Traditional Expected Utility Theory Research Articles

Blockchain-Based Design of a Government Incentive Mechanism for Manufacturing Supply Chain Data Governance

In the context of “double carbon”, constructing green supply chains is the only way to implement sustainable development strategies in the manufacturing industry. This paper, therefore, examines the manufacturing supply chain for low-carbon products. More recently, the lack of technical information flow due to data barriers up and down the supply chain has led to high energy consumption, the serious waste of raw materials, and the substandard production of green products. Therefore, the level of supply chain data governance must be improved to enhance the sustainability of the supply chain. By studying blockchain-based data governance and government policy incentives for manufacturing supply chains, this study constructed an evolutionary game model based on prospect theory for the tripartite relation of government, manufacturers, and retailers. The difference between the perceived and actual value was introduced into a three-way evolutionary game model based on prospect theory to optimize the practical implications of the model. The model was then simulated using system dynamics. Through the simulation, it could be concluded that the ability of the three-way evolutionary game to reach the optimal stability point is only related to the sensitivity of the retailer’s perceived value. Additionally, the outcome of the three-way evolutionary game can be unstable, with changes in perceived value sensitivity. Finally, relevant policy recommendations are made. The innovation of this study is establishing a data governance platform that uses data governance to build green supply chains. Additionally, the government was added to the subjects of the game to explore the role of government policy in data governance and sustainable development. In addition, the evolutionary game model was incorporated with prospect theory and traditional expected utility theory, and the rational deficits and preferences of decision makers were taken into account, which brings the results closer to the reality of the situation.

A Joint Optimization Model considering the Product User's Risk Preference for Supply System Disruption

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.

Prospect Theoretic Analysis of Privacy-Preserving Mechanism

We study a problem of privacy-preserving mechanism design. A data collector wants to obtain data from individuals to perform some computations. To relieve the privacy threat to the contributors, the data collector adopts a privacy-preserving mechanism by adding random noise to the computation result, at the cost of reduced accuracy. Individuals decide whether to contribute data when faced with the privacy issue. Due to the intrinsic uncertainty in privacy protection, we model individuals' privacy-related decision using Prospect Theory. Such a theory more accurately models individuals' behavior under uncertainty than the traditional expected utility theory, whose prediction always deviates from practical human behavior. We show that the data collector's utility maximization problem involves a polynomial of high and fractional order, the root of which is difficult to compute analytically. We get around this issue by considering a large population approximation, and obtain a closed-form solution that well approximates the precise solution. We discover that the data collector who considers the more realistic Prospect Theory based individual decision modeling would adopt a more conservative privacy-preserving mechanism, compared with the case based on the expected utility theory modeling. We also study the impact of Prospect Theory parameters, and concludes that more loss-averse or risk-seeking individuals will trigger a more conservative mechanism. When individuals have different Prospect Theory parameters, simulations demonstrate that the privacy protection first becomes stronger and then becomes weaker as the heterogeneity increases from a low value to a high one.

How to select a promising enterprise for venture capitalists with prospect theory under intuitionistic fuzzy circumstance?

Venture capitalists are confronted with decision-making problems under an uncertain investment circumstance, which lead them to rely heavily on intuition and psychological state to make their decisions in most cases. It has attracted more and more researches to focus on such behaviors of venture capitalists in recent years and along with the development of behavioral finance in venture capital field. Through the review of framework of venture capitalists’ decision-making and the investigation of background of venture capital market in China, we find that the decision-making framework, which is based on the traditional expected utility theory with hypothesis that the investor is completely rational, is inconsistent with reality. Moreover, the decision-making information is often depicted as crisp number with probability that couldn’t express the uncertainty which is the fundamental nature of venture capital circumstance. Therefore, a novel approach that considers both the bounded rationality of venture capitalists and the uncertain circumstance of venture capital in decision-making is urgent to select the promising enterprise in China. Fortunately, prospect theory, as one of the greatest achievement in behavioral finance, thinks that venture capitalists focus on subjective probabilities instead of objective probabilities and show the behavior characterized by risk aversion over gains and risk seeking over losses, which can depict the psychological state of venture capitalists properly. Furthermore, the intuitionistic fuzzy information can express essential uncertainty in venture capital circumstance accurately. Therefore, we adopt Intuitionistic Fuzzy Prospect Theory that integrates both the merits of prospect theory and intuitionistic fuzzy information to aid venture capitalists to make a better decision in the real world. In this paper, the detailed steps of how to select a promising enterprise has been illustrated. Then, a practical example of selecting the promising enterprise for venture capitalists in Ali Capital is provided to exhibit the process of application and exemplify the feasibility of this selecting approach. Meanwhile, from the comparison of ranking results derived by TOPSIS and this approach, we find that the approach proposed in this paper is superior to the TOPSIS in selecting the promising enterprise according to the subsequent funded results of enterprises.

Predicting Empirical Patterns in Viewing Japanese TV Dramas Using Case-Based Decision Theory

AbstractThis article empirically analyzes consumer behavior of viewing TV dramas using case-based decision theory. The theory addresses an economic situation with structural ignorance, where states of the world are not naturally given nor simply formulated for a decision-maker. Under this theory, consumers make decisions based on subjective evaluations of previous purchases for similar goods. Our empirical analysis is concerned with viewing decisions on getsuku, the Japanese TV dramas broadcast at 9 pm Monday by the Fuji Television Network. The regularity of the schedule and the long-sustaining popularity of the program enable us to easily collect consumer data. Then, we conduct a web survey of individual audiences on subjective evaluations of previously watched dramas. For our empirical analysis, we utilize a simple linear model of the case-based model that allows the incorporation of flexible inference techniques. Our results demonstrate better performance of the case-based models than models based on traditional expected utility theory regarding both statistical model selection and one-step-ahead prediction. We also reveal that the successful performance of the case-based model in our analysis depends on the availability of individual subjective evaluations and that it is difficult to replace the individual-specific information using demographic information and aggregate data.

Predicting Empirical Patterns in Viewing Japanese TV Dramas Using Case-Based Decision Theory

This article empirically analyzes consumer behavior of viewing TV dramas using case-based decision theory. The theory addresses an economic situation with structural ignorance, where states of the world are not naturally given nor simply formulated for a decision-maker. Under this theory, consumers make decisions based on subjective evaluations of previous purchases for similar goods. Our empirical analysis is concerned with viewing decisions on getsuku, the Japanese TV dramas broadcast at 9pm Monday by the Fuji Television Network. The regularity of the schedule and the long-sustaining popularity of the program enable us to easily collect consumer data. Then, we conduct a web survey of individual audiences on subjective evaluations of previously watched dramas. For our empirical analysis, we utilize a simple linear model of the case-based model that allows the incorporation of flexible inference techniques. Our results demonstrate better performance of the case-based models than models based on traditional expected utility theory regarding both statistical model selection and one-step-ahead prediction. We also reveal that the successful performance of the case-based model in our analysis depends on the availability of individual subjective evaluations and that it is difficult to replace the individual-specific information using demographic information and aggregate data.

Expected utility theory with non-commutative probability theory

In this paper, we extend von Neumann and Morgenstern’s expected utility approach to a non-commutative probability theory. We introduce a new representation of the decision maker’s set of events which extends the canonical representation. We reformulate von Neumann and Morgenstern’s approach to modeling decision maker behavior by non-commutative probability theory. We introduce a set of preference axioms similar to von Neumann and Morgenstern’s axioms, and show that they lead to a generalization of the expected utility theorem. Our generalization allows for decision makers to make an intuitive distinction between representations of a set of events. We find that this methodology enables several paradoxes and inconsistencies in traditional expected utility theory (e.g., Allais paradox, etc.) to be solved or better understood.

The Game Model with Emotional Factors of Public and Media in Public Emergencies Management Engineering

The current researches on the game behavior between the media and the public in emergency management engineering are limited to the traditional expected utility theory, the theory suppose the both sides are rational people, so the theory researches often do not match the reality. This paper introduces emotional factors and makes a new study on the game model. At first, according to the analysis of the relations between the public and the media in emergencies, we begin to study the impacts of emotions on the game behavior, and then set up the RDEU game model Based on emotional function; At last, it takes a Nash Equilibrium dynamic evolution modeling analysis by numerical example.

Cumulative prospect theory challenges traditional expected utility theory

The Cumulative Prospect Theory (CPT) uses piecewise value functions instead of consumer utility and provides alternative assumptions for investment behaviour approximated by power value function. In this study, our aim to find a generalized value function that will make the value function introduced by Kahneman–Tversky (1992) a special case. This functional form of the value function determine the appropriate parameter of the values function. We believe that if one can approximate the original CPT value function by other types of functions, the optimization problem and the many other implications can be compared to choose the best model depending on the focus of the problems. This, eventually, could result in improving the theory in both theoretical and empirical points of views.

Are NFL Coaches Risk and Loss Averse? Evidence from Their Use of Kickoff Strategies

Quantitative analysis of football play calling suggests that NFL coaches do not choose their strategies optimally. They tend to be overly cautious. One possible explanation for this finding is that NFL coaches are averse to risk and loss. We propose a prospect theory based model of coaches' utility and estimate the model's parameters using kickoff data from the 2009 NFL season. Using an outcome measure of points scored on the initial post-kickoff possession we analyze two strategic kickoff decisions that involve risk-reward tradeoffs: the decision to kick a surprise onside kickoff or a regular kickoff, and the decision to accept a touchback or run the ball out of the endzone. Surprise onside kickoffs may yield a more favorable mean points scored value for the kicking team than a regular kickoff, yet surprise onside kickoffs are infrequently used (and thus the same size is small and the p-value of significance test is 0.68). Coaches appear averse to the possible loss involved in the surprise onside kickoff. Running the ball out yields a higher mean points scored for the receiving team than accepting a touchback, but it entails some risk (fumbles are lost in 2 percent of returns). Nevertheless, declining the touchback option and running the ball out is very common. Coaches do not appear excessively risk averse when presented with this choice over possible gains. Prospect theory models allow for risk aversion over possible gains, as in traditional expected utility theory, and in addition they permit an asymmetric aversion to losses. A prospect theory model therefore seems suitable for our analysis of kickoff strategies. We estimate a risk aversion coefficient value of 0.66 and a loss aversion coefficient value of 1.55, where values 1 indicate risk and loss aversion, respectively. Our analysis supports the notion that NFL coaches are both modestly risk averse and loss averse. In other words, coaches display diminishing sensitivity to changes in scoring outcomes as they move further from a reference point (zero), and for scoring gains and losses of equal magnitude they suffer more from a loss than they enjoy from a gain. This result may explain their propensity for making conservative strategic choices that, at first glance, appear sub-optimal.

Optimal Choice in the Face of Risk: Decision Theory Meets Evolution

The problem of how to make optimal choices in the face of risk (or uncertainty) arises in both economics/decision theory and also evolutionary biology; in the former, ‘optimal’ means utility maximizing, while in the latter it means fitness maximizing. This article explores the links, thematic and formal, between the economic and evolutionary theories of optimal choice in risky situations, with particular reference to the relationship between utility and fitness. It is argued that the link is strongest between evolution and ‘nonexpected’ utility theory, rather than traditional expected utility theory.

A Pedagogical Note on Risk Framing

AbstractThis article presents several classroom games that help illustrate the effect of risk framing on choices under uncertainty. These games are presented in the context of Kahneman and Tversky's prospect theory that is an alternative to the traditional expected utility theory. Expected utility theory is a prescriptive model of decision making that explains how people should react to risk, while prospect theory is a descriptive model that explains how people actually do react to risk. These classroom games can be used in introductory risk management courses, insurance courses, and financial risk management courses to help students to understand the effect of context on choices made under uncertainty. Although results from actual classroom experiments are included, these results are not meant to be indicative of normal results but are rather illustrative of the type of results that may arise when alternative framing contexts are used.

A Prediction Model for Individual Decision Making Behavior under Fuzzy Decision Problems

Most decision problems are inherently fuzzy due to of unable precisely defined or the lack of enough information. Thus these decision problems can properly be formulated by fuzzy sets. Such problems differ from the traditional mathematical programming problem of maximizing a well-defined function over a set of alternatives represented by crisp values. However, when alternatives are represented in terms of fuzzy values, one important question remains: how can one choose one alternative from a given set of alternatives so as to achieve, in some sense, ”the best” overall fuzzy value. Current fuzzy decision making methods do not account for subjective values of the decision maker. In this paper the concept of the decision maker's attitude toward fuzziness is defined and a new fuzzy decision making method is derived. The feasibility and validity of the new method is ascertained through experiments. Benchmarking with the indices of preference reversals revealed by the traditional expected utility theory, the new approach leads to better prediction of individual's actual choices.

CAUSES OF THE ALLAIS CHOICE (a1, b2)

Paradox has been an instrument to challenge the traditional expected utility theory. Paradox arises from the inconsistency between the empirical or experimental results and the theoretical deductions. In the expected utility theory field, there are many paradoxes or effects showing behaviors that are contradictory to the “theoretical” ones. Many studies questioned the validity of the expected utility theory by means of these paradoxes; while many others disqualified the expected utility theory as a descriptive model for human decision making behaviors. Among these paradoxes or effects, the Allais Paradox raised by Allais in 1953 is the most famous one.

Crime and Punishment: Further Results

I consider a general specification of criminals' objective function and argue that, when the general non-expected utility theory is substituted for the traditional expected utility theory, the high-fine-low-probability result (Becker, 1968) only holds under specific and strong restrictions.

Discussion of an Experimental Investigation of Auditor-Auditee Interaction Under Ambiguity

Experimental economics studies in auditing typically test for economic forces in multiperson settings and ignore psychological considerations. Behavioral studies in auditing typically test for psychological forces in single-person settings and ignore economic forces. Zimbelman and Waller [1999] (hereafter ZW) manipulate and test for both economic and psychological forces in a multiperson setting. The authors construct an experiment that places auditors in a strategic setting that also subjects them to the psychological force of ambiguity (uncertainty regarding a probability distribution). The auditors respond both to auditees' economic incentives and to ambiguity; auditees also appear to respond to the ambiguity faced by the auditors, although evidence of this effect is weaker. These results show how psychological responses to ambiguity can percolate through a strategic setting, from the auditors (who face ambiguity directly) to the managers (who face the auditors who face ambiguity). The audit setting is a natural one in which to integrate psychological and economic theory. The typical audit environment contains significant ambiguity, and the natural response to such ambiguity (to collect more data than would be predicted by traditional expected-utility theory) has important economic ramifications. The single-person environment of most behavioral studies would fail to capture the complete effects of ambiguity on audit behavior and outcomes, because the likelihood of

Decision Analysis at the Crossroads

Empirically observed violations of traditional expected utility theory have prompted several new theories, putting the future of decision analysis at the crossroads. This essay describes the current status of the literature through selected examples of decision making scenarios. It concludes that if all relevant consequences of all alternatives are included in the analysis, then expected utility theory would prove to be valid. Practitioners who use techniques or software based on expected utility theory need only to make sure that they have considered all relevant consequences, and need not worry about the new non‐expected utility theories.

Sources of Bias in Assessment Procedures for Utility Functions

Utility functions are an important component of normative decision analysis, in that they characterize the nature of people's risk-taking attitudes. In this paper we examine various factors that make it difficult to speak of the utility function for a given person. Similarly we show that it is questionable to pool risk-propensity data across studies (for descriptive purposes) that differ in the elicitation methods employed. The following five sources of bias or indeterminacy are hypothesized and demonstrated. First, certainty equivalence methods generally yield greater risk-seeking than probability equivalence methods. Second, the probability and outcome levels used in reference lotteries induce systematic bias. Third, combining gain and loss domains yields different utility measures than separate examinations of the two domains. Fourth, whether a risk is assumed or transferred away exerts a significant influence on people's preferences in ways counter to expected utility theory. Finally, context or framing differences strongly affect choice in a nonnormative manner. The above five factors are first discussed as essential choices to be made by the decision scientist in constructing Von Neumann-Morgenstern utility functions. Next, each is examined separately in view of existing literature, and demonstrated via experiments. The emerging picture is that basic preferences under uncertainty exhibit serious incompatibilities with traditional expected utility theory. An important implication of this paper is to commence development of a systematic theory of utility encoding which incorporates the many information processing effects that influence people's expressed risk preferences.