如果你也在 怎样代写微观经济学Microeconomics这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。微观经济学Microeconomics是主流经济学的一个分支，研究个人和公司在做出有关稀缺资源分配的决策时的行为以及这些个人和公司之间的互动。微观经济学侧重于研究单个市场、部门或行业，而不是宏观经济学所研究的整个国民经济。
微观经济学Microeconomics的一个目标是分析在商品和服务之间建立相对价格的市场机制，并在各种用途之间分配有限资源。微观经济学显示了自由市场导致理想分配的条件。它还分析了市场失灵，即市场未能产生有效的结果。微观经济学关注公司和个人，而宏观经济学则关注经济活动的总和，处理增长、通货膨胀和失业问题以及与这些问题有关的国家政策。微观经济学还处理经济政策（如改变税收水平）对微观经济行为的影响，从而对经济的上述方面产生影响。特别是在卢卡斯批判之后，现代宏观经济理论大多建立在微观基础上，即基于微观层面行为的基本假设。
my-assignmentexpert™ 微观经济学Microeconomics作业代写，免费提交作业要求， 满意后付款，成绩80\%以下全额退款，安全省心无顾虑。专业硕 博写手团队，所有订单可靠准时，保证 100% 原创。my-assignmentexpert™， 最高质量的微观经济学Microeconomics作业代写，服务覆盖北美、欧洲、澳洲等 国家。 在代写价格方面，考虑到同学们的经济条件，在保障代写质量的前提下，我们为客户提供最合理的价格。 由于统计Statistics作业种类很多，同时其中的大部分作业在字数上都没有具体要求，因此微观经济学Microeconomics作业代写的价格不固定。通常在经济学专家查看完作业要求之后会给出报价。作业难度和截止日期对价格也有很大的影响。
想知道您作业确定的价格吗? 免费下单以相关学科的专家能了解具体的要求之后在1-3个小时就提出价格。专家的 报价比上列的价格能便宜好几倍。
my-assignmentexpert™ 为您的留学生涯保驾护航 在经济Economy作业代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的微观经济学Microeconomics代写服务。我们的专家在经济Economy代写方面经验极为丰富，各种微观经济学Microeconomics相关的作业也就用不着 说。
我们提供的微观经济学Microeconomics及其相关学科的代写，服务范围广, 其中包括但不限于:
经济代写|微观经济学作业代写Microeconomics代考|The satisfaction principle
The satisfaction principle is the only principle common to both evolutionist and classical game theory. It requires that, in addition to the set of players, the set of strategies available to each of them, one specifies for each player a utility function, which defines his payoff at each issue of the game (which may be repeated). It specifies the set of players, the set of strategies available to each of them as well as his utility function, i. e. his payoff at each issue of the stage game. The opportunities and preferences of the players are supposed to be given (to the modeller) at the beginning of the game and stay unchanged.
The standard illustration, systematically used in the future, is the “technology game”. It represents the coordination problem faced by two firms, 1 and 2 , which have to choose among two technologies, A and B. The technology $\mathrm{B}$, in contrast to the technology $\mathrm{A}$, is a state-of- the-art- tech-nology: both firms, if they both choose $B$, better perform -they both get 4 than if they both choose A -they both get 2 . When one firm uses technology $\mathrm{A}$ and the other technology $\mathrm{B}$, the first firm gets $b$ and the second $c$, these parameters being further specified. Note that this game is symmetric whatever the values assigned to $b$ and $c$ (firms $\mathrm{A}$ and $\mathrm{B}$ have the same strategy set and achieve the same payoffs in symmetric issues). The corresponding game matrix, a symmetric matrix, is the following:
Table 3.1. Technology game
\begin{tabular}{lll}
\hline & A2 & B2 \
\hline $\mathrm{A} 1$ & $\mathbf{( 2 , 2 )}$ & $\mathbf{( b , c )}$ \
$\mathrm{B} 1$ & $\mathbf{( c , b )}$ & $\mathbf{( 4 , 4 )}$ \
\hline
\end{tabular}
In a first variant of the technology game, looking like Rousseau’s stag hunt game, one states $b=1$ and $c=0$. It means that the inferior technology $\mathrm{A}$, better mastered than technology B, can be used alone (with a reduced payoff). Conversely, the superior technology B, which needs to develop, is bad when used alone. In this variant, the technology A is less risky than technology B, in that it yields a payoff which less depends on the choice of the other firm (the payoff is between 1 and 2 for technology A, in contrast to 0 and 4 for technology B). The corresponding matrix is the following (variant 1):
Table 3.2. Technology game, variant 1
\begin{tabular}{ccc}
\hline & A2 & B2 \
\hline A1 & $(\mathbf{2 , 2})$ & $(\mathbf{1}, \mathbf{0})$ \
$\mathrm{B} 1$ & $\mathbf{( 0 , 1 )}$ & $\mathbf{( 4 , 4 )}$ \
\hline
\end{tabular}
经济代写|微观经济学作业代写Microeconomics代考|The confrontation principle
Players play repeatedly, a finite or infinite number of times, a $n$-player stage game. The stage game is a non cooperative game in the usual sense, in normal or extensive form. The payoffs of the players are aggregated thanks to some discount rate (see chapter 1 ).
The $n$-player stage game is supposed to be played by $n$ players or, more usually in evolutionist games, by $n$ populations of agents, each agent of the population $i$ playing the role of player i. In each period, several $n$-uplets of individuals are randomly drawn from the $n$ populations (or subsets of these $n$ populations), one agent from each population, and each $n$-uplet of agents plays the game. The interactions may be more or less numerous at each period. On one side, a single $n$-uplet is constituted in a random way. On the other side, all possible $n$-uplets are formed. In the technology game, if each firm is represented by an equal number of agents (each agent having a given technology), each agent of one population may meet one firm of the other population or many combinations can be sorted out.
A usual distinction about meetings concerns the “multi-population” or the “mono-population” approach. The multi-population approach is available for any game and corresponds to differenciated players. Agents from a given population meet agents from the other populations. By contrast, the mono-population approach is reserved to symmetric games when players are considered as interchangeable. Agents form a unique population and meet any agents from that population. For a symmetric game, the two approaches are then available while only the first is available for non symmetric games. For example, one may study the technology game with two populations of agents, namely if the two firms are not located in the same place and if the game can only confront firms not located in a same place, but also with only one population of agents if the firms are interchangeable.
经济代写|微观经济学作业代写MICROECONOMICS代考|The information principle
In classical economics, the players are endowed with much structural information and the structure of the game may even be common knowledge. In evolutionist game theory, the required structural information is much less constraining. A player usually (at least partially) knows his strategy set, but he may ignore his preferences over the possible issues. A player may ignore the characteristics of the other players (their strategy sets, their information, their preferences), the way he is confronted to them, and even that he is involved in a game. For example, in the technology game, each player may partly or completely ignore the costs and the efficiency of each technology given the choice of the other firm, and he may be unaware of the fact that the opponent has also to choose between the same two technologies.
In evolutionary games, factual information grows thanks to repetition. The repetition of the same stage game allows each agent to gather two types of information. On the one hand, he gathers information on the past actions played by his opponents (he is supposed to know his own past actions). On the other hand, he gets information on the utility provided by each of his past played action. He may also collect some information about the payoffs achieved by the opponents for different profiles of played strategies. Information is supposed to be non ambiguous, in the sense that one can categorize it (information on actions, information on utilities) and that it is one to one associated to a given player and to a given action. In-formation is also more or less reliable; so the information may consist in an interval of values including the true one, sometimes in probability distributions over such an interval. In the technology game for example, repetition of the game may lead each firm to observe the technology chosen by the opponent and may provide, with more or less reliability, information on the payoffs achieved thanks to this choice.
Usually, the information which can be collected by a player over time is limited in space: an agent is namely unable to gather information outside of his “information neighborhood”. This neighborhood is generally included in the interaction neighborhood, but not necessarily. Even inside the information neighborhood, a player may only get information on a random sample of agents. The size of this sample may range from one agent to the cardinality of the neighborhood set. A player may even only learn (from an organization external to the game) aggregated information about the most played past action or statistics about the payoffs assigned to some actions. Finally information gathering is not systematic during time. In the technology game for example, a firm may deliberately choose to observe the opponent’s technology choice only in some periods, more or less spaced out, the interval between two information periods being regular or random.
Up to now, we only talked about passive information, that is to say information which automatically derives from the play of the game. But a player may also be more active in his search of information. On the one hand, he may buy the information collected by external specialized organizations, or get information by communicating with the other players. On the other hand, he may actively construct his information, for example by deliberately testing new strategies, in order to lead the players to a new trajectory of actions and payoffs, which provides him new original information. Such a behavior is frequent when the stage game is not well-known in the early periods of play. If so, exploration of new actions allows to investigate new parts of the game matrix (or of the game tree); for example, it allows to discover personal payoffs assigned to actions and reactions to actions, which have not been played up to now. In the technology game, a firm, even if satisfied with the older technology A, may test the new technology $\mathrm{B}$, in order to discover if it really leads to the expected efficiency; a player may also discover, by switching to the new technology, that this switch leads the opponent to opt for a third technology, not available to himself, a possibility he ignored before the switch.
微观经济学代写
经济代写|微观经济学作业代写MICROECONOMICS代考|THE SATISFACTION PRINCIPLE
满足原则是进化论和经典博弈论唯一共同的原则。它要求，除了参与者集合、每个参与者可用的策略集合之外，还需要为每个参与者指定一个效用函数，该函数定义他在每个博弈问题上的收益在H一世CH米一种是b和r和p和一种吨和d. 它指定了参与者的集合、他们每个人可用的策略集合以及他的效用函数，即他在每个阶段博弈中的收益。应该给予玩家的机会和偏好吨这吨H和米这d和ll和r在游戏开始时保持不变。
未来系统使用的标准插图是“技术游戏”。它代表了两个公司 1 和 2 面临的协调问题，它们必须在 A 和 B 两种技术中进行选择。技术乙, 与技术相反一种, 是最先进的技术：两家公司，如果他们都选择乙，更好的表现——他们都得到 4 ，而不是他们都选择 A ——他们都得到 2 。当一家公司使用技术时一种和其他技术乙，第一家公司得到b第二个C，这些参数被进一步指定。请注意，无论分配给什么值，这个游戏都是对称的b和C F一世r米s$一种$一种nd$乙$H一种在和吨H和s一种米和s吨r一种吨和G是s和吨一种nd一种CH一世和在和吨H和s一种米和p一种是这FFs一世ns是米米和吨r一世C一世ss在和s. 对应的博弈矩阵，一个对称矩阵，如下：
表 3.1。科技游戏
\begin{tabular}{lll} \hline & A2 & B2 \ \hline $\mathrm{A} 1$ & $\mathbf{( 2 , 2 )}$ & $\mathbf{( b , c )}$ \ $\mathrm{B} 1$ & $\mathbf{( c , b )}$ & $\mathbf{( 4 , 4 )}$ \ \hline \end{表格}\begin{tabular}{lll} \hline & A2 & B2 \ \hline $\mathrm{A} 1$ & $\mathbf{( 2 , 2 )}$ & $\mathbf{( b , c )}$ \ $\mathrm{B} 1$ & $\mathbf{( c , b )}$ & $\mathbf{( 4 , 4 )}$ \ \hline \end{表格}
在技术游戏的第一个变体中，看起来像卢梭的猎鹿游戏，有人说b=1和C=0. 意味着技术低劣一种，比技术B好掌握，可以单独使用在一世吨H一种r和d在C和dp一种是这FF. 反之，需要开发的优越技术B，单独使用时效果不佳。在这个变体中，技术 A 的风险低于技术 B，因为它产生的收益较少依赖于其他公司的选择吨H和p一种是这FF一世sb和吨在和和n1一种nd2F这r吨和CHn这l这G是一种,一世nC这n吨r一种s吨吨这0一种nd4F这r吨和CHn这l这G是乙. 对应的矩阵如下在一种r一世一种n吨1:
表 3.2。科技游戏，变体 1
\begin{tabular}{ccc} \hline & A2 & B2 \ \hline A1 & $(\mathbf{2 , 2})$ & $(\mathbf{1}, \mathbf{0})$ \ $\mathrm {B} 1$ & $\mathbf{( 0 , 1 )}$ & $\mathbf{( 4 , 4 )}$ \ \hline \end{表格}\begin{tabular}{ccc} \hline & A2 & B2 \ \hline A1 & $(\mathbf{2 , 2})$ & $(\mathbf{1}, \mathbf{0})$ \ $\mathrm {B} 1$ & $\mathbf{( 0 , 1 )}$ & $\mathbf{( 4 , 4 )}$ \ \hline \end{表格}
经济代写|微观经济学作业代写MICROECONOMICS代考|THE CONFRONTATION PRINCIPLE
玩家反复玩，有限或无限次，n-玩家舞台游戏。舞台博弈是通常意义上的非合作博弈，具有正常或广泛的形式。由于一些折扣率，玩家的收益被汇总s和和CH一种p吨和r1.
这n-玩家舞台游戏应该由n玩家，或者更常见的是在进化论游戏中，通过n代理人群体，人口中的每个代理人一世扮演玩家 i 的角色。在每个时期，几个n- 个人的uplets随机抽取n人口这rs在bs和吨s这F吨H和s和$n$p这p在l一种吨一世这ns，每个群体中的一个代理，每个n-uplet 代理玩游戏。每个时期的交互可能或多或少。一方面，单n-uplet 以随机方式构成。另一边，一切皆有可能n-uplets 形成。在技术博弈中，如果每个公司由相同数量的代理人代表和一种CH一种G和n吨H一种在一世nG一种G一世在和n吨和CHn这l这G是，一个群体的每个代理人可能会遇到另一个群体的一个公司，或者可以整理出许多组合。
会议的一个常见区别在于“多群体”或“单一群体”方法。多种群方法适用于任何游戏，并对应差异化的玩家。来自给定群体的代理人遇到来自其他群体的代理人。相比之下，当玩家被认为是可互换的时，单一种群方法只适用于对称游戏。代理人形成一个独特的群体，并会见该群体中的任何代理人。对于对称游戏，两种方法都可用，而只有第一种方法可用于非对称游戏。例如，可以研究具有两个代理群体的技术博弈，即如果两个公司不在同一个地方，如果博弈只能对抗不在同一个地方的公司，
经济代写|微观经济学作业代写MICROECONOMICS代考|THE INFORMATION PRINCIPLE
在古典经济学中，参与者被赋予了大量的结构信息，博弈的结构甚至可能是常识。在进化论博弈论中，所需的结构信息的约束要小得多。一个玩家通常一种吨l和一种s吨p一种r吨一世一种ll是知道他的策略集，但他可能会忽略他对可能问题的偏好。一个玩家可能会忽略其他玩家的特征吨H和一世rs吨r一种吨和G是s和吨s,吨H和一世r一世nF这r米一种吨一世这n,吨H和一世rpr和F和r和nC和s，他面对他们的方式，甚至他参与了一场游戏。例如，在技术博弈中，每个玩家可能会部分或完全忽略每项技术的成本和效率，给对方公司的选择，而他可能不知道对手也必须在相同的两者之间进行选择。技术。
在进化游戏中，事实信息由于重复而增长。同一阶段游戏的重复允许每个智能体收集两种类型的信息。一方面，他收集对手过去的行动信息H和一世ss在pp这s和d吨这ķn这在H一世s这在np一种s吨一种C吨一世这ns. 另一方面，他得到关于他过去的每一个动作所提供的效用的信息。他还可以收集一些关于对手在不同策略配置文件中获得的回报的信息。信息应该是明确的，因为人们可以对其进行分类一世nF这r米一种吨一世这n这n一种C吨一世这ns,一世nF这r米一种吨一世这n这n在吨一世l一世吨一世和s并且它与给定的玩家和给定的动作是一对一的。信息也或多或少可靠；因此，信息可能包含在一个包含真实值的区间中，有时包含在这样一个区间上的概率分布中。例如，在技术博弈中，博弈的重复可能会导致每家公司观察对手选择的技术，并可能或多或少地提供有关由于这种选择而获得的收益的信息。
通常，玩家随着时间的推移可以收集的信息在空间上是有限的：代理即无法在他的“信息邻域”之外收集信息。该邻域一般包含在交互邻域中，但不一定。即使在信息邻域内，玩家也只能获得随机代理样本的信息。该样本的大小可能从一个代理到邻域集的基数不等。玩家甚至可能只会学习Fr这米一种n这rG一种n一世和一种吨一世这n和X吨和rn一种l吨这吨H和G一种米和关于过去玩得最多的动作的汇总信息或分配给某些动作的收益的统计信息。最后，信息收集在时间上不是系统的。例如，在技术博弈中，公司可能故意选择仅在某些时期内观察对手的技术选择，或多或少地间隔开，两个信息时期之间的间隔是规则的或随机的。
到目前为止，我们只讨论了被动信息，即从游戏中自动得出的信息。但是玩家在搜索信息时也可能更加积极。一方面，他可能会购买外部专业组织收集的信息，或者通过与其他玩家交流来获取信息。另一方面，他可能会主动构建自己的信息，例如通过刻意测试新策略，以引导玩家进入新的行动和收益轨迹，从而为他提供新的原始信息。当舞台游戏在游戏初期并不为人所知时，这种行为很常见。如果是这样，对新动作的探索允许研究游戏矩阵的新部分这r这F吨H和G一种米和吨r和和; 例如，它允许发现分配给动作的个人回报和对动作的反应，这些是迄今为止尚未播放的。在技术博弈中，企业即使对旧技术 A 感到满意，也可能会测试新技术乙，以发现它是否真的达到了预期的效率；玩家还可能通过转换到新技术发现，这种转换导致对手选择第三种技术，而他自己没有，这种可能性在转换之前被他忽略了。
经济代写|微观经济学作业代写MICROECONOMICS代考 请认准UprivateTA™. UprivateTA™为您的留学生涯保驾护航。
