更改

[[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)\geqU^*-\epsilon</math>.

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

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

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

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

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

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。

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.<ref>{{cite journal |doi=10.1007/s11633-008-0063-6 |author=Tsang, E.P.K. |title=Computational intelligence determines effective rationality |journal= International Journal of Automation and Computing|volume=5 |issue=1 |pages=63–6 |year=2008 |s2cid=9769519 }}</ref> [[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.<ref>{{cite book |last1=Marwala |first1= Tshilidzi| last2=Hurwitz |first2= Evan |title=Artificial Intelligence and Economic Theory: Skynet in the Market |year=2017 |publisher=[[Springer Science+Business Media|Springer]] |location=London |isbn=978-3-319-66104-9}}</ref>

==Relationship to behavioral economics==

==Relationship to behavioral economics==