Y. Vorobeychik | Vanderbilt University (original) (raw)
Papers by Y. Vorobeychik
Proceedings of the AAAI Conference on Artificial Intelligence
To penetrate sensitive computer networks, attackers can use spear phishing to sidestep technical ... more To penetrate sensitive computer networks, attackers can use spear phishing to sidestep technical security mechanisms by exploiting the privileges of careless users. In order to maximize their success probability, attackers have to target the users that constitute the weakest links of the system. The optimal selection of these target users takes into account both the damage that can be caused by a user and the probability of a malicious e-mail being delivered to and opened by a user. Since attackers select their targets in a strategic way, the optimal mitigation of these attacks requires the defender to also personalize the e-mail filters by taking into account the users' properties. In this paper, we assume that a learned classifier is given and propose strategic per-user filtering thresholds for mitigating spear-phishing attacks. We formulate the problem of filtering targeted and non-targeted malicious e-mails as a Stackelberg security game. We characterize the optimal filterin...
adaptive agents and multi-agents systems, May 6, 2013
Counterinsurgency, which is the effort to mitigate support for an opposing organization, is one s... more Counterinsurgency, which is the effort to mitigate support for an opposing organization, is one such domain that has been studied recently and past work has modeled the problem as an influence blocking maximization that features an influencer and a mitigator. While past work has introduced scalable heuristic techniques for generating effective strategies using a double oracle algorithm, it has not addressed the issue of uncertainty and asymmetric information, which is the topic of this paper.
adaptive agents and multi-agents systems, Jun 4, 2012
We introduce a novel framework for computing optimal randomized security policies in networked do... more We introduce a novel framework for computing optimal randomized security policies in networked domains which extends previous approaches in several ways. First, we extend previous linear programming techniques for Stackelberg security games to incorporate benefits and costs of arbitrary security configurations on individual assets. Second, we offer a principled model of failure cascades that allows us to capture both the direct and indirect value of assets, and extend this model to capture uncertainty about the structure of the interdependency network. Third, we extend the linear programming formulation to account for exogenous (random) failures in addition to targeted attacks. Fourth, we allow the attacker to choose among several capabilities in attacking a target, and, in a limited way, allow the attacker to attack multiple targets simultaneously. The goal of our work is twofold. First, we offer techniques to compute optimal security strategies in realistic settings involving interdependent security. Second, our computational framework enables us to attain theoretical insights about security on networks.
Physical review. E, 2016
We present a characterization of short-term stability of Kauffman's NK (random) Boolean netwo... more We present a characterization of short-term stability of Kauffman's NK (random) Boolean networks under arbitrary distributions of transfer functions. Given such a Boolean network where each transfer function is drawn from the same distribution, we present a formula that determines whether short-term chaos (damage spreading) will happen. Our main technical tool which enables the formal proof of this formula is the Fourier analysis of Boolean functions, which describes such functions as multilinear polynomials over the inputs. Numerical simulations on mixtures of threshold functions and nested canalyzing functions demonstrate the formula's correctness.
We present a series of results providing evidence that the incentive problem with VCG-based mecha... more We present a series of results providing evidence that the incentive problem with VCG-based mechanisms is not very severe. Our first result uses average-case analysis to show that if an algorithm can solve the allocation problem well for a large proportion of instances, incentives to lie essentially disappear. We next show that even if such in- centives exist, a simple enhancement of the mechanism makes it unlikely that any player will find an improving deviation. In the experimental part of the paper, we demonstrate that incentives to lie decrease with increasing problem complexity. However, we also note that if incentives to lie do exist, they can have a negative impact on welfare.
![Research paper thumbnail of Price prediction in a trading agent competition [Extended Abstract]](https://attachments.academia-assets.com/91014044/thumbnails/1.jpg)
](Proceedings of the 4th ACM conference on Electronic commerce - EC '03, 2003
The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of tra... more The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game.
Lecture Notes in Computer Science, 2011
We report on a series of behavioral experiments in social networks in which human subjects contin... more We report on a series of behavioral experiments in social networks in which human subjects continuously choose to play either a dominant role (called a King) or a submissive one (called a Pawn). Kings receive a higher payoff rate, but only if all their network neighbors are Pawns, and thus the maximum social welfare states correspond to maximum independent sets. We document that fairness is of vital importance in driving interactions between players. First, we find that payoff disparities between network neighbors gives rise to conflict, and the specifics depend on the network topology. However, allowing Kings to offer "tips" or side payments to their neighbors substantially reduces conflict, and consistently increases social welfare. Finally, we observe that tip reductions lead to increased conflict. We describe these and a broad set of related findings.
When learning, such as classification, is used in adversarial settings, such as intrusion detecti... more When learning, such as classification, is used in adversarial settings, such as intrusion detection, intelligent adversaries will attempt to evade the resulting policies. The literature on adversarial machine learning aims to develop learning algorithms which are robust to such adversarial evasion, but exhibits two significant limitations: a) failure to account for operational constraints and b) a restriction that decisions are deterministic. To overcome these limitations, we introduce a conceptual separation between learning, used to infer attacker preferences, and operational decisions, which account for adversarial evasion, enforce operational constraints, and naturally admit randomization. Our approach gives rise to an intractably large linear program. To overcome scalability limitations, we introduce a novel method for estimating a compact parity basis representation for the operational decision function. Additionally, we develop an iterative constraint generation approach which embeds adversary's best response calculation, to arrive at a scalable algorithm for computing near-optimal randomized operational decisions. Extensive experiments demonstrate the efficacy of our approach. 1 Introduction Success of machine learning across a variety of domains has naturally led to its adoption as a tool in security
Proceedings of the 13th ACM Conference on Electronic Commerce - EC '12, 2012
We report on an extensive series of behavioral experiments in which 36 human subjects collectivel... more We report on an extensive series of behavioral experiments in which 36 human subjects collectively build a communication network over which they must solve a competitive coordination task for monetary compensation. There is a cost for creating network links, thus creating a tension between link expenditures and collective and individual incentives. Our most striking finding is the poor performance of the subjects, especially compared to our long series of prior experiments. We demonstrate that the subjects built difficult networks for the coordination task, and compare the structural properties of the built networks to standard generative models of social networks. We also provide extensive analysis of the individual and collective behavior of the subjects, including free riding and factors influencing edge purchasing decisions.
International Journal of Electronic Business, 2008
We analyze symmetric pure strategy equilibria in dynamic sponsored search auction games using sim... more We analyze symmetric pure strategy equilibria in dynamic sponsored search auction games using simulations, restricting the strategies to several in a class of greedy bidding strategies introduced by Cary et al. We show that a particular convergent strategy, "balanced bidding", also exhibits high stability to deviations in the dynamic setting. On the other hand, a cooperative strategy which yields high payoffs to all players is not sustainable in equilibrium play. Additionally, we analyze a repeated game in which each stage is a static complete-information sponsored search game. In this setting, we demonstrate a collusion strategy which yields high payoffs to all players and empirically show it to be sustainable over a range of settings. Finally, we show how a collusive strategy profile can arise even in the case of incomplete information.
ACM SIGecom Exchanges, 2009
Keyword auctions account for an enormous proportion of revenue for the major search engines. Cons... more Keyword auctions account for an enormous proportion of revenue for the major search engines. Consequently, substantial literature analyzing alternative auction designs has sprouted in recent years. We contribute to this growing literature by engaging in a simulation-based analysis of strategic interactions in keyword auctions which are difficult to analyze in closed form. In this letter we provide an overview of two specific efforts in this direction. The first effort presents analysis of the dynamic bidding strategies, while the second effort is performed in a static context, but involves a close analysis of several classes of Bayesian bidding strategies. Both these efforts attempt to bridge the gap between the mostly theoretical literature to date and real auctions.
ACM SIGecom Exchanges, 2011
Security, like many other complex decisions, is generally approached with a divide-and-conquer mi... more Security, like many other complex decisions, is generally approached with a divide-and-conquer mindset. Consequences of security failures, however, can rarely be completely localized: an explosion or a fire at one building can affect neighboring structures, a debt crisis in Greece resonates throughout the tightly connected European and US financial markets, and a breach of security at one computer can facilitate access to others on the same network. It is thus crucial to view security holistically, and devise security strategies that explicitly account for interdependencies between valuable assets. Here we provide an overview of two recent approaches to security with network effects. The first approach takes a centralized perspective, attempting to compute an optimal security configuration for all interdependent assets. This approach explicitly accounts for an intelligent adversary optimally attacking one of the assets. The second approach studies the impact of decentralized decision making when local failures can propagate in complex ways through the entire system, but assumes that initial failures are random.
Many advanced technical tools are available to prevent attacks on national infrastructure. Nevert... more Many advanced technical tools are available to prevent attacks on national infrastructure. Nevertheless, while traditional analyses of security problems have succeeded in producing good technical solutions, they have often ignored the human factor integral to these problems. Human attackers (who may be individuals or state-level attackers) expend substantial effort to breach security because they have the incentive for doing so. People involved in implementing security follow individual incentives, which need not align with global security concerns; consequently, desired security solutions are often implemented poorly, or not at all. This complex interplay between individual incentives and global (organizational and/or national) goals can be modeled and analyzed using game theoretic techniques. By analyzing not only what is possible, but also what is motivated, a holistic approach to security problems can be developed, informing policy and providing tools to policy makers. Most of the work described here was performed in collaboration with a diverse group of people. Chapter 3 was a joint effort with Joshua Letchford from Duke University. Chapter 4 was a collaboration with Milind Tambe and Bo An from USC and Satinder Singh from the University of Michigan. Chapter 5 was a collaboration with Sandians Jackson Mayo, Rob Armstrong, and Joe Ruthruff. A portion of Chapter 3 was published in the Conference on Uncertainty in Artificial Intelligence, 2012, and I would like to thank anonymous referees for their comments and suggestions. A portion of Chapter 4 was published in the National Conference on Artificial Intelligence, 2012, and I would like to thank the referees for their comments, and Vincent Conitzer (from Duke University) from demonstrating that our original claim about stationary Markov policies was incorrect and contributing to its correction. The material in Chapter 5 was published in Physical Review Letters, 2011, and I would like to thank the anonymous referees for their comments and suggestions. The material in Chapter 6 benefited from discussions with Noel Nachtigal,
Autonomous Agents & Multiagent Systems/Agent Theories, Architectures, and Languages, 2008
We define the class of games called simulation-based games, in which the payos are available as a... more We define the class of games called simulation-based games, in which the payos are available as an output of an or- acle (simulator), rather than specified analytically or us- ing a payo matrix. We then describe a convergent algo- rithm based on a hierarchical application of simulated an- nealing for estimating Nash equilibria in simulation-based games with finite-dimensional strategy sets.
Autonomous Agents & Multiagent Systems/Agent Theories, Architectures, and Languages, 2010
A dynamic model of a multiagent system defines a proba- bility distribution over possible system ... more A dynamic model of a multiagent system defines a proba- bility distribution over possible system behaviors over time. Alternative representations for such models present trade- os in expressive power, and accuracy and cost for inferen- tial tasks of interest. In a history-dependent representation, behavior at a given time is specified as a probabilistic func- tion of some portion of system
2014 7th International Symposium on Resilient Control Systems (ISRCS), 2014
In this paper, we propose a scheme for a resilient distributed consensus problem through a set of... more In this paper, we propose a scheme for a resilient distributed consensus problem through a set of trusted nodes within the network. Currently, algorithms that solve resilient consensus problem demand networks to have high connectivity to overrule the effects of adversaries, or require nodes to have access to some non-local information. In our scheme, we incorporate the notion of trusted nodes to guarantee distributed consensus despite any number of adversarial attacks, even in sparse networks. A subset of nodes, which are more secured against the attacks, constitute a set of trusted nodes. It is shown that the network becomes resilient against any number of attacks whenever the set of trusted nodes form a connected dominating set within the network. We also study a relationship between trusted nodes and the network robustness. Simulations are presented to illustrate and compare our scheme with the existing ones.
2013 International Conference on Social Computing, 2013
Influence blocking games have been used to model adversarial domains with a social component, suc... more Influence blocking games have been used to model adversarial domains with a social component, such as counterinsurgency. In these games, a mitigator attempts to minimize the efforts of an influencer to spread his agenda across a social network. Previous work has assumed that the influence graph structure is known with certainty by both players. However, in reality, there is often significant information asymmetry between the mitigator and the influencer. We introduce a model of this information asymmetry as a two-player zero-sum Bayesian game. Nearly all past work in influence maximization and social network analysis suggests that graph structure is fundamental in strategy generation, leading to an expectation that solving the Bayesian game exactly is crucial. Surprisingly, we show through extensive experimentation on synthetic and real-world social networks that many common forms of uncertainty can be addressed nearoptimally by ignoring the vast majority of it and simply solving an abstracted game with a few randomly chosen types. This suggests that optimal strategies of games that do not model the full range of uncertainty in influence blocking games are typically robust to uncertainty about the influence graph structure.
When exploring a game over a large strategy space, it may not be feasible or cost-effective to ev... more When exploring a game over a large strategy space, it may not be feasible or cost-effective to evaluate the payoff of every r elevant strategy profile. For example, determining a profile payoff f or a procedurally defined game may require Monte Carlo simulatio n or other costly computation. Analyzing such games poses a search problem, with the goal of identifying
Physical Review Letters, 2011
We introduce noncooperatively optimized tolerance (NOT), a generalization of highly optimized tol... more We introduce noncooperatively optimized tolerance (NOT), a generalization of highly optimized tolerance (HOT) that involves strategic (game theoretic) interactions between parties in a complex system. We illustrate our model in the forest fire (percolation) framework. As the number of players increases, our model retains features of HOT, such as robustness, high yield combined with high density, and self-dissimilar landscapes, but also develops features of self-organized criticality (SOC) when the number of players is large enough. For example, the forest landscape becomes increasingly homogeneous and protection from adverse events (lightning strikes) becomes less closely correlated with the spatial distribution of these events. While HOT is a special case of our model, the resemblance to SOC is only partial; for example, the distribution of cascades, while becoming increasingly heavy-tailed as the number of players increases, also deviates more significantly from a power law in this regime. Surprisingly, the system retains considerable robustness even as it becomes fractured, due in part to emergent cooperation between neighboring players. At the same time, increasing homogeneity promotes resilience against changes in the lightning distribution, giving rise to intermediate regimes where the system is robust to a particular distribution of adverse events, yet not very fragile to changes.
Proceedings of the AAAI Conference on Artificial Intelligence
To penetrate sensitive computer networks, attackers can use spear phishing to sidestep technical ... more To penetrate sensitive computer networks, attackers can use spear phishing to sidestep technical security mechanisms by exploiting the privileges of careless users. In order to maximize their success probability, attackers have to target the users that constitute the weakest links of the system. The optimal selection of these target users takes into account both the damage that can be caused by a user and the probability of a malicious e-mail being delivered to and opened by a user. Since attackers select their targets in a strategic way, the optimal mitigation of these attacks requires the defender to also personalize the e-mail filters by taking into account the users' properties. In this paper, we assume that a learned classifier is given and propose strategic per-user filtering thresholds for mitigating spear-phishing attacks. We formulate the problem of filtering targeted and non-targeted malicious e-mails as a Stackelberg security game. We characterize the optimal filterin...
adaptive agents and multi-agents systems, May 6, 2013
Counterinsurgency, which is the effort to mitigate support for an opposing organization, is one s... more Counterinsurgency, which is the effort to mitigate support for an opposing organization, is one such domain that has been studied recently and past work has modeled the problem as an influence blocking maximization that features an influencer and a mitigator. While past work has introduced scalable heuristic techniques for generating effective strategies using a double oracle algorithm, it has not addressed the issue of uncertainty and asymmetric information, which is the topic of this paper.
adaptive agents and multi-agents systems, Jun 4, 2012
We introduce a novel framework for computing optimal randomized security policies in networked do... more We introduce a novel framework for computing optimal randomized security policies in networked domains which extends previous approaches in several ways. First, we extend previous linear programming techniques for Stackelberg security games to incorporate benefits and costs of arbitrary security configurations on individual assets. Second, we offer a principled model of failure cascades that allows us to capture both the direct and indirect value of assets, and extend this model to capture uncertainty about the structure of the interdependency network. Third, we extend the linear programming formulation to account for exogenous (random) failures in addition to targeted attacks. Fourth, we allow the attacker to choose among several capabilities in attacking a target, and, in a limited way, allow the attacker to attack multiple targets simultaneously. The goal of our work is twofold. First, we offer techniques to compute optimal security strategies in realistic settings involving interdependent security. Second, our computational framework enables us to attain theoretical insights about security on networks.
Physical review. E, 2016
We present a characterization of short-term stability of Kauffman's NK (random) Boolean netwo... more We present a characterization of short-term stability of Kauffman's NK (random) Boolean networks under arbitrary distributions of transfer functions. Given such a Boolean network where each transfer function is drawn from the same distribution, we present a formula that determines whether short-term chaos (damage spreading) will happen. Our main technical tool which enables the formal proof of this formula is the Fourier analysis of Boolean functions, which describes such functions as multilinear polynomials over the inputs. Numerical simulations on mixtures of threshold functions and nested canalyzing functions demonstrate the formula's correctness.
We present a series of results providing evidence that the incentive problem with VCG-based mecha... more We present a series of results providing evidence that the incentive problem with VCG-based mechanisms is not very severe. Our first result uses average-case analysis to show that if an algorithm can solve the allocation problem well for a large proportion of instances, incentives to lie essentially disappear. We next show that even if such in- centives exist, a simple enhancement of the mechanism makes it unlikely that any player will find an improving deviation. In the experimental part of the paper, we demonstrate that incentives to lie decrease with increasing problem complexity. However, we also note that if incentives to lie do exist, they can have a negative impact on welfare.
Proceedings of the 4th ACM conference on Electronic commerce - EC '03, 2003
The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of tra... more The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game.
Lecture Notes in Computer Science, 2011
We report on a series of behavioral experiments in social networks in which human subjects contin... more We report on a series of behavioral experiments in social networks in which human subjects continuously choose to play either a dominant role (called a King) or a submissive one (called a Pawn). Kings receive a higher payoff rate, but only if all their network neighbors are Pawns, and thus the maximum social welfare states correspond to maximum independent sets. We document that fairness is of vital importance in driving interactions between players. First, we find that payoff disparities between network neighbors gives rise to conflict, and the specifics depend on the network topology. However, allowing Kings to offer "tips" or side payments to their neighbors substantially reduces conflict, and consistently increases social welfare. Finally, we observe that tip reductions lead to increased conflict. We describe these and a broad set of related findings.
When learning, such as classification, is used in adversarial settings, such as intrusion detecti... more When learning, such as classification, is used in adversarial settings, such as intrusion detection, intelligent adversaries will attempt to evade the resulting policies. The literature on adversarial machine learning aims to develop learning algorithms which are robust to such adversarial evasion, but exhibits two significant limitations: a) failure to account for operational constraints and b) a restriction that decisions are deterministic. To overcome these limitations, we introduce a conceptual separation between learning, used to infer attacker preferences, and operational decisions, which account for adversarial evasion, enforce operational constraints, and naturally admit randomization. Our approach gives rise to an intractably large linear program. To overcome scalability limitations, we introduce a novel method for estimating a compact parity basis representation for the operational decision function. Additionally, we develop an iterative constraint generation approach which embeds adversary's best response calculation, to arrive at a scalable algorithm for computing near-optimal randomized operational decisions. Extensive experiments demonstrate the efficacy of our approach. 1 Introduction Success of machine learning across a variety of domains has naturally led to its adoption as a tool in security
Proceedings of the 13th ACM Conference on Electronic Commerce - EC '12, 2012
We report on an extensive series of behavioral experiments in which 36 human subjects collectivel... more We report on an extensive series of behavioral experiments in which 36 human subjects collectively build a communication network over which they must solve a competitive coordination task for monetary compensation. There is a cost for creating network links, thus creating a tension between link expenditures and collective and individual incentives. Our most striking finding is the poor performance of the subjects, especially compared to our long series of prior experiments. We demonstrate that the subjects built difficult networks for the coordination task, and compare the structural properties of the built networks to standard generative models of social networks. We also provide extensive analysis of the individual and collective behavior of the subjects, including free riding and factors influencing edge purchasing decisions.
International Journal of Electronic Business, 2008
We analyze symmetric pure strategy equilibria in dynamic sponsored search auction games using sim... more We analyze symmetric pure strategy equilibria in dynamic sponsored search auction games using simulations, restricting the strategies to several in a class of greedy bidding strategies introduced by Cary et al. We show that a particular convergent strategy, "balanced bidding", also exhibits high stability to deviations in the dynamic setting. On the other hand, a cooperative strategy which yields high payoffs to all players is not sustainable in equilibrium play. Additionally, we analyze a repeated game in which each stage is a static complete-information sponsored search game. In this setting, we demonstrate a collusion strategy which yields high payoffs to all players and empirically show it to be sustainable over a range of settings. Finally, we show how a collusive strategy profile can arise even in the case of incomplete information.
ACM SIGecom Exchanges, 2009
Keyword auctions account for an enormous proportion of revenue for the major search engines. Cons... more Keyword auctions account for an enormous proportion of revenue for the major search engines. Consequently, substantial literature analyzing alternative auction designs has sprouted in recent years. We contribute to this growing literature by engaging in a simulation-based analysis of strategic interactions in keyword auctions which are difficult to analyze in closed form. In this letter we provide an overview of two specific efforts in this direction. The first effort presents analysis of the dynamic bidding strategies, while the second effort is performed in a static context, but involves a close analysis of several classes of Bayesian bidding strategies. Both these efforts attempt to bridge the gap between the mostly theoretical literature to date and real auctions.
ACM SIGecom Exchanges, 2011
Security, like many other complex decisions, is generally approached with a divide-and-conquer mi... more Security, like many other complex decisions, is generally approached with a divide-and-conquer mindset. Consequences of security failures, however, can rarely be completely localized: an explosion or a fire at one building can affect neighboring structures, a debt crisis in Greece resonates throughout the tightly connected European and US financial markets, and a breach of security at one computer can facilitate access to others on the same network. It is thus crucial to view security holistically, and devise security strategies that explicitly account for interdependencies between valuable assets. Here we provide an overview of two recent approaches to security with network effects. The first approach takes a centralized perspective, attempting to compute an optimal security configuration for all interdependent assets. This approach explicitly accounts for an intelligent adversary optimally attacking one of the assets. The second approach studies the impact of decentralized decision making when local failures can propagate in complex ways through the entire system, but assumes that initial failures are random.
Many advanced technical tools are available to prevent attacks on national infrastructure. Nevert... more Many advanced technical tools are available to prevent attacks on national infrastructure. Nevertheless, while traditional analyses of security problems have succeeded in producing good technical solutions, they have often ignored the human factor integral to these problems. Human attackers (who may be individuals or state-level attackers) expend substantial effort to breach security because they have the incentive for doing so. People involved in implementing security follow individual incentives, which need not align with global security concerns; consequently, desired security solutions are often implemented poorly, or not at all. This complex interplay between individual incentives and global (organizational and/or national) goals can be modeled and analyzed using game theoretic techniques. By analyzing not only what is possible, but also what is motivated, a holistic approach to security problems can be developed, informing policy and providing tools to policy makers. Most of the work described here was performed in collaboration with a diverse group of people. Chapter 3 was a joint effort with Joshua Letchford from Duke University. Chapter 4 was a collaboration with Milind Tambe and Bo An from USC and Satinder Singh from the University of Michigan. Chapter 5 was a collaboration with Sandians Jackson Mayo, Rob Armstrong, and Joe Ruthruff. A portion of Chapter 3 was published in the Conference on Uncertainty in Artificial Intelligence, 2012, and I would like to thank anonymous referees for their comments and suggestions. A portion of Chapter 4 was published in the National Conference on Artificial Intelligence, 2012, and I would like to thank the referees for their comments, and Vincent Conitzer (from Duke University) from demonstrating that our original claim about stationary Markov policies was incorrect and contributing to its correction. The material in Chapter 5 was published in Physical Review Letters, 2011, and I would like to thank the anonymous referees for their comments and suggestions. The material in Chapter 6 benefited from discussions with Noel Nachtigal,
Autonomous Agents & Multiagent Systems/Agent Theories, Architectures, and Languages, 2008
We define the class of games called simulation-based games, in which the payos are available as a... more We define the class of games called simulation-based games, in which the payos are available as an output of an or- acle (simulator), rather than specified analytically or us- ing a payo matrix. We then describe a convergent algo- rithm based on a hierarchical application of simulated an- nealing for estimating Nash equilibria in simulation-based games with finite-dimensional strategy sets.
Autonomous Agents & Multiagent Systems/Agent Theories, Architectures, and Languages, 2010
A dynamic model of a multiagent system defines a proba- bility distribution over possible system ... more A dynamic model of a multiagent system defines a proba- bility distribution over possible system behaviors over time. Alternative representations for such models present trade- os in expressive power, and accuracy and cost for inferen- tial tasks of interest. In a history-dependent representation, behavior at a given time is specified as a probabilistic func- tion of some portion of system
2014 7th International Symposium on Resilient Control Systems (ISRCS), 2014
In this paper, we propose a scheme for a resilient distributed consensus problem through a set of... more In this paper, we propose a scheme for a resilient distributed consensus problem through a set of trusted nodes within the network. Currently, algorithms that solve resilient consensus problem demand networks to have high connectivity to overrule the effects of adversaries, or require nodes to have access to some non-local information. In our scheme, we incorporate the notion of trusted nodes to guarantee distributed consensus despite any number of adversarial attacks, even in sparse networks. A subset of nodes, which are more secured against the attacks, constitute a set of trusted nodes. It is shown that the network becomes resilient against any number of attacks whenever the set of trusted nodes form a connected dominating set within the network. We also study a relationship between trusted nodes and the network robustness. Simulations are presented to illustrate and compare our scheme with the existing ones.
2013 International Conference on Social Computing, 2013
Influence blocking games have been used to model adversarial domains with a social component, suc... more Influence blocking games have been used to model adversarial domains with a social component, such as counterinsurgency. In these games, a mitigator attempts to minimize the efforts of an influencer to spread his agenda across a social network. Previous work has assumed that the influence graph structure is known with certainty by both players. However, in reality, there is often significant information asymmetry between the mitigator and the influencer. We introduce a model of this information asymmetry as a two-player zero-sum Bayesian game. Nearly all past work in influence maximization and social network analysis suggests that graph structure is fundamental in strategy generation, leading to an expectation that solving the Bayesian game exactly is crucial. Surprisingly, we show through extensive experimentation on synthetic and real-world social networks that many common forms of uncertainty can be addressed nearoptimally by ignoring the vast majority of it and simply solving an abstracted game with a few randomly chosen types. This suggests that optimal strategies of games that do not model the full range of uncertainty in influence blocking games are typically robust to uncertainty about the influence graph structure.
When exploring a game over a large strategy space, it may not be feasible or cost-effective to ev... more When exploring a game over a large strategy space, it may not be feasible or cost-effective to evaluate the payoff of every r elevant strategy profile. For example, determining a profile payoff f or a procedurally defined game may require Monte Carlo simulatio n or other costly computation. Analyzing such games poses a search problem, with the goal of identifying
Physical Review Letters, 2011
We introduce noncooperatively optimized tolerance (NOT), a generalization of highly optimized tol... more We introduce noncooperatively optimized tolerance (NOT), a generalization of highly optimized tolerance (HOT) that involves strategic (game theoretic) interactions between parties in a complex system. We illustrate our model in the forest fire (percolation) framework. As the number of players increases, our model retains features of HOT, such as robustness, high yield combined with high density, and self-dissimilar landscapes, but also develops features of self-organized criticality (SOC) when the number of players is large enough. For example, the forest landscape becomes increasingly homogeneous and protection from adverse events (lightning strikes) becomes less closely correlated with the spatial distribution of these events. While HOT is a special case of our model, the resemblance to SOC is only partial; for example, the distribution of cascades, while becoming increasingly heavy-tailed as the number of players increases, also deviates more significantly from a power law in this regime. Surprisingly, the system retains considerable robustness even as it becomes fractured, due in part to emergent cooperation between neighboring players. At the same time, increasing homogeneity promotes resilience against changes in the lightning distribution, giving rise to intermediate regimes where the system is robust to a particular distribution of adverse events, yet not very fragile to changes.