Bounded rationality is the idea that rationality is limited when individuals make decisions, and under these limitations, rational individuals will select a decision that is satisfactory rather than optimal.
Limitations include the difficulty of the problem requiring a decision, the cognitive capability of the mind, and the time available to make the decision. Decision-makers, in this view, act as satisficers, seeking a satisfactory solution, with everything that they have at the moment rather than an optimal solution. Therefore, humans do not undertake a full cost-benefit analysis to determine the optimal decision, but rather, choose an option that fulfills their adequacy criteria.
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 model.Mancur Olson, Jr. ( 1971). The Logic of Collective Action: Public Goods and the Theory of Groups, 2nd ed. Harvard University Press, Description, Table of Contents, and preview. The concept of bounded rationality complements the idea of rationality as optimization, which views decision-making as a fully rational process of finding an optimal choice given the information available. Therefore, bounded rationality can be said to address the discrepancy between the assumed perfect rationality of human behaviour (which is utilised by other economics theories), and the reality of human cognition. In short, 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. The concept of bounded rationality continues to influence (and be debated in) different disciplines, including political science, economics, psychology, law, philosophy, and cognitive science.
Background and motivation was coined by Herbert A. Simon, where it was proposed as an alternative basis for the mathematical and neoclassical economic modelling of decision-making, as used in economics, political science, and related disciplines. Many economics models assume that agents are on average rational, and can in large quantities be approximated to act according to their preferences in order to maximise utility. 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." Soon after the term bounded rationality appeared, studies in the topic area began examining the issue in depth. A study completed by Maurice Allais in 1953 began to generate ideas of the irrationality of decision making as he found that given preferences, individuals will not always choose the most rational decision and therefore the concept of rationality was not always reliable in economic predictions.Schiliró, D. (2013). Bounded Rationality: Psychology, Economics and the Financial Crises. Theoretical and Practical Research in Economic Fields, 4(1(7)), 97-108.
In Models of Man, Simon argues 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".Oliver E. Williamson, p. 553, citing Simon. 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.
Simon describes a number of dimensions along which classical models of rationality can be made somewhat more realistic, while remaining within the vein of fairly rigorous formalization. These include:
limiting the types of utility functions recognizing the costs of gathering and processing information the possibility of having a vector or multi-valued utility function
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. An example of behaviour inhibited by heuristics can be seen when comparing the cognitive strategies utilised in simple situations (e.g. tic-tac-toe), in comparison to strategies utilised in difficult situations (e.g. chess). Both games, as defined by game theory economics, are finite games with perfect information, and therefore equivalent. However, within chess, mental capacities and abilities are a binding constraint, therefore optimal choices are not a possibility. Thus, in order to test the mental limits of agents, complex problems, such as those within chess, should be studied to test how individuals work around their cognitive limits, and what behaviours or heuristics are used to form solutions
Anchoring and adjustment heuristics give some explanation to bounded rationality and why decision makers do not make rational decisions. A study undertaken by Zenko et al. showed that the amount of physical activity completed by decision makers was able to be influenced by anchoring and adjustment as most decision makers would typically be considered irrational and would unlikely do the amount of physical activity instructed and it was shown that these decision makers use anchoring and adjustment to decide how much exercise they will complete.Zenko, Z., Ekkekakis, P., & Kavetsos, G. (2016). Changing Minds: Bounded Rationality and Heuristic Processes in Exercise-Related Judgments and Choices. Sport, Exercise, and Performance Psychology, 5(4), 337-351. https://doi.org/10.1037/spy0000069
Other heuristics that are closely related to the concept of bounded rationality include the availability heuristic and representativeness heuristic. The availability heuristic refers to how people tend to overestimate the likelihood of events that are easily brought to mind, such as vivid or recent experiences. This can lead to biased judgments based on incomplete or unrepresentative information. The representativeness heuristic states that people often judge the probability of an event based on how closely it resembles a typical or representative case, ignoring other relevant factors like base rates or sample size. Examples
Bounded rationality can have significant effects on political decision-making, voter behavior, and policy outcomes. A prominent example of this is heuristic-based voting. According to the theory of bounded rationality, individuals have limited time, information, and cognitive resources to make decisions. In the context of voting, this means that most voters cannot realistically gather and process all available information about candidates, issues, and policies. Even if such information were available, the time and effort required to analyze it would be prohibitively high for many voters. As a result, voters often resort to heuristics, which allow voters to make decisions based on cues like party affiliation, candidate appearance, or single-issue positions, rather than engaging in a comprehensive evaluation of all relevant factors. For example, a voter who relies on the heuristic of party affiliation may vote for a candidate whose policies do not actually align with their interests, simply because the candidate belongs to their preferred party.
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. Rubinstein argues that consistency in reaching final decision for the same level of information must factor in the decision making procedure itself. 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. Moreover, Gigerenzer claimed, agents react relative to their environment and use their cognitive processes to adapt accordingly.
Huw Dixon later argued that it may not be necessary to analyze in detail the process of reasoning underlying bounded rationality. 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 , then the set of epsilon-optimizing options can be defined as all options such that:
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 (closer to optimal) decisions than one that has poorer heuristics and algorithms. And many artificial intelligence systems show that strategic use of more computationally intensive inference can optimize performance at lower resource cost.
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.
The model of bounded rationality also extends to bounded self-interest, 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.
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.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
Using the concept of an "adaptive toolbox," a repertoire of fast and frugal rules for decision making under uncertainty, the work by Gerd Gigerenzer and colleagues on ecological rationality views bounded rationality neither as optimization under constraints nor as the study of people's reasoning fallacies. The strategies in the adaptive toolbox apply to situations where optimization and, for the most part, calculations of probabilities and utilities are not feasible. Ecological rationality explores when social norms, imitation, and other cultural tools function as rational strategies and shows how smart heuristics can exploit the structure of real decision-making environments.
Behavioral economics