arXiv subjects

Y. Narahari

Publications and source records attributed to Y. Narahari.

43 records · Page 3Linked to original sources

arXiv2012

Mechanism Design for Time Critical and Cost Critical Task Execution via Crowdsourcing

An exciting application of crowdsourcing is to use social networks in complex task execution. In this paper, we address the problem of a planner who needs to incentivize agents within a network in order to seek their help in executing an {\em atomic task} as well as in recruiting other agents to execute the task. We study this mechanism design problem under two natural resource optimization settings: (1) cost critical tasks, where the planner's goal is to minimize the total cost, and (2) time critical tasks, where the goal is to minimize the total time elapsed before the task is executed. We identify a set of desirable properties that should ideally be satisfied by a crowdsourcing mechanism. In particular, {\em sybil-proofness} and {\em collapse-proofness} are two complementary properties in our desiderata. We prove that no mechanism can satisfy all the desirable properties simultaneously. This leads us naturally to explore approximate versions of the critical properties. We focus our attention on approximate sybil-proofness and our exploration leads to a parametrized family of payment mechanisms which satisfy collapse-proofness. We characterize the approximate versions of the desirable properties in cost critical and time critical domain.

cs.GT↗

arXiv2012

Optimal Mix of Incentive Strategies for Product Marketing on Social Networks

We consider the problem of devising incentive strategies for viral marketing of a product. In particular, we assume that the seller can influence penetration of the product by offering two incentive programs: a) direct incentives to potential buyers (influence) and b) referral rewards for customers who influence potential buyers to make the purchase (exploit connections). The problem is to determine the optimal timing of these programs over a finite time horizon. In contrast to algorithmic perspective popular in the literature, we take a mean-field approach and formulate the problem as a continuous-time deterministic optimal control problem. We show that the optimal strategy for the seller has a simple structure and can take both forms, namely, influence-and-exploit and exploit-and-influence. We also show that in some cases it may optimal for the seller to deploy incentive programs mostly for low degree nodes. We support our theoretical results through numerical studies and provide practical insights by analyzing various scenarios.

cs.SI↗

arXiv2011

Topologies and Price of Stability of Complex Strategic Networks with Localized Payoffs : Analytical and Simulation Studies

We analyze a network formation game in a strategic setting where payoffs of individuals depend only on their immediate neighbourhood. We call these payoffs as localized payoffs. In this game, the payoff of each individual captures (1) the gain from immediate neighbors, (2) the bridging benefits, and (3) the cost to form links. This implies that the payoff of each individual can be computed using only its single-hop neighbourhood information. Based on this simple model of network formation, our study explores the structure of networks that form, satisfying one or both of the properties, namely, pairwise stability and efficiency. We analytically prove the pairwise stability of several interesting network structures, notably, the complete bi-partite network, complete equi-k-partite network, complete network and cycle network, under various configurations of the model. We validate and extend these results through extensive simulations. We characterize topologies of efficient networks by drawing upon classical results from extremal graph theory and discover that the Turan graph (or the complete equi-bi-partite network) is the unique efficient network under many configurations of parameters. We examine the tradeoffs between topologies of pairwise stable networks and efficient networks using the notion of price of stability, which is the ratio of the sum of payoffs of the players in an optimal pairwise stable network to that of an efficient network. Interestingly, we find that price of stability is equal to 1 for almost all configurations of parameters in the proposed model; and for the rest of the configurations of the parameters, we obtain a lower bound of 0.5 on the price of stability. This leads to another key insight of this paper: under mild conditions, efficient networks will form when strategic individuals choose to add or delete links based on only localized payoffs.

cs.SI↗

arXiv2011

Incentive Compatible Influence Maximization in Social Networks and Application to Viral Marketing

Information diffusion and influence maximization are important and extensively studied problems in social networks. Various models and algorithms have been proposed in the literature in the context of the influence maximization problem. A crucial assumption in all these studies is that the influence probabilities are known to the social planner. This assumption is unrealistic since the influence probabilities are usually private information of the individual agents and strategic agents may not reveal them truthfully. Moreover, the influence probabilities could vary significantly with the type of the information flowing in the network and the time at which the information is propagating in the network. In this paper, we use a mechanism design approach to elicit influence probabilities truthfully from the agents. We first work with a simple model, the influencer model, where we assume that each user knows the level of influence she has on her neighbors but this is private information. In the second model, the influencer-influencee model, which is more realistic, we determine influence probabilities by combining the probability values reported by the influencers and influencees. In the context of the first model, we present how VCG (Vickrey-Clarke-Groves) mechanisms could be used for truthfully eliciting the influence probabilities. Our main contribution is to design a scoring rule based mechanism in the context of the influencer-influencee model. In particular, we show the incentive compatibility of the mechanisms when the scoring rules are proper and propose a reverse weighted scoring rule based mechanism as an appropriate mechanism to use. We also discuss briefly the implementation of such a mechanism in viral marketing applications.

cs.GT↗

arXiv2010

Multi-Armed Bandit Mechanisms for Multi-Slot Sponsored Search Auctions

In pay-per click sponsored search auctions which are currently extensively used by search engines, the auction for a keyword involves a certain number of advertisers (say k) competing for available slots (say m) to display their ads. This auction is typically conducted for a number of rounds (say T). There are click probabilities mu_ij associated with each agent-slot pairs. The goal of the search engine is to maximize social welfare of the advertisers, that is, the sum of values of the advertisers. The search engine does not know the true values advertisers have for a click to their respective ads and also does not know the click probabilities mu_ij s. A key problem for the search engine therefore is to learn these click probabilities during the T rounds of the auction and also to ensure that the auction mechanism is truthful. Mechanisms for addressing such learning and incentives issues have recently been introduced and are aptly referred to as multi-armed-bandit (MAB) mechanisms. When m = 1, characterizations for truthful MAB mechanisms are available in the literature and it has been shown that the regret for such mechanisms will be O(T^{2/3}). In this paper, we seek to derive a characterization in the realistic but non-trivial general case when m > 1 and obtain several interesting results.

cs.GT↗

arXiv2009

Design of an Optimal Bayesian Incentive Compatible Broadcast Protocol for Ad hoc Networks with Rational Nodes

Nodes in an ad hoc wireless network incur certain costs for forwarding packets since packet forwarding consumes the resources of the nodes. If the nodes are rational, free packet forwarding by the nodes cannot be taken for granted and incentive based protocols are required to stimulate cooperation among the nodes. Existing incentive based approaches are based on the VCG (Vickrey-Clarke-Groves) mechanism which leads to high levels of incentive budgets and restricted applicability to only certain topologies of networks. Moreover, the existing approaches have only focused on unicast and multicast. Motivated by this, we propose an incentive based broadcast protocol that satisfies Bayesian incentive compatibility and minimizes the incentive budgets required by the individual nodes. The proposed protocol, which we call {\em BIC-B} (Bayesian incentive compatible broadcast) protocol, also satisfies budget balance. We also derive a necessary and sufficient condition for the ex-post individual rationality of the BIC-B protocol. The {\em BIC-B} protocol exhibits superior performance in comparison to a dominant strategy incentive compatible broadcast protocol.

cs.GT↗

arXiv2009

On Framework and Hybrid Auction Approach to the Spectrum Licensing Procedure

Inspired by the recent developments in the field of Spectrum Auctions, we have tried to provide a comprehensive framework for the complete procedure of Spectrum Licensing. We have identified the various issues the Governments need to decide upon while designing the licensing procedure and what are the various options available in each issue. We also provide an in depth study of how each of this options impact the overall procedure along with theoretical and practical results from the past. Lastly we argue as to how we can combine the positives two most widely used Spectrum Auctions mechanisms into the Hybrid Multiple Round Auction mechanism being proposed by us.

cs.GT↗