arXiv subjects

Y Narahari

Publications and source records attributed to Y Narahari.

7 recordsLinked to original sources

arXiv2022

On Achieving Leximin Fairness and Stability in Many-to-One Matchings

The past few years have seen a surge of work on fairness in allocation problems where items must be fairly divided among agents having individual preferences. In comparison, fairness in settings with preferences on both sides, that is, where agents have to be matched to other agents, has received much less attention. Moreover, two-sided matching literature has largely focused on ordinal preferences. This paper initiates the study of fairness in stable many-to-one matchings under cardinal valuations. Motivated by real-world settings, we study leximin optimality over stable many-to-one matchings. We first investigate matching problems with ranked valuations where all agents on each side have the same preference orders or rankings over the agents on the other side (but not necessarily the same valuations). Here, we provide a complete characterisation of the space of stable matchings. This leads to FaSt, a novel and efficient algorithm to compute a leximin optimal stable matching under ranked isometric valuations (where, for each pair of agents, the valuation of one agent for the other is the same). Building upon FaSt, we present an efficient algorithm, FaSt-Gen, that finds the leximin optimal stable matching for a more general ranked setting. When there are exactly two agents on one side who may be matched to many agents on the other, strict preferences are enough to guarantee an efficient algorithm. We next establish that, in the absence of rankings and under strict preferences (with no restriction on the number of agents on either side), finding a leximin optimal stable matching is NP-Hard. Further, with weak rankings, the problem is strongly NP-Hard, even under isometric valuations. In fact, when additivity and non-negativity are the only assumptions, we show that, unless P=NP, no efficient polynomial factor approximation is possible.

cs.GT↗

arXiv2022

On the Coexistence of Stability and Incentive Compatibility in Fractional Matchings

Stable matchings have been studied extensively in social choice literature. The focus has been mostly on integral matchings, in which the nodes on the two sides are wholly matched. A fractional matching, which is a convex combination of integral matchings, is a natural extension of integral matchings. The topic of stability of fractional matchings has started receiving attention only very recently. Further, incentive compatibility in the context of fractional matchings has received very little attention. With this as the backdrop, our paper studies the important topic of incentive compatibility of mechanisms to find stable fractional matchings. We work with preferences expressed in the form of cardinal utilities. Our first result is an impossibility result that there are matching instances for which no mechanism that produces a stable fractional matching can be incentive compatible or even approximately incentive compatible. This provides the motivation to seek special classes of matching instances for which there exist incentive compatible mechanisms that produce stable fractional matchings. Our study leads to a class of matching instances that admit unique stable fractional matchings. We first show that a unique stable fractional matching for a matching instance exists if and only if the given matching instance satisfies the conditional mutual first preference (CMFP) property. To this end, we provide a polynomial-time algorithm that makes ingenious use of envy-graphs to find a non-integral stable matching whenever the preferences are strict and the given instance is not a CMFP matching instance. For this class of CMFP matching instances, we prove that every mechanism that produces the unique stable fractional matching is (a) incentive compatible and further (b) resistant to coalitional manipulations.

cs.GT↗

arXiv2021

Improving Teacher-Student Interactions in Online Educational Forums using a Markov Chain based Stackelberg Game Model

With the rapid proliferation of the Internet, the area of education has undergone a massive transformation in terms of how students and instructors interact in a classroom. Online learning now takes more than one form, including the use of technology to enhance a face-to-face class, a hybrid class that combines both face-to-face meetings and online work, and fully online courses. Further, online classrooms are usually composed of an online education forum (OEF) where students and instructor discuss open-ended questions for gaining better understanding of the subject. However, empirical studies have repeatedly shown that the dropout rates in these online courses are very high partly due to the lack of motivation among the enrolled students. We undertake an empirical comparison of student behavior in OEFs associated with a graduate-level course during two terms. We identify key parameters dictating the dynamics of OEFs like effective incentive design, student heterogeneity, and super-posters phenomenon. Motivated by empirical observations, we propose an analytical model based on continuous time Markov chains (CTMCs) to capture instructor-student interactions in an OEF. Using concepts from lumpability of CTMCs, we compute steady state and transient probabilities along with expected net-rewards for the instructor and the students. We formulate a mixed-integer linear program which views an OEF as a single-leader-multiple-followers Stackelberg game. Through simulations, we observe that students exhibit varied degree of non-monotonicity in their participation (with increasing instructor involvement). We also study the effect of instructor bias and budget on the student participation levels. Our model exhibits the empirically observed super-poster phenomenon under certain parameter configurations and recommends an optimal plan to the instructor for maximizing student participation in OEFs.

cs.CY↗

arXiv2019

Optimal Mechanisms for Selling Two Items to a Single Buyer Having Uniformly Distributed Valuations

We consider the design of a revenue-optimal mechanism when two items are available to be sold to a single buyer whose valuation is uniformly distributed over an arbitrary rectangle $[c_1,c_1+b_1]\times[c_2,c_2+b_2]$ in the positive quadrant. We provide an explicit, complete solution for arbitrary nonnegative values of $(c_1,c_2,b_1,b_2)$. We identify eight simple structures, each with at most $4$ (possibly stochastic) menu items, and prove that the optimal mechanism has one of these eight structures. We also characterize the optimal mechanism as a function of $(c_1,c_2,b_1,b_2)$. The structures indicate that the optimal mechanism involves (a) an interplay of individual sale and a bundle sale when $c_1$ and $c_2$ are low, (b) a bundle sale when $c_1$ and $c_2$ are high, and (c) an individual sale when one of them is high and the other is low. To the best of our knowledge, our results are the first to show the existence of optimal mechanisms with no exclusion region. We further conjecture, based on promising preliminary results, that our methodology can be extended to a wider class of distributions.

cs.GT↗

arXiv2019

On Optimal Mechanisms in the Two-Item Single-Buyer Unit-Demand Setting

We consider the problem of designing a revenue-optimal mechanism in the two-item, single-buyer, unit-demand setting when the buyer's valuations, $(z_1, z_2)$, are uniformly distributed in an arbitrary rectangle $[c,c+b_1]\times[c,c+b_2]$ in the positive quadrant. We provide a complete and explicit solution for arbitrary nonnegative values of $(c,b_1,b_2)$. We identify five simple structures, each with at most five (possibly stochastic) menu items, and prove that the optimal mechanism has one of the five structures. We also characterize the optimal mechanism as a function of $b_1, b_2$, and $c$. When $c$ is low, the optimal mechanism is a posted price mechanism with an exclusion region; when $c$ is high, it is a posted price mechanism without an exclusion region. Our results are the first to show the existence of optimal mechanisms with no exclusion region, to the best of our knowledge.

cs.GT↗

arXiv2016

A Truthful Mechanism with Biparameter Learning for Online Crowdsourcing

We study a problem of allocating divisible jobs, arriving online, to workers in a crowdsourcing setting which involves learning two parameters of strategically behaving workers. Each job is split into a certain number of tasks that are then allocated to workers. Each arriving job has to be completed within a deadline and each task has to be completed satisfying an upper bound on probability of failure. The job population is homogeneous while the workers are heterogeneous in terms of costs, completion times, and times to failure. The job completion time and time to failure of each worker are stochastic with fixed but unknown means. The requester is faced with the challenge of learning two separate parameters of each (strategically behaving) worker simultaneously, namely, the mean job completion time and the mean time to failure. The time to failure of a worker depends on the duration of the task handled by the worker. Assuming non-strategic workers to start with, we solve this biparameter learning problem by applying the Robust UCB algorithm. Then, we non-trivially extend this algorithm to the setting where the workers are strategic about their costs. Our proposed mechanism is dominant strategy incentive compatible and ex-post individually rational with asymptotically optimal regret performance.

cs.AI↗

arXiv2010

An Optimal Multi-Unit Combinatorial Procurement Auction with Single Minded Bidders

The current art in optimal combinatorial auctions is limited to handling the case of single units of multiple items, with each bidder bidding on exactly one bundle (single minded bidders). This paper extends the current art by proposing an optimal auction for procuring multiple units of multiple items when the bidders are single minded. The auction minimizes the cost of procurement while satisfying Bayesian incentive compatibility and interim individual rationality. Under appropriate regularity conditions, this optimal auction also satisfies dominant strategy incentive compatibility.

cs.GT↗