2020/06/10 by Violet Xinying Chen, John Hooker, Chen, Violet Xinying +1
Decision Sciences · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Decision-Making and Behavioral Economics #FOS: Computer and information sciences #FOS: Mathematics #Game Theory and Voting Systems #Health Systems, Economic Evaluations, Quality of Life #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2006.05963
openalex publication_date 2020/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Optimization models generally aim for efficiency by maximizing total benefit or minimizing cost. Yet a trade-off between fairness and efficiency is an important element of many practical decisions. We propose a principled and practical method for balancing these two criteria in an optimization model. Following a critical assessment of existing schemes, we define a set of social welfare functions (SWFs) that combine Rawlsian leximax fairness and utilitarianism and overcome some of the weaknesses of previous approaches. In particular, we regulate the equity/efficiency trade-off with a single parameter that has a meaningful interpretation in practical contexts. We formulate the SWFs using mixed integer constraints and sequentially maximize them subject to constraints that define the problem at hand. After providing practical step-by-step instructions for implementation, we demonstrate the method on problems of realistic size involving healthcare resource allocation and disaster preparation. The solution times are modest, ranging from a fraction of a second to 18 seconds for a given value of the trade-off parameter.