Conflict analysis and game theory models are applied to a case study in the upper Rio Grande river basin. The objective is to find which theory best describes past developments in the Rio Grande river basin and the status quo of water use strategies employed by the players (decision makers). By assuming that these past properties will propagate in the future, the preferable change in the equilibrium solution is derived under climate fluctuation, coupled with future population growth scenarios. Past and future Rio Grande resource allocation conflicts are analyzed using (1) multicriterion decision making (MCDM) techniques, such as distance based approach of compromise programming and outranking technique of the ELECTRE family and (2) voting scheme approach of game theory. MCDM and game theory model cases are classified according to the following categories: 1. If decision makers consider each other payoff or if an authority above forces them to consider each other's payoffs, then the conflict analysis problem is a multiactor/ multiobjective problem. 2. If decision makers only care about their own payoff and not what other players payoff are, then the conflict analysis problem is described and solved by game theoretic models. Fifteen decision makers from the Rio Grande water allocation and water management conflict are used as an example to present the different approaches to conflict modeling. From the MCDM techniques used, namely the compromise programming of distance-based approach and the ELECTRE family of outranking relation, the former method stands out as being the most flexible and comprehensive methodology. Though these two methods are conceptually different, for this case study, both methods give approximately the same results. For the game theory analysis, the special voting scheme stands out as being the preferred approach because it better reflects the decision maker's preference and it also is easy to implement and apply. Finally, the climate change scenarios are considered, the 1XCO₂ and the 2XCO₂. Results obtained from these two scenarios indicate the Rio Grande river will face extreme water shortages that will require the development of a different set of water release rules.

This dissertation presents efficient, on-line, convergent methods to find defense strategies against attacks in dynamic multi-stage attacker-defender games including adaptive learning. This effort culminated in four papers submitted to high quality journals and a book and they are partially published. The first paper presents a novel fictitious play approach to describe the interactions between the attackers and network administrator along a dynamic game. Multi-objective optimization methodology is used to predict the attacker's best actions at each decision node. The administrator also keeps track of the attacker's actions and updates his knowledge on the attacker's behavior and objectives after each detected attack, and uses this information to update the prediction of the attacker's future actions to find its best response strategies. The second paper proposes a Dynamic game tree based Fictitious Play (DFP) approach to describe the repeated interactive decision processes of the players. Each player considers all possibilities in future interactions with their uncertainties, which are based on learning the opponent's decision process (including risk attitude, objectives). Instead of searching the entire game tree, appropriate future time horizons are dynamically selected for both players. The administrator keeps tracking the opponent's actions, predicts the probabilities of future possible attacks, and then chooses its best moves. The third paper introduces an optimization model to maximize the deterministic equivalent of the random payoff function of a computer network administrator in defending the system against random attacks. By introducing new variables the transformed objective function becomes concave. A special optimization algorithm is developed which requires the computation of the unique solution of a single variable monotonic equation. The fourth paper, which is an invited book chapter, proposes a discrete-time stochastic control model to capture the process of finding the best current move of the defender. The defender's payoffs at each stage of the game depend on the attacker's and the defender's accumulative efforts and are considered random variables due to their uncertainty. Their certain equivalents can be approximated based on their first and second moments which is chosen as the cost functions of the dynamic system. An on-line, convergent, Scenarios based Proactive Defense (SPD) algorithm is developed based on Differential Dynamic Programming (DDP) to solve the associated optimal control problem.

In an interval society, voters are represented by intervals on the real line, corresponding to their approval sets on a linear political spectrum. I imagine the society to be a representative democracy, and ask how to choose members of the society as representatives. Following work in mathematical psychology by Coombs and others, I develop a measure of the compatibility (political similarity) of two voters. I use this measure to determine the popularity of each voter as a candidate. I then establish local “agreeability” conditions and attempt to find a lower bound for the popularity of the best candidate. Other results about certain special societies are also obtained

Research on auctions that involve more than one identical item for sale was,almost non-existing in the 90’s, but has since then been getting increasing attention. External incentives for this research have come from the US spectrum, sales, the European 3G mobile-phone auctions,  and Internet auctions. The policy relevance and the huge amount of money involved in many of them have helped the theory and experimental research advance. But in auctions where values are equal across bidders, common value auctions, that is, when the value depends on some outside parameter, equal to all bidders, the research is still embryonic. This thesis contributes to the topic with three studies. The first uses a Bayesian game to model a simple multi-unit common value auction, the task being to compare equilibrium strategies and the seller’s revenue from three auction formats; the discriminatory, the uniform and the Vickrey auction. The second study conducts an economic laboratory experiment on basis of the first study. The third study comprises an experiment on the multi-unit common value uniform auction and compares the dynamic and the static environments of this format. The most salient result in both experiments is that subjects overbid. They are victims of the winner’s curse and bid above the expected value, thus earning a negative profit. There is some learning, but most bidders continue to earn a negative profit also in later rounds. The competitive effect when participating in an auction seems to be stronger than the rationality concerns. In the first experiment, subjects in the Vickrey auction do somewhat better in small groups than subjects in the other auction types and, in the second experiment, subjects in the dynamic auction format perform much better than subjects in the static auction format; but still, they overbid. Due to this overbidding, the theoretical (but not the behavioral) prediction that the dynamic auction should render more revenue than the static fails inthe second experiment. Nonetheless, the higher revenue of the static auction comes at a cost; half of the auctions yield negative profits to the bidders, and the winner’s curse is more severely widespread in this format. Besides, only a minority of the bidders use the equilibrium bidding strategy.The bottom line is that the choice between the open and sealed-bid formats may be more important than the choice of price mechanism, especially in common value settings.

‘Game Theory’ is the formal study of conflict and cooperation. The theory is based on a set of tools that have been developed in order to assist with the modelling and analysis of individual, independent decision makers. These actions potentially affect any decisions, which are made by other competitors. Therefore, it is well suited and capable of addressing the various issues linked to wireless communications. This work presents a Green Game-Based Hybrid Vertical Handover Model. The model is used for heterogeneous wireless networks, which combines both dynamic (Received Signal Strength and Node Mobility) and static (Cost, Power Consumption and Bandwidth) factors. These factors control the handover decision process; whereby the mechanism successfully eliminates any unnecessary handovers, reduces delay and overall number of handovers to 50% less and 70% less dropped packets and saves 50% more energy in comparison to other mechanisms. A novel Game-Based Multi-Interface Fast-Handover MIPv6 protocol is introduced in this thesis as an extension to the Multi-Interface Fast-handover MIPv6 protocol. The protocol works when the mobile node has more than one wireless interface. The protocol controls the handover decision process by deciding whether a handover is necessary and helps the node to choose the right access point at the right time. In addition, the protocol switches the mobile nodes interfaces ‘ON’ and ‘OFF’ when needed to control the mobile node’s energy consumption and eliminate power lost of adding another interface. The protocol successfully reduces the number of handovers to 70%, 90% less dropped packets, 40% more received packets and acknowledgments and 85% less end-to-end delay in comparison to other Protocols. Furthermore, the thesis adapts a novel combination of both game and auction theory in dynamic resource allocation and price-power-based routing in wireless Ad-Hoc networks. Under auction schemes, destinations nodes bid the information data to access to the data stored in the server node. The server will allocate the data to the winner who values it most. Once the data has been allocated to the winner, another mechanism for dynamic routing is adopted. The routing mechanism is based on the source-destination cooperation, power consumption and source-compensation to the intermediate nodes. The mechanism dramatically increases the seller’s revenue to 50% more when compared to random allocation scheme and briefly evaluates the reliability of predefined route with respect to data prices, source and destination cooperation for different network settings. Last but not least, this thesis adjusts an adaptive competitive second-price pay-to-bid sealed auction game and a reputation-based game. This solves the fairness problems associated with spectrum sharing amongst one primary user and a large number of secondary users in a cognitive radio environment. The proposed games create a competition between the bidders and offers better revenue to the players in terms of fairness to more than 60% in certain scenarios. The proposed game could reach the maximum total profit for both primary and secondary users with better fairness; this is illustrated through numerical results.

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.