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  1. Free, publicly-accessible full text available October 30, 2024
  2. Free, publicly-accessible full text available September 1, 2024
  3. For the assignment problem where multiple indivis- ible items are allocated to a group of agents given their ordinal preferences, we design randomized mechanisms that satisfy first-choice maximality (FCM), i.e., maximizing the number of agents as- signed their first choices, together with Pareto- efficiency (PE). Our mechanisms also provide guarantees of ex-ante and ex-post fairness. The generalizedeager Boston mechanism is ex-ante envy-free, and ex-post envy-free up to one item (EF1). The generalized probabilistic Boston mech- anism is also ex-post EF1, and satisfies ex-ante ef- ficiency instead of fairness. We also show that no strategyproof mechanism satisfies ex-post PE, EF1, and FCM simultaneously. In doing so, we expand the frontiers of simultaneously providing efficiency and both ex-ante and ex-post fairness guarantees for the assignment problem. 
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    Free, publicly-accessible full text available August 19, 2024
  4. Free, publicly-accessible full text available July 7, 2024
  5. The Condorcet criterion (CC) is a classical and well-accepted criterion for voting. Unfortunately, it is incompatible with many other desiderata including participation (PAR), half-way monotonicity (HM), Maskin monotonicity (MM), and strategy-proofness (SP). Such incompatibilities are often known as impossibility theorems, and are proved by worst-case analysis. Previous work has investigated the likelihood for these impossibilities to occur under certain models, which are often criticized of being unrealistic.We strengthen previous work by proving the first set of semi-random impossibilities for voting rules to satisfy CC and the more general, group versions of the four desiderata: for any sufficiently large number of voters n, any size of the group 1

     
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    Free, publicly-accessible full text available June 27, 2024
  6. In the assignment problem, the goal is to assign indivisible items to agents who have ordinal preferences, efficiently and fairly, in a strategyproof manner. In practice, first-choice maximality, i.e., assigning a maximal number of agents their top items, is often identified as an important efficiency criterion and measure of agents' satisfaction. In this paper, we propose a natural and intuitive efficiency property, favoring-eagerness-for-remaining-items (FERI), which requires that each item is allocated to an agent who ranks it highest among remaining items, thereby implying first-choice maximality. Using FERI as a heuristic, we design mechanisms that satisfy ex-post or ex-ante variants of FERI together with combinations of other desirable properties of efficiency (Pareto-efficiency), fairness (strong equal treatment of equals and sd-weak-envy-freeness), and strategyproofness (sd-weak-strategyproofness). We also explore the limits of FERI mechanisms in providing stronger efficiency, fairness, or strategyproofness guarantees through impossibility results.

     
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  7. Gerrymandering is the manipulation of redistricting to influence the results of a set of elections for local representatives. Gerrymandering has the potential to drastically swing power in legislative bodies even with no change in a population’s political views. Identifying gerrymandering and measuring fairness using metrics of proposed district plans is a topic of current research, but there is less work on how such plans will be perceived by voters. Gathering data on such perceptions presents several challenges such as the ambiguous definitions of ‘fair’ and the complexity of real world geography and district plans. We present a dataset collected from an online crowdsourcing platform on a survey asking respondents to mark which of two maps of equal population distribution but different districts appear more ‘fair’ and the reasoning for their decision. We performed preliminary analysis on this data and identified which of several commonly suggested metrics are most predictive of the responses. We found that the maximum perimeter of any district was the most predictive metric, especially with participants who reported that they made their decision based on the shape of the districts. 
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  8. Voting is used widely to identify a collective decision for a group of agents, based on their preferences. In this paper, we focus on evaluating and designing voting rules that support both the privacy of the voting agents and a notion of fairness over such agents. To do this, we introduce a novel notion of group fairness and adopt the existing notion of local differential privacy. We then evaluate the level of group fairness in several existing voting rules, as well as the trade-offs between fairness and privacy, showing that it is not possible to always obtain maximal economic efficiency with high fairness or high privacy levels. Then, we present both a machine learning and a constrained optimization approach to design new voting rules that are fair while maintaining a high level of economic efficiency. Finally, we empirically examine the effect of adding noise to create local differentially private voting rules and discuss the three-way trade-off between economic efficiency, fairness, and privacy.This paper appears in the special track on AI & Society. 
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