Bounded rationality
From Wikipedia, the free encyclopedia
Bounded rationality
is the idea that in decision-making, rationality of individuals is
limited by the information they have, the cognitive limitations of their
minds, and the finite amount of time they have to make a decision. It
was proposed by Herbert A. Simon as an alternative basis for the mathematical modeling of decision making, as used in economics 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]
Another way to look at bounded rationality is that, because
decision-makers lack the ability and resources to arrive at the optimal
solution, they instead apply their rationality only after having greatly
simplified the choices available. Thus the decision-maker is a satisficer, one seeking a satisfactory solution rather than the optimal one.[2]
Simon used the analogy of a pair of scissors, where one blade is the
"cognitive limitations" of actual humans and the other the "structures
of the environment"; minds with limited cognitive resources can thus be
successful by exploiting pre-existing structure and regularity in the
environment.[1]
Some models of human behavior in the social sciences assume that humans can be reasonably approximated or described as "rational" entities (see for example rational choice theory). Many economics
models assume that people are on average rational, and can in large
enough quantities be approximated to act according to their preferences.
The concept of bounded rationality revises this assumption to account
for the fact that perfectly rational decisions are often not feasible in
practice because of the finite computational resources available for
making them.
Contents |
Models
The term is thought to have been coined by Herbert A. Simon. In Models of Man, Simon points out that most people are only partly rational, and are emotional/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" (Williamson,
p. 553, citing Simon). 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 what sorts of utility functions there might be.
- 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, and their inability
to process and compute the expected utility of every alternative
action. Deliberation costs might be high and there are often other
concurrent economic activities also requiring decisions.
Daniel Kahneman
proposes bounded rationality as a model to overcome some of the
limitations of the rational-agent models in economic literature.
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.
Gerd Gigerenzer
argues that most decision theorists who have discussed bounded
rationality have not really followed Simon's ideas about it. Rather,
they have either considered how people's decisions might be made
sub-optimal by the limitations of human rationality, or have constructed
elaborate optimising models of how people might cope with their
inability to optimize. Gigerenzer instead proposes to examine simple
alternatives to a full rationality analysis as a mechanism for
decision-making, and he and his colleagues have shown that such simple heuristics frequently lead to better decisions than the theoretically optimal procedure.
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.
Satisficing
From Wikipedia, the free encyclopedia
Satisficing, a portmanteau of satisfy and suffice,[1] is a decision-making strategy that attempts to meet an acceptability threshold. This is contrasted with optimal decision-making,
an approach that specifically attempts to find the best option
available. A satisficing strategy may often be (near) optimal if the
costs of the decision-making process itself, such as the cost of
obtaining complete information, are considered in the outcome
calculation.
The word satisfice was given its current meaning by Herbert A. Simon in 1956,[2] although the idea "was first posited in Administrative Behavior, published in 1947."[3][4] He pointed out that human beings lack the cognitive resources to optimize:
we usually do not know the relevant probabilities of outcomes, we can
rarely evaluate all outcomes with sufficient precision, and our memories
are weak and unreliable. A more realistic approach to rationality takes
into account these limitations: This is called bounded rationality.
"Satisficing" can also be regarded as combining "satisfying" and "sacrificing."[citation needed] In this usage the satisficing solution satisfies some criteria and sacrifices others.
Some consequentialist theories in moral philosophy use the concept of satisficing in the same sense, though most call for optimization instead.
In decision making, satisficing explains the tendency to select the
first option that meets a given need or select the option that seems to
address most needs rather than the “optimal” solution.
- Example: A task is to sew a patch onto a pair of jeans. The best needle to do the threading is a 4 inch long needle with a 3 millimeter eye. This needle is hidden in a haystack along with 1000 other needles varying in size from 1 inch to 6 inches. Satisficing claims that the first needle that can sew on the patch is the one that should be used. Spending time searching for that one specific needle in the haystack is a waste of energy and resources.
Satisficing also occurs in consensus building when the group looks
towards a solution everyone can agree on even if it may not be the best.
- Example: A group spends hours projecting the next fiscal year's budget. After hours of debating they eventually reach a consensus, only to have one person speak up and ask if the projections are correct. When the group becomes upset at the question, it is not because this person is wrong to ask, but rather because they have come up with a solution that works. The projection may not be what will actually come, but the majority agrees on one number and thus the projection is good enough to close the book on the budget.
In many circumstances, the individual may be uncertain about what
constitutes a satisfactory outcome. For example, an individual who only
seeks a satisfactory retirement income may not know what level of wealth
is required—given uncertainty about future prices—to ensure a
satisfactory income. In this case, the individual can only evaluate
outcomes on the basis of their probability of being satisfactory.
If the individual chooses that outcome which has the maximum chance
of being satisfactory, then this individual's behavior is theoretically
indistinguishable from that of an optimizing individual under certain
conditions[6][7][8]
Satisficing is often a good option when making a decision, but it can
also be detrimental if used the wrong way. For example, when
considering a medical issue such as a diagnosis, satisficing is not the
best decision making strategy to use. On the other hand, when choosing
an outfit or an option from a menu, it can be helpful. When there is an
unlimited amount of information available and it is necessary to
eliminate options, satisficing is beneficial because it helps the person
making the decision effectively and efficiently reach a conclusion.[9]
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