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Mihaela van der Schaar

Publications and source records attributed to Mihaela van der Schaar.

At least 343 records · Page 19Linked to original sources
arXiv2011

Robust Additively Coupled Games

We study the robust Nash equilibrium (RNE) for a class of games in communications systems and networks where the impact of users on each other is an additive function of their strategies. Each user measures this impact, which may be corrupted by uncertainty in feedback delays, estimation errors, movements of users, etc. To study the outcome of the game in which such uncertainties are encountered, we utilize the worst-case robust optimization theory. The existence and uniqueness conditions of RNE are derived using finite-dimensions variational inequalities. To describe the effect of uncertainty on the performance of the system, we use two criteria measured at the RNE and at the equilibrium of the game without uncertainty. The first is the difference between the respective social utility of users and, the second is the differences between the strategies of users at their respective equilibria. These differences are obtained for the case of a unique NE and multiple NEs. To reach the RNE, we propose a distributed algorithm based on the proximal response map and derive the conditions for its convergence. Simulations of the power control game in interference channels, and Jackson networks validate our analysis.

cs.GT↗
arXiv2011

Strategic Learning and Robust Protocol Design for Online Communities with Selfish Users

This paper focuses on analyzing the free-riding behavior of self-interested users in online communities. Hence, traditional optimization methods for communities composed of compliant users such as network utility maximization cannot be applied here. In our prior work, we show how social reciprocation protocols can be designed in online communities which have populations consisting of a continuum of users and are stationary under stochastic permutations. Under these assumptions, we are able to prove that users voluntarily comply with the pre-determined social norms and cooperate with other users in the community by providing their services. In this paper, we generalize the study by analyzing the interactions of self-interested users in online communities with finite populations and are not stationary. To optimize their long-term performance based on their knowledge, users adapt their strategies to play their best response by solving individual stochastic control problems. The best-response dynamic introduces a stochastic dynamic process in the community, in which the strategies of users evolve over time. We then investigate the long-term evolution of a community, and prove that the community will converge to stochastically stable equilibria which are stable against stochastic permutations. Understanding the evolution of a community provides protocol designers with guidelines for designing social norms in which no user has incentives to adapt its strategy and deviate from the prescribed protocol, thereby ensuring that the adopted protocol will enable the community to achieve the optimal social welfare.

cs.LG↗
arXiv2011

Robust Stackelberg game in communication systems

This paper studies multi-user communication systems with two groups of users: leaders which possess system information, and followers which have no system information using the formulation of Stackelberg games. In such games, the leaders play and choose their actions based on their information about the system and the followers choose their actions myopically according to their observations of the aggregate impact of other users. However, obtaining the exact value of these parameters is not practical in communication systems. To study the effect of uncertainty and preserve the players' utilities in these conditions, we introduce a robust equilibrium for Stackelberg games. In this framework, the leaders' information and the followers' observations are uncertain parameters, and the leaders and the followers choose their actions by solving the worst-case robust optimizations. We show that the followers' uncertain parameters always increase the leaders' utilities and decrease the followers' utilities. Conversely, the leaders' uncertain information reduces the leaders' utilities and increases the followers' utilities. We illustrate our theoretical results with the numerical results obtained based on the power control games in the interference channels.

cs.IT↗
arXiv2011

Reputation-based Incentive Protocols in Crowdsourcing Applications

Crowdsourcing websites (e.g. Yahoo! Answers, Amazon Mechanical Turk, and etc.) emerged in recent years that allow requesters from all around the world to post tasks and seek help from an equally global pool of workers. However, intrinsic incentive problems reside in crowdsourcing applications as workers and requester are selfish and aim to strategically maximize their own benefit. In this paper, we propose to provide incentives for workers to exert effort using a novel game-theoretic model based on repeated games. As there is always a gap in the social welfare between the non-cooperative equilibria emerging when workers pursue their self-interests and the desirable Pareto efficient outcome, we propose a novel class of incentive protocols based on social norms which integrates reputation mechanisms into the existing pricing schemes currently implemented on crowdsourcing websites, in order to improve the performance of the non-cooperative equilibria emerging in such applications. We first formulate the exchanges on a crowdsourcing website as a two-sided market where requesters and workers are matched and play gift-giving games repeatedly. Subsequently, we study the protocol designer's problem of finding an optimal and sustainable (equilibrium) protocol which achieves the highest social welfare for that website. We prove that the proposed incentives protocol can make the website operate close to Pareto efficiency. Moreover, we also examine an alternative scenario, where the protocol designer aims at maximizing the revenue of the website and evaluate the performance of the optimal protocol.

cs.AI↗
arXiv2011

Business Mode Selection in Digital Content Markets

In this paper, we consider a two-sided digital content market, and study which of the two business modes, i.e., Business-to-Customer (B2C) and Customer-to-Customer (C2C), should be selected and when it should be selected. The considered market is managed by an intermediary, through which content producers can sell their contents to consumers. The intermediary can select B2C or C2C as its business mode, while the content producers and consumers are rational agents that maximize their own utilities. The content producers are differentiated by their content qualities. First, given the intermediary's business mode, we show that there always exists a unique equilibrium at which neither the content producers nor the consumers change their decisions. Moreover, if there are a sufficiently large number of consumers, then the decision process based on the content producers' naive expectation can reach the unique equilibrium. Next, we show that in a market with only one intermediary, C2C should be selected if the intermediary aims at maximizing its profit. Then, by considering a particular scenario where the contents are not highly substitutable, we prove that when the intermediary chooses to maximize the social welfare, C2C should be selected if the content producers can receive sufficient compensation for content sales, and B2C should be selected otherwise.

cs.SI↗
arXiv2011

The Theory of Intervention Games for Resource Sharing in Wireless Communications

This paper develops a game-theoretic framework for the design and analysis of a new class of incentive schemes called intervention schemes. We formulate intervention games, propose a solution concept of intervention equilibrium, and prove its existence in a finite intervention game. We apply our framework to resource sharing scenarios in wireless communications, whose non-cooperative outcomes without intervention yield suboptimal performance. We derive analytical results and analyze illustrative examples in the cases of imperfect and perfect monitoring. In the case of imperfect monitoring, intervention schemes can improve the suboptimal performance of non-cooperative equilibrium when the intervention device has a sufficiently accurate monitoring technology, although it may not be possible to achieve the best feasible performance. In the case of perfect monitoring, the best feasible performance can be obtained with an intervention scheme when the intervention device has a sufficiently strong intervention capability.

cs.GT↗
arXiv2011

Minimizing weighted sum download time for one-to-many file transfer in peer-to-peer networks

This paper considers the problem of transferring a file from one source node to multiple receivers in a peer-to-peer (P2P) network. The objective is to minimize the weighted sum download time (WSDT) for the one-to-many file transfer. Previous work has shown that, given an order at which the receivers finish downloading, the minimum WSD can be solved in polynomial time by convex optimization, and can be achieved by linear network coding, assuming that node uplinks are the only bottleneck in the network. This paper, however, considers heterogeneous peers with both uplink and downlink bandwidth constraints specified. The static scenario is a file-transfer scheme in which the network resource allocation remains static until all receivers finish downloading. This paper first shows that the static scenario may be optimized in polynomial time by convex optimization, and the associated optimal static WSD can be achieved by linear network coding. This paper then presented a lower bound to the minimum WSDT that is easily computed and turns out to be tight across a wide range of parameterizations of the problem. This paper also proposes a static routing-based scheme and a static rateless-coding-based scheme which have almost-optimal empirical performances. The dynamic scenario is a file-transfer scheme which can re-allocate the network resource during the file transfer. This paper proposes a dynamic rateless-coding-based scheme, which provides significantly smaller WSDT than the optimal static scenario does.

cs.IT↗
arXiv2011

Social Norms for Online Communities

Sustaining cooperation among self-interested agents is critical for the proliferation of emerging online social communities, such as online communities formed through social networking services. Providing incentives for cooperation in social communities is particularly challenging because of their unique features: a large population of anonymous agents interacting infrequently, having asymmetric interests, and dynamically joining and leaving the community; operation errors; and low-cost reputation whitewashing. In this paper, taking these features into consideration, we propose a framework for the design and analysis of a class of incentive schemes based on a social norm, which consists of a reputation scheme and a social strategy. We first define the concept of a sustainable social norm under which every agent has an incentive to follow the social strategy given the reputation scheme. We then formulate the problem of designing an optimal social norm, which selects a social norm that maximizes overall social welfare among all sustainable social norms. Using the proposed framework, we study the structure of optimal social norms and the impacts of punishment lengths and whitewashing on optimal social norms. Our results show that optimal social norms are capable of sustaining cooperation, with the amount of cooperation varying depending on the community characteristics.

cs.SI↗
arXiv2011

Peer-to-Peer Multimedia Sharing based on Social Norms

Empirical data shows that in the absence of incentives, a peer participating in a Peer-to-Peer (P2P) network wishes to free-riding. Most solutions for providing incentives in P2P networks are based on direct reciprocity, which are not appropriate for most P2P multimedia sharing networks due to the unique features exhibited by such networks: large populations of anonymous agents interacting infrequently, asymmetric interests of peers, network errors, and multiple concurrent transactions. In this paper, we design and rigorously analyze a new family of incentive protocols that utilizes indirect reciprocity which is based on the design of efficient social norms. In the proposed P2P protocols, the social norms consist of a social strategy, which represents the rule prescribing to the peers when they should or should not provide content to other peers, and a reputation scheme, which rewards or punishes peers depending on whether they comply or not with the social strategy. We first define the concept of a sustainable social norm, under which no peer has an incentive to deviate. We then formulate the problem of designing optimal social norms, which selects the social norm that maximizes the network performance among all sustainable social norms. Hence, we prove that it becomes in the self-interest of peers to contribute their content to the network rather than to free-ride. We also investigate the impact of various punishment schemes on the social welfare as well as how should the optimal social norms be designed if altruistic and malicious peers are active in the network. Our results show that optimal social norms are capable of providing significant improvements in the sharing efficiency of multimedia P2P networks.

cs.MM↗
arXiv2010

Designing Incentive Schemes Based on Intervention: The Case of Imperfect Monitoring

We propose an incentive scheme based on intervention to sustain cooperation among self-interested users. In the proposed scheme, an intervention device collects imperfect signals about the actions of the users for a test period, and then chooses the level of intervention that degrades the performance of the network for the remaining time period. We analyze the problems of designing an optimal intervention rule given a test period and choosing an optimal length of the test period. The intervention device can provide the incentive for cooperation by exerting intervention following signals that involve a high likelihood of deviation. Increasing the length of the test period has two counteracting effects on the performance: It improves the quality of signals, but at the same time it weakens the incentive for cooperation due to increased delay.

cs.GT↗
arXiv2010

Designing Incentive Schemes Based on Intervention: The Case of Perfect Monitoring

This paper studies a class of incentive schemes based on intervention, where there exists an intervention device that is able to monitor the actions of users and to take an action that affects the payoffs of users. We consider the case of perfect monitoring, where the intervention device can immediately observe the actions of users without errors. We also assume that there exist actions of the intervention device that are most and least preferred by all the users and the intervention device, regardless of the actions of users. We derive analytical results about the outcomes achievable with intervention, and illustrate our results with an example based on the Cournot model.

cs.GT↗
arXiv2010

Structural Solutions to Dynamic Scheduling for Multimedia Transmission in Unknown Wireless Environments

In this paper, we propose a systematic solution to the problem of scheduling delay-sensitive media data for transmission over time-varying wireless channels. We first formulate the dynamic scheduling problem as a Markov decision process (MDP) that explicitly considers the users' heterogeneous multimedia data characteristics (e.g. delay deadlines, distortion impacts and dependencies etc.) and time-varying channel conditions, which are not simultaneously considered in state-of-the-art packet scheduling algorithms. This formulation allows us to perform foresighted decisions to schedule multiple data units for transmission at each time in order to optimize the long-term utilities of the multimedia applications. The heterogeneity of the media data enables us to express the transmission priorities between the different data units as a priority graph, which is a directed acyclic graph (DAG). This priority graph provides us with an elegant structure to decompose the multi-data unit foresighted decision at each time into multiple single-data unit foresighted decisions which can be performed sequentially, from the high priority data units to the low priority data units, thereby significantly reducing the computation complexity. When the statistical knowledge of the multimedia data characteristics and channel conditions is unknown a priori, we develop a low-complexity online learning algorithm to update the value functions which capture the impact of the current decision on the future utility. The simulation results show that the proposed solution significantly outperforms existing state-of-the-art scheduling solutions.

cs.MM↗
arXiv2010

Intervention Mechanism Design for Networks With Selfish Users

We consider a multi-user network where a network manager and selfish users interact. The network manager monitors the behavior of users and intervenes in the interaction among users if necessary, while users make decisions independently to optimize their individual objectives. In this paper, we develop a framework of intervention mechanism design, which is aimed to optimize the objective of the manager, or the network performance, taking the incentives of selfish users into account. Our framework is general enough to cover a wide range of application scenarios, and it has advantages over existing approaches such as Stackelberg strategies and pricing. To design an intervention mechanism and to predict the resulting operating point, we formulate a new class of games called intervention games and a new solution concept called intervention equilibrium. We provide analytic results about intervention equilibrium and optimal intervention mechanisms in the case of a benevolent manager with perfect monitoring. We illustrate these results with a random access model. Our illustrative example suggests that intervention requires less knowledge about users than pricing.

cs.GT↗
arXiv2010

Reinforcement Learning in BitTorrent Systems

Recent research efforts have shown that the popular BitTorrent protocol does not provide fair resource reciprocation and may allow free-riding. In this paper, we propose a BitTorrent-like protocol that replaces the peer selection mechanisms in the regular BitTorrent protocol with a novel reinforcement learning (RL) based mechanism. Due to the inherent opration of P2P systems, which involves repeated interactions among peers over a long period of time, the peers can efficiently identify free-riders as well as desirable collaborators by learning the behavior of their associated peers. Thus, it can help peers improve their download rates and discourage free-riding, while improving fairness in the system. We model the peers' interactions in the BitTorrent-like network as a repeated interaction game, where we explicitly consider the strategic behavior of the peers. A peer, which applies the RL-based mechanism, uses a partial history of the observations on associated peers' statistical reciprocal behaviors to determine its best responses and estimate the corresponding impact on its expected utility. The policy determines the peer's resource reciprocations with other peers, which would maximize the peer's long-term performance, thereby making foresighted decisions. We have implemented the proposed reinforcement-learning based mechanism and incorporated it into an existing BitTorrent client. We have performed extensive experiments on a controlled Planetlab test bed. Our results confirm that our proposed protocol (1) promotes fairness in terms of incentives to each peer's contribution e.g. high capacity peers improve their download completion time by up to 33\%, (2) improves the system stability and robustness e.g. reducing the peer selection luctuations by 57\%, and (3) discourages free-riding e.g. peers reduce by 64\% their upload to \FR, in comparison to the regular \BT~protocol.

cs.NI↗
arXiv2010

Cognitive MAC Protocols Using Memory for Distributed Spectrum Sharing Under Limited Spectrum Sensing

The main challenges of cognitive radio include spectrum sensing at the physical (PHY) layer to detect the activity of primary users and spectrum sharing at the medium access control (MAC) layer to coordinate access among coexisting secondary users. In this paper, we consider a cognitive radio network in which a primary user shares a channel with secondary users that cannot distinguish the signals of the primary user from those of a secondary user. We propose a class of distributed cognitive MAC protocols to achieve efficient spectrum sharing among the secondary users while protecting the primary user from potential interference by the secondary users. By using a MAC protocol with one-slot memory, we can obtain high channel utilization by the secondary users while limiting interference to the primary user at a low level. The results of this paper suggest the possibility of utilizing MAC design in cognitive radio networks to overcome limitations in spectrum sensing at the PHY layer as well as to achieve spectrum sharing at the MAC layer.

cs.NI↗
arXiv2010

Near-Optimal Deviation-Proof Medium Access Control Designs in Wireless Networks

Distributed medium access control (MAC) protocols are essential for the proliferation of low cost, decentralized wireless local area networks (WLANs). Most MAC protocols are designed with the presumption that nodes comply with prescribed rules. However, selfish nodes have natural motives to manipulate protocols in order to improve their own performance. This often degrades the performance of other nodes as well as that of the overall system. In this work, we propose a class of protocols that limit the performance gain which nodes can obtain through selfish manipulation while incurring only a small efficiency loss. The proposed protocols are based on the idea of a review strategy, with which nodes collect signals about the actions of other nodes over a period of time, use a statistical test to infer whether or not other nodes are following the prescribed protocol, and trigger a punishment if a departure from the protocol is perceived. We consider the cases of private and public signals and provide analytical and numerical results to demonstrate the properties of the proposed protocols.

cs.NI↗
arXiv2010

Structural Solutions For Additively Coupled Sum Constrained Games

We propose and analyze a broad family of games played by resource-constrained players, which are characterized by the following central features: 1) each user has a multi-dimensional action space, subject to a single sum resource constraint; 2) each user's utility in a particular dimension depends on an additive coupling between the user's action in the same dimension and the actions of the other users; and 3) each user's total utility is the sum of the utilities obtained in each dimension. Familiar examples of such multi-user environments in communication systems include power control over frequency-selective Gaussian interference channels and flow control in Jackson networks. In settings where users cannot exchange messages in real-time, we study how users can adjust their actions based on their local observations. We derive sufficient conditions under which a unique Nash equilibrium exists and the best-response algorithm converges globally and linearly to the Nash equilibrium. In settings where users can exchange messages in real-time, we focus on user choices that optimize the overall utility. We provide the convergence conditions of two distributed action update mechanisms, gradient play and Jacobi update.

cs.GT↗