Strategic and Secure Interactions in Networks

The goal of this dissertation is to understand how network plays a role in shaping certain strategic interactions, in particular biased voting and bargaining, on networks; and to understand how interactions can be made secure when they are constrained by the network topology. Our works take an interdisciplinary approach by drawing on theories and models from economics, sociology, as well as computer science, and using methodologies that include both theories and behavioral experiments. First, we consider biased voting in networks, which models distributed collective decision making processes where individuals on a network must balance between their private biases or preferences with a collective goal of consensus. Our study of this problem is two-folded. On the theoretical side, we start by introducing a diffusion model called biased voter model, which is a natural extension of the classic voter model. Among other results, we show in the presence of private biases, no matter how small, there exists certain networks where it takes exponential time to converge to a consensus through distributed interaction in networks. This is a stark and interesting contrast to the well-known result that it always takes polynomial time to converge in the voter model, when there are no private biases. On the experimental side, a group human subjects were arranged in various carefully designed virtual networks to solve the biased voting problem. Along with analyses of how collective and individual performance vary with network structure and incentives generally, we find there are well-studied network topologies in which the minority preference consistently wins globally, and that the presence of “extremist” individuals, or the awareness of opposing incentives, reliably improve collective performance Second, we consider bargaining in networks, which has long been studied by economists and sociologists. A basic premise behind the many theoretical study of bargaining in networks is that pure topological differences in agents’ network positions endow them with different bargaining power. As a complementary to these theories, we again conducted a series of highly controlled behavioral experiments, where human subjects were arranged in various carefully designed virtual networks to playing bargaining games. Along with other findings of how individual and collective performance vary with network structures and individual playing styles, we find that the number of neighbors one can negotiate with confers bargaining power, whereas the limit on the number of deals one can close undermines it, and we find that competitions from distant part of the network that are invisible locally also play a significant and subtle role in shaping bargaining powers. And last, we consider the question of how interactions in networks can be made secure. Traditional methods and tools from cryptography, for example secure multi-party computation, can be applied only if each party can talk to everyone else directly; but cannot be directly applied if interactions are distributed over a network without completely eradicating the distributed nature. We develop a general ‘compiler’ that turns each algorithm from a broad class collectively known as message-passing algorithms into a secure one that has exactly the same functionality and communication pattern. And we show a fundamental trade-off between preserving the distributed nature of communication and the level of security one can hope for

Learning with bounded memory.

The paper studies infinite repetition of finite strategic form games. Players use a learning behavior and face bounds on their cognitive capacities. We show that for any given beliefprobability over the set of possible outcomes where players have no experience. games can be payoff classified and there always exists a stationary state in the space of action profiles. In particular, if the belief-probability assumes all possible outcomes without experience to be equally likely, in one class of Prisoners' Dilemmas where the average defecting payoff is higher than the cooperative payoff and the average cooperative payoff is lower than the defecting payoff, play converges in the long run to the static Nash equilibrium while in the other class of Prisoners' Dilemmas where the reserve holds, play converges to cooperation. Results are applied to a large class of 2 x 2 games.Cognitive complexity; Bounded logistic quantal response learning; Long run outcomes;

Enlace de retorno satelital DVB-RCS2 : modelagem de fila e otimização de alocação de recursos baseada em teoria dos jogos

Tese (doutorado) — Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2022.É esperado que satélites tenham um papel fundamental no futuro dos sistemas de comunicação, integrando-se às infraestruturas terrestres. Esta dissertação de mestrado propõe três contribuições principais: primeiramente, se apresenta um arcabouço de simulação capaz de prover detalhes da performance de redes de comunicação satelital em cenários realistas. Este arcabouço aplica uma metodologia orientada a eventos, modelando a rede de comunicação como um sistema baseado em eventos discretos (DES), focando no enlace de retorno do protocolo DVB-RCS2. Três diferentes cenários simulados demonstram os possíveis usos das saídas do simulador para entender o comportamento dinâmico da rede e alcançar um ponto ótimo de operação do sistema. Cada cenário explora uma característica diferente do simulador, enquanto cobre um grande território de usuários, que em nosso caso estudo o país de escolha foi o Brasil. Em um segundo tópico, este trabalho introduz um novo algoritmo modificado do método de alocação de timeslots baseado em teoria dos jogos, aplicando-se no protocolo DVB-RCS2. Este procedimento considera a eficiência espectral do terminal como um parâmetro de peso para o problema de otimização convexa resultante da solução da barganha de Nash. Este novo método garante o cumprimento dos requisitos de Qualidade de Serviço (QoS) enquanto provê uma medida de justiça maior; os resultados mostram uma melhoria de 5% na medida de justiça, com uma diminuição de 75% no desvio padrão de justiça entre os quadros, também alcançando um aumento de 12% na satisfação individual média pela alocação de capacidade aos terminais. Por final, apresentamos uma modelagem alternativa para o enlace de retorno do DVB-RCS2 usando cadeias de Markov, predizendo parâmetros tradicionais de fila como a intensidade de tráfego, tempo médio de espera, dentre outros. Utilizamos dados coletados de uma série de simulações usando o arcabouço orientado a eventos para validar o modelo de filas como uma aproximação numérica útil para o cenário real de aplicação. Nós apresentamos o algoritmo de alocação de controle do parâmetro alfa (GTAC) que consegue controlar o tempo médio de espera de um RCST na fila, respeitando um limiar de tempo enquanto otimiza a taxa média média de transmissão de dados dos terminais.Satellite networks are expected to play a vital role in future communication systems, with complex features and seamless integration with ground-based infrastructure. This dissertation proposes three main contributions: firstly, it presents a novel simulation framework capable of providing a detailed assessment of a satellite communication’s network performance in realistic scenarios, employing an event-driven methodology and modeling the communications network as a DES (discrete event system). This work focuses on the return link of the Digital Video Broadcast Return Channel via Satellite (DVB-RCS2) standard. Three different scenarios demonstrate possible uses of the simulator’s output to understand the network’s dynamic behavior and achievable optimal system operation. Each scenario explores a different feature of the simulator. The simulated range covers a large territory with thousands of users, which in our case study was the country of Brazil. In the second theme, this work introduces a novel algorithm modification for the conventional game theory-based time slot assignment method, applying it to the DVB-RCS system. This procedure considers the spectral efficiency as a weighting parameter. We use it as an input for the resulting convex optimization problem of the Nash Bargaining Solution. This approach guarantees the fulfillment of Quality of Service (QoS) constraints while maintaining a higher fairness measure; results show a 5% improvement in fairness, with a 73% decrease in the standard deviation of fairness between frames, while also managing to reach a 12.5% increase in average normalized terminal BTU allocation satisfaction. Lastly, we present an alternative queuing model analysis for the DVB-RCS2 return link using Markov chains, developed to predict traditional queue parameters such as traffic intensity, average queue size, average waiting time, among others. We used data gathered from a series of simulations using the DES framework to validate this queuing model as a useful numerical approximation to the real application scenario, and, by the end of the scope, we present the alpha allocation algorithm (GTAC) that can maintain the average waiting time of a terminal in the queue to a threshold while optimizing the average terminal throughput

A game-based approach towards human augmented image annotation.

PhDImage annotation is a difficult task to achieve in an automated way. In this thesis, a human-augmented approach to tackle this problem is discussed and suitable strategies are derived to solve it. The proposed technique is inspired by human-based computation in what is called “human-augmented” processing to overcome limitations of fully automated technology for closing the semantic gap. The approach aims to exploit what millions of individual gamers are keen to do, i.e. enjoy computer games, while annotating media. In this thesis, the image annotation problem is tackled by a game based framework. This approach combines image processing and a game theoretic model to gather media annotations. Although the proposed model behaves similar to a single player game model, the underlying approach has been designed based on a two-player model which exploits the player’s contribution to the game and previously recorded players to improve annotations accuracy. In addition, the proposed framework is designed to predict the player’s intention through Markovian and Sequential Sampling inferences in order to detect cheating and improve annotation performances. Finally, the proposed techniques are comprehensively evaluated with three different image datasets and selected representative results are reported

Intelligent Agents for Active Malware Analysis

The main contribution of this thesis is to give a novel perspective on Active Malware Analysis modeled as a decision making process between intelligent agents. We propose solutions aimed at extracting the behaviors of malware agents with advanced Artificial Intelligence techniques. In particular, we devise novel action selection strategies for the analyzer agents that allow to analyze malware by selecting sequences of triggering actions aimed at maximizing the information acquired. The goal is to create informative models representing the behaviors of the malware agents observed while interacting with them during the analysis process. Such models can then be used to effectively compare a malware against others and to correctly identify the malware famil

Aspirations, adaptive learning and cooperation in repeated games

Game Theory;Repeated Games

Dynamic Legislative Policy Making

We prove existence of stationary Markov perfect equilibria in an infinite-horizon model of legislative policy making in which the policy outcome in one period determines the status quo in the next. We allow for a multidimensional policy space and arbitrary smooth stage utilities. We prove that all such equilibria are essentially in pure strategies and that proposal strategies are differentiable almost everywhere. We establish upper hemicontinuity of the equilibrium correspondence, and we derive conditions under which each equilibrium of our model determines a unique invariant distribution characterizing long run policy outcomes. We illustrate the equilibria of the model in a numerical example of policy making in a single dimension, and we discuss extensions of our approach to accommodate much of the institutional structure observed in real-world politics.

Finding Multiple Equilibria for Raiffa–Kalai–Smorodinsky and Nash Bargaining Equilibria in Electricity Markets: A Bilateral Contract Model

In a deregulated market, energy can be exchanged like a commodity and the market agents including generators, distributors, and the end consumers can trade energy independently settling the price, volume, and the supply terms. Bilateral contracts (BCs) have been applied to hedge against price volatility in the electricity spot market. This work introduces a model to find all solutions for the equilibria implementing the Raiffa–Kalai–Smorodinski (RKS) and the Nash Bargaining Solution (NBS) approaches in an electricity market based on BCs. It is based on creating “holes” around an existing equilibrium within the feasibility set, yielding a new (smaller) feasibility set at each iteration. This research has two players: a generation company (GC) and an electricity supplier company (ESC), aiming to achieve the highest profit for each of them. The results present all possible RKS and NBS, in addition to showing all assigned energies for a case study at different time frames. The multiple equilibria solutions allow the ESC and the GC to apply different strategies knowing that they can still achieve an optimal solution