Bounded rationality (1980S)

Developed by American behaviorist Herbert Simon (1916-2001), bounded rationality is an analysis of decision-making which accepts that there are cognitive limits to an individual’s knowledge and capacity to act rationally.

Also see: uncertainty, bernouilli’s hypothesis



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”.[6] 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:

  • 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 strategies in easy situations (e.g Tic-tac-toe) verses the strategies in difficult situations (e.g Chess). Both games, as defined by game theory economics are finite games with perfect information and therefore equivalent.[7] However, within chess mental capacities and abilities are a binding constraint therefore optimal choices are not a possibility.[7] Thus, in order to test the mental limits of agents, complex problems such as chess should be studied to test how individuals work around their cognitive limits and what behaviours or heuristics are used to form solutions [8]

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.[9] 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.[5] Moreover, Gigerenzer states, agents react relative to their environment and use their cognitive processes to adapt accordingly.[1]

Huw Dixon later argues that it may not be necessary to analyze in detail the process of reasoning underlying bounded rationality.[10] 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 {\displaystyle U^{*}}, then the set of epsilon-optimizing options S(ε) can be defined as all those options s such that:

{\displaystyle U(s)\geq U^{*}-\epsilon }.

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.[11] 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.[12]

Relationship to Behavioral Economics

Bounded rationality implies the idea that humans take reasoning shortcuts that may lead to sub-optimal 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.[13][14] 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.[15][16]

Bounded rationality was shown to be essential to predict human sociability properties in a particular model by Vernon L. Smith and Michael J. Campbell.[17] There, an agent-based model correctly predicts that agents are averse to resentment and punishment, there is an asymmetry between gratitude/reward and resentment/punishment, and the diminishing of this aversion as expected payoff increases – which captures two essential properties of prospect theory. 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.[18]

Relationship to Psychology

The collaborative works of Daniel Kahneman and Amos Tversky expand upon Herbert A. Simon’s ideas in the attempt to create a map of bounded rationality. The research attempted to explore the choices made by what was assumed as rational agents compared to the choices made by individuals optimal beliefs and their satisficing behaviour.[19] Kahneman cites that the research contributes mainly to the school of psychology due to imprecision of psychological research to fit the formal economic models, however the theories are useful to economic theory as a way to expand simple and precise models and cover diverse psychological phenomena.[19] Three major topics covered by the works of Daniel Kahneman and Amos Tversky include Heuristics of judgement, risky choice and framing effect, which were a culmination of research that fit under what was defined by Herbert A. Simon as the Psychology of Bounded Rationality.[20] In contrast to the work of Simon; Kahneman and Tversky aimed to focus on the effects bounded rationality had on simple tasks which therefore placed more emphasis on errors in cognitive mechanisms irrespective of the situation.[7]

Influence on social network structure

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[21] 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.

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