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OCR for page 17
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Research Briefing Panel on
Decision Making and Problem Solving
Herbert A. Simon (Chairman), Carneg~e
Mellon University, Pittsburgh, Pa.
George B. Dantzig, Stanford University,
Stanford, Calif.
Robin Hogarth, University of Chicago,
Chicago, ID.
Charles R. Plott, California Institute of
Technology, Pasadena, Calif.
Howard Raiffa, Harvard Business School,
Boston, Mass.
Thomas C. Schelling, Harvard University,
Cambridge, Mass.
Kenneth A. Shepsle, Washington
University, St. Louis, Mo.
Richard Thaler, Cornell University, Ithaca,
N.Y.
18
Amos Tversky, Stanford University,
Stanford, Calif.
Sidney Winter, Yale University, New
Haven, Conn.
Staff
David A. Goslin, Executive Director,
Commission on Behavioral and Social
Sciences and Education
Karan Ford, Administrative Secretary
Allan R. Hoffman, Executive Director,
Committee on Science, Engineering,
and Public Policy
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Report of the
Research Briefing Panel on
Decision Making and Problem Solving
INTRODUCTION
The work of managers, of scientists, of
engineers, of lawyers the work that steers
the course of society and its economic and
governmental organizations is largely work
of making decisions and solving problems.
It is work of choosing issues that require
attention, setting goals, finding or design-
ing suitable courses of action, and evaluat-
ing anct choosing among alternative actions.
The first three of these activities fixing
agendas, setting goals, and designing ac-
tions are usually called problem solving; the
last, evaluating and choosing, is usually
called decision making. Nothing is more im-
portant for the well-being of society than
that this work be performed effectively, that
we address successfully the many problems
requiring attention at the national level (the
budget and trade deficits, AIDS, national
security, the mitigation of earthquake dam-
age), at the level of business organizations
(product improvement, efficiency of pro-
duction, choice of investments), and at the
level of our individual lives (choosing a ca-
reer or a school, buying a house).
The abilities and skills that determine the
quality of our decisions and problem solu
19
lions are stored not only in more than 200
million human heads, but also in tools and
machines, and especially today in those ma-
chines we call computers. This fund of brains
and its attendant machines form the basis
of our American ingenuity, an ingenuity that
has permitted U.S. society to reach remark-
able levels of economic productivity.
rT' . . -
1 nere are no more promising or 1mpor-
tant targets for basic scientific research than
understanding how human minds, with and
without the help of computers, solve prob-
lems and make decisions effectively, and
improving our problem-solving and deci-
sion-making capabilities. In psychology,
economics, mathematical statistics, opera-
tions research, political science, artificial in-
telligence, and cognitive science, major
research gains have been made during the
past half century in understanding problem
solving and decision making. The progress
already achieved holds forth the promise of
exciting new advances that will contribute
substantially to our nation's capacity for
dealing intelligently with the range of is-
sues, large and small, that confront us.
Much of our existing knowledge about
decision making and problem solving, de-
rived from this research, has already been
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put to use in a wide variety of applications,
including procedures used to assess drug
safety, inventory control methods for in-
dustry, the new expert systems that em-
body artificial intelligence techniques,
procedures for modeling energy and envi-
ronmental systems, and analyses of the sta-
bilizing or destabilizing effects of alternative
defense strategies. (Application of the new
inventory control techniques, for example,
has enabled American corporations to re-
duce their inventories by hundreds of mil-
lions of clolIars since World War I! without
increasing the incidence of stockouts. ~ Some
of the knowledge gained through the re-
search describes the ways in which people
actually go about making decisions and
solving problems; some of it prescribes bet-
ter methods, offering advice for the im-
provement of the process.
Central to the body of prescriptive knowI-
edge about decision making has been the
theory of subjective expected utility (SEU),
a sophisticated mathematical mode} of choice
that lies at the foundation of most contem-
porary economics, theoretical statistics, and
operations research. SEU theory defines the
conditions of perfect utility-maximizing ra-
tionality in a world of certainty or in a world
in which the probability distributions of all
relevant variables can be provided by the
decision makers. (In spirit, it might be com-
pared with a theory of ideal gases or of fric-
tioniess bocties sliding down inclined planes
in a vacuum.) SEU theory deals only with
decision making; it has nothing to say about
how to frame problems, set goals, or cle-
velop new alternatives.
Prescriptive theories of choice such as SEU
are complemented by empirical research that
shows how people actually make decisions
(purchasing insurance, voting for political
candidates, or investing in securities), and
research on the processes people use to solve
problems (designing switchgear or finding
chemical reaction pathways). This research
demonstrates that people solve problems by
selective, heuristic search through large
20
problem spaces and large data bases, using
means-ends analysis as a principal tech-
nique for guiding the search. The expert sys-
tems that are now being produced by
research on artificial intelligence and ap-
plied to such tasks as interpreting oil-weD
drilling logs or making medical diagnoses
are outgrowths of these research findings
on human problem solving.
What chiefly distinguishes the empirical
research on decision making and problem
solving from the prescriptive approaches
derived from SEU theory is the attention
that the former gives to the limits on human
rationality. These limits are imposed by the
complexity of the world in which we live,
the incompleteness and inadequacy of hu-
man knowledge, the inconsistencies of in-
dividual preference and belief, the conflicts
of value among people and groups of peo-
ple, and the inadequacy of the computa-
tions we can carry out, even with the aid of
the most powerful computers. The real world
of human decisions is not a world of ideal
gases, frictionIess planes, or vacuums. To
bring it within the scope of human thinking
powers, we must simplify our problem for-
mulations drastically, even leaving out much
or most of what is potentially relevant.
The descriptive theory of problem solving
and decision making is centrally concerned
with how people cut problems down to size:
how they apply approximate, heuristic tech-
niques to handle complexity that cannot be
handled exactly. Out of this descriptive
theory is emerging an augmented and
amended prescriptive theory, one that takes
account of the gaps and elements of un-
realism in SEU theory by encompassing
problem solving as well as choice and de-
manding only the kinds of knowledge, con-
sistency, and computational power that are
attainable in the real world.
The growing realization that coping with
complexity is central to human decision
making strongly influences the directions of
research in this domain. Operations re-
search and artificial intelligence are forging
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DECISION MAKING AND PROBLEM SOLVING
powerful new computational tools; at the
same time, a new body of mathematical
theory is evolving around the topic of com-
putational complexity. Economics, which has
traditionally derived both its descriptive and
prescriptive approaches from SEU theory,
is now paying a great deal of attention to
uncertainty and incomplete information; to
so-called "agency theory," which takes ac-
count of the institutional framework within
which decisions are made; and to game
theory, which seeks to deal with interindi-
vidual and intergroup processes in which
there is partial conflict of interest. Econo-
mists and political scientists are also increas-
ingly buttressing the empirical foundations
of their field by studying individual choice
behavior directly and by studying behavior
in experimentally constructed markets and
simulated political structures.
The following pages contain a fuller out-
line of current knowledge about decision
making and problem solving and a brief re-
view of current research directions in these
fields as well as some of the principal re-
search opportunities.
DECISION MAKING
SEU THEORY
The development of SEU theory was a
major intellectual achievement of the first
half of this century. It gave for the first time
a formally axiomatized statement of what it
would mean for an agent to behave in a
consistent, rational manner. It assumed that
a decision maker possessed a utility func-
tion (an ordering by preference among all
the possible outcomes of choice), that all the
alternatives among which choice could be
made were known, and that the conse-
quences of choosing each alternative could
be ascertained (or, in the version of the theory
that treats of choice under uncertainty, it
assumed that a subjective or objective prob-
ability distribution of consequences was as-
sociated with each alternative). By admitting
21
subjectively assigned probabilities, SEU
theory opened the way to fusing subjective
opinions with objective data, an approach
that can also be user! in man-machine de-
cision-making systems. In the probabilistic
version of the theory, Bayes's rule pre-
scribes how people should take account of
new information and how they should re-
spond to incomplete information.
The assumptions of SEU theory are very
strong, permitting correspondingly strong
inferences to be made from them. Although
the assumptions cannot be satisfied even
remotely for most complex situations in the
real world, they may be satisfied appro~a-
mately in some microcosms problem sit-
uations that can be isolated from the worId's
complexity and dealt with independently.
For example, the manager of a commercial
cattle-feeding operation might isolate the
problem of finding the least expensive mix
of feeds available in the market that would
meet all the nutritional requirements of his
cattle. The computational too! of linear pro-
gramming, which is a powerful method for
maximizing goal achievement or minimiz-
ing costs while satisfying all kinds of side
conditions (in this case, the nutritional re-
quirements), can provide the manager with
an optimal feed mix optimal within the
limits of approximation of his mode! to real-
worId conditions. Linear programming and
related operations research techniques are
now used widely to make decisions when-
ever a situation that reasonably fits their as-
sumptions can be carved out of its complex
surround. These techniques have been es-
pecially valuable aids to middle manage-
ment in dealing with relatively well-
structured decision problems.
Most of the tools of modern operations
research not only linear programming, but
also integer programming, queuing theory,
decision trees, and other widely used tech-
niques use the assumptions of SEU theory.
They assume that what is desired is to max-
imize the achievement of some goal, under
specified constraints and assuming that all
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alternatives and consequences (or their
probability distributions) are known. These
tools have proven their usefulness in a wide
variety of applications.
THE LIMITS OF RATIONALITY
operations research tools have also un-
derscored dramatically the limits of SEU
theory in dealing with complexity. For ex-
ample, present and prospective computers
are not even powerful enough to provide
exact solutions for the problems of optimal
scheduling and routing of jobs through a
typical factory that manufactures a variety
of products using many different tools and
machines. And the mere thought of using
these computational techniques to deter-
mine an optimal national policy for energy
production or an optimal economic policy
reveals their limits.
Computational complexity is not the only
factor that limits the literal application of
SEU theory. The theory also makes enor-
mous demands on information. For the util-
ity function, the range of available
alternatives and the consequences following
from each alternative must all be known.
Increasingly, research is being directed at
decision making that takes realistic account
of the compromises and approximations that
must be made in order to fit real-worId prob-
lems to the informational and computa-
tional limits of people and computers, as
well as to the inconsistencies in their values
and perceptions. The study of actual deci-
sion processes (for example, the strategies
used by corporations to make their invest-
ments) reveals massive and unavoidable de-
partures from the framework of SEU theory.
The sections that follow describe some of
the things that have been learned about
choice under various conditions of incom-
plete information, limited computing power,
inconsistency, and institutional constraints
on alternatives. Game theory, agency theory,
choice under uncertainty, and the theory of
markets are a few of the directions of this
research, with the aims both of constructing
prescriptive theories of broader application and
of providing more realistic descriptions and
explanations of actual decision making within
U.S. economic and political institutions.
i IMITED RATIONALITY
IN ECONOMIC THEORY
Although the limits of human rationality
were stressed by some researchers in the
1950s, only recently has there been exten-
sive activity in the field of economics aimed
at developing theories that assume less than
fully rational choice on the part of business
firm managers and other economic agents.
The newer theoretical research undertakes
to answer such questions as the following:
· Are market equilibria alterec! by the de-
partures of actual choice behavior from the
behavior of fully rational agents predicted
by SEU theory?
· Under what circumstances do the pro-
cesses of competition "police" markets in
such a way as to cancel out the effects of
the departures from full rationality?
· In what ways are the choices made by
boundedly rational agents different from
those made by fully rational agents?
Theories of the firm that assume man-
agers are aiming at "satisfactory" profits or
that their concern is to maintain the firm's
share of market in the industry make quite
different predictions about economic equi-
librium than those derived from the as-
sumption of profit maximization. Moreover,
the classical theory of the firm cannot ex-
plain why economic activity is sometimes
organized around large business firms and
sometimes around contractual networks of
individuals or smaller organizations. New
theories that take account of differential ac-
cess of economic agents to information,
combined with differences in self-interest,
are able to account for these important phe-
nomena, as well as provide explanations for
22
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DECISION MAKING AND PROBLEM SOLVING
the many forms of contracts that are used
in business. Incompleteness anc! asymme-
try of information have been shown to be
essential for explaining how individuals and
business firms decide when to face uncer-
tainty by insuring, when by hedging, and
when by assuming the risk.
Most current work in this domain still as-
sumes that economic agents seek to maxi-
mize utility, but within limits posed by the
incompleteness and uncertainty of the in-
formation available to them. An important
potential area of research is to discover how
choices will be changed if there are other
departures from the axioms of rational
choice for example, substituting goals of
reaching specified aspiration levels (satis-
ficing) for goals of maximizing.
Applying the new assumptions about
choice to economics leads to new empiri-
cally supported theories about decision
making over time. The classical theory of
perfect rationality leaves no room for re-
grets, second thoughts, or "weakness of
will." It cannot explain why many individ-
uals enroll In Christmas savings plans, which
earn interest well below the market rate.
More generally, it does not lead to correct
conclusions about the important social is-
sues of saving and conservation. The effect
of pensions and social security on personal
saving has been a controversial issue in eco-
nomics. The standard economic model pre-
dicts that an increase in required pension
saving will reduce other saving dollar for
dollar; behavioral theories, on the other hand,
predict a much smaller offset. The empirical
evidence indicates that the offset is indeed
very small. Another empirical finding is that
the method of payment of wages and sal-
aries affects the saving rate. For example,
annual bonuses produce a higher saving rate
than the same amount of income paid in
monthly salaries. This finding implies that
saving rates can be influenced by the way
compensation is framed.
If individuals fail to discount properly for
the passage of time, their decisions will not
23
be optimal. For example, air conditioners
vary greatly in their energy efficiency; the
more efficient models cost more initially but
save money over the long run through lower
energy consumption. It has been found that
consumers, on average, choose air condi-
tioners that imply a discount rate of 25 per-
cent or more per year, much higher than
the rates of interest that prevailed at the
time of the study.
As recently as five years ago, the evidence
was thought to be unassailable that markets
like the New York Stock Exchange work ef-
ficiently-that prices reflect all available in-
formation at any given moment in time, so
that stock price movements resemble a ran-
dom walk and contain no systematic infor-
mation that could be exploited for profit.
I:cecent~y, nowever, substantial departures
from the behavior predicted by the efficient-
market hypothesis have been detected. For
example, small firms appear to earn inexpli-
cably high returns on the market prices of
their stock, while firms that have very low
price-earnings ratios and firms that have
lost much of their market value in the recent
past also earn abnormally high returns. All
of these results are consistent with the em-
pirical finding that decision makers often
overreact to new information, in violation
of Bayes's rule. In the same way, it has been
found that stock prices are excessively vol-
atile- that they fluctuate up and clown more
rapidly and violently than they would if the
market were efficient.
There has also been a long-standing puz-
zle as to why firms pay dividencls. Consid-
ering that dividends are taxed at a higher
rate than capital gains, taxpaying investors
should prefer, under the assumptions of
perfect rationality, that their firms reinvest
earnings or repurchase shares instead of
paying dividends. (The investors could sim-
ply sell some of their appreciated shares to
obtain the income they require.) The solu-
tion to this puzzle also requires models of
investors that take account of limits on ra-
tionality.
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THE THEORY OF GAMES
In economic, political, and other social sit-
uations in which there is actual or potential
conflict of interest, especially if it is com-
bined with incomplete information, SEU
theory faces special difficulties. In markets
in which there are many competitors (e.g.,
the wheat market), each buyer or seller can
accept the market price as a "given" that
will not be affected materially by the actions
of any single individual. Under these con-
ditions, SEU theory makes unambiguous
predictions of behavior. However, when a
market has only a few suppliers say, for
example, two matters are quite different.
In this case, what it is rational to do depends
on what one's competitor is going to do,
and vice versa. Each supplier may try to
outwit the other. What then is the rational
decision?
The most ambitious attempt to answer
questions of this kind was the theory of
games, developed by van Neumann and
Morgenstern and published in its full form
in 1944. But the answers provided by the
theory of games are sometimes very puz-
zling and ambiguous. In many situations,
no single course of action dominates all the
others; instead, a whole set of possible so-
lutions are all equally consistent with the
postulates of rationality.
One game that has been studied exten-
sively, both theoretically and empirically, is
the Prisoner's Dilemma. In this game be-
tween two players, each has a choice be-
tween two actions, one trustful of the other
player, the other mistrustful or exploitative.
If both players choose the trustful alterna-
tive, both receive small rewards. If both
choose the exploitative alternative, both are
punished. If one chooses the trustful alter-
native and the other the exploitative alter-
native, the former is punished much more
severely than in the previous case, while the
latter receives a substantial reward. If the
other player's choice is fixed but unknown,
it is advantageous for a player to choose the
exploitative alternative, for this will give him
the best outcome in either case. But if both
adopt this reasoning, they will both be pun-
ished, whereas they could both receive re-
wards if they agreed upon the trust choice
(and did not Welch on the agreement).
The terms of the game have an unsettling
resemblance to certain situations in the re-
lations between nations or between a com-
pany and the employees' union. The
resemblance becomes stronger if one imag-
ines the game as being played repeatedly.
Analyses of "rational" behavior under as-
sumptions of intended utility maximization
support the conclusion that the players wiD
(ought to?) always make the mistrustful
choice. Nevertheless, in laboratory experi-
ments with the game, it is often found that
players (even those who are expert in game
theory) adopt a "tit-for-tat" strategy. That
is, each plays the trustful, cooperative strat-
egy as long as his or her partner does the
same. If the partner exploits the player on
a particular trial, the player then plays the
exploitative strategy on the next trial and
continues to do so until the partner switches
back to the trustful strategy. Under these
conditions, the game frequently stabilizes
with the players pursuing the mutually
trustful strategy and receiving the rewarcls.
With these empirical findings in hand,
theorists have recently sought and found
some of the conditions for attaining this kind
of benign stability. It occurs, for example, if
the players set aspirations for a satisfactory
reward rather than seeking the maximum
reward. This result is consistent with the
finding that in many situations, as in the
Prisoner's Dilemma game, people appear to
satisfice rather than attempting to optimize.
The Prisoner's Dilemma game illustrates
an important point that is beginning to be
appreciated by those who clo research on
decision making. There are so many ways
in which actual human behavior can depart
from the SEU assumptions that theorists
seeking to account for behavior are con-
frontec! with an embarrassment of riches.
24
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DECISION MAKING AND PROBLEM SOLVING
To choose among the many alternative
models that could account for the anomalies
of choice, extensive empirical research is
called for to see how people do make their
choices, what beliefs guide them, what in-
formation they have available, and what part
of that information they take into account
and what part they ignore. In a world of
limited rationality, economics and the other
decision sciences must closely examine the
actual limits on rationality in order to make
accurate predictions and to provide sound
advice on public policy.
EMPIRICAE STUDIES OF CHOICE
UNDER UNCERTAINTY
During the past 10 years, empirical stud-
ies of human choices in which uncertainty,
inconsistency, and incomplete information
are present have produced a rich collection
of findings which only now are beginning
to be organized under broad generaliza-
tions. Here are a few examples. When peo-
ple are given information about the
probabilities of certain events (e.g., how
many lawyers and how many engineers are
in a population that is being sampled), and
then are given some additional information
as to which of the events has occurred (which
person has been sampled from the popu-
lation), they tend to ignore the prior prob-
abilities in favor of incomplete or even quite
irrelevant information about the individual
event. Thus, if they are told that 70 percent
of the population are lawyers, and if they
are then given a noncommittal description
of a person (one that could equally well fit
a lawyer or an engineers, half the time they
will predict that the person is a lawyer and
half the time that he is an engineer-even
though the laws of probability dictate that
the best forecast is always to predict that the
person is a lawyer.
People commonly misjudge probabilities
in many other ways. Asked to estimate the
probability that 60 percent or more of the
25
babies born in a hospital during a given week
are male, they ignore information about the
total number of births, although it is evident
that the probability of a departure of this
magnitude from the expected value of 50
percent is smaller if the total number of births
is larger (the standard error of a percentage
varies inversely with the square root of the
population size).
There are situations in which people as-
sess the frequency of a class by the ease with
which instances can be brought to mind. In
one experiment, subjects heard a list of names
of persons of both sexes and were later asked
to judge whether there were more names of
men or women on the list. In lists presented
to some subjects, the men were more fa-
mous than the women; in other lists, the
women were more famous than the men.
For all lists, subjects judged that the sex that
had the more famous personalities was the
more numerous.
The way in which an uncertain possibility
is presented may have a substantial effect
on how people respond to it. When asked
whether they would choose surgery in a hy-
pothetical medical emergency, many more
people said that they would when the chance
of survival was given as 80 percent than
when the chance of death was given as 20
percent.
On the basis of these studies, some of the
general heuristics, or rules of thumb, that
people use in making judgments have been
compilecl- heuristics that produce biases
toward classifying situations according to
their representativeness, or toward judging
frequencies according to the availability of
examples in memory, or toward interpre-
tations warped by the way in which a prob-
lem has been framed. These findings have
important implications for public policy. A
recent example is the lobbying effort of the
credit card industry to have differentials be-
tween cash and credit prices labeled "cash
discounts" rather than "credit surcharges."
The research findings raise questions about
how to phrase cigarette warning labels or
OCR for page 26
frame truth-in-lending laws and informed-
consent laws.
METHODS OF EMPIRICAL RESEARCH
Finding the underlying bases of human
choice behavior is difficult. People cannot
always, or perhaps even usually, provide
veridical accounts of how they make up their
minds, especially when there is uncertainty.
In many cases, they can predict how they
will behave (pre-election polls of voting in-
tentions have been reasonably accurate when
carefully taken), but the reasons people give
for their choices can often be shown to be
rationalizations and not closely related to
their real motives.
Students of choice behavior have steadily
improved their research methods. They
question respondents about specific situa-
tions, rather than asking for generaliza-
tions. They are sensitive to the dependence
of answers on the exact forms of the ques-
tions. They are aware that behavior in an
experimental situation may be different from
behavior in real life, and they attempt to
provide experimental settings and motiva-
tions that are as realistic as possible. Using
thinking-aloud protocols and other ap-
proaches, they try to track the choice be-
havior step by step, instead of relying just
on information about outcomes or querying
respondents retrospectively about their
choice processes.
Perhaps the most common method of em-
pirical research in this field is still to ask
people to respond to a series of questions.
But data obtained by this method are being
supplemented by data obtained from care-
fully designed laboratory experiments and
from observations of actual choice behavior
(for example, the behavior of customers in
supermarkets). In an experimental study of
choice, subjects may trade in an actual mar-
ket with real (if modest) monetary rewards
and penalties. Research experience has also
demonstrated the feasibility of making cli-
rect observations, over substantial periods
of time, of the decision-making processes in
business and governmental organizations-
for example, observations of the procedures
that corporations use in making new in-
vestments in plant and equipment. Confi-
dence in the empirical findings that have
been accumulating over the past several de-
cades is enhanced by the general consis-
tency that is observed among the data
obtained from quite different settings using
different research methods.
There still remains the enormous and
challenging task of putting together these
findings into an empirically founded theory
of decision making. With the growing avail-
ability of data, the theory-building enter-
prise is receiving much better guidance from
the facts than it did in the past. As a result,
we can expect it to become correspondingly
more effective in arriving at realistic models
of behavior.
PROBLEM SOLVING
The theory of choice has its roots mainly
in economics, statistics, and operations re-
search and only recently has received much
attention from psychologists; the theory of
problem solving has a very different history.
Problem solving was initially studied prin-
cipally by psychologists, and more recently
by researchers in artificial intelligence. It has
received rather scant attention from econ-
omists.
CONTEMPORARY
PROBEEM-SOEVING THEORY
Human problem solving is usually stud-
ied in laboratory settings, using problems
that can be solved in relatively short periods
of time (seldom more than an hour), and
often seeking a maximum density of data
about the solution process by asking sub-
jects to think aloud while they work. The
thinking-aloud technique, at first viewed with
suspicion by behaviorists as subjective and
"introspective," has received such careful
26
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DECISION MAKING AND PROBLEM SOLVING
methodological attention in recent years that
it can now be used dependably to obtain
data about subjects' behaviors in a wide range
of settings.
The laboratory study of problem solving
has been supplemented by field studies of
professionals solving real-worId prob-
lems for example, physicians making diag-
noses and chess grandmasters analyzing
game positions, and, as noted earlier, even
business corporations making investment
decisions. Currently, historical records, in-
cluding laboratory notebooks of scientists,
are also being used to study problem-solv-
ing processes in scientific discovery. Al-
though such records are far less "dense"
than laboratory protocols, they sometimes
permit the course of discovery to be traced
in considerable detail. Laboratory note-
books of scientists as distinguished as Charles
Darwin, Michael Faraday, Antoine-Laurent
T~avoisier, and Hans Krebs have been used
successfully in such research.
From empirical studies, a description can
now be given of the problem-solving pro-
cess that holds for a rather wide range of
activities. First, problem solving generally
proceeds by selective search through large
sets of possibilities, using rules of thumb
(heuristics) to guide the search. Because the
possibilities in realistic problem situations
are generally multitudinous, trial-and-error
search would simply not work; the search
must be highly selective. Chess grandmas-
ters seldom examine more than a hundred
of the vast number of possible scenarios that
confront them, and similar small numbers
of searches are observed in other kinds of
problem-solving search.
One of the procedures often used to guide
search is "hill climbing," using some mea-
sure of approach to the goal to determine
where it is most profitable to look next. An-
other, and more powerful, common pro-
ced~ure is means-ends analysis. In means-
ends analysis, the problem solver compares
the present situation with the goal, detects
a difference between them, and then searches
27
memory for actions that are likely to reduce
the difference. Thus, if the difference is a
50-mile distance from the goal, the problem
solver will retrieve from memory knowl-
edge about autos, carts, bicycles, and other
means of transport; walling and flying will
probably be discarded as inappropriate for
that distance.
The third thing that has been learner! about
problem solving especially when the sol-
ver is an expert-is that it relies on large
amounts of information that are stored in
memory and that are retrievable whenever
the solver recognizes cues signaling its rel-
evance. Thus, the expert knowledge of a
diagnostician is evoked by the symptoms
presented by the patient; this knowledge
leads to the recollection of what additional
information is needed to discriminate among
alternative diseases and, finally, to the di-
agnosis.
In a few cases, it in-as been possible to
estimate how many patterns an expert must
be able to recognize in order to gain access
to the relevant knowledge stored in mem-
ory. A chess master must be able to recog-
nize about 50,000 different configurations of
chess pieces that occur frequently in the
course of chess games. A medical diagnos-
tician must be able to recognize tens of thou-
sands of configurations of symptoms; a
botanist or zoologist specializing in taxon-
omy, tens or hundreds of thousands of fea-
tures of specimens that define their species.
For comparison, college graduates typically
have vocabularies in their native languages
of 50,000 to 200,000 words. (However, these
numbers are very small in comparison with
the real-word situations the expert faces:
there are perhaps loi20 branches in the game
tree of chess, a game played with only six
kinds of pieces on an ~ x ~ board.)
One of the accomplishments of the con-
temporary theory of problem solving has
been to provide an explanation for the phe-
nomena of intuition and judgment fre-
quently seen in experts' behavior. The store
of exr)ert knowledge, "indexed" by the rec
OCR for page 28
ognition cues that make it accessible and
combined with some basic inferential ca-
pabilities (perhaps in the form of means-
ends analysis), accounts for the ability of
experts to find satisfactory solutions for dif-
ficult problems, and sometimes to find them
almost instantaneously. The expert's "in-
tuition" and "judgment" derive from this
capability for rapid recognition linked to a
large store of knowledge. When immediate
intuition fails to yield a problem solution or
when a prospective solution needs to be
evaluated, the expert fats back on the slower
processes of analysis and inference.
EXPERT SYSTEMS IN ARTIFICIAL INTELLIGENCE
Over the past 30 years, there has been
close teamwork between research in psy-
chology and research in computer science
aimed at developing intelligent programs.
Artificial intelligence (Al) research has both
borrowed from and contributed to research
on human problem solving. Today, artificial
intelligence is beginning to produce sys-
tems, applied to a variety of tasks, that can
solve difficult problems at the level of
professionally trained humans. These Al
programs are usually called expert systems.
A description of a typical expert system
would resemble closely the description given
above of typical human problem solving; the
differences between the two would be dif-
ferences in degree, not in kind. An Al expert
system, relying on the speed of computers
and their ability to retain large bodies of
transient information in memory, will gen-
erally use "brute force" sheer computa-
tional speed and power more freely than
a human expert can. A human expert, in
compensation, will generally have a richer
set of heuristics to guide search and a larger
vocabulary of recognizable patterns. To the
observer, the computer's process will ap-
pear the more systematic and even com-
puIsive, the human's the more intuitive. But
these are quantitative, not qualitative, dif-
ferences.
The number of tasks for which expert sys-
tems have been built is increasing rapidly.
One is medical diagnosis (two examples are
the CADUCEUS and MYCIN programs).
Others are automatic design of electric mo-
tors, generators, anc! transformers (which
predates by a decade the invention of the
term "expert systems"), the configuration
of computer systems from customer speci-
fications, and the automatic generation of
reaction paths for the synthesis of organic
molecules. All of these (and others) are ei-
ther being used currently in professional or
industrial practice or at least have reached
a level at which they can produce a profes-
sionally acceptable product.
Expert systems are generally constructed
in close consultation with the people who
are experts in the task domain. Using stan-
dard techniques of observation and inter-
rogation, the heuristics that the human expert
uses, implicitly and' often unconsciously, to
perform the task are gradually educed, made
explicit, and incorporated in program struc-
tures. Although a great deal has been learned
about how to do this, improving techniques
for designing expert systems is an important
current direction of research. It is especially
important because expert systems, once built,
cannot remain static but must be modifiable
to incorporate new knowledge as it becomes
available.
DEALING WITH {EE-STRUCTURED PROBLEMS
In the 1950s and 1960s, research on prob-
lem solving focused on clearly structured
puzzle-like problems that were easily brought
into the psychological laboratory and that
were within the range of computer pro-
gramming sophistication at that time. Com-
puter programs were written to discover
proofs for theorems in Euclidean geometry
or to solve the puzzle of transporting mis-
sionaries and cannibals across a river.
Choosing chess moves was perhaps the most
complex task that received attention in the
early years of cognitive science and AI.
28
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DECISION MAKING AND PROBLEM SOLVING
As understanding grew of the methods
needed to handle these relatively simple
tasks, research aspirations rose. The next
main target, in the 1960s and 1970s, was to
find methods for solving problems that in-
volved large bodies of semantic informa-
tion. Medical diagnosis and interpreting mass
spectrogram data are examples of the kinds
of tasks that were investigated during this
period and for which a good level of un-
derstanding was achieved. They are tasks
that, for all of the knowledge they call upon,
are still well structured, with clear-cut goals
and constraints.
The current research target is to gain an
understanding of problem-solving tasks
when the goals themselves are complex and
sometimes ill defined, and when the very
nature of the problem is successively trans-
formed in the course of exploration. To the
extent that a problem has these character-
istics, it is usually called ill structured. Be-
cause ambiguous goals and shifting problem
formulations are typical characteristics of
problems of design, the work of architects
offers a good example of what is involved
in solving ill-structured problems. An ar-
chitect begins with some very general spec-
ifications of what is wanted by a client. The
initial goals are modified and substantially
elaborated as the architect proceeds with the
task. Initial design ideas, recorded in draw-
ings and diagrams, themselves suggest new
criteria, new possibilities, and new require-
ments. Throughout the whole process of de-
sign, the emerging conception provides
continual feedback that reminds the archi-
tect of additional considerations that need
to be taken into account.
With the current state of the art, it is just
beginning to be possible to construct pro-
grams that simulate this kind of flexible
problem-solving process. What is called for
is an expert system whose expertise in-
cludes substantial knowledge about design
criteria as wed as knowledge about the means
for satisfying those criteria. Both kinds of
knowledge are evoked in the course of the
29
design activity by the usual recognition pro-
cesses, and the evocation of design criteria
and constraints continually modifies and re-
molds the problem that the design system
is addressing. The large data bases that can
now be constructed to aid in the manage-
ment of architectural and construction proj-
ects provide a framework into which Al tools,
fashioned along these lines, can be incor-
porated.
Most corporate strategy problems and
governmental policy problems are at least
as ill structured as problems of architectural
or engineering design. The tools now being
forged for aiding architectural design will
provide a basis for building tools that can
aid in formulating, assessing, and monitor-
ing public energy or environmental policies,
or in guiding corporate product and invest-
ment strategies.
SETTING THE AGENDA
AND REPRESENTING A PRosEEM
The very first steps in the problem-solv-
ing process are the least understood. What
brings (and should bring) problems to the
head of the agenda? And when a problem
is identified, how can it be represented in a
way that facilitates its solution?
The task of setting an agenda is of utmost
importance because both individual human
beings and human institutions have limited
capacities for dealing with many tasks si-
multaneously. While some problems are re-
ceiving full attention, others are neglected.
When new problems come thick and fast,
"fire fighting" replaces planning and delib-
eration. The facts of limited attention span,
both for individuals and for institutions like
the Congress, are well known. However,
relatively little has been accomplished to-
ward analyzing or designing effective
agenda-setting systems. A beginning could
be made by the study of "alerting" orga-
nizations like the Office of Technology As-
sessment or military and foreign affairs
intelligence agencies. Because the research
OCR for page 30
and development function in industry is also
in considerable part a task of monitoring
current and prospective technological ad-
vances, it could also be studied profitably
from this standpoint.
The way in which problems are repre-
sented has much to do with the quality of
the solutions that are found. The task of
designing highways or dams takes on an
entirely new aspect if human responses to
a changed environment are taken into ac-
count. (New transportation routes cause
people to move their homes, and people
show a considerable propensity to move into
zones that are subject to flooding when par-
tial protections are erected.) Very different
social welfare policies are usually proposed
in response to the problem of providing in-
centives for economic independence than
are proposed in response to the problem of
taking care of the needy. Early management
information systems were designed on the
assumption that information was the scarce
resource; today, because designers recog-
nize that the scarce resource is managerial
attention, a new framework produces quite
different designs.
The representation or "framing" of prob-
lems is even less well understood than
agenda setting. Today's expert systems make
use of problem representations that already
exist. But major advances in human knowI-
edge frequently derive from new ways of
thinking about problems. A large part of the
history of physics in nineteenth-century En-
gland can be written in terms of the shift
from action-at-a-distance representations to
the field representations that were devel-
oped by the applied mathematicians at
Cambridge.
Today, developments in computer-aided
design (CAD) present new opportunities to
provide human designers with computer-
generated representations of their prob-
lems. Effective use of these capabilities re-
quires us to understand better how people
extract information from diagrams and other
displays and how displays can enhance hu
man performance in design tasks. Research
on representations is fundamental to the
progress of CAD.
COMPUTATION AS PROBLEM SOEVING
Nothing has been said so far about the
radical changes that have been brought about
in problem solving over most of the do-
mains of science and engineering by the
standard uses of computers as computa-
tional devices. Although a few examples
come to mind in which artificial intelligence
has contributed to these developments, they
have mainly been brought about by research
in the individual sciences themselves, com-
bined with work in numerical analysis.
Whatever their origins, the massive com-
putational applications of computers are
changing the conduct of science in numer-
ous ways. There are new specialties emerg-
ing such as "computational physics" and
"computational chemistry." Computa-
tion that is to say, problem solving- be-
comes an object of explicit concern to
scientists, side by sicle with the substance
of the science itself. Out of this new aware-
ness of the computational component of sci
... . . . . . . .
enht~c inquiry is arising an increasing
interaction among computational specialists
in the various sciences and scientists con-
cerned with cognition and Al. This inter-
action extends well beyond the traditional
area of numerical analysis, or even the newer
subject of computational complexity, into the
heart of the theory of problem solving.
Physicists seeking to handle the great mass
of bubble-chamber data produced by their
instruments began, as early as the 1960s, to
look to Al for pattern recognition methods
as a basis for automating the analysis of their
data. The construction of expert systems to
interpret mass spectrogram data and of other
systems to design synthesis paths for chem-
ical reactions are other examples of problem
solving in science, as are programs to aid in
matching sequences of nucleic acids in DNA
30
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DECISION MAKING AND PROBLEM SOLVING
and RNA and amino acid sequences in pro-
teins.
Theories of human problem solving and
learning are also beginning to attract new
attention within the scientific community as
a basis for improving science teaching. Each
advance in the understanding of problem
solving and learning processes provides new
insights about the ways in which a learner
must store and index new knowledge and
procedures if they are to be useful for solv-
ing problems. Research on these topics is
also generating new ideas about how effec-
tive learning takes place for example, how
students can learn by examining and ana-
lyzing worked-out examples.
EXTENSIONS OF THEORY
Opportunities for advancing our under-
standing of decision making and problem
solving are not limited to the topics dealt
with above, and in this section, just a few
indications of additional promising direc-
tions for research are presented.
DECISION MAKING OVER TIME
The time dimension is especially trouble-
some in decision making. Economics has long
used the notion of time discounting and in-
terest rates to compare present with future
consequences of decisions, but as noted
above, research on actual decision making
shows that people frequently are inconsis-
tent in their choices between present and
future. Although time discounting is a pow-
erful idea, it requires fixing appropriate dis-
count rates for individual, and especially
social, decisions. Additional problems arise
because human tastes and priorities change
over time. Classical SEU theory assumes a
fixed, consistent utility function, which does
not easily accommodate changes in taste. At
the other extreme, theories postulating a
limited attention span do not have ready
ways of ensuring consistency of choice over
time.
31
AGGREGATION
In applying our knowledge of decision
malting and problem solving to society-wide,
or even organization-wide, phenomena, the
problem of aggregation must be solved; that
is, ways must be found to extrapolate from
theories of individual decision processes to
the net effects on the whole economy, pol-
ity, and society. Because of the wide variety
of ways in which any given decision task
can be approached, it is unrealistic to pos-
tulate a "representative firm" or an "eco-
nomic man," and to simply lump together
the behaviors of large numbers of suppos-
edly identical individuals. Solving the ag-
gregation problem becomes more important
as more of the empirical research effort is
directed toward studying behavior at a de-
tailed, microscopic level.
ORGAN~zAT~oNs
Related to aggregation is the question of
how decision making and problem solving
change when attention turns from the be-
havior of isolated individuals to the behav-
ior of these same individuals operating as
members of organizations or other groups.
When people assume organizational posi-
tions, they adapt their goals and values to
their responsibilities. Moreover, their deci-
sions are influenced substantially by the
patterns of information flow and other com-
munications among the various organiza-
tion units.
Organizations sometimes display sophis-
ticated capabilities far beyond the under-
standing of single individuals. They
sometimes make enormous blunders or find
themselves incapable of acting. Organiza-
tional performance is highly sensitive to the
quality of the routines or "performance pro-
grams" that govern behavior and to the
adaptability of these routines in the face of
a changing environment. In particular, the
"peripheral vision" of a complex organiza-
tion is limited, so that responses to novelty
OCR for page 32
in the environment may be made in inap-
propriate and quasi-automatic ways that
cause major failure.
Theory development, formal modeling,
laboratory experiments, and analysis of his-
torical cases are all going forward in this
important area of inquiry. Although the de-
cision-making processes of organizations
have been studied in the field on a limited
scale, a great many more such intensive
studies will be needed before the full range
of techniques used by organizations to make
their decisions is understood, and before the
strengths and weaknesses of these tech-
niques are grasped.
LEARNING
Until quite recently, most research in cog-
nitive science and artificial intelligence had
been aimed at understanding how intelli-
gent systems perform their work. Only in
the past five years has attention begun to
turn to the question of how systems become
intelligent-how they learn. A number of
promising hypotheses about learning mech-
anisms are currently being explored. One is
the so-cared connexionist hypothesis, which
postulates networks that learn by changing
the strengths of their interconnections in re-
sponse to feedback. Another learning mech-
anism that is being investigated is the
adaptive production system, a computer
program that learns by generating new in-
structions that are simply annexed to the
existing program. Some success has been
achieved in constructing adaptive produc-
tion systems that can learn to solve equa-
tions in algebra and to do other tasks at
comparable levels of difficulty.
Learning is of particular importance for
successful adaptation to an environment that
is changing rapidly. Because that is exactly
the environment of the 1980s, the trend to-
ward broadening research on decision mak-
ing to include learning and adaptation is
welcome.
This section has by no means exhausted
the areas in which exciting and important
research can be launched to deepen under-
standing of decision making and problem
solving. But perhaps the examples that have
been provided are sufficient to convey the
promise and significance of this field of in-
quiry today.
CURRENT RESEARCH PROGRAMS
Most of the current research on decision
making and problem solving is carried on
in universities, frequently with the support
of government funding agencies and pri-
vate foundations. Some research is done by
consulting firms in connection with their de-
velopment and application of the tools of
operations research, artificial intelligence,
and systems modeling. In some cases, gov-
ernment agencies and corporations have
supported the development of planning
models to aid them in their policy plan-
ning for example, corporate strategic
planning for investments and markets and
government planning of environmental and
· · ~ · · -
energy pot uncles. 1 here IS an 1ncreasmg num-
ber of cases in which research scientists are
devoting substantial attention to improving
the problem-solving and decision-making
tools in their disciplines, as we noted in the
examples of automation of the processing
of bubble-chamber tracks and of the inter-
pretation of mass spectrogram data.
To use a generous estimate, support for
basic research in the areas described in this
document is probably at the level of tens of
millions of dollars per year, and almost cer-
tainly, it is not as much as $100 million. The
principal costs are for research personnel
and computing equipment, the former being
considerably larger.
Because of the interdisciplinary character
of the research domain, federal research
support comes from a number of different
agencies, and it is not easy to assess the total
picture. Within the National Science Foun-
dation (NSF), the grants of the decision and
32
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DECISION MAKING AND PROBLEM SOLVING
management sciences, political science and
the economics programs in the Social Sci-
ences Division are to a considerable extent
devoted to projects in this domain. Smaller
amounts of support come from the memory
and cognitive processes program in the Di-
vision of Behavioral and Neural Sciences,
and perhaps from other programs. The
"software" component of the new NSF Di-
rectorate of Computer Science and Engi-
neering contains programs that have also
provided important support to the study of
decision making and problem solving.
The Office of Naval Research has, over
the years, supported a wide range of studies
of decision making, including important early
support for operations research. The main
source of funding for research in Al has been
the Defense Advanced Research Projects
Agency (DARPA) in the Department of De-
fense; important support for research on ap-
plications of AT to medicine has been
provided by the National Institutes of Health.
Relevant economics research is also funded
by other federal agencies,including the
Treasury Department, the Bureau of Labor
Statistics, and the Federal Reserve Board. In
recent years, basic studies of decision mak-
ing have received only relatively minor sup-
port from these sources, but because of the
relevance of the research to their missions,
they could become major sponsors.
Although a number of projects have been
and are funded by private foundations, there
appears to be at present no foundation for
which decision making and problem solving
are a major focus of interest.
In sum, the pattern of support for re-
search in this field shows a healthy diversity
but no agency with a clear lead responsi-
bility, unless it be the rather modestly fundec!
program in decision and management sci-
ences at NSF. Perhaps the largest scale of
support has been provided by DARPA,
where decision making and problem solv-
ing are only components within the larger
area of artificial intelligence and certainly
not highly visible research targets.
33
The character of the funding require-
ments in this domain is much the same as
in other fields of research. A rather intensive
use of computational facilities is typical of
most, but not an, of the research. Anc! be-
cause the field is gaining new recognition
and growing rapidly, there are special needs
for the support of graduate students and
postdoctoral training. In the computing-in-
tensive part of the domain, desirable re-
search funding per principal investigator
might average $250,000 per year; in empir-
ical research involving field studies anct large-
scale experiments, a similar amount; and in
other areas of theory and laboratory exper-
imentation, somewhat less.
RESEARCH OPPORTUNITIES:
SUMMARY
The study of decision making and prob-
lem solving has attracted much attention
through most of this century. By the end of
WorIct War it, a powerful prescriptive theory
of rationality, the theory of subjective ex-
pected utility (SEU), had taken form; it was
followecl by the theory of games. The past
40 years have seen widespread applications
of these theories in economics, operations
research, and statistics, and, through these
disciplines, to decision making in business
and government.
The main limitations of SEU theory and
the developments based on it are its relative
neglect of the limits of human (and com-
puter) problem-solving capabilities in the face
of real-worId complexity. Recognition of
these limitations has produced an increas-
ing volume of empirical research aimed at
discovering how humans cope with com-
plexity and reconcile it with their bounded
computational powers. Recognition that hu-
man rationality is limited occasions no sur-
prise. What is surprising are some of the
forms these limits take and the kinds of de-
partures from the behavior predicted by the
SEU mode] that have been observed. Ex-
tending empirical knowlecige of actual hu
OCR for page 34
.
man cognitive processes and of techniques for
dealing with complexity continues to be a re-
search goal of very high priority. Such em-
p~ncal knowledge is needed both to build valid
theories of how the U.S. society and economy
operate and to build prescriptive tools for de-
cision making that are compatible with exist-
ing computational capabilities.
The complementary fields of cognitive
psychology and artificial intelligence have
produced in the past 30 years a fairly weD-
developed theory of problem solving that
lencts itself wed to computer simulation, both
for purposes of testing its empirical validity
and for augmenting human problem-solv-
ing capacities by the construction of expert
systems. Problem-solving research today is
being extended into the domain of ill-struc-
tured problems and applied to the task of
formulating problem representations. The
processes for setting the problem agenda,
which are still very little explored, deserve
more research attention.
The growing importance of computa-
tional techniques in all of the sciences has
attracted new attention to numerical anal-
ysis and to the topic of computational com-
plexity. The need to use heuristic as well as
rigorous methods for analyzing very com-
plex domains is beginning to bring about a
wide interest, in various sciences, in the
possible application of problem-solving the-
vies to computation.
Opportunities abound for productive re-
search in decision making and problem
solving. A few of the directions of research
that look especially promising and signifi-
cant follow:
· A substantially enlarged program of
empirical studies, involving direct obser-
vation of behavior at the level of the indi-
vidual and the organization, and including
both laboratory and field experiments, will
be essential in sifting the wheat from the
chaff in the large body of theory that now
exists and in giving direction to the devel-
opment of new theory.
/
· Expanded research on expert systems
wiD require extensive empirical study of ex-
pert behavior and will provide a setting for
basic research on how is-structured prob-
lems are, ant! can be, solved.
· Decision making in organizational set-
tings, which is much less well understood
than indiviclual decision making and prob-
lem solving, can be studied with great profit
using already established methods of in-
quiry, especially through intensive long-
range studies within individual organiza-
tions.
· The resolution of conflicts of values (in-
dividual and group) and of inconsistencies
in belief will continue to be highly produc-
tive directions of inquiry, addressed to is-
sues of great importance to society.
· Setting agendas and framing problems
are two related but poorly understood pro-
cesses that require special research attention
and that now seem open to attack.
These five areas are examples of especially
pro~sing research opportunities drawn from
the much larger set that are described or
hinted at in this report.
The tools for decision making developed
by previous research have already found ex-
tensive application in business and govern-
ment organizations. A number of such
applications have been mentioned in this
report, but they so pervade organizations,
especially at the middle management and
professional levels, that people are often un-
aware of their origins.
Although the research domain of decision
making and problem solving is alive and
well today, the resources devoted to that
research are modest in scale (of the order of
tens of millions rather than hundreds of mil-
lions of dollars). They are not commensur-
ate with either the identified research
opportunities or the human resources avail-
able for exploiting them. The prospect of
throwing new light on the ancient problem
of mind and the prospect of enhancing the
powers of mind with new computational
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DECISION MAKING AND PROBLEM SOLVING
tools are attracting substantial numbers of
first-rate young scientists. Research prog-
ress is not limited either by lack of excellent
research problems or by lack of human tal-
ent eager to get on with the job.
Gaining a better understanding of how
problems can be solved and decisions made
is essential to our national goal of increasing
productivity. The first industrial revolution
showed us how to do most of the worId's
heavy work with the energy of machines
35
instead of human muscle. The new indus-
trial revolution is showing us how much of
the work of human thinking can be done by
and in cooperation with intelligent ma-
chines. Human minds with computers to
aid them are our principal productive re-
source. Understanding how that resource
operates is the main road open to us for
becoming a more productive society and a
society able to deal with the many complex
problems in the world today.
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Representative terms from entire chapter:
artificial intelligence
