Should Responsibility Affect Who Gets a Kidney?
Responsibility and Healthcare2024
Most people have two kidneys and can live comfortably with only one, if it functions well enough. If both kidneys completely fail, however, patients will die quickly unless they receive kidney dialysis or transplant. Dialysis has severe costs in money, time and discomfort, and patients face approximately a 40 percent chance of mortality after five years on dialysis (USRDS 2020: chapter 5). For these reasons, many dialysis patients eventually need a kidney transplant, after which they have a survival rate of 90 percent after 3–5 years (Briggs, 2001). Unfortunately, there are about 98,000 people in the USA waiting for a kidney transplant, but only around 20,000 kidneys become available each year (OPTN, nd).
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Computing optimal equilibria and mechanisms via learning in zero-sum extensive-form games
Advances in Neural Information Processing Systems2024
We introduce a new approach for computing optimal equilibria via learning in games. It applies to extensive-form settings with any number of players, including mechanism design, information design, and solution concepts such as correlated, communication, and certification equilibria. We observe that optimal equilibria are minimax equilibrium strategies of a player in an extensive-form zero-sum game. This reformulation allows to apply techniques for learning in zero-sum games, yielding the first learning dynamics that converge to optimal equilibria, not only in empirical averages, but also in iterates. We demonstrate the practical scalability and flexibility of our approach by attaining state-of-the-art performance in benchmark tabular games, and by computing an optimal mechanism for a sequential auction design problem using deep reinforcement learning.
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Similarity-based cooperative equilibrium
Advances in Neural Information Processing Systems2024
As machine learning agents act more autonomously in the world, they will increasingly interact with each other. Unfortunately, in many social dilemmas like the one-shot Prisoner’s Dilemma, standard game theory predicts that ML agents will fail to cooperate with each other. Prior work has shown that one way to enable cooperative outcomes in the one-shot Prisoner’s Dilemma is to make the agents mutually transparent to each other, ie, to allow them to access one another’s source code (Rubinstein, 1998; Tennenholtz, 2004)–or weights in the case of ML agents. However, full transparency is often unrealistic, whereas partial transparency is commonplace. Moreover, it is challenging for agents to learn their way to cooperation in the full transparency setting. In this paper, we introduce a more realistic setting in which agents only observe a single number indicating how similar they are to each other. We prove that this allows for the same set of cooperative outcomes as the full transparency setting. We also demonstrate experimentally that cooperation can be learned using simple ML methods.
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Pacing equilibrium in first price auction markets
Management Science2022
Mature internet advertising platforms offer high-level campaign management tools to help advertisers run their campaigns, often abstracting away the intricacies of how each ad is placed and focusing on aggregate metrics of interest to advertisers. On such platforms, advertisers often participate in auctions through a proxy bidder, so the standard incentive analyses that are common in the literature do not apply directly. In this paper, we take the perspective of a budget management system that surfaces aggregated incentives—instead of individual auctions—and compare first and second price auctions. We show that theory offers surprising endorsement for using a first price auction to sell individual impressions.
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Safe Pareto improvements for delegated game playing
Autonomous Agents and Multi-Agent Systems2022
A set of players delegate playing a game to a set of representatives, one for each player. We imagine that each player trusts their respective representative’s strategic abilities. Thus, we might imagine that per default, the original players would simply instruct the representatives to play the original game as best as they can. In this paper, we ask: are there safe Pareto improvements on this default way of giving instructions? That is, we imagine that the original players can coordinate to tell their representatives to only consider some subset of the available strategies and to assign utilities to outcomes differently than the original players. Then can the original players do this in such a way that the payoff is guaranteed to be weakly higher than under the default instructions for all the original players?
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