Editing Bounded rationality (section)

==Model extensions==
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 as decision-makers with the same information are also not able to analyse the situation equally thus reach the same rational decision.<ref name=":3">{{cite book |author=Rubinstein, Ariel |title=Modeling bounded rationality |publisher=MIT Press |year=1997 |url = http://arielrubinstein.tau.ac.il/book-br.html | isbn=9780262681001 }}</ref> Rubinstein argues that consistency in reaching final decision for the same level of information must factor in the decision making procedure itself.<ref name=":3" /> This puts the study of decision procedures on the research agenda.

[[Gerd Gigerenzer]] stated that decision theorists, to some extent, have not 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.<ref name="Olson"/> Moreover, Gigerenzer claimed, agents react relative to their environment and use their cognitive processes to adapt accordingly.<ref name=":0" />

[[Huw Dixon]] later argued 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 |year=1992 |pages=[https://archive.org/details/artificialintell0000unse_a9c0/page/131 131–154] |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 display="block"> 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.

From a computational point of view, decision procedures can be encoded in [[algorithm]]s 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 (closer to 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>

The model of bounded rationality also extends to bounded self-interest,<ref>{{Cite journal|last1=Gotsis|first1=George|last2=Kortezi|first2=Zoe|date=2011-01-01|title=Bounded self-interest: a basis for constructive organizational politics|url=https://doi.org/10.1108/01409171111117889|journal=Management Research Review|volume=34|issue=4|pages=450–476|doi=10.1108/01409171111117889|issn=2040-8269}}</ref> in which humans are sometimes willing to forsake their own self-interests for the benefits of others due to incomplete information that the individuals have at the time being. This is something that had not been considered in earlier economic models.<ref>{{Cite journal |last=Kirchgässner |first=Gebhard |date=2005 |title=The Weak Rationality Principle in Economics |journal=Cesifo Working Paper Series |url=https://ideas.repec.org/p/ces/ceswps/_1410.html |language=en}}</ref>

The theory of rational inattention, an extension of bounded rationality, studied by Christopher Sims, found that decisions may be chosen with incomplete information as opposed to affording the cost to receive complete information. This shows that decision makers choose to endure bounded rationality.<ref>Sent, E. (2018). Rationality and Bounded Rationality: You can't have one without the Other. The European Journal of the History of Economic Thought, 25(6), 1370-1386. https://doi.org/10.1080/09672567.2018.1523206</ref>

On the other hand, another extension came from the notion of bounded rationality and was explained by Ulrich Hoffrage and Torsten Reimer in their studies of a "fast and frugal heuristic approach". The studies explained that complete information sometimes is not needed as there are easier and simpler ways to reach the same optimal outcome.<ref name=":4">{{Cite journal |last1=Hoffrage |first1=Ulrich |last2=Reimer |first2=Torsten |date=2004-01-01 |title=Models of Bounded Rationality: The Approach of Fast and Frugal Heuristics |url=https://www.researchgate.net/publication/23779792 |journal=Management Revue. The International Review of Management Studies |volume=15 |issue=4 |pages=437–459 |doi=10.5771/0935-9915-2004-4-437|s2cid=153412733 |doi-access=free }}</ref> However, this approach which is usually known as the [[gaze heuristic]] was explained to be the theory for non-complex decision making only.<ref name=":4" />