Fairness-efficiency Tradeoff Research Articles

Tipping the balance between fairness and efficiency through temporoparietal stimulation

Maximizing the welfare of society requires distributing goods between groups of people with different preferences. Such decisions are difficult because different moral principles impose irreconcilable solutions. For example, utilitarian efficiency (maximize overall outcome across individuals) may need trade-off against Rawlsian fairness norms (maximize the outcome for the worst-off individual). We identify a brain mechanism enabling decision-makers to solve such trade-offs between efficiency and fairness using separate neuroimaging and sham-controlled brain stimulation experiments. As activity in the temporoparietal junction (TPJ) increases, people are more likely to implement the Rawlsian fairness criterion rather than efficiency or inequality concerns. Strikingly, reducing TPJ excitability with brain stimulation reduces the concern for fairness in fairness-efficiency trade-offs. Moreover, the reduced fairness concerns statistically relate to stimulation-induced reductions in perspective-taking skills as measured in a separate task. Together, our findings not only reveal the neural underpinning of efficiency-fairness trade-offs but also recast the role of TPJ in social decision-making by showing that its perspective-taking function serves to promote fairness for the worst-off rather than efficiency or equality.

Accurate preference-based method to obtain the deterministically optimal and satisfactory fairness-efficiency trade-off

The resource allocation problem is a classic multi-objective challenge, particularly when balancing the fairness-efficiency trade-off. To achieve a deterministically optimal and satisfactory solution, researchers frequently employ preference-based methods, including selecting among Pareto solutions based on the decision-maker's a posteriori preference and using deterministic models incorporating a priori preferences. In this study, we address two main challenges—specifically, (1) the limitations in measuring the abstract concepts of fairness and efficiency and (2) finding a deterministically optimal and satisfactory balance between fairness and efficiency. We apply a Gini impurity index derived from the classification and regression tree to calculate fairness, ensuring the Gini index function's differentiability. Additionally, we unify the scales of fairness and efficiency to facilitate calculation. Using accurate preference information, we employ the extended interval goal programming method to solve the model and achieve a deterministically optimal and satisfactory solution. The comparative analysis results demonstrate that our model (1) efficiently addresses the real-world water resource allocation problem concerning the fairness-efficiency trade-off; and (2) generates fewer penalties, with an average improvement ratio of 8% in the case study, using more refined penalty functions that align closer to the decision-maker's real and nonlinear preferences.

Measuring and Mitigating Group Inequalities In Resource Allocation

Resource allocation, an integral part of socio-economic governance, profoundly influences individual prosperity and has the potential to mitigate or exacerbate socioeconomic disparities. This paper addresses the challenge of equitably allocating finite resources among individuals by answering two fundamental questions: (1) how to accurately measure and test group disparities and (2) how to optimally distribute resources while ensuring group fairness. We propose the Group Beneficiary Disparity (GBD) metric – an evaluation tool engineered to systematically gauge inequalities in a binary beneficiary/non-beneficiary context. The GBD provides decision-makers and planners with a powerful tool to audit social programs and optimize policies from a lens of group equality. We argue that utilitarian decision-makers cannot fully eliminate group disparities even when operating under social welfare constraints. To address this issue, we propose a new resource allocation optimization model, called A-FARM (Asymptotically Fair Allocation of Resources Model), with asymptotic group fairness guarantees. A-FARM partitions individuals into distinct, non-overlapping units and distributes resources among these units based on a utility-based allocation mechanism. Finally, we evaluate the performance of our proposed algorithm using both simulated and real-world data. Our results demonstrate that, A-FARM enables decision-makers to (1) achieve maximum efficiency under group fairness constraints and (2) perform a fairness-efficiency trade-off.

Reshaping National Organ Allocation Policy

Working with U.S. policymakers to redesign national organ allocation The Organ Procurement & Transplantation Network (OPTN), which manages transplantation activities in the United States, recently partnered with the MIT Operations Research Center to design and implement novel organ allocation policies that are more equitable, efficient, and inclusive. National organ allocation policies need to strike a delicate balance between efficiency and fairness in multiple objectives, reconciling often disparate value judgments and priorities from many different stakeholders. In “Reshaping National Organ Allocation Policy,” T. Papalexopoulos, J. Alcorn, and D. Bertsimas et al. introduced a novel optimization- and machine learning-based framework to aid policy design and navigate challenging fairness-efficiency tradeoffs. The authors collaborated with the OPTN to apply the framework to the design of a new national allocation policy for lungs, which was implemented in March 2023 and is anticipated to reduce waitlist mortality by approximately 20%. Based on this success, the authors are now working toward the redesign of the entire U.S. organ allocation system, including kidneys, pancreata, hearts, and livers.

Fair and Efficient Online Allocations

Trade-Offs in Dynamic Allocation Problems Food rescue organizations often receive donations to allocate to food pantries or families. Donations are unpredictable, and goods are often perishable; as a result, allocations have to be made within a short time frame after arrival without knowledge of future arrivals. It is important that donations go to organizations that are able to use them; at the same time, organizations that serve different communities should be treated equitably. In “Fair and Efficient Online Allocations,” Benadè, Kazachkov, Procaccia, Psomas, and Zeng study fairness-efficiency trade-offs in such online allocation problems. Against adversarial arrivals, no algorithm can provide nontrivial guarantees for both these objectives simultaneously. When item values are drawn from (potentially correlated) distributions, there is no trade-off, and a simultaneously fair and efficient algorithm is presented.

Fair navigation planning: A resource for characterizing and designing fairness in mobile robots

In recent years, the development and deployment of autonomous systems such as mobile robots have been increasingly common. Investigating and implementing ethical considerations such as fairness in autonomous systems is an important problem that is receiving increased attention, both because of recent findings of their potential undesired impacts and a related surge in ethical principles and guidelines. In this paper we take a new approach to considering fairness in the design of autonomous systems: we examine fairness by obtaining formal definitions, applying them to a system, and simulating system deployment in order to anticipate challenges. We undertake this analysis in the context of the particular technical problem of robot navigation. We start by showing that there is a fairness dimension to robot navigation, and we then collect and translate several formal definitions of distributive justice into the navigation planning domain. We use a walkthrough example of a rescue robot to bring out design choices and issues that arise during the development of a fair system. We discuss indirect discrimination, fairness-efficiency trade-offs, the existence of counter-productive fairness definitions, privacy and other issues. Finally, we elaborate on important aspects of a research agenda and reflect on the adequacy of our methodology in this paper as a general approach to responsible innovation in autonomous systems.

Social Crowdsourcing to Friends: An Incentive Mechanism for Multi-Resource Sharing

In this paper, we propose a novel game-based incentive mechanism for multi-resource sharing, where users are motivated to share their idle resources in view of conditional voluntary. Through social networking service platforms, such a crowdsourcing service fully explores the significant influence and computing potential of mobile social networks. Specifically, a combination of task allocation process, profit transfer process, and reputation updating process are involved in this sharing incentive mechanism, satisfying truthfulness, individual rationality, and robustness. To maintain the social fairness-efficiency tradeoff, we further develop a resource sharing algorithm on the basis of dominant resource fairness, revealing that the sacrifice of fairness properties is necessary for the improvement of efficiency. Real-world traces from Facebook are numerically studied, validating social fairness and efficiency of our social crowdsourcing mechanism.

Multiresource Allocation: Fairness–Efficiency Tradeoffs in a Unifying Framework

Quantifying the notion of fairness is underexplored when there are multiple types of resources and users request different ratios of the different resources. A typical example is data centers processing jobs with heterogeneous resource requirements on CPU, memory, network bandwidth, etc. In such cases, a tradeoff arises between equitability, or fairness, and efficiency. This paper develops a unifying framework addressing the fairness-efficiency tradeoff in light of multiple types of resources. We develop two families of fairness functions that provide different tradeoffs, characterize the effect of user requests' heterogeneity, and prove conditions under which these fairness measures satisfy the Pareto efficiency, sharing incentive, and envy-free properties. Intuitions behind the analysis are explained in two visualizations of multiresource allocation. We also investigate people's fairness perceptions through an online survey of allocation preferences.

Introducing Fairness-Efficiency Trade-off for Energy Savings in Wireless Cooperative Networks

The focus of this paper is on a wireless cooperative network architecture, where a group of users exploits short-range wireless links to share the costs of a cellular download. To maximize the efficiency of the communication system, an optimization of parameters such as download time, monetary cost, and energy consumption can be implemented. Following this approach different portions of data shall be assigned for download to the involved users, which will then cooperatively exchange the contents on the short-range link. However, the policy of task assignment to the user terminals has a direct influence on the payoff of the single users, raising fairness issues in real implementation scenarios. Focusing on the energy savings introduced by the wireless cooperative network, in this paper we address the fairness issue by relying on game theoretic bargaining solutions. These solutions, have intrinsic properties to nicely model the duality between fairness and efficiency in the performances. An optimal trade-off algorithm between efficiency and fairness is then introduced, allowing the service coordinator to select the most appropriate bargaining solution and energy savings allocation under different constraints on fairness.

Fairness-related challenges in mobile opportunistic networking

The fundamental challenge in opportunistic networking, regardless of the application, is when and how to forward a message. Rank-based forwarding techniques currently represent one of the most promising methods for addressing this message forwarding challenge. While these techniques have demonstrated great efficiency in performance, they do not address the rising concern of fairness amongst various nodes in the network. Higher ranked nodes typically carry the largest burden in delivering messages, which creates a high potential of dissatisfaction amongst them. In this paper, we adopt a real-trace driven approach to study and analyze the trade-offs between efficiency, cost, and fairness of rank-based forwarding techniques in mobile opportunistic networks.Our work comprises three major contributions. First, we quantitatively analyze the trade-off between fair and efficient environments. Second, we demonstrate how fairness coupled with efficiency can be achieved based on real mobility traces. Third, we propose FOG, a real-time distributed framework to ensure efficiency–fairness trade-off using local information. Our framework, FOG, enables state-of-the-art rank-based opportunistic forwarding algorithms to ensure a better fairness–efficiency trade-off while maintaining a low overhead. Within FOG, we implement two real-time distributed fairness algorithms; Proximity Fairness Algorithm (PFA), and Message Context Fairness Algorithm (MCFA). Our data-driven experiments and analysis show that mobile opportunistic communication between users may fail with the absence of fairness in participating high-ranked nodes, and an absolute fair treatment of all users yields inefficient communication performance. Finally our analysis shows that FOG-based algorithms ensure relative equality in the distribution of resource usage among neighbor nodes while keeping the success rate and cost performance near optimal.