Research on Decision Model of Emergency Material Distribution Based on Random Information

As an important link of emergency logistics, emergency material distribution has been widely concerned and applied in the development of today’s society. From the consideration of the fairness and efficiency of material distribution, there is a kind of negative and negative relationship between them. As a lever, information updating can be used to solve the problems of fairness and efficiency in the distribution of emergency supplies. Based on fuzzy objective programming, the classification decision model of emergency materials is discussed in this paper.

Discussion of the Relationship between Fairness and Efficiency ——Based on Rawls' “A Theory of Justice”

Since China’s reform and opening up, from the Third Plenary Session of the 14th Central Committee of the Communist Party of China that put forward "efficiency first and fairness" to the 18th National Congress of the Communist Party of China, "first distribution and redistribution must deal with the relationship between fairness and efficiency, and redistribution pays more attention to fairness" Distribution policy, in 2021, the tenth meeting of the Central Finance and Economics Committee clearly stated that it is necessary to promote common prosperity in stages. The relationship between fairness and efficiency has always been the basic principle and standard for the country to formulate policies, and it is also the core issue discussed by scholars. Both utilitarianism and Rawls' two principles of justice provide us with different perspectives to explore the relationship between fairness and efficiency. This article focuses on Rawls's critique of utilitarianism and the specific content of the two justice principles, and makes a simple discussion on the relationship between fairness and efficiency and its enlightenment on the formulation of distribution policies in our country.

Alternation Measures for the Evaluation of Selfish Agents’ Turn-Taking

Multi-Agent Reinforcement Learning reductionist simulations can provide a spectrum of opportunities towards the modeling and understanding of complex social phenomena such as common-pool appropriation. In this paper, a multiplayer variant of Battle-of-the-Exes is suggested as appropriate for experimentation regarding fair and efficient coordination and turn-taking among selfish agents. Going beyond literature’s fairness and efficiency, a novel measure is proposed for turn-taking coordination evaluation, robust to the number of agents and episodes of a system. Six variants of this measure are defined, entitled Alternation Measures or ALT. ALT measures were found sufficient to capture the desired properties (alternation, fair and efficient distribution) in comparison to state-of-the-art measures, thus they were benchmarked and tested through a series of experiments with Reinforcement Learning agents, aspiring to contribute novel tools for a deeper understanding of emergent social outcomes.

A rational delegating computation protocol based on reputation and smart contract

The delegating computation has become an irreversible trend, together comes the pressing need for fairness and efficiency issues. To solve this problem, we leverage game theory to propose a smart contract-based solution. First, according to the behavioral preferences of the participants, we design an incentive contract to describe the motivation of the participants. Next, to satisfy the fairness of the rational delegating computation, we propose a rational delegating computation protocol based on reputation and smart contract. More specifically, rational participants are to gain the maximum utility and reach the Nash equilibrium in the protocol. Besides, we design a reputation mechanism with a reputation certificate, which measures the reputation from multiple dimensions. The reputation is used to assure the client’s trust in the computing party to improve the efficiency of the protocol. Then, we conduct a comprehensive experiment to evaluate the proposed protocol. The simulation and analysis results show that the proposed protocol solves the complex traditional verification problem. We also conduct a feasibility study that involves implementing the contracts in Solidity and running them on the official Ethereum network. Meanwhile, we prove the fairness and correctness of the protocol.

Nash Welfare in the Facility Location Problem

In most facility location research, either an efficient facility placement which minimizes the total cost or a fairer placement which minimizes the maximum cost are typically proposed. To find a solution that is both fair and efficient, we propose converting the agent costs to utilities and placing the facility/ies such that the product of utilities, also known as the Nash welfare, is maximized. We ask whether the Nash welfare's well-studied balance between fairness and efficiency also applies to the facility location setting, and what agent strategic behaviour may occur under this facility placement.

Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling

Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers. However, these platforms can induce unfairness either through an unequal income distribution or disparate treatment of riders. We investigate two methods to reduce forms of inequality in ride-pooling platforms: by incorporating fairness constraints into the objective function and redistributing income to drivers who deserve more. To test these out, we use New York City taxi data to evaluate their performance on both the rider and driver side. For the first method, we find that optimizing for driver fairness out-performs state-of-the-art models in terms of the number of riders serviced, showing that optimizing for fairness can assist profitability in certain circumstances. For the second method, we explore income redistribution as a method to combat income inequality by having drivers keep an $r$ fraction of their income, and contribute the rest to a redistribution pool. For certain values of $r$, most drivers earn near their Shapley value, while still incentivizing drivers to maximize income, thereby avoiding the free-rider problem and reducing income variability. While the first method is useful because it improves both rider and driver-side fairness, the second method is useful because it improves fairness without affecting profitability, and both methods can be combined to improve rider and driver-side fairness.

Attempting—and Failing—to Balance Fairness and Efficiency in the Arbitral System

The Supreme Court has actively expanded the Federal Arbitration Act into realms not originally contemplated by Congress. This harms consumers who are parties to pre-dispute, binding arbitration agreements. If consumers sign a contract containing an arbitration agreement, they may be required to arbitrate everything within the agreement’s scope, including their statutory rights. Simultaneously, the Court has restricted class action arbitration—a device on which consumers have relied when they are forced to arbitrate. The Court’s expansion of arbitration and restriction of class action arbitration has led many to distrust and advocate for changing the arbitral system. Arbitration institutions have directly reacted to the concerns about arbitration by promulgating more rules, procedures, and safeguards to make arbitration fairer for consumers. However, adding rules and procedures is probably not enough to make arbitration proceedings truly fair, and doing so creates a system that is so court-like that arbitration loses its chief benefits—affordability and efficiency. Thus, if the Court continues with its expansive arbitration jurisprudence and its anti-class action arbitration jurisprudence, institutional reaction is an unlikely solution to address arbitration’s fairness concerns.