57 research outputs found

    Groupwise Maximin Fair Allocation of Indivisible Goods

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    We study the problem of allocating indivisible goods among n agents in a fair manner. For this problem, maximin share (MMS) is a well-studied solution concept which provides a fairness threshold. Specifically, maximin share is defined as the minimum utility that an agent can guarantee for herself when asked to partition the set of goods into n bundles such that the remaining (n-1) agents pick their bundles adversarially. An allocation is deemed to be fair if every agent gets a bundle whose valuation is at least her maximin share. Even though maximin shares provide a natural benchmark for fairness, it has its own drawbacks and, in particular, it is not sufficient to rule out unsatisfactory allocations. Motivated by these considerations, in this work we define a stronger notion of fairness, called groupwise maximin share guarantee (GMMS). In GMMS, we require that the maximin share guarantee is achieved not just with respect to the grand bundle, but also among all the subgroups of agents. Hence, this solution concept strengthens MMS and provides an ex-post fairness guarantee. We show that in specific settings, GMMS allocations always exist. We also establish the existence of approximate GMMS allocations under additive valuations, and develop a polynomial-time algorithm to find such allocations. Moreover, we establish a scale of fairness wherein we show that GMMS implies approximate envy freeness. Finally, we empirically demonstrate the existence of GMMS allocations in a large set of randomly generated instances. For the same set of instances, we additionally show that our algorithm achieves an approximation factor better than the established, worst-case bound.Comment: 19 page

    An Algorithmic Approach to Address Course Enrollment Challenges

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    Robust Restless Bandits: Tackling Interval Uncertainty with Deep Reinforcement Learning

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    We introduce Robust Restless Bandits, a challenging generalization of restless multi-arm bandits (RMAB). RMABs have been widely studied for intervention planning with limited resources. However, most works make the unrealistic assumption that the transition dynamics are known perfectly, restricting the applicability of existing methods to real-world scenarios. To make RMABs more useful in settings with uncertain dynamics: (i) We introduce the Robust RMAB problem and develop solutions for a minimax regret objective when transitions are given by interval uncertainties; (ii) We develop a double oracle algorithm for solving Robust RMABs and demonstrate its effectiveness on three experimental domains; (iii) To enable our double oracle approach, we introduce RMABPPO, a novel deep reinforcement learning algorithm for solving RMABs. RMABPPO hinges on learning an auxiliary "λ\lambda-network" that allows each arm's learning to decouple, greatly reducing sample complexity required for training; (iv) Under minimax regret, the adversary in the double oracle approach is notoriously difficult to implement due to non-stationarity. To address this, we formulate the adversary oracle as a multi-agent reinforcement learning problem and solve it with a multi-agent extension of RMABPPO, which may be of independent interest as the first known algorithm for this setting. Code is available at https://github.com/killian-34/RobustRMAB.Comment: 18 pages, 3 figure

    Impact of the non-biodegradable plastics and role of microbes in biotic degradation

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    Plastic is a group of elastic organic compounds whose definition has radically changed from being a large family of useful polymers to an indispensable part of life.  We might say we are residing in the “era of plasticene”. If we simply pause and look around, we would realize that a majority of things in our daily life comprise plastic polymers.  Currently, the international production of these polymers has spiked to around 300 million metric tons annually. Surprisingly about 50 percent of the products are discarded within a year of fabrication.  Once discarded ‘outside’ they end up ‘somewhere’ and start exerting their disruptive consequences.  Despite its enormous utility, it is now being increasingly known that these polymers are surely not without their downsides.  Several steps are taken and even more, are being investigated so the mayhem of plastic doesn't prove for a "no pilot in cockpit" situation. Here we have conducted a review work of the available literature on various biological entities that can utilize plastic while at the same time focusing our attempts to assemble information regarding the probable enzymes that do it.  We have also provided a report on the effect of different plastics on the ecosystem and the various management alternatives out there

    W(0,b)W(0,b) algebra and the dual theory of 3D asymptotically flat higher spin gravity

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    BMS algebra in three spacetime dimensions can be deformed into a two parameter family of algebra known as W(a,b)W(a,b) algebra. For a=0a=0, we show that other than W(0,−1)W(0,-1), no other W(0,b)W(0,b) algebra admits a non-degenerate bilinear and thus one can not have a Chern-Simons gauge theory formulation with them. However, they may appear in a three-dimensional gravity description, where we also need to have a spin 2 generator, that comes from the (a=0,b=−1)(a=0,b=-1) sector. In the present work, we have demonstrated that the asymptotic symmetry algebra of a spin 3 gravity theory on flat spacetime has both the W(0,−1)W(0,-1) and W(0,−2)W(0,-2) algebras as subalgebras. We have also constructed a dual boundary field theory for this higher spin gravity theory by using the Chern-Simons/Wess-Zumino-Witten correspondence.Comment: 27+5 pages; 3 appendice
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