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# 经济代写|微观经济学代考Microeconomics代写|ECON2516 The evaluation principle

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## 经济代写|微观经济学代考Microeconomics代写|The evaluation principle

In a first step, a player may mainly focus on the distribution of past played strategies in his information neighborhood. Limited memory and limited ability of the player induces limits on the history of plays observed. An agent may forget any information which dates back more than $k$ periods, and he may only focus on a sample of actions, possibly randomly drawn among the $k$ last plays. On basis of this information, an agent can calculate the frequency with which each strategy has been played, as well as many other statistical properties of the sequence of past observed strategies, which may have an impact on his future play. More ambitiously, an agent can try to discover patterns of behaviors, like cycles of opponents’ actions or types of reactions to actions. For example, in the technology game, a firm may be content with observing the technology chosen by its opponent in the three last periods in order to choose the most often selected technology; but a firm may also look for some regularities, like for example a systematic imitation by the opponent agent in each period of her technology choice in preceding periods.

In a similar way, a player may focus on the payoffs associated to different past strategies, played by himself and by the agents in his information neighborhood. Of course, limited memory and limited ability may again limit the sequence of observed past payoffs. The gathered information may be used to construct statistical indicators for each strategy, like the mean payoff or the weighted sum of payoffs provided, the weights (possibly) decreasing with earlier periods. He may also observe some structural patterns about payoffs such as their dispersion. In the technology game, a firm may study the sequence of payoffs assigned to both technologies, in order to adopt the most efficient one; but it may also focus on the dispersion of the obtained payoffs, in order to adopt the less risky technology.

## 经济代写|微观经济学代考Microeconomics代写|The decision principle

A player draws on all the preceding calculated indices to make his choice. In classical game theory, it may be difficult to calculate the player’s chosen strategy, for instance when the game has no or many equilibria. By contrast, in evolutionist game theory, an agent always plays, each time he is called on to do so, despite his possible lack of information and rationality. In fact, the way he plays just takes these failings into account, being sometimes very unsophisticated from a strategic point of view. The important point is that the mere fact of playing has two consequences. On the one hand, regardless of the strategic content of the actions, playing draws the game toward a particular direction depending on the played actions. On the other hand, playing diffuses information on the game through the played actions. Diffusion of information takes a strategic dimension: each agent may be conscious that the information conveyed by his actions can be used by other players to their advantage. Acquisition of information is favored in some respects. The exploitation behavior, taking advantage of the already obtained information is often followed and completed by an exploration behavior, which aims at providing new information.

Exploitation behavior may be more or less sophisticated. At one extreme, it may reduce to “inert behavior”, which consists in repeating the same action, or rather in keeping over time the same probability distribution over actions. At the other extreme, “optimizing behavior” consists in only choosing the best action, as regards the value of an index associated to each action. Inbetween, “probabilistic behavior” consists in playing each action with a probability proportional to the value of an index assigned to each action. In fact, the probabilistic behavior has two limit cases: the one is random behavior (which leads to the play of every action with the same probability), the other is optimising behavior (only the best action, as regards the value of the index, is played). Probabilistic and optimising behavior can be adjusted to all kinds of information available, namely information on actions and information on payoffs. For example, an imitation model, in which each action is played with a probability that is function of the frequency of its play in the past, is a probabilistic behavior model, based on an action frequency index. A reinforcement model, in which the probability of play of an action is function of its performance in the past, is also a probabilistic behavior model, based on a past observed payoff index. A best-reply model, in which an agent plays the action that maximizes his payoff given the past profile of opponents’ strategies, is an optimisation model, based on a calculated utility derived from a past action index.

# 微观经济学代写

## Matlab代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。