Pareto Efficient Research Articles

Organizational form and multiple exportable goods in export rivalry trade

AbstractFocusing on the multiple exportable goods between intrabrand and interbrand competition in the export rivalry market, we analyze foreign firms' endogenous choice of organizational form in the face of tariffs. It is shown that if the degree of intrabrand competition is sufficiently high, firms provide corporate incentives (i.e., U‐form) when goods are substitutes and divisional incentives (i.e., M‐form) when goods are complements, and vice versa. This result relating to the prisoner's dilemma situation depends on which effect between the tariff level and incentive terms dominates. In the case of a low degree of intrabrand competition, choosing the M‐form (U‐form) brings about Pareto efficiency for foreign firms' profits and social welfare in the importing country when goods are substitutes (complements). In contrast to previous studies, when comparing the case of no delegation with the M‐form or U‐form, the Pareto optimum can also be achieved for both foreign firms choosing the M‐form or U‐form endogenously and the importing country, without a prisoner's dilemma involving no delegation.

Robust ex-post Pareto efficiency and fairness in random assignments: Two impossibility results

A random assignment is robust ex-post Pareto efficient whenever for any of its lottery decomposition, each deterministic assignment in its support is Pareto efficient. We show that ordinal efficiency implies robust ex-post Pareto efficiency while the reverse does not hold. We know that strategy-proof and ordinal efficient mechanisms satisfy neither equal treatment of equals nor equal division lower bound. We prove that it is not possible to avoid these two impossibilities by weakening ordinal efficiency to robust ex-post Pareto efficiency.

Panel VAR study on the effects of technical efficiency, market competition, and firm value on environmental performance

This study provides evidence on the interactions among four interconnected firm performance profiles: environmental performance (environmental score and pollutant emissions), production technical efficiency, firm market power, and firm market value. We employ two sophisticated econometric methodologies to a sample of almost 2500 observations covering about 430 US-listed manufacturing firms from 2002 to 2020. First, stochastic frontier analysis allows us to estimate time-varying technical efficiency in firm production; next, we apply an atheoretical model, the panel vector autoregression model , to identify the dynamic and causal relationships between the corporate profiles’ performance. Our results show that inefficiency is the weak point of the environmental score for the listed US manufacturing firms. Moreover, monopolistic rents enact a detrimental externality on firms’ ecological awareness. Finally, market dominance appears be a Pareto inefficiency condition. • This study provides evidence on the interactions among four interconnected firm performance profiles. • Inefficiency is the weak point of the environmental score for the listed U.S. manufacturing firms. • Monopolistic rents enact a detrimental externality on firms’ ecological awareness. • Market dominance appears be a Pareto inefficiency condition.

Optimal Distributed Frequency and Voltage Control for Zonal Electricity Markets

Zonal pricing is a well-suited mechanism to incentivize grid-supporting behavior of profit-maximizing producers and consumers operating on a large-scale power system. In zonal electricity markets, local system operators create individual price zones, which provide appropriate price signals depending on local grid conditions such as an excess or shortage of electrical energy in certain regions. In this paper, a real-time zonal pricing controller for AC power networks is presented that ensures frequency and voltage stability as well as Pareto efficiency of the resulting closed-loop equilibria. Based on a dynamic network model that takes line losses and power exchange with adjacent price zones into account, distributed continuous-time control laws are derived which require only neighbor-to-neighbor communication. Application to a real-time congestion management strategy illustrates how spatially and temporally differentiated prices enable grid-supportive operation of the participants without interventions by a superordinate control authority. Effectiveness of different zonal pricing concepts compared to an isolated grid operation is demonstrated through simulations on the IEEE-57 bus system.

Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems

A major barrier to investments in clean and future-proof energy technologies of local multi-energy systems (L-MESs) is the lack of knowledge about their impacts on profitability and carbon footprints due to their complex techno-economic interactions. To reduce this problem, decision support tools should integrate various forms of decarbonization measures. This paper proposes the Demand Response Analysis Framework (DRAF), a new open-source Python decision support tool that integrally optimizes the design and operation of energy technologies considering demand-side flexibility, electrification, and renewable energy sources. It quantifies decarbonization and cost reduction potential using multi-objective mixed-integer linear programming and provides decision-makers of L-MESs with optimal scenarios regarding costs, emissions, or Pareto efficiency. DRAF supports all steps of the energy system optimization process from time series analysis to interactive plotting and data export. It comes with several component templates that allow a quick start without limiting the modeling possibilities thanks to a generic model generator. Other key features are the access and preparation of time series, such as dynamic carbon emission factors or wholesale electricity prices; and the generation, handling, and parallel computing of scenarios. We demonstrate DRAF’s capabilities through three case studies on (1) the DR of industrial production processes, (2) the design optimization of battery and photovoltaic systems, and (3) the design optimization and DR of distributed thermal energy resources.

Pareto Efficiency Analysis of Private Toll Road Networks

Pareto Efficiency Analysis of Private Toll Road Networks

Compromising as an equal loss principle

A social choice rule aggregates the preferences of a group of individuals over a set of alternatives into a collective choice. The literature admits several social choice rules whose recommendations are supposed to reflect a compromise among individuals. We observe that all these compromise rules can be better described as procedural compromises, i.e., they impose over individuals a willingness to compromise but they do not ensure an outcome where everyone has effectively compromised. We revisit the concept of a compromise in a collective choice environment with at least three individuals having strict preferences over a finite set of alternatives. Referring to a large class of spread measures, we view the concept of compromise from an equal loss perspective, favoring an outcome where every voter concedes as equally as possible. As such, being a compromise may fail Pareto efficiency, which we ensure by asking voters to concede as equally as possible among the Pareto efficient alternatives. We show that Condorcet consistent rules, scoring rules (except antiplurality) and Brams-Kilgour compromises (except fallback bargaining) all fail to ascertain an outcome which is a compromise. A slight restriction on acceptable spread measures suffices to extend the negative result to antiplurality and fallback bargaining. This failure also prevails for social choice problems with two individuals: all well-known two-person social choice rules of the literature, namely, fallback bargaining, Pareto and veto rules, short listing and veto rank, fail to pick ex-post compromises. We conclude that there is a need to propose and study rules that satisfy this equal loss, or outcome oriented, notion of a compromise.

Implications of Bounds in Approval Voting

We analyze and show the implications of required limitations on common voting system criteria for the three different versions of voting restrictions in the election website proposed for the Saudi Chamber of Commerce Business Awards. Electoral systems properties might be considered as performance indicators of a voting system. When presented with the same underlying voter preferences, different voting systems constraints might produce different results. Those voting systems may violate or satisfy voting properties. Distinct ways meet different requirements, and it was shown that no particular method can meet all of them.The studied voting systems mechanisms on the aforementioned website are called: Single Casting, Limited Casting, and Unlimited Casting methods.The first version permits the voter to vote for one unique candidate, and corresponds to the common Single Plurality Vote mechanism. The second version permits the voter to vote for three unique candidates at a time, and it is a new method that can be described as the Approval Voting with bounded ballot size. The third version permits the voter to vote for all candidates, and corresponds to the common Approval Voting mechanism.In the long version analysis, we proved that one of the new voting schemes in the election website proposed for the SaudiChamber of Commerce Business Awards, a version of Approval Voting where the size of the ballot is bounded, a set of the properties of Approval Voting are lost, in particular Pareto efficiency, Condorcet criteria winner support, Condorcet criteria loser, and Independence to Irrelevant Alternatives.

Multiobjective and multivariable optimization for earthmoving equipment

PurposeEfficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives.Design/methodology/approachThis paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation.FindingsThe efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives.Originality/valueThis model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.

ENSURING ECONOMIC SECURITY OF TRADE ENTERPRISES IN THE FORMATION OF PRICING POLICY

The article considers the problem of ensuring the economic security of trade enterprises by forming an optimal pricing policy. The methodology of formation the minimum and maximum selling prices of trade enterprise, maintenance of margin of economic security, which based on research of turnover costs and working capital of trade enterprise is offered. Based on statistical data of trade enterprises, the types of prices by product range are determined, which form a stable economic situation and ensure economic security of trade enterprise. The necessity of formation the optimal price policy and selling prices of trade enterprises with the use of Pareto efficiency tools and construction of single-criteria and multi-criteria tasks, indicators of quality of commodity resources of trade enterprises is substantiated. Using the Excel software processor and the “Regression” function, economic-mathematical models of optimal prices for the product range of trade enterprises are built. The results testified to the effectiveness of the proposed model of the optimal pricing policy of trade enterprises, as the obtained values are within the minimum selling prices, which provides economic security of trade enterprises. The process of forming the pricing policy of trade enterprises to ensure economic security is proposed, which is based on the principles of pricing, appropriate methodological tools and monitoring of market environment factors. Organizational, economic, legal, social and market (marketing) mechanisms for the formation of effective pricing policy of trade enterprises aimed at ensuring economic security are identified. This study is practically interesting for personnel of trade enterprises, regardless of organizational-legal forms of ownership and activities, and theoretically – for researchers dealing with pricing.

Multi-objective parallel adjacent U-shaped assembly line balancing collaborated by robots and normal and disabled workers

This paper emphasises the parallel adjacent U-shaped assembly line balancing problem in which the collaborative work forces in the system comprise human workers and robots. Apart from normal workers, disabled workers are also hired to enable the company to enjoy corporate tax benefits from the government. Five objectives related to the system as a whole and another three interacting entities are optimised simultaneously. A mathematical model is formulated for the first time for this type of layout. Due to the NP-hard nature of the problem, a reference point based evolutionary algorithm, namely non-dominated sorting teaching-learning-based optimisation (NSTLBO III), is proposed to solve medium- and large-sized problems. The performances of NSTLBO III are benchmarked in the sense of Pareto efficiency with two well-known multi-objective evolutionary algorithms, i.e. non-dominated sorting genetic algorithm (NSGA III) and multi-objective evolutionary algorithm based on decomposition (MOEA/D). The experimental results show that NSTLBO III surpasses its contestants significantly concerning the convergence to the true Pareto front.

Stability kernel in finite games with perturbed payoffs

Abstract The parametric concept of equilibrium in a finite cooperative game of several players in a normal form is introduced. This concept is defined by the partitioning of a set of players into coalitions. Two extreme cases of such partitioning correspond to Pareto optimal and Nash equilibrium outcomes, respectively. The game is characterized by its matrix, in which each element is a subject for independent perturbations., i.e. a set of perturbing matrices is formed by a set of additive matrices, with two arbitrary Hölder norms specified independently in the outcome and criterion spaces. We undertake post-optimal analysis for the so-called stability kernel. The analytical expression for supreme levels of such perturbations is found. Numerical examples illustrate some of the pertinent cases.

TARA: Training and Representation Alteration for AI Fairness and Domain Generalization.

We propose a novel method for enforcing AI fairness with respect to protected or sensitive factors. This method uses a dual strategy performing training and representation alteration (TARA) for the mitigation of prominent causes of AI bias. It includes the use of representation learning alteration via adversarial independence to suppress the bias-inducing dependence of the data representation from protected factors and training set alteration via intelligent augmentation to address bias-causing data imbalance by using generative models that allow the fine control of sensitive factors related to underrepresented populations via domain adaptation and latent space manipulation. When testing our methods on image analytics, experiments demonstrate that TARA significantly or fully debiases baseline models while outperforming competing debiasing methods that have the same amount of information-for example, with (% overall accuracy, % accuracy gap) = (78.8, 0.5) versus the baseline method's score of (71.8, 10.5) for Eye-PACS, and (73.7, 11.8) versus (69.1, 21.7) for CelebA. Furthermore, recognizing certain limitations in current metrics used for assessing debiasing performance, we propose novel conjunctive debiasing metrics. Our experiments also demonstrate the ability of these novel metrics in assessing the Pareto efficiency of the proposed methods.

Hybrid modeling of collaborative freight transportation planning using agent-based simulation, auction-based mechanisms, and optimization

The sharing economy is a peer-to-peer economic model characterized by people and organizations sharing resources. With the emergence of such economies, an increasing number of logistics providers seek to collaborate and derive benefit from the resultant economic efficiencies, sustainable operations, and network resilience. This study investigates the potential for collaborative planning enabled through a Physical Internet-enabled logistics system in an urban area that acts as a freight transport hub with several e-commerce warehouses. Our collaborative freight transportation planning approach is realized through a three-layer structured hybrid model that includes agent-based simulation, auction mechanism, and optimization. A multi-agent model simulates a complex transportation network, an auction mechanism facilitates allocating transport services to freight requests, and a simulation–optimization technique is used to analyze strategic transportation planning under different objectives. Furthermore, sensitivity analyses and Pareto efficiency experiments are conducted to draw insights regarding the effect of parameter settings and multi-objectives. The computational results demonstrate the efficacy of our developed model and solution approach, tested on a real urban freight transportation network in a major US city.

Pareto efficiency in intrahousehold allocations: evidence from rice farming households in India

ABSTRACT Intrahousehold models assume that plots farmed by women are as productive as plots farmed by men within the same household. Using a large plot-level dataset on rice farming households in India, we find evidence of significant Pareto inefficiency: women’s plots produce lower yields compared to their spouse’s plots, conditional on crop, plot and other attributes. The inefficiency is larger in the left tail of the rice yield distribution and primarily attributed to child-care burdens and social-norms faced by women.

Towards Distributed Lexicographically Fair Resource Allocation with an Indivisible Constraint

In the cloud computing and big data era, data analysis jobs are usually executed over geo-distributed data centers to make use of data locality. When there are not enough resources to fully meet the demands of all the jobs, allocating resources fairly becomes critical. Meanwhile, it is worth noting that in many practical scenarios, resources waiting to be allocated are not infinitely divisible. In this paper, we focus on fair resource allocation for distributed job execution over multiple sites, where resources allocated each time have a minimum requirement. Aiming at the problem, we propose a novel scheme named Distributed Lexicographical Fairness (DLF) targeting to well specify the meaning of fairness in the new scenario considered. To well study DLF, we follow a common research approach that first analyzes its economic properties and then proposes algorithms to output concrete DLF allocations. In our study, we leverage a creative idea that transforms DLF equivalently to a special max flow problem in the integral field. The transformation facilitates our study in that by generalizing basic properties of DLF from the view of network flow, we prove that DLF satisfies Pareto efficiency, envy-freeness, strategy-proofness, relaxed sharing incentive and 12-maximin share. After that, we propose two algorithms. One is a basic algorithm that stimulates a water-filling process. However, our analysis shows that the time complexity is not strongly polynomial. Aiming at such inefficiency, we then propose a new iterative algorithm that comprehensively leverages parametric flow and push-relabel maximal flow techniques. By analyzing the steps of the iterative algorithm, we show that the time complexity is strongly polynomial.

Computational implementation

Following a theoretical analysis of the scope of Nash implementation for a given mechanism, we study the formal framework for computational identification of Nash implementability. We provide computational tools for Nash implementation in finite environments. In particular, we supply Python codes that identify (i) the domain of preferences that allows Nash implementation by a given mechanism, (ii) the maximal domain of preferences that a given mechanism Nash implements Pareto efficiency, (iii) all consistent collections of sets of a given social choice correspondence (SCC), the existence of which is a necessary condition for Nash implementation of this SCC, and (iv) check whether some of the well-known sufficient conditions for Nash implementation hold for a given SCC. Our results exhibit that the computational identification of all collections consistent with an SCC enables the planner to design appealing mechanisms.

Ex-ante Pareto Efficiency for the Random Assignment Problem

Ex-ante Pareto Efficiency for the Random Assignment Problem

Robust Ex-Post Pareto Efficiency and Fairness in Random Assignments: Two Impossibility Results

Robust Ex-Post Pareto Efficiency and Fairness in Random Assignments: Two Impossibility Results

Ex-Ante Pareto Efficiency For the Random Assignment Problem

Ex-Ante Pareto Efficiency For the Random Assignment Problem