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Gerd Gigerenzer opines that decision theorists have not really adhered to Simon's original ideas. Rather, they have considered how decisions may be crippled by limitations to rationality, or have modeled how people might cope with their inability to optimize. Gigerenzer proposes and shows that simple heuristics often lead to better decisions than theoretically optimal procedures.  If we believe that agents will choose an action that gets them "close" to the optimum, then we can use the notion of epsilon-optimization, which means we choose our actions so that the payoff is within epsilon of the optimum. If we define the optimum (best possible) payoff as <math> U^* </math>, then the set of epsilon-optimizing options S(ε) can be defined as all those options s such that:

Gerd Gigerenzer opines that decision theorists have not really adhered to Simon's original ideas. Rather, they have considered how decisions may be crippled by limitations to rationality, or have modeled how people might cope with their inability to optimize. Gigerenzer proposes and shows that simple heuristics often lead to better decisions than theoretically optimal procedures.  If we believe that agents will choose an action that gets them "close" to the optimum, then we can use the notion of epsilon-optimization, which means we choose our actions so that the payoff is within epsilon of the optimum. If we define the optimum (best possible) payoff as <math> U^* </math>, then the set of epsilon-optimizing options S(ε) can be defined as all those options s such that:

'''<font color="#ff8000">格尔德·吉格伦泽 Gerd Gigerenzer</font>'''认为，决策理论家并没有真正坚持西蒙的原始观点。相反，他们考虑了决策如何被理性的限制所削弱，或者模拟了人们如何应对他们无法优化的情况。Gigerenzer 提出并证明了简单的探索法往往比理论上的最佳程序导致更好的决策。如果我们相信行为主体会选择一个让他们“接近”最优的行动，那么我们可以使用 epsilon 优化的概念，这意味着我们选择我们的行动，使得回报在最优的 epsilon 之内。如果我们将最优(最佳可能)收益定义为 < math > u ^ * </math > ，那么 epsilon-optimization 选项集 s (ε)就可以定义为所有这些选项:

'''<font color="#ff8000">格尔德·吉格伦泽 Gerd Gigerenzer</font>'''认为，决策论专家并没有真正坚持西蒙的原始观点。相反，他们考虑了决策如何被理性的限制所削弱，或者模拟了人们如何应对他们无法优化的情况。Gigerenzer 提出并证明了简单的启发式算法往往能比理论上的最佳程序获得更好的决策。如果我们相信行为主体会选择一个让他们“接近”最优的行动，那么我们可以使用 epsilon 优化的概念——我们选择我们的行动，使得回报在最优的 epsilon 之内。如果我们将最优(最佳可能)收益定义为<math>u^*</math>，那么 epsilon-optimization 选项集s(ε)就可以定义为所有这些选项:

[[Huw Dixon]] later argues that it may not be necessary to analyze in detail the process of reasoning underlying bounded rationality.<ref>{{cite book |chapter=Some Thoughts on Artificial Intelligence and Economic Theory |editor-last=Moss |editor2-last=Rae |title=Artificial Intelligence and Economic Analysis |publisher=Edward Elgar |location= |year=1992 |pages=[https://archive.org/details/artificialintell0000unse_a9c0/page/131 131–154] |doi= |isbn=978-1852786854 |chapter-url=https://archive.org/details/artificialintell0000unse_a9c0/page/131 }}</ref>  If we believe that agents will choose an action that gets them "close" to the optimum, then we can use the notion of ''epsilon-optimization'', which means we choose our actions so that the payoff is within epsilon of the optimum. If we define the optimum (best possible) payoff as <math> U^* </math>, then the set of epsilon-optimizing options '''S(ε)''' can be defined as all those options '''s''' such that:

[[Huw Dixon]] later argues that it may not be necessary to analyze in detail the process of reasoning underlying bounded rationality.<ref>{{cite book |chapter=Some Thoughts on Artificial Intelligence and Economic Theory |editor-last=Moss |editor2-last=Rae |title=Artificial Intelligence and Economic Analysis |publisher=Edward Elgar |location= |year=1992 |pages=[https://archive.org/details/artificialintell0000unse_a9c0/page/131 131–154] |doi= |isbn=978-1852786854 |chapter-url=https://archive.org/details/artificialintell0000unse_a9c0/page/131 }}</ref>  If we believe that agents will choose an action that gets them "close" to the optimum, then we can use the notion of ''epsilon-optimization'', which means we choose our actions so that the payoff is within epsilon of the optimum. If we define the optimum (best possible) payoff as <math> U^* </math>, then the set of epsilon-optimizing options '''S(ε)''' can be defined as all those options '''s''' such that:

<math> U(s) \geq U^*-\epsilon</math>.

<math>U(s)\geqU^*-\epsilon</math>.

[ math ] u (s) geq u ^ *-epsilon.

[math]u(s)gequ^*-epsilon.

<math> U(s) \geq U^*-\epsilon</math>.

<math>U(s)\geqU^*-\epsilon</math>.

The notion of strict rationality is then a special case (ε=0). The advantage of this approach is that it avoids having to specify in detail the process of reasoning, but rather simply assumes that whatever the process is, it is good enough to get near to the optimum.

The notion of strict rationality is then a special case (ε=0). The advantage of this approach is that it avoids having to specify in detail the process of reasoning, but rather simply assumes that whatever the process is, it is good enough to get near to the optimum.

严格合理性的概念是一个特例(ε = 0)。这种方法的优点在于，它避免了详细说明推理过程，而是简单地假设无论过程是什么，它都足以接近最优值。

严格合理性的概念是一个特例(ε = 0)。虽然这种方法的优点在于避免了详细说明推理过程，但是它简单地假设无论过程是什么，都足以接近最优值。

From a computational point of view, decision procedures can be encoded in algorithms and heuristics. Edward Tsang argues that the effective rationality of an agent is determined by its computational intelligence. Everything else being equal, an agent that has better algorithms and heuristics could make "more rational" (more optimal) decisions than one that has poorer heuristics and algorithms. Tshilidzi Marwala and Evan Hurwitz in their study on bounded rationality observed that advances in technology (e.g. computer processing power because of Moore's law, artificial intelligence and big data analytics) expand the bounds that define the feasible rationality space. Because of this expansion of the bounds of rationality, machine automated decision making makes markets more efficient.

From a computational point of view, decision procedures can be encoded in algorithms and heuristics. Edward Tsang argues that the effective rationality of an agent is determined by its computational intelligence. Everything else being equal, an agent that has better algorithms and heuristics could make "more rational" (more optimal) decisions than one that has poorer heuristics and algorithms. Tshilidzi Marwala and Evan Hurwitz in their study on bounded rationality observed that advances in technology (e.g. computer processing power because of Moore's law, artificial intelligence and big data analytics) expand the bounds that define the feasible rationality space. Because of this expansion of the bounds of rationality, machine automated decision making makes markets more efficient.

从计算的角度来看，决策过程可以编码在算法和启发式。曾德昌认为，智能体的有效合理性取决于其计算智能。在其他条件相同的情况下，一个拥有更好的算法和启发式的智能体可以比那些启发式和算法较差的智能体做出“更理性”(更优化)的决策。和 Evan Hurwitz 在他们关于有限理性的研究中观察到技术的进步(例如:。由于摩尔定律、人工智能和大数据分析等因素的影响，计算机处理能力扩展了界定可行理性空间的范围。由于这种理性边界的扩展，机器自动决策使市场更有效率。

从计算的角度来看，决策过程可以在算法和启发式上编码。曾德昌认为，智能体的有效合理性取决于其计算智能。在其他条件相同的情况下，一个拥有更好的算法和启发式的智能体可以比那些启发式和算法较差的智能体做出“更理性”(更优化)的决策。他和 Evan Hurwitz 在他们关于有限理性的研究中观察到技术的进步(例如:由于摩尔定律、人工智能和大数据分析等因素的影响，计算机处理能力扩展了界定可行理性空间的范围。由于这种理性边界的扩展，机器自动决策使市场更有效率。