55,972 research outputs found

    Optimal Algorithm for Bayesian Incentive-Compatible Exploration

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    We consider a social planner faced with a stream of myopic selfish agents. The goal of the social planner is to maximize the social welfare, however, it is limited to using only information asymmetry (regarding previous outcomes) and cannot use any monetary incentives. The planner recommends actions to agents, but her recommendations need to be Bayesian Incentive Compatible to be followed by the agents. Our main result is an optimal algorithm for the planner, in the case that the actions realizations are deterministic and have limited support, making significant important progress on this open problem. Our optimal protocol has two interesting features. First, it always completes the exploration of a priori more beneficial actions before exploring a priori less beneficial actions. Second, the randomization in the protocol is correlated across agents and actions (and not independent at each decision time).Comment: EC 201

    Efficient Candidate Screening Under Multiple Tests and Implications for Fairness

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    When recruiting job candidates, employers rarely observe their underlying skill level directly. Instead, they must administer a series of interviews and/or collate other noisy signals in order to estimate the worker's skill. Traditional economics papers address screening models where employers access worker skill via a single noisy signal. In this paper, we extend this theoretical analysis to a multi-test setting, considering both Bernoulli and Gaussian models. We analyze the optimal employer policy both when the employer sets a fixed number of tests per candidate and when the employer can set a dynamic policy, assigning further tests adaptively based on results from the previous tests. To start, we characterize the optimal policy when employees constitute a single group, demonstrating some interesting trade-offs. Subsequently, we address the multi-group setting, demonstrating that when the noise levels vary across groups, a fundamental impossibility emerges whereby we cannot administer the same number of tests, subject candidates to the same decision rule, and yet realize the same outcomes in both groups

    k-server via multiscale entropic regularization

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    We present an O((logk)2)O((\log k)^2)-competitive randomized algorithm for the kk-server problem on hierarchically separated trees (HSTs). This is the first o(k)o(k)-competitive randomized algorithm for which the competitive ratio is independent of the size of the underlying HST. Our algorithm is designed in the framework of online mirror descent where the mirror map is a multiscale entropy. When combined with Bartal's static HST embedding reduction, this leads to an O((logk)2logn)O((\log k)^2 \log n)-competitive algorithm on any nn-point metric space. We give a new dynamic HST embedding that yields an O((logk)3logΔ)O((\log k)^3 \log \Delta)-competitive algorithm on any metric space where the ratio of the largest to smallest non-zero distance is at most Δ\Delta

    Superconductivity in the presence of strong electron-phonon interactions and frustrated charge order

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    We study the superconductivity of strongly coupled electron-phonon systems where the geometry of the lattice frustrates the charge order by the sign-problem-free Quantum Monte Carlo(QMC) method. The results suggest that with charge order frustrated, the superconductivity can benefit from strong electron-phonon interaction in a wide range of coupling strengths.Comment: 5 pages + supplemental materials, 5 figures; comments are welcom
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