With the increasing connectivity enabled by the Internet of Things (IoT), security becomes a critical concern, and users should invest to secure their IoT applications. Due to the massive devices in the IoT network, users cannot be aware of the security policies taken by all its connected neighbors. Instead, a user makes security decisions based on the cyber risks that he perceives by observing a selected number of nodes. To this end, we propose a model which incorporates the limited attention or bounded rationality nature of players in the IoT. Specifically, each individual builds a sparse cognitive network of nodes to respond to. Based on this simplified cognitive network representation, each user then determines his security management policy by minimizing his own real-world security cost. The bounded rational decision-makings of players and their cognitive network formations are interdependent and thus should be addressed in a holistic manner. We establish a games-in-games framework and propose a Gestalt Nash equilibrium (GNE) solution concept to characterize the decisions of agents and quantify their risk of bounded perception due to the limited attention. In addition, we design a proximal-based iterative algorithm to compute the GNE. With case studies of smart communities, the designed algorithm can successfully identify the critical users whose decisions need to be taken into account by the other users during the security management.
Humans with limited knowledge or cognitive resources are bounded rational, since they cannot pay attention to all the information @cite_36 @cite_19 . Gabaix has proposed a sparse max'' operator to model the limited attention of players in which each agent builds a simplified model of the network based on an @math norm @cite_13 . Built upon @cite_5 which includes some preliminary results, our work leverages on the established sparse max'' operator and formulates a constrained game program to capture the bounded cognition ability of players in the IoT. In addition, we further consider the risk management of each user based on their underperceived cyber risks over the network.
@cite_36 @cite_19 @cite_5 @cite_13
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With the increasing connectivity enabled by the Internet of Things (IoT), security becomes a critical concern, and users should invest to secure their IoT applications. Due to the massive devices in the IoT network, users cannot be aware of the security policies taken by all its connected neighbors. Instead, a user makes security decisions based on the cyber risks that he perceives by observing a selected number of nodes. To this end, we propose a model which incorporates the limited attention or bounded rationality nature of players in the IoT. Specifically, each individual builds a sparse cognitive network of nodes to respond to. Based on this simplified cognitive network representation, each user then determines his security management policy by minimizing his own real-world security cost. The bounded rational decision-makings of players and their cognitive network formations are interdependent and thus should be addressed in a holistic manner. We establish a games-in-games framework and propose a Gestalt Nash equilibrium (GNE) solution concept to characterize the decisions of agents and quantify their risk of bounded perception due to the limited attention. In addition, we design a proximal-based iterative algorithm to compute the GNE. With case studies of smart communities, the designed algorithm can successfully identify the critical users whose decisions need to be taken into account by the other users during the security management. With the increasing connectivity enabled by the Internet of Things (IoT), security becomes a critical concern, and the users should invest to secure their IoT applications. Due to the massive devices in the IoT network, users cannot be aware of the security policies taken by all its connected neighbors. Instead, a user makes security decisions based on the cyber risks he perceives by observing a selected number of nodes. To this end, we propose a model which incorporates the limited attention or bounded rationality nature of players in the IoT. Specifically, each individual builds a sparse cognitive network which includes the users to respond to. Based on this simplified cognitive network representation, each user then determines his security investment policy by minimizing his own real-world security cost. The bounded rational decision-makings of players and their cognitive network formations are interdependent, and thus should be addressed in a holistic manner. We propose a Gestalt Nash equilibrium (GNE) solution concept to characterize the decisions of agents. Then, we design a proximal-based iterative algorithm to compute the GNE and show its convergence. With case studies to smart home communities, the designed algorithm can successfully identify the critical users whose decisions need to be taken into account by the other users during the security investment. This paper defines and analyzes a "sparse max" operator, which is a less than fully attentive and rational version of the traditional max operator. The agent builds (as economists do) a simplified model of the world which is sparse, considering only the variables of first-order importance. His stylized model and his resulting choices both derive from constrained optimization. Still, the sparse max remains tractable to compute. Moreover, the induced outcomes reflect basic psychological forces governing limited attention. The sparse max yields a behavioral version of two basic chapters of the microeconomics textbook: consumer demand and competitive equilibrium. We obtain a behavioral version of Marshallian and Hicksian demand, the Slutsky matrix, the Edgeworth box, Roy's identity etc. The Slutsky matrix is no longer symmetric: non-salient prices are associated with anomalously small demand elasticities. Because the consumer exhibits nominal illusion, in the Edgeworth box, the offer curve is a two-dimensional surface rather than a one-dimensional curve. As a result, different aggregate price levels correspond to materially distinct competitive equilibria, in a similar spirit to a Phillips curve. This framework provides a way to assess which parts of basic microeconomics are robust, and which are not, to the assumption of perfect maximization. Cyber insurance is a valuable approach to mitigate further the cyber risk and its loss in addition to the deployment of technological cyber defense solutions, such as intrusion detection systems and firewalls. An effective cyber insurance policy can reduce the number of successful cyber attacks by incentivizing the adoption of preventative measures and the implementation of best practices of the users. To study cyber insurance in a holistic manner, we first establish a bi-level game-theoretic model that nests a zero-sum game in a moral-hazard type of principal-agent game to capture complex interactions between a user, an attacker, and the insurer. The game framework provides an integrative view of the cyber insurance and enables a systematic design of incentive compatible and attack-aware insurance policy. The framework is further extended to study a network of users and their risk interdependencies. We completely characterize the equilibrium solutions of the bi-level game. Our analytical results provide a fundamental limit on insurability, predict the Peltzman effect, and reveal the principles of zero operating profit and the linear insurance policy of the insurer. We provide analytical results and numerical experiments to corroborate the analytical results and demonstrate the network effects as a result of the strategic interactions among the three types of players. High correlation in failure of information systems due to worms and viruses has been cited as major impediment to cyber-insurance. However, of the many cyber-risk classes that influence failure of information systems, not all exhibit similar correlation properties. In this paper, we introduce a new classification of correlation properties of cyber-risks based on a twin-tier approach. At the first tier, is the correlation of cyber-risks within a firm i.e. correlated failure of multiple systems on its internal network. At second tier, is the correlation in risk at a global level i.e. correlation across independent firms in an insurer’s portfolio. Various classes of cyber-risks exhibit dierent level of correlation at two tiers, for instance, insider attacks exhibit high internal but low global correlation. While internal risk correlation within a firm influences its decision to seek insurance, the global correlation influences insurers’ decision in setting the premium. Citing real data we study the combined dynamics of the two-step risk arrival process to determine conditions conducive to the existence of cyber-insurance market. We address technical, managerial and policy choices influencing the correlation at both steps and the business implications thereof. In this paper, we consider a delegated dynamic systemic cyber risk management problem between a resource owner (principal) and a risk manager (agent). The principal can only observe cyber risk outcomes of the network rather than the efforts that the agent spends on protecting the resources. Under this asymmetric information, the principal aims to minimize the systemic cyber risks by designing a dynamic contract that specifies the compensation flows and the anticipated efforts of the manager by taking into account his incentives and rational behaviors. We formulate a bi-level mechanism design problem for dynamic contract design which can be seen as a special class of differential game. We show that the principal has rational controllability of the systemic risk by designing an incentive compatible estimator of the agent's hidden efforts. We characterize the optimal mechanism design by reformulating the problem into a stochastic optimal control program and derive the solution explicitly. We further reveal a separation principle for dynamic risk management where the effort estimation and the compensation design can be achieved separately. The emerging Internet of Things (IoT) applications that leverage ubiquitous connectivity and big data are facilitating the realization of smart everything initiatives. IoT-enabled infrastructures have naturally a multi-layer system architecture with an overlaid or underlaid device network and its coexisting infrastructure network. The connectivity between different components in these two heterogeneous networks plays an important role in delivering real-time information and ensuring a high-level situational awareness. However, IoT- enabled infrastructures face cyber threats due to the wireless nature of communications. Therefore, maintaining the network connectivity in the presence of adversaries is a critical task for the infrastructure network operators. In this paper, we establish a three-player three-stage game-theoretic framework including two network operators and one attacker to capture the secure design of multi- layer infrastructure networks by allocating limited resources. We use subgame perfect Nash equilibrium (SPE) to characterize the strategies of players with sequential moves. In addition, we assess the efficiency of the equilibrium network by comparing with its team optimal solution counterparts in which two network operators can coordinate. We further design a scalable algorithm to guide the construction of the equilibrium IoT-enabled infrastructure networks. Finally, we use case studies on the emerging paradigm of Internet of Battlefield Things (IoBT) to corroborate the obtained results. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. We consider a network creation game in which, each player (vertex) has a limited budget to establish links to other players. In our model, each link has a unit cost and each agent tries to minimize its cost which is its local diameter or its total distance to other players in the (undirected) underlying graph of the created network. Two variants of the game are studied: in the MAX version, the cost incurred to a vertex is the maximum distance between that vertex and other vertices, and in the SUM version, the cost incurred to a vertex is the sum of distances between that vertex and other vertices. We prove that in both versions pure Nash equilibria exist, but the problem of finding the best response of a vertex is NP-hard. Next, we study the maximum possible diameter of an equilibrium graph with n vertices in various cases. For infinite numbers of n, we construct an equilibrium tree with diameter Θ(n) in the MAX version. Also, we prove that the diameter of any equilibrium tree is O(log n) in the SUM version and this bound is tight. When all vertices have unit budgets (i.e. can establish link to just one vertex), the diameter in both versions is O(1). We give an example of equilibrium graph in MAX version, such that all vertices have positive budgets and yet the diameter is as large as Ω(√log n). This interesting result shows that the diameter does not decrease necessarily and may increase as the budgets are increased. For the SUM version, we prove that every equilibrium graph has diameter 2O(√log n) when all vertices have positive budgets. Moreover, if the budget of every players is at least k, then every equilibrium graph with diameter more than 3 is k-connected. We propose to add a new dimension to existing wireless multimedia communications systems by enabling competing stations to proactively engage in the resource management game by adapting their cross-layer transmission strategies. For this, we model wireless stations (WSTAs) as rational and selfish players competing for available wireless resources in a dynamic game. We focus on polling-based wireless LAN (WLAN) networks, where developing an efficient solution for managing the available transmission opportunities is of paramount importance. The resource allocation game is coordinated by a network moderator, which deploys a novel resource management based on the Vickrey-Clarke-Groves (VCG) mechanism to determine a) the amount of time to be allocated to the various users and b) the transmission cost associated to the allocated resources. The transmission cost is referred to in the VCG mechanism as "transfer" and depends not on the used resources, but rather on the inconvenience (in terms of utility impact) that it causes to other WSTAs. The transfer is introduced in order to discourage WSTAs from lying about their resource requirements. Importantly, this proposed dynamic resource management approach for wireless multimedia applications changes the passive way stations are currently adapting their cross-layer strategies by enabling them to selfishly influence the wireless systems dynamics by proactively adapting their packet scheduling strategies, error protection strategies, etc. Hence, each wireless station can play the resource management game by adapting its multimedia transmission strategy depending on the experienced channel conditions, derived video quality, attitude towards risk, willingness to pay for resources and available information about the wireless network. Our simulations show that using the VCG mechanism the WSTAs do not have any incentives to lie about their resource requirements as otherwise they will be severely penalized by a high transfer. We also show that deploying advanced cross-layer strategies for playing the resource management game significantly benefit the WSTAs' received video quality. The willingness-to-pay for resources is introduced to provide WSTAs a tool to gather additional resources whenever they need to transmit an important (part of a) video sequence by agreeing to pay for resources an increased cost. A novel risk-aware scheduling scheme is also proposed that provides WSTAs the ability to dynamically avoid network congestion and hence, reduce their incurred transfer In a complex and uncertain world, humans and animals make decisions under the constraints of limited knowledge, resources, and time. Yet models of rational decision making in economics, cognitive science, biology, and other fields largely ignore these real constraints and instead assume agents with perfect information and unlimited time. About forty years ago, Herbert Simon challenged this view with his notion of "bounded rationality." Today, bounded rationality has become a fashionable term used for disparate views of reasoning. This book promotes bounded rationality as the key to understanding how real people make decisions. Using the concept of an "adaptive toolbox," a repertoire of fast and frugal rules for decision making under uncertainty, it attempts to impose more order and coherence on the idea of bounded rationality. The contributors view bounded rationality neither as optimization under constraints nor as the study of people's reasoning fallacies. The strategies in the adaptive toolbox dispense with optimization and, for the most part, with calculations of probabilities and utilities. The book extends the concept of bounded rationality from cognitive tools to emotions; it analyzes social norms, imitation, and other cultural tools as rational strategies; and it shows how smart heuristics can exploit the structure of environments. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . Historically, papers have been physically bound to the journal in which they were published; but in the digital age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or impact factor (IF). We compare the strength of the relationship between journals' IFs and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' IFs. However, since 1990, the advent of the digital age, the relation between IFs and paper citations has been weakening. This began first in physics, a field that was quick to make the transition into the electronic domain. Furthermore, since 1990 the overall proportion of highly cited papers coming from highly cited journals has been decreasing and, of these highly cited papers, the proportion not coming from highly cited journals has been increasing. Should this pattern continue, it might bring an end to the use of the IF as a way to evaluate the quality of journals, papers, and researchers. © 2012 Wiley Periodicals, Inc. We study a setting where a set of players simultaneously invest in a shared resource. The resource has a probability of failure and a return on investment, both of which are functions of the total investment by all players. We use a simple reference dependent preference model to capture players with heterogeneous risk attitudes (risk seeking, risk neutral and risk averse). We show the existence and uniqueness of a pure strategy Nash equilibrium in this setting and examine the effect of different risk attitudes on players' strategies in the presence of uncertainty. In particular, we show that at the equilibrium, risk averse players are pushed out of the resource by risk seeking players. We compare the failure probabilities in the decentralized (game-theoretic) and centralized settings, and show that our proposed game belongs to the class of best response potential games, for which there are simple dynamics that allow all players to converge to the equilibrium. In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps. Abstract It is now well-known that one can reconstruct sparse or compressible signals accurately from a very limited number of measurements, possibly contaminated with noise. This technique known as “compressed sensing” or “compressive sampling” relies on properties of the sensing matrix such as the restricted isometry property . In this Note, we establish new results about the accuracy of the reconstruction from undersampled measurements which improve on earlier estimates, and have the advantage of being more elegant. To cite this article: E.J. Candes, C. R. Acad. Sci. Paris, Ser. I 346 (2008). This work characterizes the generalization ability of algorithms whose predictions are linear in the input vector. To this end, we provide sharp bounds for Rademacher and Gaussian complexities of (constrained) linear classes, which directly lead to a number of generalization bounds. This derivation provides simplified proofs of a number of corollaries including: risk bounds for linear prediction (including settings where the weight vectors are constrained by either L2 or L1 constraints), margin bounds (including both L2 and L1 margins, along with more general notions based on relative entropy), a proof of the PAC-Bayes theorem, and upper bounds on L2 covering numbers (with Lp norm constraints and relative entropy constraints). In addition to providing a unified analysis, the results herein provide some of the sharpest risk and margin bounds. Interestingly, our results show that the uniform convergence rates of empirical risk minimization algorithms tightly match the regret bounds of online learning algorithms for linear prediction, up to a constant factor of 2.