Superior Exploration-Exploitation Balance with Quantum-Inspired Hadamard Walks

This paper extends the analogies employed in the development of quantum-inspired evolutionary algorithms by proposing quantum-inspired Hadamard walks, called QHW. A novel quantum-inspired evolutionary algorithm, called HQEA, for solving combinatorial optimization problems, is also proposed. The novelty of HQEA lies in it's incorporation of QHW Remote Search and QHW Local Search - the quantum equivalents of classical mutation and local search, that this paper defines. The intuitive reasoning behind this approach, and the exploration-exploitation balance thus occurring is explained. From the results of the experiments carried out on the 0,1-knapsack problem, HQEA performs significantly better than a conventional genetic algorithm, CGA, and two quantum-inspired evolutionary algorithms - QEA and NQEA, in terms of convergence speed and accuracy.Comment: 2 pages, 2 figures, 1 table, late-breakin

Impacts of Game-Theoretic and Behavioral Decision-Making on the Robustness and Security of Shared Systems and Networks

We investigate the impacts of game-theoretic and behavioral decision-making in two broad classes of problems: i) resource sharing games, and ii) network security games. In the first part of the thesis, we consider a set of decision-makers or players who choose their levels of utilization of a shared resource. The return from the resource depends on the utilization by all players, and the resource is prone to failure due to overutilization. This problem has been studied in a multitude of disciplines, including engineering (to model congestion in transportation and communication networks) and economics (to model competition over open-access natural resources). We provide a mathematically rigorous characterization of the Nash equilibrium when the players have behavioral risk preferences. Specifically, we model the risk preferences of the players according to prospect theory, a widely accepted and empirically grounded behavioral model of human decision-making under risk and uncertainty. Our analysis quantifies the increase in resource utilization and failure probability at equilibrium due to higher competition and heterogeneity in the risk preferences of the players for a broad class of resource characteristics. We then investigate how behavioral players respond to economic incentives such as taxes imposed by a central planner to control the utilization of the resource. In the second part of the thesis, we consider the impacts of game-theoretic and behavioral decision-making on the security of networked systems. While networks capture relationships and interdependencies in many socio-technical systems, these interconnections often expose the entities/nodes to different types of security risks. We first investigate the impacts of nonlinear (prospect-theoretic) perception of attack/infection probabilities on the security investments at the Nash equilibria in two game-theoretic settings. Specifically, we consider i) interdependent security games, where the attack probability faced by a node depends on the decisions made by her immediate neighbors, and ii) SIS epidemics on networks, where the infection probability of a node depends on the decisions made by all nodes in the network. In both settings, we identify conditions under which nonlinear perception of probabilities lead to improved security outcomes at the respective Nash equilibria. We further characterize the structure of networks that minimize bounds on the expected fraction of nodes that are i) attacked at the equilibria in a class of interdependent security games, and ii) infected in the endemic state of SIS epidemic dynamics. Finally, we propose a game-theoretic framework to investigate the implications of decentralized defense strategies in large-scale networks against targeted attacks. In total, our investigation leads to new insights into the impacts of behavioral and game-theoretic decision-making on the security and robustness of shared systems and networks

A Dynamic Population Model of Strategic Interaction and Migration under Epidemic Risk

In this paper, we show how a dynamic population game can model the strategic interaction and migration decisions made by a large population of agents in response to epidemic prevalence. Specifically, we consider a modified susceptible-asymptomatic-infected-recovered (SAIR) epidemic model over multiple zones. Agents choose whether to activate (i.e., interact with others), how many other agents to interact with, and which zone to move to in a time-scale which is comparable with the epidemic evolution. We define and analyze the notion of equilibrium in this game, and investigate the transient behavior of the epidemic spread in a range of numerical case studies, providing insights on the effects of the agents' degree of future awareness, strategic migration decisions, as well as different levels of lockdown and other interventions. One of our key findings is that the strategic behavior of agents plays an important role in the progression of the epidemic and can be exploited in order to design suitable epidemic control measures