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Individual decision making is the foundation for almost all economic analysis. In this dissertation, I study various choice problems in the absence of perfect information.Related decisions are often observed in isolation without direct measurement of correlation in beliefs across state spaces or complementarity in tastes across prize spaces. In my first chapter, my coauthor David Ahn and I introduce a novel model with two decision problems with distinct states and prizes, which we call small worlds, without observation of bets that are contingent on the realization of both worlds. We characterize an appropriate version of subjective expected utility, where choices are made as if there is a joint distribution over the product of the state spaces and a joint utility index over pairs of prizes from both prize spaces. Turning to identification, the joint utility index over pairs of prizes and the marginal belief over each small world are identified, but the uniqueness of the joint distribution is more subtle. If the utility index is separable across prize spaces, then the correlation across state spaces is unidentified; but if there is any complementarity across prizes, then the joint distribution is exactly identified.A growing literature attributes choice anomalies, such as stochastic choices, to limited attention. Most choice models with limited attention do not model attention itself. In my second chapter, I propose a choice model with gradual attention, in which a decision maker’s attention is limited due to time constraints. With richer choice data including the associated decision time, I provide an axiomatic characterization of this model. I show the model primitives such as the preference can be uniquely identified. To make the model comparable to existing choice models with limited attention, I consider its static version without time data and show identification is not always possible, which highlights the novelty of utilizing the decision time information in understanding choice behaviors. I examine aggregation issues of this model and give necessary and sufficient conditions for the existence of a representative agent under heterogeneity. In my model, the decision maker's consideration set is growing monotonically as decision time increases. As a consequence, more time leads to better choices. I test this choice pattern using experimental data from a well-known study and find supportive evidence.In a persuasion problem, an information sender designs an experiment for an information receiver, in order to persuade the receiver to change actions. While persuasion is all about manipulating the receiver’s posterior beliefs, little is known about the effect of changes in receiver's risk attitudes on persuasion outcomes. In my third chapter, I study how the receiver’s risk attitudes affect the set of implementable outcomes in a persuasion problem. I consider a two-period consumption model, where the receiver’s future income is uncertain and he has the option to transfer his wealth intertemporally through saving. I present a local comparative statics result, showing that any marginal change in the receiver’s prudence either expands or shrinks the set of implementable persuasion outcomes. Risk aversion does not affect persuasion outcomes, except through pivoting the receiver’s optimal action under the prior belief. Other higher order risk attitudes have no direct impact on persuasion outcomes.

Logistics distribution is the terminal link that connects the manufacturer and product user and determines the efficiency of the manufacturer’s service. Therefore, the disruption risk of the joint system is an essential factor affecting the product user experience. In this paper, while considering the product user’s supply disruption risk preference (PUSDRP), a biobjective integer nonlinear programming (INLP) model with subjective cost-utility is proposed to solve the manufacturer’s combined location routing inventory problem (CLRIP). According to the user’s time satisfaction requirement, a routing change selection framework (RCSF) is designed based on the bounded rational behavior of the user. Additionally, the Lagrange Relaxation and Modified Genetic Algorithm (LR-MGA) is proposed. The LR method relaxes the model, and the MGA finds a compromise solution. The experimental results show that the biobjective cost-utility model proposed in this paper is effective and efficient. The RCSF based on user behavior is superior to the traditional expected utility theory model. The compromise solution provides a better solution for the manufacturer order allocation delivery combinatorial optimization problem. The compromise solution not only reduces the manufacturer’s total operating cost but also improves the user's subjective utility. To improve the stability of cooperation between manufacturers and users, the behavior decision-making method urges manufacturers to consider product users’ supply disruption risk preferences (PUSDRPs) in attempting to optimize economic benefits for the long term. This paper uses behavior decision-making methods to expand the ideas of the CLRIP joint system.

Distributed energy resources create a prosumers era. Peer-to-peer (P2P) energy sharing is an effective way to conduct prosumers-based energy management. Integrating prosumers’ social attributes in energy engineering substantially affects their decisions, which brings challenges to the energy scheduling under the cyber-physical-social environment. In this paper, a data-driven stochastic game model with prosumers’ social attributes is proposed for P2P energy sharing. According to social psychology, the prosumers’ social attributes are expressed as subjective probabilities, which are studied by the spatial-temporal graph convolutional networks. In the network, a double-layer feature graph is built to learn the social attributes based on the social survey data and load metering data. The P2P energy sharing incurred randomness comes from social attributes of interactive prosumers, which is formulated as a stochastic game model. In this game, a subjective utility model is proposed for prosumers, and the energy scheduling is conducted with the designed dynamic interval adjustment method. Numerical analysis reveals the results of the learning network and generalized Nash equilibrium. Through comparing with rational scenarios, the influence of prosumers’ social attributes on P2P energy sharing is concluded.

We explore the question of whether cost-free uncertain evidence is worth waiting for in advance of making a decision. A classical result in Bayesian decision theory, known as the value of evidence theorem, says that, under certain conditions, when you update your credences by conditionalizing on some cost-free and certain evidence, the subjective expected utility of obtaining this evidence is never less than the subjective expected utility of not obtaining it. We extend this result to a type of update method, a variant of Judea Pearl’s virtual conditionalization, where uncertain evidence is represented as a set of likelihood ratios. Moreover, we argue that focusing on this method rather than on the widely accepted Jeffrey conditionalization enables us to show that, under a fairly plausible assumption, gathering uncertain evidence not only maximizes expected pragmatic utility, but also minimizes expected epistemic disutility (inaccuracy).

Optimality analysis of value-based decisions in binary and multi-alternative choice settings predicts that reaction times should be sensitive only to differences in stimulus magnitudes, but not to overall absolute stimulus magnitude. Yet experimental work in the binary case has shown magnitude sensitive reaction times, and theory shows that this can be explained by switching from linear to geometric time costs, but also by nonlinear subjective utility. Thus disentangling explanations for observed magnitude sensitive reaction times is difficult. Here for the first time we extend the theoretical analysis of geometric time-discounting to ternary choices, and present novel experimental evidence for magnitude-sensitivity in such decisions, in both humans and slime moulds. We consider the optimal policies for all possible combinations of linear and geometric time costs, and linear and nonlinear utility; interestingly, geometric discounting emerges as the predominant explanation for magnitude sensitivity.

Safe traffic participation requires continuous monitoring of the environment for potential safety hazards. Here we investigate, to what extent an interface using directed tactile stimuli to communicate temporal distances to approaching objects can make drivers feel supported and influence their driving safety. In contrast to previous studies, we focus on conditions in which the driver has relatively high levels of control over the criticality of evolving hazards as well as on scenarios with laterally approaching hazards. In a dynamic driving simulator experiment, 33 participants completed a selection of overtaking and intersection scenarios with and without sensory support. Furthermore, two novel variants of the interface which differed in the resolution of the temporal distance encoding were utilized. We measured safety, quantified as minimum time-to-contact (mTTC) and rate of critical situations, and assessed the participants’ subjective understanding, acceptance and perceived helpfulness of the support functions. Participants understood the system in both variants after brief exposure. The subjective evaluation was positive with regard to helpfulness, acceptance, and custom questionnaire responses. While mTTC values did not differ between supported and baseline drives, the availability of the system reduced the occurrence of safety–critical situations in intersection scenarios. Driving statistics further reveal that the availability of the system increased the likelihood for entering system-relevant signaling ranges in overtaking scenarios. In sum, the presented work extends previous findings by showing that the subjective utility of the investigated tactile interface applies beyond critical situations and that it can also provide a safety advantage at intersections.

Social decisions are often made under great uncertainty - in situations where political principles, and even standard subjective expected utility, do not apply smoothly. In the first section, we argue that the core of this problem lies in decision theory itself - it is about how to act when we do not have an adequate representation of the context of the action and of its possible consequences. Thus, we distinguish two criteria to complement decision theory under ignorance - Laplace’s principle of insufficient reason and Wald’s maximin criterion. After that, we apply this analysis to political philosophy, by contrasting Harsanyi’s and Rawls’s theories of justice, respectively based on Laplace’s principle of insufficient reason and Wald’s maximin rule - and we end up highlighting the virtues of Rawls’s principle on practical grounds (it is intuitively attractive because of its computational simplicity, so providing a salient point for convergence) - and connect this argument to our moral intuitions and social norms requiring prudence in the case of decisions made for the sake of others.

In this paper, we provide an axiomatic foundation of pessimism and optimism towards ambiguity that emerges due to growing awareness. In our setup, this corresponds to a discovery of finer “descriptions” of the original contingencies (states). A decision-maker can form subjective probabilistic beliefs on the original state space and behaves as an expected utility maximizer. However, as finer contingencies are discovered, he may perceive ambiguity with respect to the newly identified states and thus be unable to extend her initial probabilistic beliefs to the expanded state space in an additive way. As a result, the decision-maker’s new beliefs are now “ambiguous” and represented by a probability distribution combined, for each refinement of an original state, with a degree of confidence in this probability estimate. We provide a parametric representation of preferences, identify the DM’s degree of ambiguity as well as his attitude towards ambiguity as captured by the degree of optimism and pessimism. We illustrate the relation of our model to some well-known capacities, such as the E-capacities introduced by Eichberger and Kelsey (Theory Decis 46:107–140, 1999), the JP-capacities of Jaffray and Philippe (Math Oper Res 22:165–185, 1997) and the NEO-additive capacities developed by Chateauneuf et al. (J Econ Theory, 137:538–567, 2007).

Distributed energy resources bring about challenges related to the participation of an increasing number of prosumers with strong social attributes in peer-to-peer (P2P) energy sharing markets, resulting in the increased complexity of socio-technical systems. Previous research has focused on energy sharing analysis based on rational games without considering the social attributes of prosumers, which are not typically used in real scenarios. In this article, an interdisciplinary P2P energy sharing framework that considers both technical and sociological aspects is proposed. It is based on prospect theory (PT) and stochastic game theory, in which the prosumers work as followers with subjective load strategies, while an energy sharing provider (ESP) serves as the leader with a dynamic pricing scheme. A subjective utility model with risk utility (RU) determined by PT is designed for prosumers, and a profit model for dynamic prices is suggested for ESP. Moreover, a solution algorithm that consists of interpolation and curve fitting to obtain the RU function, the aggregation of prosumers to a Markov decision process, and a differential evolution algorithm to solve the game are proposed to solve the problems of the “curse of dimensionality” and discreteness arising from the social attributes of prosumers. Numerical analysis reveals the results of the Stackelberg equilibrium and demonstrates the effectiveness of this method in terms of the social behavior of prosumers, i.e., radicalness when losing and conservatism when gaining.

Ambiguity sensitive preferences must fail either Consequentialism or Dynamic Consistency (DC), two properties that are compatible with subjective expected utility and Bayesian updating, while forming the basis of backward induction and dynamic programming. We examine the connection between these properties in a general environment of convex preferences over monetary acts and find that, far from being incompatible, they are connected in an economically meaningful way. In single-agent decision problems, positive value of information characterises one direction of DC. We propose a weakening of DC and show that one direction is equivalent to weakly valuable information, whereas the other characterises the Bayesian updating of the subjective beliefs which are revealed by trading behavior.

To optimize the investment scheme of a port in a multiport region, it is necessary to analyze users' port choice habits and the feedback relationship between users' use experience and their port choice behaviors. This paper proposes a method to analyze the process of dynamic change in users' port choices after a specific port invests to enhance its competitiveness; the paper then establishes an analytical model to measure the evolution of port market share. The case study shows that users cannot fully perceive a utility increment at a port when their loyalties are at a lower level, but their loyalty may increase as they experience satisfaction with the port's services, and ultimately, they will fully perceive the objective utility of the port. This means that users' perceived subjective utility lags behind the port's objective utility; thus, a small and undeveloped port should not invest to its maximum capacity when users' loyalties are low but should reduce the port tariff so that users experience the improved service and then invest step by step to effectively give full play to the investment.

The paper examines notions of incentive compatibility in an environment with ambiguity-averse agents. In particular, we propose the notion of maxmin transfer coalitional incentive compatibility, which is immune to coalitional manipulations and thus more stable than the individual incentive compatibility condition. The main result characterizes the set of allocations that satisfy the maxmin transfer coalitional incentive compatibility condition. We show that an allocation satisfies maxmin transfer coalitional incentive compatibility if and only if it is maxmin interim efficient. This result extends that of De Castro and Yannelis (J Econ Theory 177:678–707, 2018) in the sense that ambiguity not only resolves the conflict between efficiency and incentive compatibility, it also accommodates stability. Furthermore, this result is false in a finite economy where agents are subjective expected utility maximizers.

This review uses the empirical analysis of portfolio choice to illustrate econometric issues that arise in decision problems. Subjective expected utility (SEU) can provide normative guidance to an investor making a portfolio choice. The investor, however, may have doubts on the specification of the distribution and may seek a decision theory that is less sensitive to the specification. I consider three such theories: maxmin expected utility, variational preferences (including multiplier and divergence preferences and the associated constraint preferences), and smooth ambiguity preferences. I use a simple two-period model to illustrate their application. Normative empirical work on portfolio choice is mainly in the SEU framework, and bringing in ideas from robust decision theory may be fruitful.

I provide a novel simplified approach to Savage’s theory of subjective expected utility. Such an approach is based on abstract integral representation theorems in the space of measurable functions. The advantage of such an approach is that these results can be used to easily obtain variations on Savage’s theorem, such as representations with state-dependent utility or probability measures that can have atoms. Finally, I discuss how such an approach can be used in other settings such as decision making under ambiguity.

We model uncertain social prospects as acts mapping states of nature to (social ) outcomes. A social choice function (or SCF ) assigns an act to each profile of subjective expected utility preferences over acts. An SCF is strategyproof if no agent ever has an incentive to misrepresent her beliefs about the states of nature or her valuation of the outcomes. It is unanimous if it picks the feasible act that all agents find best whenever such an act exists. We offer a characterization of the class of strategyproof and unanimous SCFs in two settings. In the setting where all acts are feasible, the chosen act must yield the favorite outcome of some ( possibly different) agent in every state of nature. The set of states in which an agent’s favorite outcome is selected may vary with the reported belief profile; it is the union of all states assigned to her by a collection of constant, bilaterally dictatorial, or bilaterally consensual assignment rules. In a setting where each state of nature defines a possibly different subset of available outcomes, bilaterally dictatorial or consensual rules can only be used to assign control rights over states characterized by identical sets of available outcomes. (JEL D71, D81, R53)