有限理性

来自集智百科
跳到导航 跳到搜索

此词条暂由小竹凉翻译,翻译字数共1006,未经人工整理和审校,带来阅读不便,请见谅。

Bounded rationality is the idea that rationality is limited, when individuals make decisions, by the tractability of the decision problem, the cognitive limitations of the mind, and the time available to make the decision. Decision-makers, in this view, act as satisficers, seeking a satisfactory solution rather than an optimal one.

Bounded rationality is the idea that rationality is limited, when individuals make decisions, by the tractability of the decision problem, the cognitive limitations of the mind, and the time available to make the decision. Decision-makers, in this view, act as satisficers, seeking a satisfactory solution rather than an optimal one.

有限理性是这样一种观点,即当个体做出决定时,受决定问题的可控性、大脑的认知局限性以及决定时间的限制,理性 Rationality是有限的。这种观点认为,决策者会作为满足者 Satisficer,寻求一个令人满意的而不是最佳的解决方案。


Herbert A. Simon proposed bounded rationality as an alternative basis for the mathematical modeling of decision-making, as used in economics, political science and related disciplines. It complements "rationality as optimization", which views decision-making as a fully rational process of finding an optimal choice given the information available.[1] Simon used the analogy of a pair of scissors, where one blade represents "cognitive limitations" of actual humans and the other the "structures of the environment", illustrating how minds compensate for limited resources by exploiting known structural regularity in the environment.[1] Many economics models assume that people are on average rational, and can in large enough quantities be approximated to act according to their preferences. With bounded rationality, Simon's goal was "to replace the global rationality of economic man with a kind of rational behavior that is compatible with the access to information and the computational capacities that are actually possessed by organisms, including man, in the kinds of environments in which such organisms exist."[2] In short, the concept of bounded rationality revises notions of "perfect" rationality to account for the fact that perfectly rational decisions are often not feasible in practice because of the intractability of natural decision problems and the finite computational resources available for making them.

Herbert A. Simon proposed bounded rationality as an alternative basis for the mathematical modeling of decision-making, as used in economics, political science and related disciplines. It complements "rationality as optimization", which views decision-making as a fully rational process of finding an optimal choice given the information available. Simon used the analogy of a pair of scissors, where one blade represents "cognitive limitations" of actual humans and the other the "structures of the environment", illustrating how minds compensate for limited resources by exploiting known structural regularity in the environment. In short, the concept of bounded rationality revises notions of "perfect" rationality to account for the fact that perfectly rational decisions are often not feasible in practice because of the intractability of natural decision problems and the finite computational resources available for making them.

赫伯特·A·西蒙 Herbert A. Simon提出把有限理性作为决策 Decision-making数学模型的替代基础,用于经济学 Economics政治学 Political Science和相关学科。它补充了“理性即优化”的观点,该观点认为,决策是一个完全理性的,根据已有信息找到最佳选择的过程。西蒙用一把剪刀作类比,其中一把刀片代表实际人类的”认知局限” ,另一把代表”环境的结构”,说明人类如何通过利用已知的环境结构规律来弥补有限的资源。许多经济学模型假设人们一般都是理性的,并且可以根据他们的偏好来近似地行动。通过有限理性,Simon 的目标是“用一种理性行为来取代经济人的全球理性,这种理性行为在有机体(包括人类)存在的环境中实际拥有的信息获取和计算能力是相容的。”简而言之,有限理性的概念修正了“完美”理性的概念以解释这样一个事实,由于自然决策问题的难解性和有限的计算资源,完全理性的决策在实践中往往不可行。


The concept of bounded rationality continues to influence (and be debated in) different disciplines, including economics, psychology, law, political science and cognitive science.[3] Some models of human behavior in the social sciences assume that humans can be reasonably approximated or described as "rational" entities, as in rational choice theory or Downs Political Agency Models.[4]

The concept of bounded rationality continues to influence (and be debated in) different disciplines, including economics, psychology, law, political science and cognitive science. Some models of human behavior in the social sciences assume that humans can be reasonably approximated or described as "rational" entities, as in rational choice theory or Downs Political Agency Models.

有限理性的概念继续影响着不同的学科,包括经济学、心理学、法律、政治学和认知科学。社会科学 Social Science中的一些人类行为 Human Behavior模型假定,人类可以被合理地近似或描述为“理性”实体,例如理性选择理论 Rational Choice Theory或唐斯政治机构模型。


Origins

起源

The term was coined by Herbert A. Simon. In Models of Man, Simon points out that most people are only partly rational, and are irrational in the remaining part of their actions. In another work, he states "boundedly rational agents experience limits in formulating and solving complex problems and in processing (receiving, storing, retrieving, transmitting) information".[5] Simon describes a number of dimensions along which "classical" models of rationality can be made somewhat more realistic, while sticking within the vein of fairly rigorous formalization. These include:

The term was coined by Herbert A. Simon. In Models of Man, Simon points out that most people are only partly rational, and are irrational in the remaining part of their actions. In another work, he states "boundedly rational agents experience limits in formulating and solving complex problems and in processing (receiving, storing, retrieving, transmitting) information". Simon describes a number of dimensions along which "classical" models of rationality can be made somewhat more realistic, while sticking within the vein of fairly rigorous formalization. These include:

这个术语是由Herbert A.Simon 赫伯特·A·西蒙创造的。在《人类模型》一书中,西蒙指出,大多数人的行为只是部分理性的,其余是非理性的。在另一部著作中,他指出“有限理性的行为主体在制定和解决复杂问题以及处理(接收、存储、检索、传输)信息方面经验有限”。西蒙描述了一些维度,沿着这些维度,“经典”的理性模型可以变得更加现实一些,同时坚持相当严格的形式化的脉络。其中包括:


  • limiting the types of utility functions 限制效用 utility函数的类型
  • recognizing the costs of gathering and processing information 认识到收集和处理信息的成本
  • the possibility of having a "vector" or "multi-valued" utility function 向量 vector或多值效用函数的可能性


Simon suggests that economic agents use heuristics to make decisions rather than a strict rigid rule of optimization. They do this because of the complexity of the situation.

Simon suggests that economic agents use heuristics to make decisions rather than a strict rigid rule of optimization. They do this because of the complexity of the situation.

西蒙认为,经济行为主体使用启发式 heuristic而不是严格的最优化规则作出决定。他们这样做是因为情况的复杂性。

Model extensions

模型扩展


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.[6] 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.

由于决策者必须决定如何以及何时做出决定,阿里埃勒·鲁宾斯坦 Ariel Rubinstein提议通过明确规定决策程序来建立有限理性的模型。这就把决策程序的研究提上了研究日程。


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.[4]

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]\displaystyle{ 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 Gerd Gigerenzer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]\displaystyle{ 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.[7] 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]\displaystyle{ U^* }[/math], then the set of epsilon-optimizing options S(ε) can be defined as all those options s such that:

[math]\displaystyle{ U(s)\geq U^*-\epsilon }[/math].

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


[math]\displaystyle{ 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.

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。


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.[8] 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.[9]


从计算的角度来看,决策过程可以在算法 algorithm启发式 heuristic上编码。曾德昌 Edward Tsang认为,智能体的有效合理性取决于其计算智能 computational intelligence。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。

Relationship to behavioral economics

与行为经济学的关系

Bounded rationality implies the idea that humans take reasoning shortcuts that may lead to suboptimal decision-making. Behavioral economists engage in mapping the decision shortcuts that agents use in order to help increase the effectiveness of human decision-making. One treatment of this idea comes from Cass Sunstein and Richard Thaler's Nudge. Sunstein and Thaler recommend that choice architectures are modified in light of human agents' bounded rationality. A widely cited proposal from Sunstein and Thaler urges that healthier food be placed at sight level in order to increase the likelihood that a person will opt for that choice instead of a less healthy option. Some critics of Nudge have lodged attacks that modifying choice architectures will lead to people becoming worse decision-makers.

模板:Further

Bounded rationality implies the idea that humans take reasoning shortcuts that may lead to suboptimal decision-making. Behavioral economists engage in mapping the decision shortcuts that agents use in order to help increase the effectiveness of human decision-making. One treatment of this idea comes from Cass Sunstein and Richard Thaler's Nudge.[10][11] Sunstein and Thaler recommend that choice architectures are modified in light of human agents' bounded rationality. A widely cited proposal from Sunstein and Thaler urges that healthier food be placed at sight level in order to increase the likelihood that a person will opt for that choice instead of a less healthy option. Some critics of Nudge have lodged attacks that modifying choice architectures will lead to people becoming worse decision-makers.[12][13]


有限理性意味着人类走了一条可能导致次优决策的推理捷径。行为经济学家从事绘制决策捷径,给行动主体使用以帮助提高人类决策的有效性。对这一观点的论述来自于卡斯·桑斯坦和理查德·塞勒的《Nudge》。Sunstein 和 Thaler 建议选择的结构应该根据人类行为主体的有限理性来修改。Sunstein 和 Thaler提出的一个被广泛引用的建议是,为了增加人们选择健康食品而不是不健康食品的可能性,应该把健康食品放在视线范围内。一些对《Nudge》持批评态度的人指出,修改选择架构将导致人们成为更糟糕的决策者。


Bounded rationality was shown to be essential to predict human sociability properties in a particular model by Vernon L. Smith and Michael J. Campbell. There, an agent-based model correctly predicts that agents are averse to resentment and punishment, and that there is an asymmetry between gratitude/reward and resentment/punishment. The purely rational Nash equilibrium is shown to have no predictive power for that model, and the boundedly rational Gibbs equilibrium must be used to predict phenomena outlined in Humanomics.

Bounded rationality was shown to be essential to predict human sociability properties in a particular model by Vernon L. Smith and Michael J. Campbell.[14] There, an agent-based model correctly predicts that agents are averse to resentment and punishment, and that there is an asymmetry between gratitude/reward and resentment/punishment. The purely rational Nash equilibrium is shown to have no predictive power for that model, and the boundedly rational Gibbs equilibrium must be used to predict phenomena outlined in Humanomics.[15]


由 Vernon l. Smith 和 Michael j. Campbell 研究表明,在一个特定的模型中,有限理性对于预测人类的社交能力是必不可少的。在这里,一个基于个体的模型正确地预测了行为主体反对怨恨和惩罚,并且在感激/奖励和怨恨/惩罚之间存在着不对称的情况。纯理性的纳什均衡点对于这个模型没有预测能力,有限理性的吉布斯平衡 Gibbs equilibrium必须在 Humanomics 概述的现象中进行预测。

Influence on social network structure

对社会网络结构的影响

Recent research has shown that bounded rationality of individuals may influence the topology of the social networks that evolve among them. In particular, Kasthurirathna and Piraveenan have shown that in socio-ecological systems, the drive towards improved rationality on average might be an evolutionary reason for the emergence of scale-free properties. They did this by simulating a number of strategic games on an initially random network with distributed bounded rationality, then re-wiring the network so that the network on average converged towards Nash equilibria, despite the bounded rationality of nodes. They observed that this re-wiring process results in scale-free networks. Since scale-free networks are ubiquitous in social systems, the link between bounded rationality distributions and social structure is an important one in explaining social phenomena.

Recent research has shown that bounded rationality of individuals may influence the topology of the social networks that evolve among them. In particular, Kasthurirathna and Piraveenan[16] have shown that in socio-ecological systems, the drive towards improved rationality on average might be an evolutionary reason for the emergence of scale-free properties. They did this by simulating a number of strategic games on an initially random network with distributed bounded rationality, then re-wiring the network so that the network on average converged towards Nash equilibria, despite the bounded rationality of nodes. They observed that this re-wiring process results in scale-free networks. Since scale-free networks are ubiquitous in social systems, the link between bounded rationality distributions and social structure is an important one in explaining social phenomena.


最近的研究表明,个体的有限理性可能会影响在他们之间演化的社交网络的拓扑结构。特别的,Kasthurirathna 和 Piraveenan 已经表明,在社会生态系统中,平均而言,改善理性的驱动力可能是无尺度特性出现的进化原因。他们通过在一个具有分布式有限理性的初始随机网络上模拟一系列策略游戏,之后重新布线网络,使网络平均收敛到纳什平衡,尽管节点存在有限理性。他们观察到,这种重新布线的过程导致了无标度网络。因此无标度网络在社会系统中无处不在,有限理性分布和社会结构之间的联系是解释社会现象的一个重要因素。


==See also==


Notes

  1. 1.0 1.1 Gigerenzer, Gerd; Selten, Reinhard (2002). Bounded Rationality: The Adaptive Toolbox. MIT Press. ISBN 978-0-262-57164-7. https://books.google.com/books?id=dVMq5UoYS3YC&dq=%22bounded+rationality%22&printsec=frontcover. 
  2. Simon, Herbert A. (1955-02-01). "A Behavioral Model of Rational Choice". The Quarterly Journal of Economics (in English). 69 (1): 99–118. doi:10.2307/1884852. ISSN 0033-5533. JSTOR 1884852.
  3. Chater, Nick; Felin, Teppo; Funder, David C.; Gigerenzer, Gerd; Koenderink, Jan J.; Krueger, Joachim I.; Noble, Denis; Nordli, Samuel A.; Oaksford, Mike; Schwartz, Barry; Stanovich, Keith E. (2018-04-01). "Mind, rationality, and cognition: An interdisciplinary debate". Psychonomic Bulletin & Review (in English). 25 (2): 793–826. doi:10.3758/s13423-017-1333-5. ISSN 1531-5320. PMC 5902517. PMID 28744767.
  4. 4.0 4.1 Mancur Olson, Jr. ([1965] 1971). The Logic of Collective Action: Public Goods and the Theory of Groups, 2nd ed. Harvard University Press, Description, Table of Contents, and preview.
  5. Oliver E. Williamson, p. 553, citing Simon.
  6. Rubinstein, Ariel (1997). Modeling bounded rationality. MIT Press. ISBN 9780262681001. http://arielrubinstein.tau.ac.il/book-br.html. 
  7. Moss; Rae, eds. (1992). "Some Thoughts on Artificial Intelligence and Economic Theory". Artificial Intelligence and Economic Analysis. Edward Elgar. pp. 131–154. ISBN 978-1852786854. https://archive.org/details/artificialintell0000unse_a9c0/page/131. 
  8. Tsang, E.P.K. (2008). "Computational intelligence determines effective rationality". International Journal of Automation and Computing. 5 (1): 63–6. doi:10.1007/s11633-008-0063-6. Unknown parameter |s2cid= ignored (help)
  9. Marwala, Tshilidzi; Hurwitz, Evan (2017). Artificial Intelligence and Economic Theory: Skynet in the Market. London: Springer. ISBN 978-3-319-66104-9. 
  10. Thaler, Richard H., Sunstein, Cass R. (April 8, 2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press. ISBN 978-0-14-311526-7. OCLC 791403664. 
  11. Thaler, Richard H., Sunstein, Cass R. and Balz, John P. (April 2, 2010). "Choice Architecture". doi:10.2139/ssrn.1583509. SSRN 1583509. Unknown parameter |s2cid= ignored (help); Cite journal requires |journal= (help)CS1 maint: uses authors parameter (link)
  12. Wright, Joshua; Ginsberg, Douglas (February 16, 2012). "Free to Err?: Behavioral Law and Economics and its Implications for Liberty". Library of Law & Liberty.
  13. Sunstein, Cass (2009-05-13). Going to extreems: How Like Minds Unite and Divide. ISBN 9780199793143. https://books.google.com/books?id=jEWplxVkEEEC. 
  14. Michael J. Campbell; Vernon L. Smith (2020). "An elementary humanomics approach to boundedly rational quadratic models". Physica A. doi:10.1016/j.physa.2020.125309.
  15. Vernon L. Smith and Bart J. Wilson (2019). Humanomics: Moral Sentiments and the Wealth of Nations for the Twenty-First Century. Cambridge University Press. doi:10.1017/9781108185561. https://www.cambridge.org/core/books/humanomics/1B4064A206BD99DB36E794B53ADF8BB4. 
  16. Kasthurirathna, Dharshana; Piraveenan, Mahendra (2015-06-11). "Emergence of scale-free characteristics in socio-ecological systems with bounded rationality". Scientific Reports (in English). 5 (1): 10448. doi:10.1038/srep10448. ISSN 2045-2322. PMC 4464151. PMID 26065713.


Further reading

  • Elster, Jon (1983). Sour Grapes: Studies in the Subversion of Rationality. Cambridge, UK: Cambridge University Press. ISBN 978-0-521-25230-0. 
  • Felin, T., Koenderink, J., & Krueger, J. (2017). "Rationality, perception and the all-seeing eye." Psychonomic Bulletin and Review, 25: 1040-1059. DOI 10.3758/s13423-016-1198-z
  • Gershman, S.J., Horvitz, E.J., & Tenenbaum, J.B. (2015). Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science, 49: 273-278. DOI: 10.1126/science.aac6076
  • Hayek, F.A (1948) Individualism and Economic order
  • Simon, Herbert (1957). "A Behavioral Model of Rational Choice", in Models of Man, Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting. New York: Wiley.
  • Simon, Herbert (1991). "Bounded Rationality and Organizational Learning". Organization Science. 2 (1): 125–134. doi:10.1287/orsc.2.1.125.
  • Tisdell, Clem (1996). Bounded Rationality and Economic Evolution: A Contribution to Decision Making, Economics, and Management. Cheltenham, UK: Brookfield. ISBN 978-1-85898-352-3. 
  • Williamson, Oliver E. (1981). "The economics of organization: the transaction cost approach". American Journal of Sociology. 87 (3): 548–577 (press +). doi:10.1086/227496.


External links

模板:Wikiquote


编者推荐

集智文章推荐

脑力经济学:DeepMind联合哈佛、剑桥在nature人类行为上发表长文综述

研究者们从经济学视角切入脑力劳动研究,为基于脑力投入的决策提供了新线索,利用稀缺性、成本效益分析等思路揭示了一些本来可能不会被考虑的新问题,并衍生出新的理论视角。


模板:Game theory

Category:Behavioral economics

分类: 行为经济学

模板:Instecon

Category:Game theory

范畴: 博弈论


Category:Rational choice theory

范畴: 理性选择理论


This page was moved from wikipedia:en:Bounded rationality. Its edit history can be viewed at 有限理性/edithistory