Essays on decision making under variable information

Li, Hualin (2020) Essays on decision making under variable information. PhD thesis, University of Glasgow.

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Abstract

This thesis is composed of three chapters that invoke axiomatic approaches to study models of decision making under objective and variable information.
In Chapter 1, we propose a model of choice from choice architectures that refer to environments where alternatives are presented with objective and observable choice-relevant information. We identify choice architectures by directed graphs on sets of alternatives where directed edges represent choice-relevant information about the alternatives. In this domain, a choice function hence singles out a vertex from each given directed graph, whereas a choice correspondence assigns to every directed graph a set of vertices. A choice function and choice correspondence are respectively characterised by a choice procedure that separates the role of information processing from that of preferences. Notably, both choice procedures suggest the same machinery of information processing that is hinged on properties of directed graphs, hence being objective and predictable. We then explore its implications on the formation mechanism of consideration sets and the sources of the stochasticity of choice. Later in the chapter, we also study the applications in terms of demand shaping and revealing equilibrium, respectively.
Chapter 2 considers decision making under uncertainty with objective and variable information structures. We take as primitive a family of information-dependent preferences over subjective acts indexed by partitions of the state space. Each partition corresponds to an information structure. We characterise a utility representation that comprises an affine utility index over simple lotteries, a unique capacity over the state space, and for each partition, a probability measure on the $\sigma$-algebra generated by the partition. We find that such a representation is equivalent to the Choquet expected utility representation with specific machinery of non-additive belief formation. We then connect the utility representation to the definition of comparative uncertainty aversion to explore the characteristic conditions related to the translatability of uncertainty attitude among variable information structures.
In Chapter 3, we explicitly incorporate framing of information into decision making under uncertainty. As in Chapter 2, we also study a family of partition-indexed preferences over subjective acts, where we interpret each partition as a frame of information. Under a modest set of axioms, we characterise a general utility representation, which we call frame-adaptive expected utility. Having the general utility representation, we focus on two parameterised forms of frame-adaptive expected utility featuring attitude towards informativeness and degree of salience, respectively. We then apply the frame-adaptive models to the definition of comparative uncertainty aversion and that of definitive uncertainty-aversion to study the translatability of uncertainty attitude among variable frames of information. We also conduct a comparative analysis and find that the decision maker's reaction to information frames plays a role in modifying the degree of uncertainty attitude revealed from choices. Later in the chapter, we relate frame-adaptivity to ambiguity-aversion and argue that the latter can be viewed as a manifestation of the decision-maker performing frame-adaptive reasoning.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Subjects: H Social Sciences > HB Economic Theory
Colleges/Schools: College of Social Sciences > Adam Smith Business School > Economics
Supervisor's Name: Hayashi, Prof. Takashi and Lombardi, Dr. Michele and Bogomolnaia, Prof. Anna
Date of Award: December 2020
Depositing User: Hualin Li
Unique ID: glathesis:2020-81874
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 16 Dec 2020 14:06
Last Modified: 16 Dec 2020 14:17
URI: http://theses.gla.ac.uk/id/eprint/81874

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