### 统计代写 | Statistical Learning and Decision Making代考| Reinforcement learning

statistics-lab™ 为您的留学生涯保驾护航 在代写Statistical Learning and Decision Making方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写Statistical Learning and Decision Making代写方面经验极为丰富，各种代写Statistical Learning and Decision Making相关的作业也就用不着说。

• 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

## 统计代写 | Statistical Learning and Decision Making代考|History

The theory of automating the process of decision making has roots in the dreams of early philosophers, scientists, mathematicians, and writers. The ancient Greeks began incorporating automation into myths and stories as early as 800 в.c. The word automaton was first used in Homer’s Iliad, which contains references to the notion of automatic machines including mechanical tripods used to serve dinner guests. ${ }^{9}$ In the seventeenth century, philosophers proposed the use of logic rules to automatically settle disagreements. Their ideas created the foundation for mechanized reasoning.

Beginning in the late eighteenth century, inventors began creating automatic machines to perform labor. In particular, a series of innovations in the textile industry led to the development of the automatic loom, which in turn laid the foundation for the first factory robots. ${ }^{10}$ In the early nineteenth century, the use of intelligent machines to automate labor began to make its way into science fiction novels. The word robot originated in Czech writer Karel Čapek’s play titled Rossum’s Universal Robots about machines that could perform work humans would prefer not to do. The play inspired other science fiction writers to incorporate robots into their writing. In the mid-twentieth century, notable writer and professor Isaac Asimov laid out his vision for robotics in his famous Robot series.
A major challenge in practical implementations of automated decision making is accounting for uncertainty. Even at the end of the twentieth century, George Dantzig, most famously known for developing the simplex algorithm, stated in 1991:

In retrospect it is interesting to note that the original problem that started my research is still outstanding – namely the problem of planning or scheduling dynamically over time, particularly planning dynamically under uncertainty. If such a problem could be successfully solved it could (eventually through better planning) contribute to the well-being and stability of the world. ${ }^{.11}$

While decision making under uncertainty still remains an active area of research, over the past few centuries, researchers and engineers have come closer to making the concepts posed by these early dreamers possible. Current state-of-the-art decision making algorithms rely on a convergence of concepts developed in multiple disciplines including economics, psychology, neuroscience, computer science, engineering, mathematics, and operations research. This section highlights some major contributions from these disciplines. The cross-pollination between disciplines has led to many recent advances and will likely continue to support growth in the future.

## 统计代写 | Statistical Learning and Decision Making代考|Economics

Economics requires models of human decision making. One approach to building such models involves utility theory, which was first introduced in the late eighteenth century. ${ }^{12}$ Utility theory provides a means to model and compare the desirability of various outcomes. For example, utility can be used to compare the desirability of monetary quantities. In the Theory of Legislation, Jeremy Bentham summarized the nonlinearity in the utility of money:
1st. Each portion of wealth has a corresponding portion of happiness.
2nd. Of two individuals with unequal fortunes, he who has the most wealth has the most happiness.
$3^{n d}$. The excess in happiness of the richer will not be so great as the excess of his wealth. ${ }^{13}$
By combining the concept of utility with the notion of rational decision making, economists in the mid-twentieth century established a basis for the maximum expected utility principle. This principle is a key concept behind the creation of autonomous decision making agents. Utility theory also gave rise to the development of game theory, which attempts to understand the behavior of multiple agents acting in the presence of one another to maximize their interests. ${ }^{14}$

## 统计代写 | Statistical Learning and Decision Making代考|Psychology

Psychologists also study human decision making, typically from the perspective of human behavior. By studying the reactions of animals to stimuli, psychologists have been developing theories of trial-and-error learning since the nineteenth century. Researchers noticed that animals tended to make decisions based on the satisfaction or discomfort they experienced in previous similar situations. Russian psychologist Ivan Pavlov combined this idea with the concept of reinforcement

after observing the salivation patterns of dogs when fed. Psychologists found that a pattern of behavior could be strengthened or weakened using a continuous reinforcement of a particular stimulus. In the mid-twentieth century, mathematician and computer scientist Alan Turing expressed the possibility of allowing machines to learn in the same manner:
The organization of a marhine into a universal machine would he mnst impressive if the arrangements of interference involve very few inputs. The training of a human child depends largely on a system of rewards and punishments, and this suggests that it ought to be possible to carry through the organising with only two interfering inputs, one for ‘pleasure’ or ‘reward’ ( $R$ ) and the other for ‘pain’ or ‘punishment’ (P). ${ }^{15}$
The work of psychologists laid the foundation for the field of reinforcement learning, a critical technique used to teach agents to make decisions in uncertain environments. ${ }^{16}$

## 统计代写 | Statistical Learning and Decision Making代考|Economics

1。财富的每一部分都有相应的幸福部分。

3nd. 富人的幸福过剩不会像他的财富过剩那么大。13

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## MATLAB代写

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