In this thesis, the channel assignment and routing problems have been investigated for both cooperative and competitive Wireless Mesh networks (WMNs). A dynamic and distributed channel assignment scheme has been proposed which generates the network topologies ensuring less interference and better connectivity. The proposed channel assignment scheme is capable of detecting the node failures and mobility in an efficient manner. The channel monitoring module precisely records the quality of bi-directional links in terms of link delays. In addition, a Quality of Service based Multi-Radio Ad-hoc On Demand Distance Vector (QMR-AODV) routing protocol has been devised. QMR-AODV is multi-radio compatible and provides delay guarantees on end-to-end paths. The inherited problem of AODV’s network wide flooding has been solved by selectively forwarding the routing queries on specified interfaces. The QoS based delay routing metric, combined with the selective route request forwarding, reduces the routing overhead from 24% up to 36% and produces 40.4% to 55.89% less network delays for traffic profiles of 10 to 60 flows, respectively. A distributed channel assignment scheme has been proposed for competitive WMNs, where the problem has been investigated by applying the concepts from non-cooperative bargaining Game Theory in two stages. In the first stage of the game, individual nodes of the non-cooperative setup is considered as the unit of analysis, where sufficient and necessary conditions for the existence of Nash Equilibrium (NE) and Negotiation-Proof Nash Equilibrium (N-PNE) have been derived. A distributed algorithm has been presented with perfect information available to the nodes of the network. In the presence of perfect information, each node has the knowledge of interference experience by the channels in its collision domain. The game converges to N-PNE in finite time and the average fairness achieved by all the nodes is greater than 0.79 (79%) as measured through Jain Fairness Index. Since N-PNE and NE are not always a system optimal solutions when considered from the end-nodes prospective, the model is further extended to incorporate non-cooperative end-users bargaining between two end user’s Mesh Access Points (MAPs), where an increase of 10% to 27% in end-to-end throughput is achieved. Furthermore, a non-cooperative game theoretical model is proposed for end-users flow routing in a multi-radio multi-channel WMNs. The end user nodes are selfish and compete for the channel resources across the WMNs backbone, aiming to maximize their own benefit without taking care for the overall system optimization. The end-to-end throughputs achieved by the flows of an end node and interference experienced across the WMNs backbone are considered as the performance parameters in the utility function. Theoretical foundation has been drawn based on the concepts from the Game Theory and necessary conditions for the existence of NE have been extensively derived. A distributed algorithm running on each end node with imperfect information has been implemented to assess the usefulness of the proposed mechanism. The analytical results have proven that a pure strategy Nash Equilibrium exists with the proposed necessary conditions in a game of imperfect information. Based on a distributed algorithm, the game converges to a stable state in finite time. The proposed game theoretical model provides a more reasonable solution with a standard deviation of 2.19Mbps as compared to 3.74Mbps of the random flow routing. Finally, the Price of Anarchy (PoA) of the system is close to one which shows the efficiency of the proposed scheme.

Approved for public release, distribution is unlimited / Due to the rapid development of technology, many simple tasks can now be automated, leaving more difficult and cognitive tasks such as planning, decision making and design to teams. Technology also allows these teams to be distributed through time and space. While this is becoming more and more prevalent in the business world, distributed teams also exist in the military where the stresses are much different. One of the key factors associated with collaboration in military teams is situational awareness. This research used a commercial command and control type video game to investigate the issues of collaboration and situational awareness. The amount of information subjects had access to was varied to see if there was a significant impact upon their level of situational awareness which was measured by the accuracy of maps that the subjects drew. Results from this research may provide insight into how much information is needed by distributed teams and when they need it. Ideas for future research in this area have also been proposed. / Lieutenant Commander, United States Navy

Future wireless networks are expected to be highly heterogeneous and ultra dense with different types of small cells underlaid with traditional macro cells. In the presence of hundreds of different types of small cells, centralized control and manual intervention in network management will be inefficient and expensive. In this case, self-organization has been proposed as a key feature in future wireless networks. In a self-organizing network, the nodes are expected to take individual decisions on their behavior. Therefore, individual decision making in resource allocation (i.e., Distributed Resource Allocation) is of vital important. The objective of this thesis is to develop a distributed resource allocation framework for self-organizing small cell networks. Game theory is a powerful mathematical tool which can model and analyze interactive decision making problems of the agents with conflicting interests. Therefore, it is a well-appropriate tool for modeling the distributed resource allocation problem of small cell networks. In this thesis, I consider three different scenarios of distributed resource allocation in self-organizing small cell networks i.e., i). Distributed downlink power and spectrum allocation to ensure fairness for a small cell network of base stations with bounded rationality, ii). Distributed downlink power control for an ultra dense small cell network of base stations with energy constraints, iii). Distributed joint uplink-downlink power control for a small cell network of possibly deceitful nodes with full-duplexing capabilities. Specifically, I utilize evolutionary games, mean field games, and repeated games to model and analyze the three aforementioned scenarios. I also use stochastic geometry, which is a very powerful mathematical tool that can model the characteristics of the networks with random topologies, to design the payoff functions for the formulated evolutionary game and the mean field game. / October 2016

The research objective is to demonstrate that a game-implementation process can serve as a means of solving some key implementation problems and for integrating the components associated with developing a quantitative based system. Thus, the study has the following objectives: 1. To demonstrate by means of a case study example that gaming can be successfully employed as a systems implementation tool. 2. To identify a game-implementation approach which would be useful in developing and implementing a quantitative based system.

One of the most pressing concerns in homeland security is the illegal passing of weapons-grade nuclear material through the borders of the United States. If terrorists can gather the materials needed to construct a nuclear bomb or radiological dispersion device (RDD, i.e., dirty bomb) while inside the United States, the consequences would be devastating. Preventing plutonium, highly enriched uranium (HEU), tritium gas or other materials that can be used to construct a nuclear weapon from illegally entering the United States is an area of vital concern. There are enormous economic consequences when our nation's port security system is compromised. Interdicting nuclear material being smuggled into the United States on cargo containers is an issue of vital national interest, since it is a critical aspect of protecting the United States from nuclear attacks. However, the efforts made to prevent nuclear material from entering the United States via cargo containers have been disjoint, piecemeal, and reactive, not the result of coordinated, systematic planning and analysis. Our economic well-being is intrinsically linked with the success and security of the international trade system. International trade accounts for more than thirty percent of the United States economy (Rooney, 2005). Ninety-five percent of international goods that enter the United States come through one of 361 ports, adding up to more than 11.4 million containers every year (Fritelli, 2005; Rooney, 2005; US DOT, 2007). Port security has emerged as a critically important yet vulnerable component in the homeland security system. Applying game theoretic methods to counterterrorism provides a structured technique for defenders to analyzing the way adversaries will interact under different circumstances and scenarios. This way of thinking is somewhat counterintuitive, but is an extremely useful tool in analyzing potential strategies for defenders. Decision analysis can handle very large and complex problems by integrating multiple perspectives and providing a structured process in evaluating preferences and values from the individuals involved. The process can still ensure that the decision still focuses on achieving the fundamental objectives. In the decision analysis process value tradeoffs are evaluated to review alternatives and attitudes to risk can be quantified to help the decision maker understand what aspects of the problem are not under their control. Most of all decision analysis provides insight that may not have been captured or fully understood if decision analysis was not incorporated into the decision making process. All of these factors make decision analysis essentially to making an informed decision. Game theory and decision analysis both play important roles in counterterrorism efforts. However, they both have their weaknesses. Decision analysis techniques such as probabilistic risk analysis can provide incorrect assessments of risk when modeling intelligent adversaries as uncertain hazards. Game theory analysis also has limitations. For example when analyzing a terrorist or terrorist group using game theory we can only take into consideration one aspect of the problem to optimize at a time. Meaning the analysis is either analyzing the problem from the defenders perspective or from the attacker’s perspective. Parnell et al. (2009) was able to develop a model that simultaneously maximizes the effects of the terrorist and minimizes the consequences for the defender. The question this thesis aims to answer is whether investing in new detector technology for screening cargo containers is a worthwhile investment for protecting our country from a terrorist attack. This thesis introduces an intelligent adversary risk analysis model for determining whether to use new radiological screening technologies at our nation’s ports. This technique provides a more realistic risk assessment of the true situation being modeled and determines whether it is cost effective for our country to invest in new cargo container screening technology. The optimal decision determined by our model is for the United States to invest in a new detector, and for the terrorists to choose agent cobalt-60, shown in Figure 18. This is mainly due to the prominence of false alarms and the high costs associated with screening all of these false alarms, and we assume for every cargo container that sounds an alarm, that container is physically inspected. With the new detector technology the prominence of false alarms decreases and the true alarm rate increases, the cost savings associated with this change in the new technology outweighs the cost of technical success or failure. Since the United States is attempting to minimize their expected cost per container, the optimal choice is to invest in the new detector. Our intelligent adversary risk analysis model can simultaneously determine the best decision for the United States, who is trying to minimize the expected cost, and the terrorist, who is trying to maximize the expected cost to the United States. Simultaneously modeling the decisions of the defender and attacker provides a more accurate picture of reality and could provide important insights to the real situation that may have been missed with other techniques. The model is extremely sensitive to certain inputs and parameters, even though the values are in line with what is available in the literature, it is important to understand the sensitivities. Two inputs that were found to be particularly important are the expected cost for physically inspecting a cargo container, and the cost of implementing the technology needed for the new screening device. Using this model the decision maker can construct more accurate judgments based on the true situation. This increase in accuracy could save lives with the decisions being made. The model can also help the decision maker understand the interdependencies of the model and visually see how his resource allocations affect the optimal decisions of the defender and the attacker.

The present work seeks to incorporate a popular descriptive, empirically grounded model of human preference under risk, prospect theory, into the equilibrium theory of noncooperative games. Three primary, candidate definitions are systematically identified on the basis of classical characterizations of Nash Equilibrium; in addition, three equilibrium subtypes are defined for each primary definition, in order to enable modeling of players' reference points as exogenous and fixed, slowly and myopically adaptive, highly flexible and non-myopically adaptive. Each primary equilibrium concept was analyzed both theoretically and empirically; for the theoretical analyses, prospect theory, game theory, and computational complexity theory were all summoned to analysis. In chapter 1, the reader is provided with background on each of these theoretical underpinnings of the current work, the scope of the project is described, and its conclusions briefly summarized. In chapters 2 and 3, each of the three equilibrium concepts is analyzed theoretically, with emphasis placed on issues of classical interest (e.g. existence, dominance, rationalizability) and computational complexity (i.e, assessing how difficult each concept is to apply in algorithmic practice, with particular focus on comparison to classical Nash Equilibrium). This theoretical analysis leads us to discard the first of our three equilibrium concepts as unacceptable. In chapter 4, our remaining two equilibrium concepts are compared empirically, using average-level data originally aggregated from a number of studies by Camerer and Selten and Chmura; the results suggest that PT preferences may improve on the descriptive validity of NE, and pose some interesting questions about the nature of the PT weighting function (2003, Ch. 3). Chapter 5 concludes, systematically summarizes theoretical and empirical differences and similarities between the three equilibrium concepts, and offers some thoughts on future work.

"Playing with Aesthetics in Art Museums" presents a strategy for using design thinking to mediate engrossing art experiences for adult museum visitors. Built upon a substantiated family resemblance between art and play experiences, the study synthesizes a typology of aesthetic theories, ten germane tenets of game design, and a psychographic portrait of the "archetypal" museum visitor to create a practical framework for delivering engrossing art experiences to adult visitors who typically enter museums with limited art historical knowledge. The interdisciplinary approach used is intended to replace the singular methodologies (whether art historical, pedagogical or aesthetic) that have informed museum practice in the United States since the late nineteenth century.

The impact of false information injection is investigated for linear dynamic systems with multiple sensors. First, it is assumed that the system is unaware of the existence of false information and the adversary is trying to maximize the negative effect of the false information on Kalman filter's estimation performance under a power constraint. The false information attack under different conditions is mathematically characterized. For the adversary, many closed-form results for the optimal attack strategies that maximize the Kalman filter's estimation error are theoretically derived. It is shown that by choosing the optimal correlation coefficients among the false information and allocating power optimally among sensors, the adversary could significantly increase the Kalman filter's estimation errors. In order to detect the false information injected by an adversary, we investigate the strategies for the Bayesian estimator to detect the false information and defend itself from such attacks. We assume that the adversary attacks the system with certain probability, and that he/she adopts the worst possible strategy that maximizes the mean squared error (MSE) if the attack is undetected. An optimal Bayesian detector is designed which minimizes the average system estimation error instead of minimizing the probability of detection error, as a conventional Bayesian detector typically does. The case that the adversary attacks the system continuously is also studied. In this case, sparse attack strategies in multi-sensor dynamic systems are investigated from the adversary's point of view. It is assumed that the defender can perfectly detect and remove the sensors once they are corrupted by false information injected by an adversary. The adversary's goal is to maximize the covariance matrix of the system state estimate by the end of attack period under the constraint that the adversary can only attack the system a few times over the sensor and over the time, which leads to an integer programming problem. In order to overcome the prohibitive complexity of the exhaustive search, polynomial-time algorithms, such as greedy search and dynamic programming, are proposed to find the suboptimal attack strategies. As for greedy search, it starts with an empty set， and one sensor is added at each iteration, whose elimination will lead to the maximum system estimation error. The process terminates when the cardinality of the active set reaches to the sparsity constraint. Greedy search based approaches such as sequential forward selection (SFS), sequential backward selection (SBS), and simplex improved sequential forward selection (SFS-SS) are discussed and corresponding attack strategies are provided. Dynamic programming is also used in obtaining the sub-optimal attack strategy. The validity of dynamic programming lies on a straightforward but important nature of dynamic state estimation systems: the credibility of the state estimate at current step is in accordance with that at previous step. The problem of false information attack on and the Kalman filter's defense of state estimation in dynamic multi-sensor systems is also investigated from a game theoretic perspective. The relationship between the Kalman filter and the adversary can be regarded as a two-person zero-sum game. The condition under which both sides of the game will reach a Nash equilibrium is investigated.

The concern of this thesis is to discuss different multicriteria games solution concepts. Multicriteria game is a special case from the game theory if the payoff function of at least one player is a vector and the player wants to maximize all the criteria at the same time. The thesis is divided into four chapters. In the first instance a few motivation examples are introduced. Subsequently the history of the multicriteria games is mentioned. The theoretical chapter follows. It contains five sections - introduction of new definitions; the structure of the set of equilibria for two person multicriteria games; searching equilibria points by the help of scalarization of the vector-valued function; introduction of ideal equilibria points and ways how to find them; the comparison of used methods. The last solution concept is demonstrated by the real example. Finally a theoretical chapter with new results is included. 1

Contemporary Social Contract Theories The thesis is intended to be an introduction into contemporary social contract theories. John Rawls' book A Theory of Justice is a seminal work in this field. The thesis, however, does focus not only on the approach adopted by Rawls, but on the theories of other authors as well - that of J. Buchanan, D. Gauthier, T. Scanlon and R. Nozick. These remain quite unknown in the Czech context. The structure of the thesis should allow for a comparison between the respective theories as the author starts with the object of the social contract, proceeds to the parties of the contract and, finally, to its content. Special attention is devoted to the game theory which models some of the typical interpersonal interactions and, using its theoretical apparatus, can illustrate problems of social cooperation - the key theme of virtually all social contract theories. Eventually, the author tries to outline the way analytic philosophy views the social contract. In its eyes, the social contract is a necessary implication of the existence of language.