Expected Utility Theory Framework Research Articles

Impact of the Local Dynamics on Exit Choice Behaviour in Evacuation Model

This study investigated the interplay between exit selection models and local pedestrian movement patterns within floor field frameworks. Specifically, this investigation analysed the performance of a multinomial logit exit choice model, incorporating both expected utility theory and cumulative prospect theory frameworks when coupled with three distinct local-level pedestrian movement models (FF-Von Neumann, FF-Moore, and NSFF). The expected utility theory framework considers the deterministic component as a linear relationship, while the cumulative prospect theory framework further considers the decision-maker’s risky attitudes by transforming objective terms into subjective terms using a power value function. The core objective was to comprehend how local movement dynamics, as represented by the floor field models, influence decision-making during exit selection. Comparative analyses revealed intriguing variations between the three local models, despite their shared expected utility theory-based exit choice framework. These discrepancies stemmed from the diverse pedestrian trajectory behaviours generated by each model. Consequently, these local dynamics impacted the decision-maker’s assessment of critical factors, such as the number of evacuees close to the decision-maker (NCDM) and the number of evacuees close to an exit (NCE), which the exit choice model incorporates. These assessments, in turn, significantly affected higher-level decision-making. The integration of the three models with the multinomial logit exit choice model, using either cumulative prospect theory and expected utility theory frameworks, further strengthened the observed bilateral relationship. While the specific nature of this relationship varied depending on the chosen framework and its implementation details, these consistent findings demonstrate the robustness of the results. This reinforced the influence of local-level pedestrian dynamics on higher-level exit selection, highlighting the importance of accurate crowd dynamics modelling, especially when advanced exit choice models consider local movement factors.

Utility-Consistent Valuation Schemes for the Own Risk and Solvency Assessment of Life Insurance Companies

Abstract In this paper, we construct new valuation schemes for the liabilities and economic capital of insurance companies. Specifically, we first build a ‘SAHARA’ valuation framework based on Symmetric Asymptotic Hyperbolic Absolute Risk Aversion utility functions. Then, we construct a ‘SAHARA-CPT’ framework that incorporates the previous utility function as a value function and that is based on Cumulative Prospect Theory. The process used for assessing a life insurance company’s own funds consists in replacing the market-consistent parametrization with a utility-consistent parametrization that accounts for the risk aversion of the market and the long-term duration of the company’s commitments. Our illustrations show that this approach leads to a lower value of the Own Risk and Solvency Assessment and to a lower volatility of own funds. The framework that is based on cumulative prospect theory has the advantage over the expected utility theory framework that it considers a precautionary overweighting of extreme events, as a tradeoff for additional model complexity.

A satisficing approach to eliciting risk preferences

A new approach is proposed to eliciting risk preferences by framing choice over risky payoff distributions as a satisficing task. We demonstrate novel links between the information elicited from the satisficing task—which allows subjects to consider accepting a worse worst-case outcome in favor of a better best-case outcome—and portfolio choice using expected utility theory (EUT). The key tradeoff in our satisficing task can also be stated in reverse: to consider accepting less attractive potential upside gains in order to improve worst-case outcomes. Risk preferences are elicited by asking subjects to choose an acceptable worst-case portfolio outcome from a continuum of binary gambles, each with its own support and unique minimum. The worst-case aspiration represents the smallest low-state payoff in the binary gamble that the subject is willing to accept. We show analytically and empirically that choosing a most preferred worst-case aspiration maps into a logically equivalent—but psychologically distinct—process of expected utility maximization (i.e., allocating one's savings over a binary risky asset and risk-free bond using the EUT framework with a unique risk-acceptance parameter under CARA or CRRA risk preferences).

Demand for a Simple Weather Insurance Product in India: Theory and Evidence

AbstractThis article analyzes the demand for a simple rainfall‐based weather insurance product among farmers in rural India. We explore the predictions of a standard expected utility theory framework on the nature of demand in terms of price, the basis of the hedge, and risk aversion using data from a randomized control trial. We find that demand behaves as predicted: it falls with price and basis risk and is hump‐shaped in risk aversion, with price sensitivity decreasing at higher levels of basis risk. We estimate a negative price elasticity of 0.58 and find that doubling the distance to a reference weather station decreases demand by 18%. These results indicate that improving pricing and quality of insurance products can directly increase demand. In addition, we examine the impact of insurance training relative to other mechanisms designed to increase understanding. The evidence suggests that increased incentives to learn or learning by using are more effective at increasing both understanding and demand. Finally, we contribute to the scarce evidence on the demand for insurance over time. In terms of our main interventions, we find that the effect of premium subsidies persists over time, while the impact of investments in new weather stations diminishes and the effect of increased training in the first season seems to disappear during the second season. Importantly, while having previously purchased insurance does not encourage future uptake, receiving a payout does. This could reflect issues of trust in the product or the insurance company, and constitutes an important topic for future research.

Impact of cost uncertainty on pricing decisions under risk aversion

This paper studies cost uncertainty in services. Despite the fact that the service sector has become the largest component of gross domestic products in most developed economies, cost uncertainty and its impact on pricing decisions have not received much attention in the literature. In this paper, we first identify the root causes of cost uncertainty in services. Using the distinctive characteristics of services defined in the literature, we show why cost uncertainty, which has been widely neglected in the manufacturing dominated literature, is pervasive in services. Next, we investigate how cost uncertainty affects a risk-averse service provider’s pricing decisions in a make-to-order setting. Using the expected utility theory framework, we show that cost uncertainty increases the optimal price, whereas demand uncertainty reduces it. As a result of the countervailing impacts, the optimal price under risk aversion may be larger or smaller than the optimal risk-neutral price. Next, we study the problem of optimizing cost contingency in service contract pricing. We show that the optimal cost contingency decreases as the profit of the contract increases even when the utility function exhibits an increasing absolute risk aversion. Finally, we introduce various strategies to mitigate the risk of cost uncertainty observed in practice, and propose new research problems.

Are employee stock option exercise decisions better explained through the prospect theory?

In this paper, we introduce a new framework for the analysis of employee stock options exercise decisions. We develop a distorted lattice model where the exercise decision obeys a policy that maximizes the expected value of the exercise outcomes under the Cumulative Prospect Theory. Using a large US dataset of exercise transactions, we show that our framework broadly outperforms the Expected Utility Theory framework in explaining empirical exercise decisions. Interestingly, our empirical estimates of probability weighting are consistent with those from the experimental literature. We argue that this analysis provides a unifying stream for thinking about issues related to the exercise and the valuation of stock options.

Demand for Weather Hedges in India: An Empirical Exploration of Theoretical Predictions

Income risk is substantial for farmers in developing countries. Formal insurance markets for this risk are poorly developed, and as a result there has been an increasing trend to sell weather hedges to smallholder farmers to manage their risk. This paper analyzes the demand for rainfall-based weather hedges among farmers in rural India. We explore the predictions of a standard expected utility theory framework on the nature of demand for such products, in particular testing whether demand behaves as predicted with respect to price, the basis of the hedge, and risk aversion using data from a randomized control trial in which price and basis risk was varied for a series of hedging products offered to farmers. We find that demand behaves as predicted, with demand falling with price and basis risk, and appearing hump-shaped in risk aversion. Second, we analyze understanding of and demand for hedging products over time, examining the impact of increased investments in training on hedging products as well as evidence for learning by doing among farmers. We find evidence that suggests that learning by doing is more effective at increasing both understanding and demand.

Incomplete financial markets and contingent claim pricing in a dual expected utility theory framework

This paper investigates the price for contingent claims in a dual expected utility theory framework, the dual price, considering arbitrage-free financial markets. A pricing formula is obtained for contingent claims written on n underlying assets following a general diffusion process. The formula holds in both complete and incomplete markets as well as in constrained markets. An application is also considered assuming a geometric Brownian motion for the underlying assets and the Wang transform as the distortion function.

Decision-Making When Data and Inferences Are Not Conclusive: Risk-Benefit and Acceptable Regret Approach

The absolute truth in research is unobtainable, as no evidence or research hypothesis is ever 100% conclusive. Therefore, all data and inferences can in principle be considered as "inconclusive." Scientific inference and decision-making need to take into account errors, which are unavoidable in the research enterprise. The errors can occur at the level of conclusions that aim to discern the truthfulness of research hypothesis based on the accuracy of research evidence and hypothesis, and decisions, the goal of which is to enable optimal decision-making under present and specific circumstances. To optimize the chance of both correct conclusions and correct decisions, the synthesis of all major statistical approaches to clinical research is needed. The integration of these approaches (frequentist, Bayesian, and decision-analytic) can be accomplished through formal risk:benefit (R:B) analysis. This chapter illustrates the rational choice of a research hypothesis using R:B analysis based on decision-theoretic expected utility theory framework and the concept of "acceptable regret" to calculate the threshold probability of the "truth" above which the benefit of accepting a research hypothesis outweighs its risks.

Dynamic Portfolio Selection in a Dual Expected Utility Theory Framework

In this paper the dynamic portfolio selection problem is studied for the first time in a dual utility theory framework. The Wang transform is used as distortion function and well diversified optimal portfolios result both with and without short sales allowed.

Dynamic Portfolio Selection in a Dual Expected Utility Theory Framework

In this paper the dynamic portfolio selection problem is studied for the first time in a dual utility theory framework. The Wang transform is used as distortion function and well diversified optimal portfolios result both with and without short sales allowed.

Using treatment-tradeoff preferences to select diagnostic strategies: linking the ROC curve to threshold analysis.

Selection of optimal diagnostic strategies depends on the accuracy of diagnostic tests, the prevalence of disease, and the relative benefits and harms resulting from test/treatment choices. One can characterize diagnostic strategies according to their net benefit-to-harm ratios. Within the framework of expected-utility theory, the benefit-to-harm ratio is equivalent to the marginal tradeoff between erroneous and correct treatment choices. Consequently, a physician can identify his or her preferred strategy by asking the question: "How many additional treatment errors am I willing to make in order to treat one additional person correctly?" Family physicians were asked to indicate their lowest and highest acceptable tradeoff ratios in a survey about streptococcal pharyngitis. All 53 respondents indicated uncertainty about their preferred treatment-tradeoff ratios. For 58% of respondents, the midpoints of the acceptable range of tradeoff ratios corresponded to generally accepted diagnostic strategies: 1) test all or 2) treat if classic, test all others. In contrast, previously published patient utilities yield a treatment-tradeoff ratio that corresponds to the strategy of treating all cases. The analysis illustrates the linkage between the optimal operating point on a receiver operating characteristic (ROC) curve and the selection of a preferred diagnostic strategy based on treatment thresholds derived from benefit-to-harm ratios. The survey results indicate that physicians can respond directly to questions assessing their preferences for such treatment thresholds. Differences between patients and physicians have significant impacts on choices of test and treatment strategies.