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As decision-makers have to make decisions about how and when to decide, Ariel Rubinstein proposed to model bounded rationality by explicitly specifying decision-making procedures. This puts the study of decision procedures on the research agenda.

As decision-makers have to make decisions about how and when to decide, Ariel Rubinstein proposed to model bounded rationality by explicitly specifying decision-making procedures. This puts the study of decision procedures on the research agenda.

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:

格尔德 · 吉格伦泽认为，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启发法往往比理论上的最佳程序导致更好的决策。如果我们相信代理人会选择一个让他们“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:

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>

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==