### 经济代写|博弈论代写Game Theory代考|ECON40010

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 经济代写|博弈论代写Game Theory代考|Overview

This book is organized into six parts. The first two parts of the book focus primarily on game theory methods, while the next four focus on machine learning methods. However, as noted, there is considerable overlap among these general approaches in complex, interactive multi-agent settings. Game theory approaches consider opportunities for adaptation and learning, while machine learning considers the potential for adversarial attacks. The first part on game theory focuses on introductory material and uses a variety of different approaches for cyber deception to illustrate many of the common research challenges in applying game theory to cybersecurity. The next chapter looks at a broader range of applications of game theory, showing the variety of potential decision problems, beyond just deception, that can be addressed using this approach.
The remaining four parts of the book focus on a variety of topics in machine learning for cybersecurity. Part 3 considers adversarial attacks against machine learning, bridging game theory models and machine learning models. Part 4 also connects these two using generative adversarial networks to generate deceptive objects. This area synchronizes with the earlier work on game theory for deception since this method can be used to create effective decoys. The final two parts cover various applications of machine learning to specific cybersecurity problems, illustrating some of the breadth of different techniques, problems, and novel research that is covered within this topic.

The first part of the book focuses on using game-theoretic techniques to achieve security objectives using cyber deception tactics (e.g., deploying honeypots or other types of decoys in a network). We begin with a general overview of basic game theory concepts and solution techniques that will help the reader quickly acquaint themselves with the background necessary to understand the remaining chapters. Chapters 3-6 all develop new game models and algorithms for improving cyber deception capabilities; while they share many fundamental approaches, they all address unique use cases and/or challenges for using game theory in realistic cybersecurity settings. Chapter 3 focuses on using deception strategically to differentiate among different types of attackers, and addresses issues in both the modeling and scalability of solution algorithms for this type of deception. Chapter 4 focuses on the problem of modeling and solving games that are highly dynamic with many repeated interactions between defenders and attackers, and on scaling these solutions to large graphs. Chapter 5 brings in the aspect of human behavior, evaluating the game theory solutions against human opponents. Finally, Chapter 6 introduces a different framework for modeling the knowledge players have in security games and how they reason about uncertainty, as well as using formal methods to provide some guarantees on the performance of the system.

## 经济代写|博弈论代写Game Theory代考|Introduction to Game Theory

Game theory is the study of mathematical models of conflict and cooperation between intelligent decision makers (Myerson 1991). Game theory stems from discussions of card games initially (Dimand and Dimand 1996), but it is not a collection of theories about entertaining games. Instead, game theory provides an essential class of models for complex systems with multiple agents or players. It has a profound impact in economics, with several Nobel Memorial Prize laureates in economics winning the prize for their foundational work in game theory. In addition to economics, game theory has wide applications in sociology, psychology, political science, biology, and other fields. Part 2 of this book introduces how game theory can be used to analyze cybersecurity problems and improve cyber defense effectiveness.

A game consists of at least three elements: the set of players, the set of actions for players to take, the payoff function that describes how much payoff or utility each player can get under different states of the world and joint actions chosen by the players. Games can be categorized into different types based on their differences in these elements. Two-player games have been studied much more extensively than the games with three or more players. A game is zero-sum if the players’ payoffs always sum up to zero. Simultaneous games are games where all players take actions simultaneously with no further actions to be taken afterward, i.e. the game ends immediately. Even if the players do not move simultaneously, the game can still be viewed as a simultaneous game if the players who move later are completely unaware of the earlier players’ actions: they cannot even make any inference about the earlier players’ actions from whatever they observe. In contrast, a game is sequential if some players take action after other players, and they have some knowledge about the earlier actions taken by other players.

A game is with complete information if the players have common knowledge of the game being played, including each player’s action set and payoff structure. Some games are with incomplete information. For example, in a single-item auction, each player knows how much she values the item and thus her utility function, but not how much other players value it. A game is with perfect information if players take action sequentially, and all other players can observe all actions the players take. A game has perfect recall if each player does not forget any of their previous actions.

## 经济代写|博弈论代写Game Theory代考|Example Two-Player Zero-Sum Games

We start by discussing the simplest type of games: two-player zero-sum normal-form games with finite action sets. They involve two decision-makers who will each take a single action among a finite set of actions at the same time, and get a payoff based on their joint moves. Furthermore, the two players’ payoffs sum up to zero. Thus, one player’s gain or loss is the same as the other player’s loss or gain in terms of the absolute value. An example of such a game is the classic Rock-Paper-Scissors (RPS) game, whose payoff matrix is shown in Table 2.1. The game has two players. Let us refer to them as the row player (or Player 1) and the column player (or Player 2). Each player can choose among three actions: Rock, Paper, and Scissors. Rock beats Scissors; Scissors beats Paper, and Paper beats Rock. In this payoff matrix, each row shows a possible action for the row player, and each column shows a possible action for the column player. Each entry in the payoff matrix has two numbers, showing the payoff for the two players, respectively, when choosing the actions shown in the corresponding row and column. If the row player chooses Paper and the column player chooses Rock, the row player wins and gets a payoff of 1 while the column player loses and gets a payoff of $-1$ as shown by the numbers in the second row, first column. Since this is a zero-sum game, the second number in each entry is always the negation of the first number. Therefore, in some cases, we only show the payoff value for the row player in each entry in the payoff matrix for two-player zero-sum games.

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