arXiv subjects

Mihaela van der Schaar

Publications and source records attributed to Mihaela van der Schaar.

At least 307 records · Page 17Linked to original sources

arXiv2014

Towards a Theory of Societal Co-Evolution: Individualism versus Collectivism

Substantial empirical research has shown that the level of individualism vs. collectivism is one of the most critical and important determinants of societal traits, such as economic growth, economic institutions and health conditions. But the exact nature of this impact has thus far not been well understood in an analytical setting. In this work, we develop one of the first theoretical models that analytically studies the impact of individualism-collectivism on the society. We model the growth of an individual's welfare (wealth, resources and health) as depending not only on himself, but also on the level of collectivism, i.e. the level of dependence on the rest of the individuals in the society, which leads to a co-evolutionary setting. Based on our model, we are able to predict the impact of individualism-collectivism on various societal metrics, such as average welfare, average life-time, total population, cumulative welfare and average inequality. We analytically show that individualism has a positive impact on average welfare and cumulative welfare, but comes with the drawbacks of lower average life-time, lower total population and higher average inequality.

cs.SI↗

arXiv2014

Jamming Bandits

Can an intelligent jammer learn and adapt to unknown environments in an electronic warfare-type scenario? In this paper, we answer this question in the positive, by developing a cognitive jammer that adaptively and optimally disrupts the communication between a victim transmitter-receiver pair. We formalize the problem using a novel multi-armed bandit framework where the jammer can choose various physical layer parameters such as the signaling scheme, power level and the on-off/pulsing duration in an attempt to obtain power efficient jamming strategies. We first present novel online learning algorithms to maximize the jamming efficacy against static transmitter-receiver pairs and prove that our learning algorithm converges to the optimal (in terms of the error rate inflicted at the victim and the energy used) jamming strategy. Even more importantly, we prove that the rate of convergence to the optimal jamming strategy is sub-linear, i.e. the learning is fast in comparison to existing reinforcement learning algorithms, which is particularly important in dynamically changing wireless environments. Also, we characterize the performance of the proposed bandit-based learning algorithm against multiple static and adaptive transmitter-receiver pairs.

cs.IT↗

arXiv2014

Incentive Design in Peer Review: Rating and Repeated Endogenous Matching

Peer review (e.g., grading assignments in Massive Open Online Courses (MOOCs), academic paper review) is an effective and scalable method to evaluate the products (e.g., assignments, papers) of a large number of agents when the number of dedicated reviewing experts (e.g., teaching assistants, editors) is limited. Peer review poses two key challenges: 1) identifying the reviewers' intrinsic capabilities (i.e., adverse selection) and 2) incentivizing the reviewers to exert high effort (i.e., moral hazard). Some works in mechanism design address pure adverse selection using one-shot matching rules, and pure moral hazard was addressed in repeated games with exogenously given and fixed matching rules. However, in peer review systems exhibiting both adverse selection and moral hazard, one-shot or exogenous matching rules do not link agents' current behavior with future matches and future payoffs, and as we prove, will induce myopic behavior (i.e., exerting the lowest effort) resulting in the lowest review quality. In this paper, we propose for the first time a solution that simultaneously solves adverse selection and moral hazard. Our solution exploits the repeated interactions of agents, utilizes ratings to summarize agents' past review quality, and designs matching rules that endogenously depend on agents' ratings. Our proposed matching rules are easy to implement and require no knowledge about agents' private information (e.g., their benefit and cost functions). Yet, they are effective in guiding the system to an equilibrium where the agents are incentivized to exert high effort and receive ratings that precisely reflect their review quality. Using several illustrative examples, we quantify the significant performance gains obtained by our proposed mechanism as compared to existing one-shot or exogenous matching rules.

cs.GT↗

arXiv2014

Global Bandits with Holder Continuity

Standard Multi-Armed Bandit (MAB) problems assume that the arms are independent. However, in many application scenarios, the information obtained by playing an arm provides information about the remainder of the arms. Hence, in such applications, this informativeness can and should be exploited to enable faster convergence to the optimal solution. In this paper, we introduce and formalize the Global MAB (GMAB), in which arms are globally informative through a global parameter, i.e., choosing an arm reveals information about all the arms. We propose a greedy policy for the GMAB which always selects the arm with the highest estimated expected reward, and prove that it achieves bounded parameter-dependent regret. Hence, this policy selects suboptimal arms only finitely many times, and after a finite number of initial time steps, the optimal arm is selected in all of the remaining time steps with probability one. In addition, we also study how the informativeness of the arms about each other's rewards affects the speed of learning. Specifically, we prove that the parameter-free (worst-case) regret is sublinear in time, and decreases with the informativeness of the arms. We also prove a sublinear in time Bayesian risk bound for the GMAB which reduces to the well-known Bayesian risk bound for linearly parameterized bandits when the arms are fully informative. GMABs have applications ranging from drug and treatment discovery to dynamic pricing.

cs.LG↗

arXiv2014

A Dynamic Network Formation Model for Understanding Bacterial Self-Organization into Micro-Colonies

We propose a general parametrizable model to capture the dynamic interaction among bacteria in the formation of micro-colonies. micro-colonies represent the first social step towards the formation of structured multicellular communities known as bacterial biofilms, which protect the bacteria against antimicrobials. In our model, bacteria can form links in the form of intercellular adhesins (such as polysaccharides) to collaborate in the production of resources that are fundamental to protect them against antimicrobials. Since maintaining a link can be costly, we assume that each bacterium forms and maintains a link only if the benefit received from the link is larger than the cost, and we formalize the interaction among bacteria as a dynamic network formation game. We rigorously characterize some of the key properties of the network evolution depending on the parameters of the system. In particular, we derive the parameters under which it is guaranteed that all bacteria will join micro-colonies and the parameters under which it is guaranteed that some bacteria will not join micro-colonies. Importantly, our study does not only characterize the properties of networks emerging in equilibrium, but it also provides important insights on how the network dynamically evolves and on how the formation history impacts the emerging networks in equilibrium. This analysis can be used to develop methods to influence on- the-fly the evolution of the network, and such methods can be useful to treat or prevent biofilm-related diseases.

q-bio.PE↗

arXiv2014

eTutor: Online Learning for Personalized Education

Given recent advances in information technology and artificial intelligence, web-based education systems have became complementary and, in some cases, viable alternatives to traditional classroom teaching. The popularity of these systems stems from their ability to make education available to a large demographics (see MOOCs). However, existing systems do not take advantage of the personalization which becomes possible when web-based education is offered: they continue to be one-size-fits-all. In this paper, we aim to provide a first systematic method for designing a personalized web-based education system. Personalizing education is challenging: (i) students need to be provided personalized teaching and training depending on their contexts (e.g. classes already taken, methods of learning preferred, etc.), (ii) for each specific context, the best teaching and training method (e.g type and order of teaching materials to be shown) must be learned, (iii) teaching and training should be adapted online, based on the scores/feedback (e.g. tests, quizzes, final exam, likes/dislikes etc.) of the students. Our personalized online system, e-Tutor, is able to address these challenges by learning how to adapt the teaching methodology (in this case what sequence of teaching material to present to a student) to maximize her performance in the final exam, while minimizing the time spent by the students to learn the course (and possibly dropouts). We illustrate the efficiency of the proposed method on a real-world eTutor platform which is used for remedial training for a Digital Signal Processing (DSP) course.

cs.CY↗

arXiv2014

Forecasting Popularity of Videos using Social Media

This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. Social-Forecast explicitly considers the dynamically changing and evolving propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the prediction performance loss of Social-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards.

cs.LG↗

arXiv2014

Dynamic Network Formation with Incomplete Information

How do networks form and what is their ultimate topology? Most of the literature that addresses these questions assumes complete information: agents know in advance the value of linking to other agents, even with agents they have never met and with whom they have had no previous interaction (direct or indirect). This paper addresses the same questions under what seems to us to be the much more natural assumption of incomplete information: agents do not know in advance -- but must learn -- the value of linking to agents they have never met. We show that the assumption of incomplete information has profound implications for the process of network formation and the topology of networks that ultimately form. Under complete information, the networks that form and are stable typically have a star, wheel or core-periphery form, with high-value agents in the core. Under incomplete information, the presence of positive externalities (the value of indirect links) implies that a much wider collection of network topologies can emerge and be stable. Moreover, even when the topologies that emerge are the same, the locations of agents can be very different. For instance, when information is incomplete, it is possible for a hub-and-spokes network with a low-value agent in the center to form and endure permanently: an agent can achieve a central position purely as the result of chance rather than as the result of merit. Perhaps even more strikingly: when information is incomplete, a connected network could form and persist even if, when information were complete, no links would ever form, so that the final form would be a totally disconnected network. All of this can occur even in settings where agents eventually learn everything so that information, although initially incomplete, eventually becomes complete.

cs.SI↗

arXiv2014

Distributed Online Learning in Social Recommender Systems

In this paper, we consider decentralized sequential decision making in distributed online recommender systems, where items are recommended to users based on their search query as well as their specific background including history of bought items, gender and age, all of which comprise the context information of the user. In contrast to centralized recommender systems, in which there is a single centralized seller who has access to the complete inventory of items as well as the complete record of sales and user information, in decentralized recommender systems each seller/learner only has access to the inventory of items and user information for its own products and not the products and user information of other sellers, but can get commission if it sells an item of another seller. Therefore the sellers must distributedly find out for an incoming user which items to recommend (from the set of own items or items of another seller), in order to maximize the revenue from own sales and commissions. We formulate this problem as a cooperative contextual bandit problem, analytically bound the performance of the sellers compared to the best recommendation strategy given the complete realization of user arrivals and the inventory of items, as well as the context-dependent purchase probabilities of each item, and verify our results via numerical examples on a distributed data set adapted based on Amazon data. We evaluate the dependence of the performance of a seller on the inventory of items the seller has, the number of connections it has with the other sellers, and the commissions which the seller gets by selling items of other sellers to its users.

cs.SI↗

arXiv2014

Energy-Efficient Nonstationary Spectrum Sharing

We develop a novel design framework for energy-efficient spectrum sharing among autonomous users who aim to minimize their energy consumptions subject to minimum throughput requirements. Most existing works proposed stationary spectrum sharing policies, in which users transmit at fixed power levels. Since users transmit simultaneously under stationary policies, to fulfill minimum throughput requirements, they need to transmit at high power levels to overcome interference. To improve energy efficiency, we construct nonstationary spectrum sharing policies, in which the users transmit at time-varying power levels. Specifically, we focus on TDMA (time-division multiple access) policies in which one user transmits at each time (but not in a round-robin fashion). The proposed policy can be implemented by each user running a low-complexity algorithm in a decentralized manner. It achieves high energy efficiency even when the users have erroneous and binary feedback about their interference levels. Moreover, it can adapt to the dynamic entry and exit of users. The proposed policy is also deviation-proof, namely autonomous users will find it in their self-interests to follow it. Compared to existing policies, the proposed policy can achieve an energy saving of up to 90% when the number of users is high.

cs.IT↗

arXiv2014

Foresighted Demand Side Management

We consider a smart grid with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve its customers. All the entities in the system are foresighted: each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g. energy generation costs and the aggregators' costs) by dispatching the energy production among the generators. The decision making of the entities is complicated for two reasons. First, the information is decentralized: the ISO does not know the aggregators' states (i.e. their energy consumption requests from customers and the amount of energy in their storage), and each aggregator does not know the other aggregators' states or the ISO's state (i.e. the energy generation costs and the status of the transmission lines). Second, the coupling among the aggregators is unknown to them. Specifically, each aggregator's energy purchase affects the price, and hence the payments of the other aggregators. However, none of them knows how its decision influences the price because the price is determined by the ISO based on its state. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its local information. The proposed framework can achieve the social optimum despite being decentralized and involving complex coupling among the various entities.

cs.MA↗

arXiv2014

Non-stationary Resource Allocation Policies for Delay-constrained Video Streaming: Application to Video over Internet-of-Things-enabled Networks

Due to the high bandwidth requirements and stringent delay constraints of multi-user wireless video transmission applications, ensuring that all video senders have sufficient transmission opportunities to use before their delay deadlines expire is a longstanding research problem. We propose a novel solution that addresses this problem without assuming detailed packet-level knowledge, which is unavailable at resource allocation time. Instead, we translate the transmission delay deadlines of each sender's video packets into a monotonically-decreasing weight distribution within the considered time horizon. Higher weights are assigned to the slots that have higher probability for deadline-abiding delivery. Given the sets of weights of the senders' video streams, we propose the low-complexity Delay-Aware Resource Allocation (DARA) approach to compute the optimal slot allocation policy that maximizes the deadline-abiding delivery of all senders. A unique characteristic of the DARA approach is that it yields a non-stationary slot allocation policy that depends on the allocation of previous slots. We prove that the DARA approach is optimal for weight distributions that are exponentially decreasing in time. We further implement our framework for real-time video streaming in wireless personal area networks that are gaining significant traction within the new Internet-of-Things (IoT) paradigm. For multiple surveillance videos encoded with H.264/AVC and streamed via the 6tisch framework that simulates the IoT-oriented IEEE 802.15.4e TSCH medium access control, our solution is shown to be the only one that ensures all video bitstreams are delivered with acceptable quality in a deadline-abiding manner.

cs.NI↗

arXiv2013

Optimal Foresighted Multi-User Wireless Video

Recent years have seen an explosion in wireless video communication systems. Optimization in such systems is crucial - but most existing methods intended to optimize the performance of multi-user wireless video transmission are inefficient. Some works (e.g. Network Utility Maximization (NUM)) are myopic: they choose actions to maximize instantaneous video quality while ignoring the future impact of these actions. Such myopic solutions are known to be inferior to foresighted solutions that optimize the long-term video quality. Alternatively, foresighted solutions such as rate-distortion optimized packet scheduling focus on single-user wireless video transmission, while ignoring the resource allocation among the users. In this paper, we propose an optimal solution for performing joint foresighted resource allocation and packet scheduling among multiple users transmitting video over a shared wireless network. A key challenge in developing foresighted solutions for multiple video users is that the users' decisions are coupled. To decouple the users' decisions, we adopt a novel dual decomposition approach, which differs from the conventional optimization solutions such as NUM, and determines foresighted policies. Specifically, we propose an informationally-decentralized algorithm in which the network manager updates resource "prices" (i.e. the dual variables associated with the resource constraints), and the users make individual video packet scheduling decisions based on these prices. Because a priori knowledge of the system dynamics is almost never available at run-time, the proposed solution can learn online, concurrently with performing the foresighted optimization. Simulation results show 7 dB and 3 dB improvements in Peak Signal-to-Noise Ratio (PSNR) over myopic solutions and existing foresighted solutions, respectively.

cs.MM↗

arXiv2013

Demand Side Management in Smart Grids using a Repeated Game Framework

Demand side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. To provide incentives for consumers to shift their consumption to off-peak times, the utility company charges consumers differential pricing for using power at different times of the day. Consumers take into account these differential prices when deciding when and how much power to consume daily. Importantly, while consumers enjoy lower billing costs when shifting their power usage to off-peak times, they also incur discomfort costs due to the altering of their power consumption patterns. Existing works propose stationary strategies for the myopic consumers to minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested, foresighted consumers as a repeated energy scheduling game and prove that the stationary strategies are suboptimal in terms of long-term total billing and discomfort costs. Subsequently, we propose a novel framework for determining optimal nonstationary DSM strategies, in which consumers can choose different daily power consumption patterns depending on their preferences, routines, and needs. As a direct consequence of the nonstationary DSM policy, different subsets of consumers are allowed to use power in peak times at a low price. The subset of consumers that are selected daily to have their joint discomfort and billing costs minimized is determined based on the consumers' power consumption preferences as well as on the past history of which consumers have shifted their usage previously. Importantly, we show that the proposed strategies are incentive-compatible. Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50% compared to existing DSM strategies.

eess.SY↗

arXiv2013

Socially-Optimal Design of Service Exchange Platforms with Imperfect Monitoring

In service exchange platforms, anonymous users exchange services with each other: clients request services and are matched to servers who provide services. Because providing good-quality services requires effort, in any single interaction a server will have no incentive to exert effort and will shirk. We show that if current servers will later become clients and want good-quality services, shirking can be eliminated by rating protocols, which maintain ratings for each user, prescribe behavior in each client-server interaction, and update ratings based on whether observed/reported behavior conforms with prescribed behavior. The rating protocols proposed are the first to achieve social optimum even when observation/reporting is imperfect (quality is incorrectly assessed/reported or reports are lost). The proposed protocols are remarkably simple, requiring only binary ratings and three possible prescribed behaviors. Key to the efficacy of the proposed protocols is that they are nonstationary, and tailor prescriptions to both current and past rating distributions.

cs.GT↗

arXiv2013

Incentive Design for Direct Load Control Programs

We study the problem of optimal incentive design for voluntary participation of electricity customers in a Direct Load Scheduling (DLS) program, a new form of Direct Load Control (DLC) based on a three way communication protocol between customers, embedded controls in flexible appliances, and the central entity in charge of the program. Participation decisions are made in real-time on an event-based basis, with every customer that needs to use a flexible appliance considering whether to join the program given current incentives. Customers have different interpretations of the level of risk associated with committing to pass over the control over the consumption schedule of their devices to an operator, and these risk levels are only privately known. The operator maximizes his expected profit of operating the DLS program by posting the right participation incentives for different appliance types, in a publicly available and dynamically updated table. Customers are then faced with the dynamic decision making problem of whether to take the incentives and participate or not. We define an optimization framework to determine the profit-maximizing incentives for the operator. In doing so, we also investigate the utility that the operator expects to gain from recruiting different types of devices. These utilities also provide an upper-bound on the benefits that can be attained from any type of demand response program.

eess.SY↗

arXiv2013

Information Sharing in Networks of Strategic Agents

To ensure that social networks (e.g. opinion consensus, cooperative estimation, distributed learning and adaptation etc.) proliferate and efficiently operate, the participating agents need to collaborate with each other by repeatedly sharing information. However, sharing information is often costly for the agents while resulting in no direct immediate benefit for them. Hence, lacking incentives to collaborate, strategic agents who aim to maximize their own individual utilities will withhold rather than share information, leading to inefficient operation or even collapse of networks. In this paper, we develop a systematic framework for designing distributed rating protocols aimed at incentivizing the strategic agents to collaborate with each other by sharing information. The proposed incentive protocols exploit the ongoing nature of the agents' interactions to assign ratings and through them, determine future rewards and punishments: agents that have behaved as directed enjoy high ratings -- and hence greater future access to the information of others; agents that have not behaved as directed enjoy low ratings -- and hence less future access to the information of others. Unlike existing rating protocols, the proposed protocol operates in a distributed manner, online, and takes into consideration the underlying interconnectivity of agents as well as their heterogeneity. We prove that in many deployment scenarios the price of anarchy (PoA) obtained by adopting the proposed rating protocols is one. In settings in which the PoA is larger than one, we show that the proposed rating protocol still significantly outperforms existing incentive mechanisms such as Tit-for-Tat. Importantly, the proposed rating protocols can also operate efficiently in deployment scenarios where the strategic agents interact over time-varying network topologies where new agents join the network over time.

cs.GT↗

arXiv2013

Designing Efficient Resource Sharing For Impatient Players Using Limited Monitoring

The problem of efficient sharing of a resource is nearly ubiquitous. Except for pure public goods, each agent's use creates a negative externality; often the negative externality is so strong that efficient sharing is impossible in the short run. We show that, paradoxically, the impossibility of efficient sharing in the short run enhances the possibility of efficient sharing in the long run, even if outcomes depend stochastically on actions, monitoring is limited and users are not patient. We base our analysis on the familiar framework of repeated games with imperfect public monitoring, but we extend the framework to view the monitoring structure as chosen by a designer who balances the benefits and costs of more accurate observations and reports. Our conclusions are much stronger than in the usual folk theorems: we do not require a rich signal structure or patient users and provide an explicit online construction of equilibrium strategies.

cs.GT↗