Utility Research Articles

Flummoxing expectations

AbstractExpected utility theory often falls silent, even in cases where the correct rankings of options seems obvious. For instance, it fails to compare the Pasadena game to the Altadena game, despite the latter turning out better in every state. Decision theorists have attempted to fill these silences by proposing various extensions to expected utility theory. As I show in this paper, such extensions often fall silent too, even in cases where the correct ranking is intuitively obvious. But we can extend the theory further than has been done before—I offer a new extension, Invariant Value Theory, which deals neatly with those problem cases and also satisfies various desirable conditions. But other prima facie desirable conditions, including Independence, the theory violates. Is this a problem for the proposal? It may not be—in a new impossibility result, I show that no theory can satisfy Independence in full generality without violating several other conditions that together seem just as plausible.

Determining Tourist Destination Priorities Using Website-Based Particle Swarm Optimization Methods (Case Study : North Padang Lawas Regency)

. Optimization of tourist routes is very important to minimize travel time and reduce travel costs. This study focuses on optimizing tourist routes in North Padang Lawas Regency using Multi Attribute Utility Theory (MAUT), and Particle Swarm Optimization (PSO) in the context of the Traveling Salesman Problem (TSP). This study discusses problems such as the lack of priority of tourist destinations and the need for shorter travel times. The research process includes problem identification, literature review, data collection through field observations and interviews, and data processing with MAUT to prioritize destinations. The identified priority destinations are Hotel Sapadia Gunung Tua, Barumun Nagari, Batik Sekar Najogi, Durian and Manggis Agrotourism, Rumah Makan Holat Alhamdulillah, Waterboom Gunung Tua, and Candi Bahal I, II, III. Furthermore, PSO is applied to determine the optimal travel route. PSO finds a route with a total travel time of 351 minutes, although the three-day travel time is extended to 375 minutes.

Strategic safeguarding: A game theoretic approach for analyzing attacker-defender behavior in DNN backdoors

Deep neural networks (DNNs) are fundamental to modern applications like face recognition and autonomous driving. However, their security is a significant concern due to various integrity risks, such as backdoor attacks. In these attacks, compromised training data introduce malicious behaviors into the DNN, which can be exploited during inference or deployment. This paper presents a novel game-theoretic approach to model the interactions between an attacker and a defender in the context of a DNN backdoor attack. The contribution of this approach is multifaceted. First, it models the interaction between the attacker and the defender using a game-theoretic framework. Second, it designs a utility function that captures the objectives of both parties, integrating clean data accuracy and attack success rate. Third, it reduces the game model to a two-player zero-sum game, allowing for the identification of Nash equilibrium points through linear programming and a thorough analysis of equilibrium strategies. Additionally, the framework provides varying levels of flexibility regarding the control afforded to each player, thereby representing a range of real-world scenarios. Through extensive numerical simulations, the paper demonstrates the validity of the proposed framework and identifies insightful equilibrium points that guide both players in following their optimal strategies under different assumptions. The results indicate that fully using attack or defense capabilities is not always the optimal strategy for either party. Instead, attackers must balance inducing errors and minimizing the information conveyed to the defender, while defenders should focus on minimizing attack risks while preserving benign sample performance. These findings underscore the effectiveness and versatility of the proposed approach, showcasing optimal strategies across different game scenarios and highlighting its potential to enhance DNN security against backdoor attacks.

Reservation and allocation model considering the user cost and the utilization of parking space

In order to alleviate the problem of imbalance between parking supply and demand by improving the utilization rate of parking resources, and considering the game relationship between user benefits and system benefits in parking allocation, the optimization objective function of maximum parking space utilization and minimum user cost in parking reservation mode is established from the two aspects which are system optimization and user optimization respectively. Then, the optimal allocation parking integer programming model (OPA) considering users' preferences is established. An Augmented Lagrangian-Alternating Direction Method of Multipliers Algorithm is designed to solve the optimal solution of the model. Finally, in order to test the validity of the model, the proposed model and the classical distribution model are compared and analyzed under different supply and demand conditions. The results show that the performance of the proposed model in three performance metrics which are parking utilization, average user cost and request acceptance rate is significantly better than that of the classical allocation model. The research results of this paper can provide theoretical reference for the management of parking reservation platform.

Active Learning Framework for Expediting the Search of Thermodynamically Stable MXenes in the Extensive Chemical Space.

MXenes possess a wide range of materials properties owing to their compositional and stoichiometric diversities, facilitating their utilization in various technological applications such as electrodes, catalysts, and supercapacitors. To explore their applicability, identification of thermodynamically stable and synthesizable MXenes should precede. The energy above the convex hull (Ehull) calculated using the density functional theory (DFT) is a powerful scale to probe the thermodynamic stability. However, the high calculation cost of DFT limits the search space of unknown chemistry. To address this challenge, this study proposes an active learning (AL) framework consisting of a surrogate model and utility function for expeditious identification of thermodynamically stable MXenes in the extensive chemical space of 23,857 MXenes with compositional and stoichiometric diversity. Exploiting the fast inference speed and the capability of the AL framework to accurately identify stable MXenes, only 480 DFT calculations were required to identify 126 thermodynamically stable MXenes; among these, the stabilities of 89 MXenes have not been previously reported. In contrast, only two stable MXenes were identified among randomly selected 1693 MXenes, demonstrating the inefficiency of using only DFT calculations in exploring a large chemical space. The AL framework successfully minimized the number of DFT calculations while maximizing that of thermodynamically stable MXenes identified and can contribute to future studies in finding stable MXenes expeditiously.

Differences in Urban Vibrancy Enhancement among Different Mixed Land Use Types: Evidence from Shenzhen, China

Mixed land use has the advantages of promoting the economic and intensive utilization of land and improving the efficiency of land use, which can help alleviate the current urban problems and promote the sustainable development of cities. Existing studies have usually used quantitative indicators to reflect complex and diverse mixed land use situations, and the conclusions obtained usually cannot provide a basis for functional selection in mixed land use practices. Therefore, this study took Shenzhen as the study area to explore whether there are differences in the urban vibrancy enhancement among different mixed land use types. First, the block-scale mixed land use dataset of the study area was constructed. Second, the spatial distribution characteristics of the main functional types and urban vibrancy in the study area were explored. Finally, the impact of mixed land use types on urban vibrancy was explored by using a multiple linear regression model and setting land use type as the dummy variable. The results show that the number of mixed-function blocks in Shenzhen is relatively small, and the mixed land use degree still needs to be improved. Among the 12 main land use types in the study area, those containing industrial land are usually clustered in the northern industrial area of Shenzhen, those containing public or commercial service land are usually clustered in the city center, and those containing residential land are widely distributed in the study area. From the perspective of urban vibrancy, there is a phenomenon of “jobs–housing mismatch” in Shenzhen, as well as a problem of low urban vibrancy in the peripheral areas of the city. In addition, the urban vibrancy intensity of mixed land use types including residential or commercial land is higher, such as “administration+residential”, “residential+commercial”, “industrial+residential+commercial”, and “administration+residential+commercial” land, which includes residential or commercial land, is stronger, while. However, the urban vibrancy stability of mixed land use types including industrial land is higher, such as “industrial+residential” and “industrial+administration” land. The results of this study can provide a basis for future mixed land use practices in terms of land use type selection. For the urban central areas and subcenters in urban peripheral areas, mixed land use types such as “administration+residential”, “residential+commercial”, and “administration+residential+commercial” can be selected to enhance the urban vibrancy stability of the area. For industrial parks in urban peripheral areas, mixed land use types such as “industrial+residential”, “industrial+commercial”, “industrial+administration+residential”, and “administration+residential+commercial” can be selected to enhance the urban vibrancy intensity of the area.

Economic Burden Associated with Pulmonary Arterial Hypertension in the United States.

Pulmonary arterial hypertension (PAH) is a progressive disease characterized by elevated pressure in the pulmonary arteries, commonly resulting in right heart failure. PAH is associated with a high economic burden throughout the duration of the disease. This retrospective cohort study of the Milliman Contributor Health Source Data, the Medicare 100% Research Identifiable Files, and the Merative Marketscan® Commercial dataset between 2018 and 2020 identified adult patients with prevalent PAH based on the earliest qualifying diagnosis date or medication date ('index date') between January 1, 2019 and November 30, 2020. Outcomes were assessed using patient data from index date through the earliest of end of enrollment, end of data, or death (Medicare fee-for-service [FFS] only). All-cause and PAH-related medical and pharmacy costs per-patient per-month (PPPM) and healthcare resource utilization per 1000 patients were summarized. The study included 11,670 Medicare FFS, 1021 Medicare Advantage, 274 Medicaid, and 1174 commercially insured patients in the US. The annual national burden to payers was estimated to be US$3.1 billion. The PPPM payer costs ranged from US$6500 to US$14,742; out-of-pocket (OOP) costs ranged from US$341 to US$907 PPPM. Inpatient utilization rate ranged from 435 to 770 per 1000 patients for all-cause admissions and from 15 to 58 per 1000 patients for PAH-related admissions. This study demonstrates that PAH continues to be associated with a high economic burden and healthcare resource utilization across all payer types within the US healthcare system.

Scalarized utility-based multi-asset risk measures

We introduce a risk measure that simultaneously allows to minimize hedging costs and maximize expected utility in the presence of a risk constraint. We call it the scalarized utility-based multi-asset (SUBMA) risk measure. For the SUBMA risk measure we state the following results: If the utility function has constant relative risk aversion and the risk constraint is coherent, then the SUBMA risk measure is coherent. In a one-period financial market setup, we present a sufficient condition for the SUBMA risk measure to be finite-valued. We derive results about the existence of optimal payoffs. Finally, we present a dual representation for a map that generalizes classical risk measures and has not been analyzed so far. In particular, it gives us the dual representation for the SUBMA risk measure.

ADAM SMITH’S THEORY OF VALUE AND THE FALLING RATE OF PROFIT: UNCOMMON CONCEPTIONS AND COMMON MISCONCEPTIONS

Smith’s theory of value and distribution, which emphasizes labor time as the determinant of prices, has been widely misunderstood. Ricardo misinterpreted it as relevant only to primitive societies, while Marx inaccurately aligned it with his own labor theory of value. In reality, Smith’s perspective oscillates between a labor-based and a labor-commanded approach to relative prices, intended for modern economies. Neoclassical economists further distorted Smith’s views by incorporating utility theory. Moreover, while Smith is often linked to the theory of a falling rate of profit due to competition, he actually attributed it to rising capital intensity. Contrary to the belief that Smith was a staunch advocate of free markets, he supported reasonable government intervention.

Can agricultural water rights trading promote intensive and economical utilization of water resources in China based on the difference-in-differences model?

IntroductionImproving the level of intensive and economical utilization of water resources is an important step towards water and food security.MethodsBased on China’s provincial panel data from 2015 to 2021, this study uses a difference-in-differences (DID) model to examine the effect of agricultural water rights trading on intensive and economical utilization of water resources.ResultsThe results show that agricultural water rights trading can significantly promote an intensive and economical utilization of water resources at the 1% level, and the results remain valid even after a series of robustness tests. Further analysis indicates that agricultural water rights trading can promote better utilization of water resources by significantly reducing agricultural water consumption at the 1% level and reducing the proportion of agricultural water consumption at the 5% level to optimize water use structure. Moreover, in regions with scarce water resources, strong water resource constraints, more farmland water conservancy projects, and greater economic development, the promoting effect of agricultural water rights trading on the intensive and economical use of water resources is more significant.DiscussionThis study provides evidence of the impact of China’s agricultural water rights trading policy and offers new ideas and experiences to improve China’s water resource utilization efficiency. The contributions of this work are mainly threefold. First, this paper clearly distinguishes the intensive conservation of water resources, presents an evaluation index system, and complements the existing literature on measuring the intensive and economical utilization of water resources. Second, the path of agricultural water rights trading is analyzed to improve the level of intensive and economical utilization of water resources, thus enriching related research on transmission mechanisms. Third, the heterogeneity of the influence of different regional conditions on the level of intensive and economical utilization of water resources is discussed to improve relevant policies.

The current status and improvement directions of legal rules regarding Chinese national gene banks for farm animal genetic resources.

A gene bank for farm animal genetic resources (FAGR) is an important facility for the diversity conservation of FAGR. The primary purpose of gene banks for FAGR is the reconstruction of those breeds. With the advent of the post-genomic era, gene banks for FAGR have increasingly become an infrastructure for the support of livestock research and animal breeding, and a platform for international research collaboration. China is one of the richest countries in the world in terms of FAGR. Chinese National Gene Banks for FAGR (CNGBFs) have an important legal status and play an important function in the Chinese FAGR protection system. In this paper, we reviewed the current situation of CNGBFs construction, and systematically collected and analyzed legal rules related to CNGBFs. As results, those legal rules were categorized into two types: (a) organization and management, (b) activities. We summarized problems existing in the current legal rules for CNGBFs from three levels: institution, practical operation, and digital development. Improvement directions of legal rules regarding CNGBFs are proposed. They include clarifying the utilization function of CNGBFs and the ownership of FAGR in CNGBFs. Moreover, improving the mechanism of administrative management, rules on domestic and international access and benefit-sharing, and the system of digital sequence information management are also suggested. The improvements in those legal rules will contribute to the appropriate utilization of Chinese FAGR and international collaborations.

Commensurability and Characterization of Normalization Conditions for Cost-of-Living Comparisons under Time-Varying CES Preferences

We show that the normalization conditions for a CES utility function with time-varying preferences (Redding and Weinstein, 2020) must be of weighted-geometric mean form to ensure commensurability of price indexes, and numerically illustrate failure of additive normalization to satisfy commensurability.

Alternative Tree Species for Sustainable Forest Management in the Brazilian Amazon

The scarcity of hardwoods from tropical forests makes the search for alternative species necessary for commercialization. This study aimed to establish groups of timber species from the Amazon Forest with potential for logging purposes through the assessment of their physical-mechanical properties, aiming to identify alternative species that can meet the market demands. We utilized data from the Forest Products Laboratory (LPF) (containing information on basic density and other wood mechanical properties) and the Timberflow platform, as well. We applied a multivariate cluster analysis technique with the aim of grouping species based on the technological characteristics of their wood and evaluating similarity among them to obtain homogeneous groups in terms of economic potential and utilization. The results indicated four homogeneous groups: Cluster 1 (40.72% of species, basic density-db: 690 kg m−3), Cluster 2 (13.92%, db: 260 and 520 kg m−3), Cluster 3 (27.32%, db: 550 and 830 kg m−3), and Cluster 4 (18.04%, db: 830 kg m−3). Most of the 20 listed species are classified as more commercially viable (70%), with high wood density. Species identified as alternatives include Dialium guianense and Zollernia paraensis for Dipteryx odorata, Terminalia argentea for Dinizia excelsa, Terminalia amazonia and Buchenavia grandis for Goupia glabra, and Protium altissimum and Maclura tinctoria for Hymenaea courbaril. The analysis highlighted the overexploitation of a restricted group of species and the need to find alternatives to ensure the sustainability of forest management. This study contributed to identifying species that can serve as alternatives to commercial ones, promoting a more balanced and sustainable forest management.

Information Freshness in Service Queues With Strategic Customers

We examine a scenario in which a service provider updates its queue length information to customers at a specific frequency. Customers decide whether to join the queue based on the most recent update. Our objective is to determine the optimal updating frequency that benefits both the service provider and customers. Using a two-dimensional continuous-time Markov process, we model the actual and announced queue length processes. By proving the identical distributions of these two processes under a Poisson updating scheme, we derive closed-form solutions for customers’ utility functions. Our findings demonstrate that customers adopt a generalized mixed-threshold strategy at equilibrium, and their certainty about whether to join the queue or balk does not always increase with fresher information. Furthermore, we reveal that due to customers’ different sensitivities to information freshness, system performance metrics such as throughput and total customer utility exhibit non-monotonic behavior in response to the updating frequency. Consequently, providing fresher information does not necessarily lead to improved system performance. To address this, we propose algorithms to determine the optimal updating frequency for each system performance metric and identify the conditions under which different updating frequencies are optimal. We demonstrate that any positive updating frequency can achieve customer utility no worse than the no-information system. Additionally, in systems with high customer arrival rates, updating with a positive updating frequency can improve throughput. Furthermore, we prove conditions under which a positive and finite updating frequency can yield higher throughput and total customer utility compared to real-time information systems.

A Solution to Dynamic Weapon Assignment Problem Based on Game Theory for Naval Platforms

Weapon target assignment is a critical challenge in military contexts. Traditionally, commanding officers manually decide weapon assignments, but the problem’s complexity has grown over time. To address this, automated systems have been introduced. These systems fall into two categories, which are static (time-independent) and dynamic (considering changes over time). Static systems solve the problem for a single time step without considering temporal changes. Dynamic systems incorporate time as a variable, adapting to evolving scenarios. Two main approaches exist, which are asset-based and target-based. Asset-based approach maximizes the survival probability of assets, which is our focus in this study. We propose a solution using game theory that spans the entire area and all time frames. We employ game theory, treating continuous functions of time as utility functions for vessels. Continuous probability-to-kill values for weapons are defined across the area. Threat trajectories yield continuous kill probabilities for the weapons, translating to vessel utility. To avoid inefficiencies, we align individual vessel utility with global utility. The Nash Equilibrium provides the optimal weapon assignment strategy. Our study uses a naval environment for analysis. In summary, our research leverages game theory to dynamically assign weapons to naval vessels, aiming to maximize asset survival.

Determinants of Willingness to Pay for Community-Based Health Insurance in Ethiopia: National Household Survey

The existing evidence on households' participation in Ethiopia's community-based health insurance (CBHI) scheme is limited, lacks representativeness, and lacks disaggregation. Thus, this study aims to assess households' willingness to pay (WTP) for CBHI membership and identify the factors that influence their decision to enroll. From February to May 2020, a nationwide cross-sectional household survey was conducted involving CBHI member and non-member households. The primary theories that informed this study were utility theory, social capital theory, and the health belief model. Sample included 5,976 households from 166 EAs—118 in CBHI regions and 48 in non-CBHI regions. A two-stage stratified cluster sampling used to select enumeration areas (EAs) and households from within these areas. The maximum amount of money at which participants responded "yes" in the bidding game exercise was used to measure WTP. Data were analyzed using STATA Version 16. Household expenditure was adjusted for key factors. Analyses explored WTP for CBHI by region, membership, and livelihood, with WTP based on mean maximum values. Linear regression identified influencing factors. The findings reveal that 30.9% of participants were active CBHI members. The average WTP increased with each additional family member, both in rural (AMD = 9.3 [6.8, 11.9]) and urban areas (AMD = 7.2 [1.0, 13.4]). In urban areas, WTP was also positively associated with the ability to pay (ATP) for CBHI (AMD = 64.1 [6.3, 121.8]). Male respondents and those who were literate in urban areas had higher mean WTP values of AMD = 39.8 [13.1, 66.4] and AMD = 56.8 [26.1, 87.4], respectively. Additionally, holding leadership positions in health and women's development initiatives positively influenced WTP. The study found that 30.9% of participants were active CBHI members. WTP for CBHI was higher among larger families. In urban areas, WTP was positively associated with the ability to pay (ATP) for CBHI, particularly among male and literate respondents. Community leadership roles in health and women's development also positively influenced WTP. To improve the CBHI scheme, adjustments should be made to premiums based on households' ability to pay. Community engagement should be strengthened, service quality enhanced, regional disparities addressed, and performance regularly monitored.

Rational Individual and Managerial Decision Model: A Critical Review of the Standard Rationality Hypothesis

Classical economic analysis is based on the hypothesis of perfect rationality attributed to economic agents. Their behavior is considered, according to this analysis, in a narrow sense represented by Homo-oeconomicus. The latter is a calculating agent in the true meaning of the term, who calculates to optimize his choice and maximize a utility function. However, in a decision-making process, the economic agent finds himself interacting with a complex and uncertain environment. To decide, he adapts his choice behavior to an end, considering the constraints of this environment. He does not always seek the optimal solution, but he can be satisfied with the solution that he considers satisfactory. This solution highlights the role of deliberation, which considers the norms and customs of society, as well as environmental constraints. This work revisits rationality by examining the assumptions underlying decision-making behaviors. This analysis considers the real motivations of the decision-maker and their implications in his decision-making process, both in the individual context and in that of the organization. It studies the sources of the limitation of rationality and seeks to know why the decision-maker abandons optimization and is interested in satisfactory solutions in his choice. The results of this analysis recommend that bounded rational behavior can still be considered when motivations are considered. They advocate that their understanding is fundamental to refound the economic behavior of choice on hypotheses, which correspond well to the social and economic context of the decision.

Daniel Kahneman and the concept of the true self

Abstract Kahneman and Tversky's ‘Prospect Theory’ paper famously demolishes expected utility theory as a predictive device. However, it presents deviations from that theory as ‘normatively unacceptable’ and argues that decision-makers would normally correct them when possible. In a later paper, Kahneman rejects a similar argument (the ‘discovered preference’ hypothesis) advanced by Plott. Later still, Kahneman endorses Sunstein and Thaler's ‘libertarian paternalism’, which aims to help people avoid deviating from their ‘true’ preferences. I report an email correspondence between Kahneman and me in which we debated whether his position on libertarian paternalism was consistent with his critique of Plott's hypothesis.

Incomplete Objectives in Decision Making: How Omitting Objectives Affects Identifying the Most Promising Alternative

Research has shown that decision-makers omit a significant number of their objectives when making a decision. This study examines the consequences of incomplete objectives on decision making, i.e., how does omitting objectives affect identifying the most promising alternative? We investigate this question using a dataset of 945 observed decisions. These decisions were developed by students using the decision-skills and training tool entscheidungsnavi.com. The tool guides students in a step-by-step process based on value-focused thinking, multi-attribute utility theory, and debiasing methods. Results show that omitting objectives significantly reduces the chances of identifying the most promising alternative. Hence, neglecting only 20% of the objectives is sufficient to mislead more than one in four decisions. We have found three factors that influence this risk of misidentifying the most promising alternative: (1) the weight of the omitted objectives; (2) the consensus on the best alternative across all objectives; and (3) the consensus on the ranking of all alternatives across all objectives.

Robust concave utility maximization over chance constraints

This paper first studies an expected utility problem with chance constraints and incomplete information on a decision maker’s utility function. The model maximizes the worst-case expected utility of random outcome over a set of concave functions within a novel ambiguity set, while the underlying probability distribution is known with the assumption of a discretization of possible realizations for the uncertainty parameter. To obtain computationally tractable formulations, we employ a discretization approach to derive a max–min chance-constrained approximation of this problem. This approximation is further reformulated as a mixed-integer program. We show that the discrete approximation converges to the true counterpart under mild assumptions. We also present a row generation algorithm for optimizing the max–min program. A computational study for a bin-packing problem and a multi-item newsvendor problem is conducted to demonstrate the benefit of the proposed framework and the computational efficiency of our algorithm. We find that the row generation algorithm can significantly reduce the computational time, and our robust policy could achieve a better out-of-sample performance when compared with the non-robust policy and the one without the chance constraints.