Many strategic actions carry a ‘contagious’ component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block the spread of support for an insurgency, such operations lack the manpower to defend the entire population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done, we propose using game theory to develop such policies and model the interconnected network of leaders as a graph. Unlike this past work in security games, actions in these domains possess a probabilistic, non-local impact. To address this new class of security games, we combine recent research in influence-blocking maximization with a double oracle approach and create novel heuristic oracles to generate mixed strategies for a real-world leadership network from Afghanistan, synthetic leadership networks and scale-free graphs. We find that leadership networks that exhibit highly interconnected clusters can be solved equally well by our heuristic methods, but our more sophisticated heuristics outperform simpler ones in less interconnected scale-free graphs.

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.

Many real-world situations involve attempts to spread influence through a social network. For example, viral marketing is when a marketer selects a few people to receive some initial advertisement in the hopes that these `seeds' will spread the news. Even peacekeeping operations in one area have been shown to have a contagious effect on the neighboring vicinity. Each of these domains also features multiple parties seeking to maximize or mitigate a contagious effect by spreading its own influence among a select few seeds, naturally yielding an adversarial resource allocation problem. My work models the interconnected network of people as a graph and develops algorithms to optimize resource allocation in these networked competitive contagion scenarios. Game-theoretic resource allocation in the past has not considered domains with both a networked structure and contagion effects, rendering them unusable in critical domains such as rumor control, counterinsurgency, and crowd management. Networked domains without contagion effects already present computational challenges due to the large scale of the action space. To address this issue, my first contribution proposed efficient game-theoretic allocation algorithms for the graph-based urban road network domain. This work still provides the only polynomial-time algorithm for allocating vehicle checkpoints through a city, giving law enforcement officers an efficient tool to combat terrorists making their way to potential points of attack. Second, I have provided the first game-theoretic treatment for contagion mitigation in social networks and given practitioners the first principled techniques for such vital concerns as rumor control and counterinsurgency. Finally, I extended my work on game-theoretic contagion mitigation to address uncertainty about the network structure to find that, contrary to what evidence and intuition suggest, heuristic sampling approaches provide near-optimal solutions across a wide range of generative graph models and uncertainty models. Thus, despite extreme practical challenges in attaining accurate social network information, my techniques remain near-optimal across numerous forms of uncertainty in multiple synthetic and real-world graph structures. Beyond optimization of resource allocation, I have further studied contagion effects to understand the effectiveness of such resources. First, I created an evacuation simulation, ESCAPES, to explore the interaction of pedestrian fear contagion and authority fear mitigation during an evacuation. Second, using this simulator, I have advanced the frontier in contagion modeling by developing empirical evaluation methods for comparing and calibrating computational contagion models that are critical in crowd simulations and evacuation modeling. Finally, I have also conducted an examination of agent-human emotional contagion to inform the rising use of simulations for personnel training in emotionally-charged situations.

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 near optimally 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.

Nations and organizations need to secure locations of economic, military, or political importance from groups or individuals that can cause harm. The fact that there are limited security resources prevents complete security coverage, which allows adversaries to observe and exploit patterns in patrolling or monitoring and enables them to plan attacks that avoid existing patrols. The use of randomized security policies that are more difficult for adversaries to predict and exploit can counter their surveillance capabilities and improve security. In this chapter we describe the recent development of models to assist security forces in randomizing their patrols and their deployment in real applications. The systems deployed are based on fast algorithms for solving large instances of Bayesian Stackelberg games that capture the interaction between security forces and adversaries. Here we describe a generic mathematical formulation of these models, present some of the results that have allowed these systems to be deployed in practice, and outline remaining future challenges. We discuss the deployment of these systems in two real-world security applications: (1) The police at the Los Angeles International Airport uses these models to randomize the placement of checkpoints on roads entering the airport and the routes of canine unit patrols within the airport terminals. (2) The Federal Air Marshal Service (FAMS) uses these models to randomize the schedules of air marshals on international flights.

In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people’s emotions [10]. In this paper, we perform a battery of experiments to explore the existence of agent-human emotional contagion. The first study is a between-subjects design, wherein subjects were shown an image of a character’s face with either a neutral or happy expression. Findings indicate that even a still image induces a very strong increase in self-reported happiness between Neutral and Happy conditions with all characters tested. In a second study, we examine the effect of a virtual character’s presence in a strategic decision by presenting subjects with a modernized Stag Hunt game. Our experiments show that the contagion effect is substantially dampened and does not cause a consistent impact on behavior. A third study explores the impact of the strategic situation within the Stag Hunt and conducts the same experiment using a description of the same strategic situation with the decision already made. We find that the emotional impact returns, implying that the contagion effect is substantially lessened in the presence of a strategic decision.

In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people’s emotions [8]. While it has been observed in humanhuman interactions, no known studies have examined its existence in agent-human interactions. As virtual characters make their way into high-risk, high-impact applications such as psychotherapy and military training with increasing frequency, the emotional impact of the agents’ expressions must be accurately understood to avoid undesirable repercussions.

Many strategic actions carry a ‘contagious’ component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block the spread of support for an insurgency, such operations lack the manpower to defend the entire population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done, we propose using game theory to develop such policies and model the interconnected network of leaders as a graph. Unlike this past work in security games, actions in these domains possess a probabilistic, non-local impact. To address this new class of security games, recent research has used novel heuristic oracles in a double oracle formulation to generate mixed strategies. However, these heuristic oracles were evaluated only on runtime and quality scaling with the graph size. Given the complexity of the problem, numerous other problem features and metrics must be considered to better inform practical application of such techniques. Thus, this work provides a thorough experimental analysis including variations of the contagion probability average and standard deviation. We extend the previous analysis to also examine the size of the action set constructed in the algorithms and the final mixed strategies themselves. Our results indicate that game instances featuring smaller graphs and low contagion probabilities converge slowly while games with larger graphs and medium contagion probabilities converge most quickly.

In social psychology, emotional contagion describes the widely observed phenomenon of one person's emotions mimicking surrounding people's emotions [8]. While it has been observed in humanhuman interactions, no known studies have examined its existence in agent-human interactions. As virtual characters make their way into high-risk, high-impact applications such as psychotherapy and military training with increasing frequency, the emotional impact of the agents' expressions must be accurately understood to avoid undesirable repercussions.

Many real-world problems exhibit competitive situations in which a defender (a defending agent, agency, or organization) has to address misinformation spread by its adversary, e.g., health organizations cope with vaccination-related misinformation provided by anti-vaccination groups. The rise of social networks has allowed misinformation to be easily and quickly diffused to a large community. Taking into account knowledge of its adversary’s actions, the defender has to seek efficient strategies to limit the influence of the spread of misinformation by the opponent. In this paper, we address this problem as a blocking influence maximization problem using a game-theoretic approach. Two players strategically select a number of seed nodes in the social network that could initiate their own influence propagation. While the adversary attempts to maximize its negative influence, the defender tries to minimize this influence. We represent the problem as a zero-sum game and apply the Double Oracle algorithm to solve the game in combination with various heuristics for oracle phases. Our experimental results reveal that by using the game theoretic approach, we are able to significantly reduce the negative influence in comparison to when the defender does not do anything. In addition, we propose using an approximation of the payoff matrix, making the algorithms scalable to large real-world networks.

In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people’s emotions [13]. While it has been observed in humanhuman interactions, no known studies have examined its existence in agent-human interactions. As virtual characters make their way into high-risk, high-impact applications such as psychotherapy and military training with increasing frequency, the emotional impact of the agents’ expressions must be accurately understood to avoid undesirable repercussions. In this paper, we perform a battery of experiments to explore the existence of agent-human emotional contagion. The first study is a between-subjects design, wherein subjects were shown an image of a character’s face with either a neutral or happy expression. Findings indicate that even a still image induces a very strong increase in self-reported happiness between Neutral and Happy conditions with all characters tested and, to our knowledge, is the first ever study explicitly showing emotional contagion from a virtual agent to a human. We also examine the effects of participant gender, participant ethnicity, character attractiveness, and perceived character happiness and find that only perceived character happiness has a substantial impact on emotional contagion. In a second study, we examine the effect of a virtual character’s presence in a strategic situation by presenting subjects with a modernized Stag Hunt game. Our experiments show that the contagion effect is substantially dampened and does not cause a consistent impact on behavior. A third study explores the impact of the strategic decision within the Stag Hunt and conducts the same experiment using a description of the same strategic situation with the decision already made. We find that the emotional impact returns again, particularly for women, implying that the contagion effect is substantially lessened in the presence of a strategic decision.

Many strategic actions carry a 'contagious' component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block the spread of support for an insurgency, such operations lack the manpower to defend the entire population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done, we propose using game theory to develop such policies and model the interconnected network of leaders as a graph. Unlike this past work in security games, actions in these domains possess a probabilistic, non-local impact. To address this new class of security games, we combine recent research in influence blocking maximization with a double oracle approach and create novel heuristic oracles to generate mixed strategies for a real-world leadership network from Afghanistan, synthetic leadership networks, and a real social network. We find that leadership networks that exhibit highly interconnected clusters can be solved equally well by our heuristic methods, but our more sophisticated heuristics outperform simpler ones in less interconnected social networks.

In creating an evacuation simulation for training and planning, realistic agents that reproduce known phenomenon are required. Evacuation simulation in the airport domain requires additional features beyond most simulations, including the unique behaviors of first-time visitors who have incomplete knowledge of the area and families that do not necessarily adhere to often-assumed pedestrian behaviors. Evacuation simulations not customized for the airport domain do not incorporate the factors important to it, leading to inaccuracies when applied to it. In this paper, we describe ESCAPES, a multiagent evacuation simulation tool that incorporates four key features: (i) different agent types; (ii) emotional interactions; (iii) informational interactions; (iv) behavioral interactions. Our simulator reproduces phenomena observed in existing studies on evacuation scenarios and the features we incorporate substantially impact escape time. We use ESCAPES to model the International Terminal at Los Angeles International Airport (LAX) and receive high praise from security officials.

Transportation networks such as buses, trains, and airplanes carry millions of people per day and from to their destinations, making them a prime target for terrorists and extremely difficult for law enforcement agencies to protect. In 2001, the 9/11 attack on the World Trade Center in New York City with commercial airliners resulted in $27.2 billion of direct short term costs (Looney 2002) as well as a government-reported 2,974 lives lost. The 2004 Madrid commuter train bombings resulted in 191 lives lost, 1755 people wounded, and an estimated cost of 212 million Euros (Blanco et al., 2007). Finally, in the 2005 London subway and bus bombings, 52 innocent lives were lost, 700 others were injured, and the estimated economic cost was 2 billion pounds (Thornton, 2005).

In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions being influenced by surrounding people’s emotions. While the overall effect is agreed upon, the underlying mechanism of the spread of emotions has seen little quantification and application to computational agents. In this paper, we explore computational models of emotional contagion by implementing two models (Bosse et al., Durupinar et al.) and augmenting them to better model real world observations. Our additions include examining the impact of physical proximity and authority figures. We show that these additions provide substantial improvements to the qualitative trends of emotion spreading, more in line with expectations than either of the two previous models. We also evaluate their impact on evacuation safety in an evacuation simulation, ESCAPES, showing substantial differences in predicted safety based on the contagion model.

In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions being influenced by surrounding people’s emotions. While the overall effect is agreed upon, the underlying mechanism of the spread of emotions has seen little quantification and application to computational agents despite extensive evidence of its impacts in everyday life. In this paper, we examine computational models of emotional contagion by implementing two models ([2] and [8]) that draw from two separate lines of contagion research: thermodynamics-based and epidemiological-based. We first perform sensitivity tests on each model in an evacuation simulation, ESCAPES, showing both models to be reasonably robust to parameter variations with certain exceptions. We then compare their ability to reproduce a real crowd panic scene in simulation, showing that the thermodynamics-style model ([2]) produces superior results due to the ill-suited contagion mechanism at the core of epidemiological models. We also identify that a graduated effect of fear and proximity-based contagion effects are key to producing the superior results. We then reproduce the methodology on a second video, showing that the same results hold, implying generality of the conclusions reached in the first scene.

Granulocytic sarcoma, also known as chloroma, is an uncommon tumor typically seen in patients with acute myelogenous leukemia (AML). Extramedullary cardiac involvement of granulocytic sarcoma is rare and there is a paucity of information regarding its multidetector computed tomography (MDCT) imaging appearance. We present a case of extramedullary granulocytic sarcoma of the heart detected on MDCT in a young adult male with AML. An understanding of how extramedullary granulocytic sarcoma of the heart appears on MDCT may decrease or prevent delays in diagnosing this uncommon, although important, complication from AML.

We apply game-theoretic techniques to urban security deployment and propose new algorithms to efficiently solve real-world domains that are intractable with existing algorithms.