Optimal Social Welfare Research Articles

Value‐Added Service Strategies for Retailers Oriented to Uncertain Demand

ABSTRACTWhereas retailers pursue providing value‐added services (VAS) to capture additional markets and maximize profits, market demand uncertainty decreases retailers' incentives to provide VAS. Therefore, we analyze retailers' VAS provision strategies in the market demand uncertainty. We then investigate the VAS provision strategies and explore the impact of market demand uncertainty, competitive effects, and market loss effects on retailers' VAS provision strategies. We prove that when the market loss effect is high, the retailers provide VAS that even face lower consumer preferences. When the market loss effect is low, neither retailer offers VAS, even when consumer preferences are high. When the market loss effect and consumer preferences match each other, only one retailer offers VAS as an equilibrium strategy. The competition effect and market demand uncertainty exacerbate this result. Notably, the combination of VAS cost, competition effect, and market loss effect puts the retailer in a prisoner's dilemma under market demand uncertainty. Interestingly, the competition effect mitigates situations where retailers suffer from the prisoners' dilemma. Besides, the competition effect and market demand uncertainty exacerbate the difficulties of extracting optimal social welfare in different scenarios.

Strategic Behavior and Social Welfare Optimization in a Batch-service Queueing System with Threshold Policy

Strategic Behavior and Social Welfare Optimization in a Batch-service Queueing System with Threshold Policy

Resolving social dilemmas with minimal reward transfer

Social dilemmas present a significant challenge in multi-agent cooperation because individuals are incentivised to behave in ways that undermine socially optimal outcomes. Consequently, self-interested agents often avoid collective behaviour. In response, we formalise social dilemmas and introduce a novel metric, the general self-interest level, to quantify the disparity between individual and group rationality in such scenarios. This metric represents the maximum proportion of their individual rewards that agents can retain while ensuring that a social welfare optimum becomes a dominant strategy. Our approach diverges from traditional concepts of altruism, instead focusing on strategic reward redistribution. By transferring rewards among agents in a manner that aligns individual and group incentives, rational agents will maximise collective welfare while pursuing their own interests. We provide an algorithm to compute efficient transfer structures for an arbitrary number of agents, and introduce novel multi-player social dilemma games to illustrate the effectiveness of our method. This work provides both a descriptive tool for analysing social dilemmas and a prescriptive solution for resolving them via efficient reward transfer contracts. Applications include mechanism design, where we can assess the impact on collaborative behaviour of modifications to models of environments.

Optimizing Over Serial Dictatorships

Motivated by the success of the serial dictatorship mechanism in social choice settings, we explore its usefulness in tackling various combinatorial optimization problems. We do so by considering an abstract model, in which a set of agents are asked to act in a particular ordering, called the action sequence. Each agent acts in a way that gives her the maximum possible value, given the actions of the agents who preceded her in the action sequence. Our goal is to compute action sequences that yield approximately optimal total value to the agents (a.k.a., social welfare). We assume query access to the value vi(S)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$v_i(S)$$\\end{document} that the agent i gets when she acts after the agents in the ordered set S. We establish tight bounds on the social welfare that can be achieved using polynomially many queries. Even though these bounds show a marginally sublinear approximation of optimal social welfare in general, excellent approximations can be obtained when the valuations stem from an underlying combinatorial domain. Indicatively, when the valuations are defined using bipartite matchings, arborescences in directed graphs, and satisfiability of Boolean expressions, simple query-efficient algorithms yield 2-approximations. We discuss issues related to truthfulness and show how some of our algorithms can be implemented truthfully using VCG-like payments. Finally, we introduce and study the price of serial dictatorship, a notion that provides an optimistic measure of the quality of combinatorial optimization solutions generated by action sequences.

Interaction between product strategy selection and the carbon quota allocation policy

The interaction between the government’s carbon reduction policy and a firm’s product strategy has not been well studied in the literature. This paper considers the government’s two different carbon quota allocation policies for the cap-and-trade scheme and the firm’s two product strategies and investigates the interaction between them by establishing a theoretical model and deriving the optimal decisions. This paper first examines the firms’ selection of low-carbon products or ordinary product strategies under the government’s two carbon quota allocation policies and then studies the government’s optimal carbon quota allocation policy for overall social welfare, which is based on the firm’s two product strategies. Our analysis reveals that (i) when the government allocates carbon quotas aimed at reducing the firm’s total carbon emissions, the firm will choose the low-carbon product strategy. When the government allocates a carbon quota aimed at optimal total social welfare, the firm’s decision depends on the impact of total carbon emissions. (ii) To achieve optimal social welfare, the government will formulate different carbon quota allocation policies on the basis of firms’ different product strategies.

Decentralized energy trading in microgrids: a blockchain-integrated model for efficient power flow with social welfare optimization

Decentralized energy trading in microgrids: a blockchain-integrated model for efficient power flow with social welfare optimization

Equilibrium balking behavior of customers and regulation measures in a multi-server queue with threshold policy

ABSTRACT The equilibrium balking behavior of customers and some effective regulation measures in a multi-server queueing system with a threshold policy are mostly investigated in this paper. We consider four information levels: entirely visible, nearly visible, nearly invisible and entirely invisible. For each case, we compare the equilibrium and socially optimal balking strategies of customers, formulate pricing strategy and analyze the revenue of the system manager. It is found that no matter what information level, there always exist the optimal thresholds that maximize the optimal social welfare and the system manager’s revenue, respectively. Moreover, how much information the customers know has influence on the optimal social welfare. Such findings can provide guidance for the social planner to formulate pricing strategies, make appropriate threshold policies and decide what information should be disclosed to customers to improve the optimal social welfare.

Dynamic, fair, and efficient routing for cooperative autonomous vehicle fleets

This paper addresses challenges in agricultural cooperative autonomous fleet routing through the proposition, modeling, and resolution of the Dynamic Vehicle Routing Problem with Fair Profits and Time Windows (DVRP-FPTW). The aim is to dynamically optimize routes for a vehicle fleet serving tasks within assigned time windows, emphasizing fair and efficient solutions. Our DVRP-FPTW accommodates unforeseen events like task modifications or vehicle breakdowns, ensuring adherence to task demand, vehicle capacities, and autonomies. The proposed model incorporates mandatory and optional tasks, including optional ones in operational vehicle routes if not compromising the vehicles’ profits. Including asynchronous and distributed column generation heuristics, the proposed Multi-Agent-based architecture DIMASA for the DVRP-FPTW dynamically adapts to unforeseen events. Systematic Egalitarian social welfare optimization is used to iteratively maximize the profit of the least profitable vehicle, prioritizing fairness across the fleet in light of unforeseen events. This improves upon existing dynamic and multi-period VRP models that rely on prior knowledge of demand changes. Our approach allows vehicle agents to maintain privacy while sharing minimal local data with a fleet coordinator agent. We propose publicly available benchmark instances for both static and dynamic VRP-FPTW. Simulation results demonstrate the effectiveness of our DVRP-FPTW model and our multi-agent system solution approach in coordinating large, dynamically evolving cooperative autonomous fleets fairly and efficiently in close to real-time.

Absorptive capacity, product heterogeneity and technology licensing under different fee scenarios

AbstractSince a licensee does not always fully absorb technology in the process of technology licensing, based on the consideration of incomplete absorption, this paper establishes a game model consisting of a technology research and development firm and two licensee firms, to explore the most licensing strategies of the licensing firms as well as the optimal social welfare of the licensee under different fee scenarios. The results of the study show that (1) under the flat fee scenario, technology R&D enterprises will choose royalty licensing in most cases, but when the gap in the absorptive capacity of the licensed enterprise is small and the difference in the quality of the products between the two licensing parties is large, fixed‐fee licensing is a more preferable option;(2)in the differential fee scenario, technology R&D firms prefer differential fixed fee licenses;(3)when technological empowerment occurs, local social welfare is enhanced;(4)technology development firms prefer firms with high absorptive capacity, but also charge higher fees;(5)differential fees charged by technology R&D firms to authorized firms do not always lead to a relative decline in local social welfare; they can lead to a greater enhancement of social welfare within a certain interval, and the enhancement is even greater than the enhancement of social welfare brought about by uniform fees.

Federated data acquisition market: Architecture and a mean-field based data pricing strategy

With the increasing global mobile data traffic and daily user engagement, technologies, such as mobile crowdsensing, benefit hugely from the constant data flows from smartphone and IoT owners. However, the device users, as data owners, urgently require a secure and fair marketplace to negotiate with the data consumers. In this paper, we introduce a novel federated data acquisition market that consists of a group of local data aggregators (LDAs); a number of data owners; and, one data union to coordinate the data trade with the data consumers. Data consumers offer each data owner an individual price to stimulate participation. The mobile data owners naturally cooperate to gossip about individual prices with each other, which also leads to price fluctuation. It is challenging to analyse the interactions among the data owners and the data consumers using traditional game theory due to the complex price dynamics in a large-scale heterogeneous data acquisition scenario. Hence, we propose a data pricing strategy based on mean-field game (MFG) theory to model the data owners’ cost considering the price dynamics. We then investigate the interactions among the LDAs by using the distribution of price, namely the mean-field term. A numerical method is used to solve the proposed pricing strategy. The evaluations demonstrate that the proposed pricing strategy efficiently allows the data owners from multiple LDAs to reach an equilibrium on data quantity to sell regarding the current individual price scheme. The result further demonstrates that the influential LDAs determine the final price distribution. Last but not least, it shows that cooperation among mobile data owners leads to optimal social welfare even with the additional cost of information exchange.

A comparative analysis of government intervention under the EPR system: Eco-design and authorized remanufacturing

The extended producer responsibility system and government intervention are among the essential requirements for constructing sustainable economies. In a closed-loop supply chain consisting of an original equipment manufacturer (OEM) and a third-party remanufacturer (TPR), this paper develops four Stackelberg game models for four modes of government intervention under patent licensing: no intervention, tax and subsidy, tax and subsidy plus eco-design tax deduction, and eco-design tax deduction only. The impacts of the different government interventions on the optimal production decision, social welfare, and environment are analyzed and compared. The results indicate that, compared with no government intervention, government tax and subsidy decrease the supply of new products, increase supply of remanufactured products, and raise recycling rates of end-of-life products; on top of tax and subsidy, the government’s eco-design tax deduction can further increase OEM and TPR profits; the environmental impact under different government intervention depends on the ratio of the environmental impacts of remanufactured to new products and the level of eco-design effort. This study provides decision makers (i.e., OEM and TPR) and policy makers (i.e., governments) with new managerial insights on the benefits of eco-design and effective mechanisms to promote it.

Research on the Optimal Leasing Strategy of Electric Vehicle Manufacturers

In the context of actively and steadily implementing the “dual carbon” strategy, two competing electric vehicle manufacturers (manufacturers m1 and m2) were selected as research objects to construct two different leasing strategy models for electric vehicle manufacturers, namely, m1 provided a unit rental electric vehicle strategy and m2 provided a fixed rental electric vehicle strategy. We studied the optimal car rental strategy and pricing of the two manufacturers under the situation of m2 providing and not providing rental service efforts, and the influence of relevant factors on the optimal decision are explored. It shows that the price of electric vehicles rented by consumers per unit increases with the combined effect of the coefficient of rental service effort and the marginal cost of the rental service effort, while the price of fixed rental electric vehicles decreases with the combined effect of both. When the unit rental preference coefficient is large, the unit rental of electric vehicles will give m1 maximum profit. When the rental service effort coefficient is high, m2 is the most profitable. The efforts to provide leasing services of m2 increase their own interests to a certain extent. The greater the effort coefficient of leasing services, the smaller the marginal cost of leasing services, and the optimal social welfare reaches the maximum. The conclusion of the article can provide relevant leasing insights for electric vehicle manufacturers and also provide certain theoretical guidance for promoting electric vehicle leasing service strategies.

Composite Optimization With Coupling Constraints via Penalized Proximal Gradient Method in Asynchronous Networks

In this paper, we consider a composite optimization problem with linear coupling constraints in a multi-agent network. In this problem, the agents cooperatively optimize a strongly convex cost function which is the linear sum of individual cost functions composed of smooth and possibly non-smooth components. To solve this problem, we propose an asynchronous penalized proximal gradient (Asyn-PPG) algorithm, a variant of classical proximal gradient method, with the presence of the asynchronous updates of the agents and uniform communication delays in the network. Specifically, we consider a slot-based asynchronous network (SAN), where the whole time domain is split into sequential time slots and each agent is permitted to execute multiple updates during a slot by accessing the historical state information of the agents. By the Asyn-PPG algorithm, an explicit convergence rate can be guaranteed based on deterministic analysis. The feasibility of the proposed algorithm is verified by solving a consensus-based distributed regression problem and a social welfare optimization problem in the electricity market.

An incentive compatible ZD strategy-based data sharing model for federated learning: A perspective of iterated prisoner's dilemma

Federated learning has been increasingly adopted as an effective means to cope with the significant increase in the volume of training data needed for machine learning and address the privacy concerns in using these data. However, moral hazard may occur when individual data providers (IDPs) use smaller amounts or low-quality data to train their local models and submit these low-quality results (gradients) to free-ride on the benefits of the federated learning. Therefore, federated learning operators often face the dilemma of encouraging more IDPs to participate in data sharing and ensuring truthful contributions from IDPs to obtain high-quality global training results. This article proposes a spontaneous cooperative data-sharing model to address this dilemma. Through an iterated prisoner's dilemma model solved by the zero-determinant (ZD) strategy, we show that the optimal ZD strategies of all IDPs are to maximize their training efforts when participating in federated learning. According to the comparisons with other approaches through simulations, we demonstrate that either the two-IDP with binary strategies case or the multi-IDP with continuous strategies case could result in the optimal individual utility and social welfare. Therefore, the proposed spontaneous cooperative model effectively avoids the existing moral hazard problem in federated learning and provides a viable instrument for the federated learning operator to maximize the performance of the global model without the need to evaluate the quality of local gradients.

Heterogeneity of delay information and revenue-cost structure in a multi-server queue with threshold policy: Customer behaviors and optimization

This paper studies customers' equilibrium joining behavior in a multi-server queue with threshold policy, and then puts forward effective schemes to control customers' behaviors and to optimize social welfare. The stream of customers consists of two parts: customers in class 1 and customers in class 2, and they wait for service in order until the system is empty. Then the servers take a vacation and they would be reactivated as soon as the queue length reaches a threshold N. The two types of customers are heterogeneous in service revenue and delay sensitivity, i.e., they have different revenue-cost structures. Besides, customers in class 1 can access to the delay information on servers' status and queue length as they arrive, but customers in class 2 can not. Distinguishing the two scenarios of whether there is a joining threshold for customers in class 1 in vacation state, we get the two types of customers' equilibrium joint joining strategies, respectively, and observe the interaction between the behaviors of the two types of customers. Moreover, we also get their socially optimal joint joining strategy for comparisons and for making control measures, and discuss the influence of the fraction of customers in class 1 on social welfare and system throughput. We find that in any scenario, the equilibrium social welfare is always unimodal with respect to the fraction of customers in class 1, which indicates that the higher fraction of customers in class 1 is not necessarily beneficial. However, on the premise of effectively controlling customer behavior, this high fraction is still necessary to optimize social welfare and increase system throughput, although the optimal social welfare is also unimodal.

The role of green reputation, carbon trading and government intervention in determining the green bond pricing: An externality perspective

Green bonds, due to their inherent externalities, pose challenges in achieving equilibrium prices under optimal public resource allocation and social welfare without governmental intervention. While numerous studies have explored green bond pricing via government subsidies and other strategies, this paper diverges by targeting the equilibrium price of green bonds as the optimal public resource allocation, specifically from an externality's standpoint. We integrate factors such as the cap-and-trade system, carbon taxes, government subsidies, and green reputation into the fractional jump-diffusion model. This is further combined with real options theory and the fast Fourier transform method to derive the green bond pricing equation. Through optimization techniques, we delve into the influences of various factors and the interplay of different governmental strategies. Numerical findings suggest that, in an ideal scenario where the government grants adequate carbon allowances to green bond issuers, the carbon emission trading mechanism can facilitate the bond in achieving its ideal equilibrium price. Conversely, when the government is restricted by carbon constraints, preventing green bond issuers from acquiring ample carbon allowances, supplementary measures like government subsidies and carbon tax policies can complement the cap-and-trade system, ensuring green bonds attain their ideal equilibrium price. Furthermore, given the carbon emission reduction level of green projects, our model can determine the optimal government subsidy ratio and the ideal carbon tax price.

Experimental Design Networks: A Paradigm for Serving Heterogeneous Learners Under Networking Constraints

Significant advances in edge computing capabilities enable learning to occur at geographically diverse locations. In general, the training data needed in those learning tasks are not only heterogeneous but also not fully generated locally. In this paper, we propose an experimental design network paradigm, wherein learner nodes train possibly different Bayesian linear regression models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners, and the decision variables are the data transmission strategies subject to network constraints. We first show that, assuming Poisson data streams in steady state, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1-1/e$</tex-math> </inline-formula> factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling. Finally, we complement our theoretical findings through extensive experiments. Our numerical evaluation shows that the proposed algorithm outperforms several baseline algorithms both in maximizing the global objective and in the quality of the trained models.

Social Welfare-Based Task Assignment in Mobile Crowdsensing

Mobile crowdsensing (MCS) is a novel data-collection paradigm in the internet of things. Social welfare is an important factor in the task allocation because it integrates the interests of all parties involved in MCS and represents societal satisfaction. The ultimate goal of task allocation is to maximize social welfare as much as possible. Existing social welfare optimization research does not consider the moral and psychological characteristics of people in the real world. In this study, the real-world situation is considered. A task allocation strategy, which includes two stages, is formulated for task allocation. A generalized shortest path algorithm and an optimal pricing algorithm are proposed for each stage. To evaluate the proposed algorithms, extensive simulation experiments are conducted on two real-world datasets. The experimental results demonstrate that the proposed algorithms produce the desired effects, and the proposed strategy significantly increases social welfare by 19% compared to another method.

Decision models of emission reduction considering CSR under reward-penalty policy.

For the two emission reduction technologies of clean process (CT Mode) and end-of-pipe pollution control technology (ET Mode), this paper constructs production and low-carbon R&D decision-making models considering consumers' green preference, and discusses the impact of social responsibility on firm's decision-making, profit and social welfare. Then, the difference of optimal decision, profit and social welfare is analyzed when the firm adopt two emission reduction technologies with or without reward-penalty policy. The main conclusions of this paper are as follows: (1) Whether using clean process technology or end-of-pipe pollution control technology, consumers' green preference behavior can increase corporate profit. When consumers' green preference is small, consumers' green preference is negatively correlated with social welfare. When consumers' green preference is large, consumers' green preference is positively correlated with social welfare. (2) Corporate social responsibility is conducive to improving the level of social welfare, not conducive to the increase of corporate profits. (3) When the reward and punishment intensity is small, the reward-penalty policy cannot effectively motivate the firm to assume social responsibility. Only when the reward and punishment reaches a certain level, the mechanism can have an incentive effect on the firm, and the government can actively implement the mechanism. (4) When the market scale is small, the adoption of end-of-pipe pollution control technology is more beneficial to the firm; When the market scale is large, it is beneficial for the firm to adopt clean technology. (5) If the efficiency of end-of-pipe pollution control and emission reduction is much higher than that of clean process, the firm should choose end-of-pipe pollution control technology, otherwise choose clean process.

Combinatorial Civic Crowdfunding with Budgeted Agents: Welfare Optimality at Equilibrium and Optimal Deviation

Civic Crowdfunding (CC) uses the ``power of the crowd" to garner contributions towards public projects. As these projects are non-excludable, agents may prefer to ``free-ride," resulting in the project not being funded. Researchers introduce refunds for single project CC to incentivize agents to contribute, guaranteeing the project's funding. These funding guarantees are applicable only when agents have an unlimited budget. This paper focuses on a combinatorial setting, where multiple projects are available for CC and agents have a limited budget. We study specific conditions where funding can be guaranteed. Naturally, funding the optimal social welfare subset of projects is desirable when every available project cannot be funded due to budget restrictions. We prove the impossibility of achieving optimal welfare at equilibrium for any monotone refund scheme. Further, given the contributions of other agents, we prove that it is NP-Hard for an agent to determine its optimal strategy. That is, while profitable deviations may exist for agents instead of funding the optimal welfare subset, it is computationally hard for an agent to find its optimal deviation. Consequently, we study different heuristics agents can use to contribute to the projects in practice. We demonstrate the heuristics' performance as the average-case trade-off between the welfare obtained and an agent's utility through simulations.