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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Bounded rationality | 2/4 | https://en.wikipedia.org/wiki/Bounded_rationality | reference | science, encyclopedia | 2026-05-05T14:37:28.847131+00:00 | kb-cron |
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
U
∗
{\displaystyle U^{*}}
, then the set of epsilon-optimizing options
S
(
ϵ
)
{\textstyle S(\epsilon )}
can be defined as all options
s
{\textstyle s}
such that:
U
(
s
)
≥
U
∗
−
ϵ
.
{\displaystyle U(s)\geq U^{*}-\epsilon .}
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. 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 ==