Utility Research Articles

Edge computing collaborative offloading strategy for space‐air‐ground integrated networks

SummaryDue to geographical factors, it is impossible to build large‐scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay‐sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space‐air‐ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay‐sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space‐ground integrated network and insufficient energy of local user equipment, firstly, a satellite‐UAV cluster‐ground three‐layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non‐cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO‐SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO‐SG reduces the total system latency during task offloading by about 13 and the energy consumption of the edge server by about 35.

The COVID-19 pandemic, lifetime consumption, and endogenous discounting under a Stone–Geary-type preference: given the passage of three years

ABSTRACT In this paper, we consider the impact of the COVID-19 pandemic on medium- to long-term lifetime consumption profiles. Previous studies have focused on the attributes of health damage that are the primary determinant of people’s time preference rate. This approach has also been applied to research on the impacts of the COVID-19 pandemic on individual consumption profiles. However, such a framework has one drawback: consumption levels among the aged were estimated to be much lower than they actually were. We address this drawback and further brush up on the existing results by adopting a Stone–Geary-type utility function. Despite this, the key findings based on the analysis are maintained. This paper attempts to make new estimates that also consider the experience of the pandemic over 3 years. Taken together, the negative impact on economic welfare is large in scale and may cause long-term problems in terms of social policy that cannot be overlooked.

A performance comparison of utility functions for game theory based weapon-target assignment

The weapon-target assignment problem has been considered as an essential issue for military applications to provide a protection for defended assets. The goal of a typical weapon-target assignment problem is to maximize the expected survivability of the valuable assets. In this study, defense of naval vessels that encounter aerial targets is considered. The vessels are assumed to have different types of weapons having various firepower and cost as well as the incoming targets may have different attack capabilities. In a typical scenario, in addition to protecting assets, it is also desirable to minimize the cost of weapons. Therefore, an asset-based static weapon-target assignment problem is considered in order to both maximize the expected survivability of the assets and minimize the weapon budget. Thus, a co-operative game theory based solution is proposed which relates the utilities of the individuals to the global utility and reach the Nash equilibrium.

An Agent-based Approach to Study Spatial Structure Effects on Estimated Distance Deterrence in Commuting

AbstractWe provide an experimental design to study how estimates of the distance deterrence parameter in a standard doubly-constrained gravity model respond to different patterns of spatial structure. The design is defined as an agent-based simulation framework that simultaneously considers the response of commuting and migration to changes in the spatial structure, accounting for labor and housing market issues. The agents are equipped with a utility function and are expected to maximize their utilities corresponding to their disposable income net of commuting costs and house value. We study how the estimates of the distance deterrence parameter respond to where the central business district is located, to the clustering of local and basic sector jobs, to changes in the road transportation network, and to the compactness in the system of towns in the geography.

ÜÇ SӘRBӘSTLİK DӘRӘCӘLİ ROBOT MANİPULYATOR SİSTEMİ ÜÇÜN OPTİMAL PİD TӘNZİMLӘYİCİSİNİN MODELLӘŞDİRİLMӘSİ

. In this research work, the application and analysis of the optimization methodology based on the dynamic flying objects algorithm to design the robot manipulator based on the optimal PID controller is considered. Based on the dynamic analysis of robot manipulators, the interaction between the torques created by the actuators and the position and speed of the manipulator was investigated. The optimal PID control law obtained from the proposed algorithm is applied to the robot system. The proposed controller optimizes the trajectory of the robot's end-effector for input during variable time and hardens the robot against perturbing effects. To achieve a highly adaptive optimization process, the correct formulation of the utility function leads to optimal solutions. Three different objective functions were used in the process of optimization of control parameters in the robot system and their results were compared. In this work, a new debugging methodology for trajectory tracking in robotic manipulator systems is presented. Optimum benefit is obtained by using the proposed PID regulation law. The obtained results are satisfactory and competitive. It has been determined that the problems of the smooth control system in the places where the robot system is highly non-linear are regulated by classical methods.

Determining the most useful renewable energy alternative for Turkey by combining MAUT and TAOV methods

This study focuses on various Multi-Criteria Decision Making (MCDM) methods developed in the academic literature to facilitate the decision-making process. These methods are primarily aimed at establishing a ranking of alternatives based on specific criteria, with the ultimate goal of determining the most optimal alternative. In this work, we propose a novel integrated MCDM approach that combines the Total Area Based on Orthogonal Vectors (TAOV) method with the Multi-Attribute Utility Theory (MAUT) method. Within this framework, criteria weights are determined using the MAUT method, while the TAOV method is employed to ascertain the ranking of alternatives. The primary objective of this approach is to identify the most suitable renewable energy resource for the context of Turkey. Based on a comprehensive review of relevant literature regarding the evaluation of renewable energy sources in Turkey, a set of critical criteria influencing the decision-making process is delineated. These criteria encompass a range of factors including efficiency, construction duration, cost, government incentives, economic lifespan, external dependence, employment opportunities, social acceptance, spatial requirements, and environmental considerations such as greenhouse gas emissions. The outcomes of this study reveal that, according to the proposed approach, the most beneficial renewable energy resources for Turkey are ranked as follows: hydroelectric, wind, solar, biomass, and geothermal energy sources, respectively.

A utility-based three-way group decision consensus model with overlapping subgroups

This paper introduces a relative utility function-based consensus method for three-way group decision (TWGD) in a social network scenario where overlapping subgroups and non-overlapping subgroups co-exist. Firstly, we construct a three-way decision (TWD) model rooted in utility theory, which proposes a novel objective calculation method for the relative utility functions based on evaluation values. The relative utility matrices of decision makers (DMs) are derived on this basis. Secondly, a utility and trust-driven overlapping clustering algorithm is designed to maximize the overall trust level and the difference between subgroup clustering centers, aiming to divide the group into several manageable subgroups. In addition, we identify contrarian DMs, triggering a delegation-exit mechanism, and determine the weights of subgroups and DMs. Thirdly, a three-way group consensus decision model is formulated to provide targeted adjustment strategies for different types of DMs. To improve group consensus, we address the influence of key DMs on the opinions of overlapping subgroups with a minimum adjustment consensus model. Finally, we demonstrate the feasibility and scientificity of the proposed method through the illustrative example, sensitivity analysis, and comparative analysis.

THE METHOD OF RANKING EFFECTIVE PROJECT SOLUTIONS IN CONDITIONS OF INCOMPLETE CERTAINTY

The subject of research in the article is the process of ranking options in support systems for project decision-making under conditions of incomplete certainty. The goal of the work is to increase the efficiency of technologies for automated design of complex systems due to the development of a combined method of ranking effective options for building objects in conditions of incomplete certainty of input data. The following tasks are solved in the article: analysis of the current state of the problem of ranking options in support systems for project decision-making; decomposition of problems of system optimization of complex design objects and support of project decision-making; development of a variant ranking method that combines the procedures of lexicographic optimization and cardinal ordering in conditions of incomplete certainty of input data. The following methods are used: systems theory, utility theory, optimization, operations research, interval and fuzzy mathematics. Results. According to the results of the analysis of the problem of supporting project decision-making, the existence of the problem of correctly reducing subsets of effective options for ranking, taking into account factors that are difficult to formalize and the experience of the decision-maker (DM), was established. Decomposition of the problems of system optimization of complex design objects and support for project decision-making was carried out. For the case of ordinalistic presentation of preferences between local criteria, an estimate of the size of the rational reduction of subsets of optimal and suboptimal options for each of the indicators is proposed. Its use allows for one approach to obtain a subset of effective variants of a given capacity for analysis and final selection of the DM. A method of transforming the ordinalistic presentation of preferences between local criteria to their quantitative presentation in the form of weighting coefficients is proposed. Conclusions. The developed methods expand the methodological foundations of the automation of processes supporting the adoption of multi-criteria project decisions. They make it possible to correctly reduce the set of effective alternatives in conditions of incomplete certainty of the input data for the final choice, taking into account factors that are difficult to formalize, knowledge and experience of ODA. The practical use of the obtained results will allow to reduce the time and capacity complexity of the procedures for supporting project decision-making, and due to the use of the technology of selection of subsets of effective options with intervally specified characteristics - to guarantee the quality of project decisions and to provide a more complete assessment of them.

Shared Capitalism and Corporate Sustainability: Broad-Based Employee Share Ownership, CEO Ownership, and Corporate Environmental Performance

This research proposes that broad-based employee share ownership (ESO) affects corporate environmental performance (CEP). Drawing upon corporate governance literature, social exchange theory, and stakeholder utility theory, we propose that employees as owners adopt more favorable attitudes toward beneficial outcomes for CEP, and that the broad-based impact of ESO overwhelms the impact of CEO ownership. Also, we propose that these relationships are contingent upon trade union presence as a form of worker voice that amplifies the ESO influence on CEP. The empirical results from U.S. publicly traded companies from 2009 to 2016 indicate that broad-based ESO is linked to positive CEP by companies, and provides some support for all proposed hypotheses. This study provides scholarly and practical implications for the effects of shared capitalist modes of ownership and employee engagement on corporate sustainability.

FORECASTING TECHNOLOGY ADOPTION BEHAVIOUR AND AGRICULTURAL PRODUCTION GROWTH

In this paper, we proposed a simple mathematical model for farmers in the Ranchi district to adopt and implement agricultural technologies. We used the Fisher-Pry model, which is a very effective model in the study of technological adoption. The model's output is a Sigmoid curve, or S-shaped curve, that develops exponentially at first, then approximately linearly, and lastly asymptotically. We have studied the models of technology spread using a basic case study and brought in a real-world situation. Mathematically, it is possible to predict that adoption will rise to the number m/2 when 50% of the farmers have adopted the technology, after which it will slow down. In a real-life situation, the point of inflection might occur before or after m/2.We also collected data on some of the food crops, and net irrigated area of Ranchi district from 2011 to 2020, and evaluated it graphically/mathematically. We examined the advantages of technology in agriculture using utility functions, by employing basic scenarios and selecting between different technologies for adoption

Optimal Income Taxation

This article explores subjects in optimal income taxation characterized by recent research interest, practical importance in light of concerns about inequality, potential for misunderstanding, and prospects for advancement. Throughout the analysis, paths for further investigation are highlighted. Areas of focus include multidimensional abilities and endogenous wages; asymmetric information and the income of founders; production and consumption externalities from labor effort; market power and rents; behavioral phenomena relating to perceptions of the income tax schedule, myopic labor supply, and the interactions of savings, savings policies, and labor supply; optimal income transfers; the relationship between optimal income taxation and the use of other instruments; and issues relating to the social welfare function and utility functions, including non-welfarist objectives, welfare weights, heterogeneous preferences, and taxation of the family. (JEL D63, D82, D91, H21, H24, H53, J22)

Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks

Mobile edge computing (MEC) enables computation intensive applications in the Internet of Vehicles (IoV) to no longer be limited by device resources. However, the lack of an effective task scheduling strategy will seriously affect users’ quality of experience (QoE). In this paper, a task type-based task offloading and resource allocation strategy is proposed to reduce delay and energy consumption during task execution. First, we establish communication, computing, and system cost models based on task offloading schemes, and model the joint optimization problem of task offloading and resource allocation as a Markov decision process. The utility function is obtained based on the task completion rate and the system cost. Second, an algorithm framework based on multi-agent deep deterministic policy gradient (MADDPG) is designed to solve the difficulty that traditional single-agent reinforcement learning algorithms are difficult to converge in a dynamic environment. In distributed scenarios, the proposed framework can also reduce system costs while handling more tasks. Finally, federated learning is introduced in the training process to reduce the impact of non-IID data while protecting privacy. Simulation results show that the proposed algorithm can effectively improve system processing efficiency and reduce device energy consumption compared to the popular reinforcement learning algorithms.

Revealed preference and revealed preference cycles: A survey

Afriat’s Theorem (1967) states that a dataset can be thought of as being generated by a consumer maximizing a continuous and increasing utility function if and only if it is free of revealed preference cycles containing a strict relation. The latter property is often known by its acronym, GARP (for generalized axiom of revealed preference). This paper surveys extensions and applications of Afriat’s seminal result. We focus on those results where the consistency of a dataset with the maximization of a utility function satisfying some property can be characterized by a suitably modified version of GARP.

Recycling channel strategy in the presence of free-riding in the carbon neutrality era

Recycling has become a critical response to the goals of reaching a carbon peak and achieving carbon neutrality. This study explores the effects of consumer free-riding behavior, the quality of recycling services, and the costs of channel transfers on the profitability of manufacturers and retailers in a dual-channel closed-loop supply chain (CLSC), focusing on the importance of recycling practices for carbon neutrality. Using consumer utility theory and a Stackelberg game model, we analyze the dynamics among these factors. Our results show that: (i) Consumer free-riding behavior slightly increases market demand and recycling volumes, enhancing profitability for both manufacturers and retailers in the dual-channel CLSC. (ii) The quality of recycling services and the transfer costs associated with retailer free-riding behavior jointly influence the profits of manufacturers and retailers. (iii) The effect of free-riding behavior on recycling services affects both forward sales and reverse recycling channels equally. This study provides valuable insights for decision-making in sustainable development practices in the recycling sector, significantly contributing to the goal of achieving carbon neutrality.

Beyond financial wealth: The experienced utility of collectibles

We argue that the utility of specific assets, in our case collectibles, is not only derived from the financial outcome, but also from the conditions that prevail until a financial outcome may be realized. Therefore, we derive a multi-attribute utility function that measures financial returns — using a mean-variance utility function — on the one hand, and non-financial returns — using an experienced utility function — on the other. We then reveal the trade-off between financial and non-financial utility by analyzing 363 owners of collectibles. We divide the owners into the group of collectors and the group of investors, based on their self-reported motivation. Our results suggest that collectors receive almost no utility from financial returns, but rather from experience. The opposite is the case for investors. Our findings help to explain the reported financial underperformance of collectibles and suggest to adjust existing models of utility.

Cheap Talk with Transparent and Monotone Motives from a Seller to an Informed Buyer

We develop a model of cheap talk with transparent and monotone motives from a seller to an informed buyer. By transparent and monotone motives, we mean that the seller’s preference does not depend on the state of the world and is increasing in the choice(s) of the buyer regardless of the state of the world. We first show that if the buyer is completely uninformed, only the babbling equilibrium exists. Then, we obtain our main result that even if the buyer has the slightest information, full revelation can be supported by using the crosschecking strategy of the buyer if and only if the seller has a CARA (constant absolute risk aversion) utility function unless the buyer has too much information. In this equilibrium, the buyer can punish the seller who sends a message far above the buyer’s information by ignoring the seller’s message. Paradoxically, no information and too much information of the buyer both eliminate the fully revealing equilibrium with the crosschecking strategy. We also obtain a counterintuitive result that the seller prefers a more informed buyer than a less informed buyer.

Effects of dietary fermented Saccharomyces cerevisiae extract (Hilyses) supplementation on growth, hematology, immunity, antioxidants, and intestinal health in Nile tilapia

This study investigated the effects of dietary supplementation with the product Hilyses on growth performance, feed utilization, nutrient composition, hematological parameters, serum biochemistry, immune function, antioxidant status, and digestive enzyme activity in juvenile Nile tilapia (Oreochromis niloticus, initial body weight 4.24 ± 0.01 g). The fish were fed diets supplemented with Hilyses at concentrations of 0, 1, 2, or 3 g/kg for a period of 8 weeks. The results showed that supplementation with Hilyses at levels up to 2 g/kg diet significantly improved final body weight, weight gain, specific growth rate, feed efficiency ratio, protein efficiency ratio, apparent protein utilization, and energy utilization compared to the control diet without Hilyses. Carcass crude protein content and moisture were significantly higher in Hilyses-fed groups, while crude lipid content decreased at the 3 g/kg supplementation level. Hilyses supplementation enhanced various hematological parameters, including increased red blood cell count, total leukocyte count, hemoglobin concentration, hematocrit, and mean corpuscular volume. Serum biochemistry and immune function markers like total protein, albumin, complement component C3, IgM, and IgG were significantly elevated in the 2 and 3 g/kg Hilyses groups. Antioxidant enzyme activities (catalase, glutathione peroxidase, total superoxide dismutase) were enhanced, and lipid peroxidation was reduced, in the 2 g/kg Hilyses group. Digestive enzyme activities, particularly protease and lipase, were also improved with Hilyses supplementation. Histological examination showed reduced lipid deposition in the liver and increased branching of intestinal villi at the 2 g/kg Hilyses level. Overall, these results indicated that dietary Hilyses supplementation at 2 g/kg diet optimizes growth, feed utilization, nutrient composition, hematology, immunity, antioxidant status, and digestive function in juvenile Nile tilapia.

Simulated maximum likelihood estimation of the sequential search model

AbstractWe propose a new approach to simulate the likelihood of the sequential search model. By allowing search costs to be heterogeneous across consumers and products, we directly compute the joint probability of the search and purchase decisions when consumers are searching for the idiosyncratic preference shocks in their utility functions. Under the assumptions of Weitzman’s sequential search algorithm, the proposed procedure recursively makes random draws for each quantity that requires numerical integration while enforcing the conditions stipulated by the algorithm. In an extensive simulation study, we compare the proposed method with existing likelihood simulators that have recently been used to estimate the sequential search model. The proposed method attributes the uncertainty in the search order to the consumer-product-level distribution of search costs and the uncertainty in the purchase decision to the distribution of match values across consumers and products. This results in more precise estimation and an improvement in prediction accuracy. We also show that the proposed method allows for different assumptions on the search cost distribution and that it recovers consumers’ relative preferences even if the utility function and/or the search cost distribution is mis-specified. We then apply our approach to online search data from Expedia for field-data validation. From a substantive perspective, we find that search costs and “position” effects affect products in the lower part of the product listing page more than they do those in the upper part of the page.

Integrated nonurban-urban flood management using multi-objective optimization of LIDs and detention dams based on game theory approach

Flood events in urban and non-urban areas have long-term devastating impacts on communities resulting in casualties and economic damages. Different interconnected concepts of flood management lead decision-makers to broaden their focus from traditional points of view to transcend the disciplinary boundaries of hydrological modeling. So, evaluating interconnected aspects of flood control requires configuring an Integrated Flood Management (IFM) system. In this research, a novel comprehensive framework is proposed to control floods in urban and non-urban areas using Low-Impact Development (LID) practices and detention dams, simultaneously. In the first step, urban and non-urban areas of the Darakeh catchment in Tehran, Iran, as the study area, are modeled using Storm Water Management Model (SWMM). Next, by coupling the SWMM with Pareto Envelope-based Selection Algorithm II (PESA-II), optimal management scenarios are generated considering four hydrological-economic objectives. Each management scenario specifies the optimal sites and dimensions of detention dams and the optimal location and area of LIDs in non-urban and urban areas, respectively. Indeed, the innovative IFM framework provides a multi-dimensional trade-off between upstream and downstream flood protection. Then, interactions among the main stakeholders with conflicting interests are incorporated into utility functions. Ultimately, using COmplex PRoportional ASsessment (COPRAS) and Nash bargaining methods the most capable management scenarios are selected based on stakeholders' preferences. Results showed that the IFM framework can be significantly effective in flood severity attenuation. Based on the findings, proposed method warrants peak flow reduction up to 60.04 and 53.50 percent at the inlet and outlet of the urban area. Besides, the Nash bargaining approach tends to select a scenario with a higher peak flow reduction compared to the COPRAS method. Also, results signify that each stakeholder's relative importance and preferences can remarkably impact the decision-making process. In addition to resolving conflicts arising from implementing flood control measures, this framework can incorporate divergent aspects of flood management into a reliable system.

Association of Inpatient Occupational Therapy Utilization With Reduced Risk for Psychiatric Readmission Among Veterans.

The authors sought to investigate whether utilization of inpatient occupational therapy (OT) was associated with reduced risk for 30-day psychiatric readmission in the Veterans Health Administration (VHA). The authors conducted a secondary analysis of VHA medical record data for veterans who received inpatient psychiatric care from 2015 to 2020 (N=176,889). Mixed-effects logistic regression was used to model psychiatric readmission within 30 days of discharge (yes or no) as a function of inpatient psychiatric OT utilization (none, one, two, three, or four or more encounters) and other care utilization (e.g., previous psychiatric hospitalization), as well as clinical (e.g., primary diagnosis), sociodemographic (e.g., race-ethnicity), and facility (e.g., complexity) characteristics. Sensitivity analyses were conducted to evaluate the robustness of findings (e.g., stratification by discharge disposition). Relatively few veterans received inpatient psychiatric OT (26.2%), and 8.4% were readmitted within 30 days. Compared with veterans who did not receive inpatient psychiatric OT, those with one (OR=0.76), two (OR=0.64), three (OR=0.67), or four or more encounters (OR=0.64) were significantly (p<0.001) less likely to be readmitted within 30 days. These findings were consistent across all sensitivity analyses. Veterans who received inpatient OT services were less likely to experience psychiatric readmission. A clear dose-response relationship between inpatient psychiatric OT and readmission risk was not identified. These findings suggest that OT services may facilitate high-value inpatient psychiatric care in the VHA by preventing readmissions that stymie recovery and incur high costs. Future research may establish the causality of this relationship, informing policy regarding increased access to inpatient psychiatric OT.