Social Cost Minimization Research Articles

Do Chilean cities have the right set of public transport modes?

Although there are various studies that evaluate the optimal design of public transport systems in radial structures, there are few that analyse which urban characteristics are relevant to recommend different public transport modes, particularly in small towns that lack the resources needed for such studies. Based on results from social cost minimization models, we classify small and medium-sized cities of Chile into three categories according to population, density, location of the city centre, and topography. For smaller towns (type A) subsidies to regular public transport seem unnecessary. In intermediate cities (type B) our models recommend promoting public transport services through shared taxis, a typical transport mode in Chilean cities in which a taxi carries up to 4 passengers on fixed routes or zones. Finally, in the largest cities in the sample (type C), it is recommended to subsidize the operation of bus services that produce less externalities than shared taxis, particularly congestion in the city centre. The methodology used can be extended to more complex structures and incorporate data from other countries, in order to formulate useful public policies in diverse contexts, in particular to address the lack of accessibility for vulnerable groups and zones. That said, transitions between categories are challenging for the governance of public transport systems and should be considered in further studies.

Optimal management of park-and-ride points and shared parking spaces in the context of autonomous driving

This paper studies how to set the location of park-and-ride and the pricing of shared parking spaces to achieve the desired goals in the context of autonomous vehicles (AV). Assuming that a linear city does not allow single-occupancy private cars to enter the city center, travelers can only choose to park and ride or take a shared autonomous vehicle to reach the city center. Under the condition of autonomous driving, since the car can park autonomously, there are three ways to park: park the car home, park the car in a free parking space near the transfer point, and park the car in a shared parking space provided by residents near the transfer point. Starting from the two goals of profit maximization and social cost minimization, this paper explores the optimal parameter settings under the two goals respectively. The results show that the optimal park-and-ride point and the optimal shared parking space pricing under the two goals are both obtained when the supply of shared parking spaces is equal to the demand. This study can provide theoretical reference and decision-making basis for the optimal allocation of resources in the era of autonomous driving.

On Scheduling Mechanisms Beyond the Worst Case

AbstractThe problem of scheduling unrelated machines has been studied since the inception of algorithmic mechanism design (Nisan and Ronen, Algorithmic mechanism design(extended abstract). In: Proceedings of the Thirty First Annual ACM Symposium on Theory of Computing (STOC), pp. 129–140, 1999. It is a resource allocation problem that entails assigning m tasks to n machines for execution. Machines are regarded as strategic agents who may lie about their execution costs so as to minimize their time cost. To address the situation when monetary payment is not an option to compensate the machines’ costs, Koutsoupias (Theory Comput Syst 54:375–387, 2014) devised two truthful mechanisms, K and P respectively, that achieves an approximation ratio of $$\frac{n+1}{2}$$ n + 1 2 and n, for social cost minimization. In addition, no truthful mechanism can achieve an approximation ratio better than $$\frac{n+1}{2}$$ n + 1 2 . Hence, mechanism K is optimal. While the approximation ratio provides a strong worst-case guarantee, it also limits us to a comprehensive understanding of mechanism performance on various inputs. This paper investigates these two scheduling mechanisms beyond the worst case. We first show that mechanism K achieves a smaller social cost than mechanism P on every input. That is, mechanism K is pointwise better than mechanism P. Next, for each task, when machines’ execution costs are independent and identically drawn from a task-specific distribution, we show that the average-case approximation ratio of mechanism K converges to a constant determined by the task-specific distribution. This bound is tight for mechanism K. For a better understanding of this distribution-dependent constant, on the one hand, we estimate its value by plugging in a few common distributions; on the other, we show that this converging bound improves a known bound (Zhang in Algorithmica 83(6):1638–1652, 2021)) which only captures the single-task setting. Last, we find that the average-case approximation ratio of mechanism P converges to the same constant.

Implementation of the international convention on the removal of wrecks: Legal obligations, model selections, and perspectives from China

The International Convention on the Removal of Wrecks mandates that registered owners are liable for wreck removal and associated costs. This article explores different national implementations of the WRC, and critically evaluates the convention's approach to liability limits. Employing a mixed-methods approach, this study offers preliminary findings that support three core aspects of the WRC: strict liability, compulsory insurance, and liability limitation. These elements are shown to collectively incentivize the minimization of social costs, ensure funding for wreck operations, and assist shipowners in securing cost-effective insurance. Despite widespread international support for strict liability and compulsory insurance, the WRC lacks a unified approach to capping liability and the application of the Convention to territorial seas remains optional, resulting in diverse implementations at the national level. For instance, China utilizes a dual framework that integrates both the convention and domestic laws, incorporating both hard and soft legal measures. With increasing vessel sizes and the rise of unmanned vessels, the complexity of legal and risk issues escalates for shipowners. Consequently, the article advocates for policy reforms: encouraging contracting parties to extend the WRC's application to territorial waters, permitting shipowners to limit their liability, and regularly updating liability caps to reflect actual wreck removal costs. It further recommends that affected states apply principles of proportionality and reasonableness in evaluating wreck hazards and selecting removal methods, while curbing excessive administrative discretion. These policy-oriented recommendations are intended to enhance the effectiveness and equity of the Convention's application.

Differentially private and truthful auction-based resource procurement for budget-constrained DAG applications in clouds

With the rapid proliferation of emerging scientific computing, more and more cloud users (CUs) with large-scale applications are seeking efficient executing paradigms in clouds. Following this trend, a cloud market with multiple cloud service providers (CSPs) is emerging, which aims to provide efficient cross-cloud services to CUs through the cooperation among CSPs. In the market, CUs can procure different virtual machine resources and their combinations from one or more CSP(s) in a pay-as-you-go way. In such a procurement process, it is challenging to achieve privacy protection and truthfulness of CSPs while enabling flexible resource combinations. As such, we design a differentially private and truthful auction-based resource procurement mechanism (DTARP) in clouds. Specifically, we first propose a budget-aware resource profile determination algorithm (BRPD) to obtain a set of resource profiles, in order to bridge the difference between application tasks and resources under the budget constraint. Next, we design a differential privacy-based winning CSP selection algorithm to choose a set of winning CSPs, and the payment calculation algorithm to calculate the payment for each winning CSP. Strict theoretical analysis proves that the proposed DTARP achieves differential privacy, truthfulness, individual rationality, computational efficiency, and approximate social cost minimization while guaranteeing the efficient completion of application tasks under the budget. Extensive simulation results also demonstrate the effectiveness of the proposed DTARP.

A bi-level optimization framework for the power-side virtual power plant participating in day-ahead wholesale market as a price-maker considering uncertainty

This paper develops a novel bilevel decision-making framework for a power-side virtual power plant (VPP) comprising refined power to gas (P2G), wherein the upper-level is the cost minimization objective of VPP, while the lower-level is social cost minimization objective of wholesale electricity market. Meanwhile, wind power uncertainty is handled by distributionally robust optimization method. By utilizing Karush-Kuhn Tucker optimality condition and strong duality theory, the bilevel model is interpreted as a tractable single-level mixed integer programming model. Simulation results show that: relative to the models with removing uncertainty and refined P2G device, 1) the proposed approach realizes outstanding economic benefit by obtaining an increase with a rate by 7.47 % and 105.09 % in total actual profit, respectively. Regarding environmental benefit, CO2 emission volume experiences a 0.89 % decline and 0.90 % increase, and the CO2 intensity of the proposed strategy is the lowest with the value of 193. 25kg/MWh. 2) The proposed model can achieve the economic and low-carbon dispatching of the VPP. Wind power curtailment in the proposed model witnesses a trivial increase and reduced by 53.19 %, respectively. 3) The model exhibits merits due to its trade-off between robustness and computational complexity.

Gasoline and electric vehicle ownership rationing over time: Lottery vs. First-come-first-served schemes

This paper addresses the ownership rationing issues of gasoline vehicles (GV) and electric vehicles (EV) in a given time horizon. A state-owned vehicle manufacturer is assumed to be the producer of these vehicles. Two typical rationing schemes for the GV and EV ownership that have been implemented in some Chinese megacities, namely a simultaneous lottery scheme for both GV and EV and a hybrid scheme consisting of lottery for GV and first-come-first-served (FCFS) for EV, are investigated. Multi-period social cost minimization models are proposed for determining the optimal auto quota (i.e., the number of vehicles to be produced) and the optimal frequency of ownership allocation for each scheme in the given time horizon. In the proposed models, residents’ heterogeneity is considered in terms of their values of time (VOTs). The properties of the proposed models are analytically explored, and a comparison between the lottery mode and the FCFS mode is made. The results show that the lottery mode outperforms the FCFS mode in terms of the total social cost. The VOT threshold of the subsequent participants in auto rationing schemes tends to be higher than that of the preceding participants. The decision of EV subsidy should be cautiously made due to a tug of war between the resultant decreased pollutant emissions and increased traffic congestion. The implementation of the auto ownership rationing schemes can cause inequity issue in terms of the changes of travel costs of different-income residents. A Pareto-improvement strategy is presented to balance equity and efficiency of the rationing schemes.

Bargaining‐based energy sharing framework for multiple CCHP systems with a shared energy storage provider

AbstractIn the present day, when centralized energy storage technology is becoming increasingly mature, the cooperative energy sharing framework between the combined cooling, heating, and power (CCHP) systems and a shared energy storage provider (SESP) can effectively promote the local consumption of renewable energy and reduce the social energy cost. In this study, the optimal operation models of CCHP and SESP are first developed by considering the electric energy trading of the CCHP and the SESP. Then the CCHP–SESP cooperative operation model is established based on Nash bargaining theory, followed by equating it into two subproblems of coalition social energy cost minimization and electric energy trading payment negotiation. The alternating direction method of multipliers approach is introduced to solve the energy‐sharing problems in a distributed way. Numerical simulation results show the effectiveness of the proposed energy‐sharing framework. Simulation results show that the proposed bargaining‐based energy‐sharing framework can significantly reduce the social energy cost and improve the utilization rate of energy storage resources.

Dynamic pricing analysis of railway freight transport under different carbon emission penalties

in order to better promote the transportation industry to reduce carbon emissions and improve the competitiveness of railway freight in the whole freight market, to achieve the “double carbon” strategy and promote the “transit rail”. In this paper, considering the dynamic pricing of freight volume and freight price, a two-layer programming model is established under the minimum social total cost of railway and road. The upper level considers the income of railway freight, and the lower level considers the total social cost of freight transportation, so as to realize the increase of railway freight revenue and the decrease of the general total expenditure paid by freight owners. Considering the transportation of bulk commodity freight from shandong to sichuan, it is concluded that the railway freight market has a good development prospect under the consideration of the total social expenditure such as carbon emission. Under the carbon emission charge, the appropriate increase of railway freight rate will not lead to the reduction of railway freight volume. As rail freight becomes more competitive under increasingly stringent carbon emissions penalties, the volume of rail freight will continue to rise even after appropriate increases in rail freight fees. At this point, rail freight companies can transport more goods and gain higher returns, slightly increasing the overall cost borne by shippers in a broad sense, and helping to reduce carbon emissions in the process of freight transport.

Distributed Algorithm for Constrained Social Cost Minimization Problem of Heterogeneous Linear Multi-Agent Systems Against DoS Attacks

Distributed Algorithm for Constrained Social Cost Minimization Problem of Heterogeneous Linear Multi-Agent Systems Against DoS Attacks

Towards sustainable UAV operations: Balancing economic optimization with environmental and social considerations in path planning

Unmanned Aerial Vehicles (UAVs), i.e., drones, are expected to be widely used in various applications, such as parcel delivery and passenger transport, with the benefits of mitigating traffic congestion and reducing carbon emissions. In this paper, we study a UAV path planning problem under uncertain weather conditions, and design a data-driven dynamic decision support system for multiple types of UAVs. To this end, we categorize all relevant costs into three types, namely, economic, environmental, and social costs, and formulate a nonlinear two-stage stochastic programming model to establish optimal paths for UAV missions under weather uncertainty. We then discretize the nonlinear model and propose a tight linear approximation for the discretized problem to allow for a near real-time implementation. To quantify weather uncertainty, we propose a weather scenario generation algorithm to map ensemble-based weather forecast information to airspace blockage maps. With comprehensive computational studies through simulations, we show that our proposed stochastic approach can lower operating costs by an average of around 6%, where the savings increase as weather conditions become more severe and complex. We also find that, for missions operated by small UAVs, it is not sufficient to determine a path solely based on economic cost minimization, but it should rather be through total cost minimization, which involves environmental and social costs. Considering only the economic cost in the optimization may lead to much higher non-economic costs. However, for missions operated by large UAVs, it is sufficient to determine paths through economic cost optimization, as including environmental and social costs in the optimization process does not result in solutions that are much different from those obtained by considering only the economic costs. For both small and large UAVs, a path established solely through environmental or social cost minimization may not be economically sustainable, as doing so would imply very high economic costs.

Distributed algorithm for social cost minimization with second-order nonlinear dynamics over weight-unbalanced digraphs

In this paper, the emerging social cost minimization framework of second-order nonlinear systems over weight-unbalanced digraphs is studied. It provides a general framework containing several important classes of problems. Therein each local cost function depends on a global decision variable consisting of all the agents’ decisions not limited to the form of aggregative terms. Under the partial decision and cost function information setting, distributed algorithms are designed based on the state feedback and left eigenvector estimation mechanism to overcome the challenges posed by second-order nonlinear systems and weight-unbalanced digraphs. The asymptotic convergence of the algorithm is demonstrated via the Lyapunov stability theory. Finally, numerical simulations of the vehicle monitoring problem are provided to support the algorithm design.

An Incentive Auction for Heterogeneous Client Selection in Federated Learning

Federated Learning (FL) is a new distributed machine learning (ML) approach which enables thousands of mobile devices to collaboratively train artificial intelligence (AI) models using local data without compromising user privacy. Although FL represents a promising computing paradigm, such training process can not be fully realized without an appropriate economic mechanism that incentivizes the participation of heterogeneous clients. This work targets social cost minimization, and studies the incentive mechanism design in FL through a procurement auction. Different from existing literature, we consider a practical scenario of FL where clients are selected and scheduled at different global iterations to guarantee the completion of the FL job, and capture the distinct feature of FL that the number of global iterations is determined by the local accuracy of all participants to balance between computation and communication. Our auction framework <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> first decomposes the social cost minimization problem into a series of winner determination problems (WDPs) based on the number of global iterations. To solve each WDP, <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> invokes a greedy algorithm to determine the winners, and a payment algorithm for computing remuneration to winners. Finally, <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> returns the best solution among all WDPs. We carried out theoretical analysis to prove that <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> is truthful, individual rational, computationally efficient, and achieves a near-optimal social cost. We further extend our model to consider multiple FL jobs with corresponding budgets and propose another efficient algorithm <inline-formula><tex-math notation="LaTeX">$A_{FL-M}$</tex-math></inline-formula> to solve the extended problem. We conduct large-scale simulations based on the real-world data and testbed experiments by adopting FL frameworks FAVOR and CoCoA. Simulation and experiment results show that both <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$A_{FL-M}$</tex-math></inline-formula> can reduce the social cost by up to 55&#x0025; compared with state-of-the-art algorithms.

A Nash bargaining model for energy sharing between micro-energy grids and energy storage

Micro-energy grids (MEGs) play a crucial role in achieving diverse energy utilization and promoting low-carbon economies. However, the intermittent nature of distributed generation and the inter-coupling of multiple energy types pose challenges to efficient energy management in MEGs. In this paper, we propose a novel Nash bargaining game-based electricity-gas energy-sharing model for MEGs. Our model incorporates bus structure-based energy storage combined with power-to-gas technology, which enables MEGs to establish an integrated electricity-gas energy-sharing network. This network facilitates both direct sharing and buffered sharing of energy, addressing the limitations of intermittent generation and enhancing overall energy management. To address the issue of computational complexity, the proposed Nash bargaining game model is decomposed into two sub-problems: social energy cost minimization and benefits sharing. Through optimization, we obtain optimal energy-sharing profiles and energy-sharing payments. Moreover, we consider the investment payback period of energy storage and adjust the initial benefit-sharing results accordingly. Case studies demonstrate that our model significantly improves economic performance, increasing the self-sufficiency ratio of MEGs to 64% and energy storage efficiency to 63% compared to operating independently. Additionally, the multiplayer Nash bargaining game model facilitates Pareto optimal benefits for MEGs and energy storage by capturing their interactive dynamics.

Cooperative coordination between port microgrid and berthed ships with emission limitation and peak awareness

Due to growing concerns on energy efficiency and environmental issues, most ports are increasingly being equipped with cold ironing (CI) facilities, i.e., shoreside power supply facilities, which provide energy to ships at berth from the shoreside grid. The emergence of CI has coupled the energy connection between port microgrid and berthed ships. However, simply treating CI demands from berthed ships as passive loads, without considering the energy coordination, will increase the operational burden of the port. In this paper, an incentive-based cooperative coordination framework between port microgrid and berthed ships is proposed, where the flexibility of CI power is used as a demand response tool. With the aim of minimizing the net cost comprising energy cost and potential demand charge, the port will differentially coordinate berthed ships to adjust their CI demand requests, to alleviate peak burden. The social cost minimization as well as net cost reduction of each participant will be achieved in this cooperative coordination framework. Asymmetric Nash bargaining is adopted to design incentive mechanism, where a novel index, termed as contribution rate is proposed to evaluate the participants’ contributions to the coordination. Distributed solution is proposed to solve the optimization problem, to guarantee the autonomy and information privacy of participants. Simulation results demonstrate the validity of the proposed method in achieving benefits for both port and berthed ships.

Applied Study of the Fluidization Model of Logistics Transportation through the Prism of the Impact Generated on the Environment

A real problem of today's society is the loss of human lives due to road accidents and the pollution caused by freight transport through metropolitan areas. The restrictions imposed in the near future for freight transport could reduce its efficiency and create many more problems. Using data centralization and developing applications or algorithms dedicated to the freight transportation sectors, routes and emissions can be managed much more efficiently. In this work, general aspects are presented, as well as a route optimization model for freight transport, taking into account the environmental impact, based on a heuristic algorithm, that of the ant colony (ACO). A multitude of studies has focused on what represents the benefits created by the applicability of solutions rather than on generalities and perspectives. The paper aims to highlight the usefulness of an optimization model of freight transport routes and the minimization of time and social costs. The study will show us that an optimized route for freight transport has a huge impact on costs, but also on time efficiency and polluting emissions.

GAIMMO: A Grade-Driven Auction-Based Incentive Mechanism With Multiple Objectives for Crowdsourcing Managed by Blockchain

Blockchain has been applied for decentralized crowdsourcing management by deploying a number of miners to reach a consensus on crowdsourced task allocation and payment decision. In a blockchain-based crowdsourcing system (BCS), incentive becomes essential to motivate the participation and cooperation of all system entities. However, existing literature scarcely investigates how to motivate heterogeneous crowdsourcers, workers, and miners simultaneously toward satisfying multiple objectives without the support of centralized management. In this article, we propose GAIMMO, a novel grade-driven auction-based incentive mechanism for BCS with multiple objectives in mind: crowdsourcer utility maximization, social welfare maximization, social grade maximization, and social cost minimization. Concretely, we propose a grade-based task sorting (GTS) algorithm to determine the service priority of heterogeneous crowdsourcers in order to motivate their cooperative behaviors, which consequently maximizes crowdsourcer utility when combining with the carefully designed utility functions of other system entities. We propose a grade-based utility function of workers and employ a hierarchical premium-based task assignment (PTA) algorithm to realize social welfare maximization, social grade maximization, and social cost minimization. We further propose a fixed-grade-sum and grade-based reward-sharing (FGSGRS) method to encourage fast block generation and motivate high-grade miners without damaging the profits of the crowdsourcers. We conduct simulation-based experiments to show the effectiveness and advance of our proposed incentive mechanism in stimulating the participation willingness of high-grade system entities and achieving the multiple objectives.

Incentivizing Federated Learning Under Long-Term Energy Constraint via Online Randomized Auctions

Mobile users are often reluctant to participate in federated learning to train models, due to the excessive consumption of the limited resources such as the mobile devices’ energy. We propose an auction-based online incentive mechanism, FLORA, which allows users to submit bids dynamically and repetitively and compensates such bids subject to each user’s long-term battery capacity. We formulate a nonlinear mixed-integer program to capture the social cost minimization in the federated learning system. Then we design multiple polynomial-time online algorithms, including a fractional online algorithm and a randomized rounding algorithm to select winning bids and control training accuracy, as well as a payment allocation algorithm to calculate the remuneration based on the bid-winning probabilities. Maintaining the satisfiable quality of the global model that is trained, our approach works on the fly without relying on the unknown future inputs, and achieves provably a sublinear regret and a sublinear fit over time while attaining the economic properties of truthfulness and individual rationality in expectation. Extensive trace-driven evaluations have confirmed the practical superiority of FLORA over existing alternatives.

EdgeDR: An Online Mechanism Design for Demand Response in Edge Clouds

The computing frontier is moving from centralized mega datacenters towards distributed cloudlets at the network edge. We argue that cloudlets are well-suited for handling power demand response to help the grid maintain stability due to more flexible workload management attributed to their distributed nature. However, they also require computing demand response to avoid overload and maintain reliability. To this end, we propose a novel online market mechanism, EdgeDR, to achieve cost efficiency in edge demand response programs. At a high level, we observe that the cloudlet operator can dynamically switch on/off entire cloudlets to compensate for the energy reduction required by the power grid or provide enough computing resources to the edge service. We formulate a long-term social cost minimization problem and decompose it into a series of one-round procurement auctions. In each auction instance, we propose to let the cloudlet tenants bid with cost functions of their two-dimension service quality degradation tolerance, and let the cloudlet operator choose the service quality, manage the workload, and schedule the cloudlet activation status. In addition, we present a dynamic payment mechanism for the operator to balance the tradeoff between short-term profit and long-term benefit in more practical scenarios. Via rigorous analysis, we exhibit that our bidding policy is individually rational and truthful; our workload management algorithm has near-optimal performance in each auction; and our overall online algorithm achieves a provable competitive ratio. We further confirm the performance of our mechanism through extensive trace-driven simulations.

Unlock the Sharing Economy: The Case of the Parking Sector for Recurrent Commuting Trips

This study examines the pricing strategy of a parking sharing platform that rents the daytime-usage rights of private parking spaces from parking owners and sells them to parking users. In an urban area with both shared parking and curbside parking, a choice equilibrium model is proposed to predict the number of shared parking users under any given pricing strategy of the platform. We analytically analyze how the pricing strategy of the platform (price charged on users and rent paid to parking owners or sharers) would affect the parking choice equilibrium and several system efficiency metrics. It is shown that the platform is profitable when some parking owners have a relatively small inconvenience cost from sharing their spaces, but its profit is always negative at minimum social cost. Numerical studies are conducted to illustrate the analytical results and provide further understanding.