p.5 This book is about fast and frugal heuristics for making decisions - how they work, and when and why
they succeed. These heuristics can be seen as models of the behavior of both living organisms and artificial systems. From
a descriptive standpoint, they are intended to capture how real minds make decisions under constraints of limited time and
knowledge. From an engineering standpoint, these heuristics suggest ways to build artificially intelligent systems - artificial
decision makers that are not paralyzed by the need for vast amounts of knowledge or extensive computational power. These two
applications of fast and frugal heuristics do not exclude one another - indeed, the decision tree in figure 1.1
[JLJ not reproduced here - classifies incoming heart attack victims as low risk or high risk patients] could be used
to describe the behavior of an unaided human mind or could be built into an emergency-room machine... We propose
replacing the image of an omniscient mind computing intricate probabilities and utilities with that of a bounded mind reaching
into an adaptive toolbox filled with fast and frugal heuristics.
p.13 The second component of [Herbert] Simon's view of bounded rationality, environmental structure,
is of crucial importance because it can explain when and why simple heuristics perform well: if the structure of the
heuristic is adapted to that of the environment. Simon's (1956a) classic example concerns foraging organisms that
have a single need: food. One organism lives in an environment in which little heaps of food are randomly distributed; it
can get away with a simple heuristic, that is, run around randomly until a heap of food is found. For this, the organism needs
some capacity for vision and movement, but it does not need a capacity for learning. . A second organism lives in an environment
where food is not distributed randomly but comes in hidden patches whose locations can be inferred from cues. This organism
can use more sophisticated strategies, such as learning the association between cues and food, and a memory for storing the
information. The general point is that to understand which heuristic an organism employs, and when and why the heuristic
works well, one needs to look at the structure of the information in the environment. Simon (1956a) was not the only
one to make this important point; it was made both before his work.. and at various times since... including the extreme statement
that only the environment need be studied, not the mechanisms of the mind... We use the term "ecological rationality"
to bring environmental structure back into bounded rationality. A heuristic is ecologically rational to the degree
that it is adapted to the structure of an environment... Thus, simple heuristics and environmental structure
can both work hand in hand to provide a realistic alternative to the ideal of optimization, whether unbounded or constrained.
p.20 Robustness goes hand in hand with speed, accuracy, and especially information frugality.
Fast and frugal heuristics can reduce overfitting by ignoring the noise inherent in many cues and looking instead for the
"swamping forces" reflected in the most important cues. Thus, simply using only one or a few of the most important cues can
automatically yield robustness.
p.22 To conclude: Heuristics are not simply hobbled versions of optimal strategies. There are no optimal
strategies in many real-world environments in the first place. This does not mean, though, that there are no performance criteria
in the real world. As a measure of success of a heuristic, we compare its performance with the actual requirements of its
environment, which can include making accurate decisions, in a minimal amount of time, and using a minimal amount of information.
p.24 Bounded rationality. Decision-making agents in the real world must arrive at their inferences
using realistic amounts of time, information, and computational resources... Ecological rationality. Decision-making
agents can exploit the structure of information in the environment to arrive at more adaptively useful outcomes.
To understand how different heuristics can be ecologically rational, we characterize the ways information can be structured
in different decision environments and how heuristics can tap that structure to be fast, frugal, accurate, and otherwise adaptive
at the same time.
p.26 As used by [Albert] Einstein, then, a heuristic is an approach to a problem that is
necessarily incomplete given the knowledge available, and hence unavoidably false, but which is useful nonetheless
for guiding thinking in appropriate directions.
p.30 Just as a mechanic will pull out specific wrenches, pliers, and spark-plug gap gauges for each task
in maintaining a car's engine rather than merely hitting everything with a large hammer, different domains of thought
require different specialized tools. This is the basic idea of the adaptive toolbox: the collection of specialized
cognitive mechanisms that evolution has built into the human mind for specific domains of inference and reasoning, including
fast and frugal heuristics... But we believe that simple heuristics can be used singly and in combination to account for a
great variety of higher order mental processes that may at first glance seem to require more complex explanation...
p.32 How does the mind choose which heuristic in the adaptive toolbox to apply to a specific problem?...
In cases where there is more than one applicable heuristic, the knowledge that the decision maker has can be used to select
the heuristic... Other external factors, such as time pressure and success, may further help to select heuristics... The tools
in the adaptive toolbox are made from more primitive components, including the heuristic principles of information search,
stopping, and decision discussed earlier.
p.120-121 the reason for the success of a heuristic can be found in its ecological rationality,
more precisely, in the fit between the structural properties of the heuristic and the structure of the environment it is applied
to... In the words "structure of the environment", we are using a shorthand for the structure of information
that is known about an environment.
p.146 All that is needed to make a choice is to identify the alternative that scores the highest with respect
to the criterion; it is not necessary to know either the exact criterion values [score] or the rank order of the inferior
alternatives.
p.166 Shanteau (1992) ["How Much Information Does an Expert Use", Acta Psychologica 81] argued
that the amount of information used is not connected with expertise. The assumption that experts make better judgements because
they use more information does not appear to be true. Instead Shanteau assumed that the difference between novices
and experts is the ability to discriminate between relevant and irrelevant cues. Experts seem
to use the same number of cues but are more likely to use cues that are more useful for making appropriate decisions.
Klayman (1985) examined the influence of cognitive capacity. He showed that children with high cognitive capacity acquire
more information for important decisions where several alternatives are available.
p.167 Keeping in mind that heuristics are only models of actual reasoning, we have presented evidence that
heuristics with cue-wise information search can describe individuals' decision strategies for choice tasks. Individuals seem
to use a fast and frugal noncompensatory strategy (LEX) with a simple stopping rule.
p.208 The adaptive process of knowledge updating relieves us of the need to store everything we
have thought, said, or experienced in the past. Updating makes us smart by preventing us from using information that
may be outdated due to changes in the environment. As [Sir Frederic] Bartlett put it: "In a world of constantly changing
environment, literal recall is extraordinarily unimportant"... Adaptive updating has an uninvited byproduct: hindsight
bias. But this by-product may be a relatively low price to pay for a memory that works fast and frugally.
p.329 Getting Along in a Deceptively Simple Environment: Chess
In 1992, Herbert Simon wrote: "It is difficult to predict when computers will defeat the best human
player....It can only be a matter of a few years before technological advances end the human supremacy at chess"
(Simon & Schaeffer, 1992, pp. 14-15). Simon's prediction was right, and five years later, in 1997, [May 11] a computer
called Deep Blue finally defeated Garry Kasparov, the world's best human chess player, in a tournament. This watershed event
in the development of artificial intelligence was also a significant moment in the development of satisficing mechanisms.
The game of chess occurs in a small field (an area of 64 discrete squares), with a limited set of alternate events at each
point in the game (16 pieces per player, each with a defined set of legal moves), and a single goal (the capture of the opponent's
king). What could be simpler? But as anyone knows who has attempted to play the game, or who has tried to program
a computer to play it, finding the one best solution to the problem of putting an opponent in checkmate is unimaginably difficult.
There are simply too many possible lines of play to simulate them all, even with the most powerful computers... The attempt
to limit the scope of the search in chess dates at least to the work of Herbert Simon and George Baylor (Baylor
& Simon, 1966)... They reasoned that one major goal of the game is to reduce the opponent's mobility...
and so paths were sought solely on the basis of how much mobility the opponent had following each move... Clever design of
the evaluation function is the difference between an artificial world champion and a machine that races toward bad outcomes.
Indeed, the creators of Deep Blue credited their recent victory over the world champion to their much improved evaluation
function... The key to improved chess is thus recognized to be clever algorithms - in other words, those
that are fast, frugal, and accurate - rather than more powerful attempts... These algorithms must find the cues
that are subtle but available on the board and provide the most diagnostic information about the outcome of various moves;
in other words, they must capitalize on the structure of the environment... Since the cleverness of the evaluation
function, rather than the power of the computer, has proved to be a better road to improved chess, perhaps chess research in
the future will return to the satisficing notions with which it began.
p.360 There are two reasons for the surprising performance of fast and frugal heuristics: their
exploitation of environment structure and their robustness (generalizing appropriately to new situations as opposed
to overfitting - see chapter 1). Ecological rationality is not a feature of a heuristic, but a consequence of the
match between heuristic and environment... By matching these structures of information in the environment with the
structure implicit in their building blocks, heuristics can be accurate without being too complex. In addition, by
being simple, these heuristics can avoid being too closely matched to any particular environment - that is, they
can escape the curse of overfitting, which often strikes more complex, parameter-laden models. This marriage of structure
with simplicity produces the counterintuitive situations in which there is little trade-off between being fast and frugal
and being accurate.
