, referred to as sample features, to understand why individuals deviation from the Bayesian benchmark when updating beliefs. We find that, subjects systematically under-infer the information contained in each sample feature. Furthermore, the magnitude of under-inference significantly varies across sample features. Specifically, under-inference is least severe with Sample Proportion (the relative frequency of different outcomes in the realized signals), compared to more informative features such as Sample Count (the absolute number of different outcomes in the realized signals). We also find that the standard measure of informativeness used in information theory does not fully explain subjects' preferences for sample features. Subjects demonstrate a strict preference for the information contained in the Sample Proportion over those without it and undervalue the usefulness of sample size. Combining preference and belief updating behaviors, we find that subjects deviate less from the Bayesian benchmark when provided with a more-preferred feature than a less-preferred one. These results suggest that some biases in signal usage is more likely an intentional deviation rather than a result of inattentive heuristics. In Chapter 3, a joint work with Xin Jiang, we introduce a novel elicitation method, called the Dynamic Binary Method (DBM), designed to address the common challenge individuals face in pinpointing the best point estimate of their beliefs, particularly when their beliefs are imprecise. Unlike Classical Methods (CM), which require respondents to make absolute judgments and form a point estimate of their true beliefs, DBM guides them through a series of binary relative judgments, enabling them to express interval beliefs by exiting the process at any step. To assess the empirical validity of DBM, we conduct both within-subject and between-subject experiments using a diverse range of perception tasks drawn from previous literature and CM as a benchmark of performances in each task. We find that DBM does not perform significantly differently from CM at the aggregate level, regardless of whether the perception questions use artificial/laboratory settings or real-life settings, and irrespective of the measurement used. Notably, DBM outperforms CM when the objective truth is extreme. Furthermore, we find a negative correlation between the length of stated beliefs in tasks using DBM and their accuracy. Additionally, we find that the length stated in DBM can predict respondents' performance in CM tasks at the aggregate level, albeit not strictly in a monotonic manner. Finally, we explore methods to use DBM-collected data for predicting stated point beliefs in DBM, offering insights into potential applications of the method beyond its immediate implementation.
546 $a English
590 $a School code: 0035
650 4 $a Experimental psychology. $3 2144733
650 4 $a Behavioral psychology. $3 2122788
653 $a Bayesian manner
653 $a Risk management
653 $a Classical behavioral puzzle
653 $a Probability Matching
653 $a Correlation-Invariant Stochastic Choice
690 $a 0501
690 $a 0511
690 $a 0623
690 $a 0384
710 2 $a University of California, Santa Barbara. $b Economics. $e degree granting institution. $3 3815285
720 1 $a Oprea, Ryan $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-03B.
790 $a 0035
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31332077