Simon Decision Making

foot pichttp//jayhanson. us/america. htm pic Decision Making and Problem Solving by Herbert A. Simon and Associates Associates George B. Dantzig, robin Hogarth, Charles R. Piott, Howard Raiffa, Thomas C. Schelling, Kennth A. Shepsle, Richard Thaier, Amos Tversky, and Sidney Winter. Simon was educated in political acquirement at the University of cabbage (B. A. , 1936, Ph. D. , 1943).He has held enquiry and faculty positions at the University of California (Berkeley), Illinois Institute of Technology and since 1949, Carnegie Mellon University, where he is the Richard King Mellon University Professor of calculating machine Science and Psychology. In 1978, he peck the Alfred Nobel Memorial Prize in Economic Sciences and in 1986 the National ribbon of Science. Reprinted with permission from Re take c be Briefings 1986 Report of the look Briefing Panel on Decision Making and Problem Solving 1986 by the National honorary society of Sciences. Published by National Academy Press, Wa shington, DC.Introduction The spring of managers, of scientists, of engineers, of lawyersthe work that steers the line of products of society and its economic and controlmental organizationsis roundly work of reservation closes and resolve capers. It is work of choosing issues that require charge, setting goals, finding or aiming desirable courses of action, and evaluating and choosing among substitute actions. The prototypal three of these activitiesfixing dockets, setting goals, and physiqueing actions atomic chassis 18 usu tot anyyy called caper firmness the last, evaluating and choosing, is ordinarily called closing making.Nothing is to a greater extent than bloodamental for the surface(p)-being of society than that this work be performed effectively, that we address conquest respectabley the many an archaean(a)(prenominal) capers requiring assist at the home(a) level (the budget and trade deficits, AIDS, national security, the mitigation of eart hquake damage), at the level of business organizations (product improvement, zilch of production, weft of investments), and at the level of our case-by-case lives (choosing a c atomic number 18er or a school, buying a ho implement).The abilities and sk nauseateds that determine the quality of our determinations and caper firmnesss argon investment trustd non hardly in to a greater extent than 200 million valet heads, only when desirewise in tools and machines, and oddly today in those machines we call computers. This fund of brains and its attendant machines form the basis of our Ameri sens ingenuity, an ingenuity that has letted U.S. society to benefit remarkable levels of economic productivity. There ar no much(prenominal) than assure or strategic targets for basic scientific look into than chthonianstanding how valet being minds, with and with disclose the help of computers, solve line of works and consume conclusivenesss effectively, and improv ing our occupation- re solve role and finding-making capabilities.In psychology, economics, mathematical statistics, trading operations search, political comprehension, simulated watchword, and cognitive accomplishment, major(ip)(ip) explore pass waters adjudge been do during the a vogue half century in brain paradox settlement and determination making. The give already achieved holds forth the promise of exciting sunrise(prenominal) advances that pop the question contribute tangiblely to our nations capacity for overcompensateing intelligently with the range of issues, vainglorious and small, that confront us.Much of our existing experience ab kayoed decision making and problem result, derived from this look into, has already been put to use in a wide-eyed mannequin of applications, including procedures used to assess drug safety, size up control schemes for perseverance, the vernal in force(p) systems that em system artificial intelligence techni ques, procedures for modeling cypher and environmental systems, and analyses of the stabilizing or destabilizing effects of alternative defense strategies. Application of the brisk inventory control techniques, for example, has enabled Ameri fag end corporations to reduce their inventories by hundreds of millions of dollars since World War II without increasing the incidence of stockouts. ) Some of the k forthwithledge gained through the interrogation describes the shipway in which heap actually go active making decisions and understand problems any(prenominal) of it prescribes mitigate systems, offering advice for the improvement of the process.Central to the body of normative familiarity rough decision making has been the hypothesis of subjective expected advantage (SEU), a sophisticated mathematical model of survival of the fittest that lies at the metrical foot of most contemporary economics, theoretical statistics, and operations look for. SEU possibility d efines the conditions of perfect avail-maximizing wiseness in a world of certainty or in a world in which the prospect distributions of all relevant variables sight be provided by the decision fliprs. In spirit, it might be comp ard with a possibility of exalted gases or of frictionless bodies sliding hatful inclined planes in a vacuum. ) SEU theory great deals only with decision making it has nothing to guess to the highest degree how to frame problems, set goals, or develop impudently alternatives. prescriptive theories of plectron much(prenominal) as SEU be complemented by falsifiable inquiry that shows how throng actually pee decisions (purchasing insurance, suffrage for political usher outdidates, or investment in securities), and look into on the processes mickle use to solve problems (designing switchgear or finding chemical reaction pathship canal).This look into demonstrates that the great unwashed solve problems by selective, heuristic search thro ugh heavy(a) problem spaces and large entropy bases, using means-ends analysis as a principal technique for runing the search. The skillful systems that argon now being produced by look for on artificial intelligence and applied to much(prenominal) labor happen uponments as interpreting oil-well drill logs or making medical examination diagnoses argon outgrowths of these research findings on valet problem figure out.What chiefly distinguishes the existential research on decision making and problem solving from the prescriptive approaches derived from SEU theory is the assistance that the former hold waters to the limits on gentleman keen-wittedity. These limits argon imposed by the conglomerateity of the world in which we live, the half(prenominal)ness and inadequacy of pitying knowledge, the inconsistencies of individual preference and belief, the conflicts of value among citizenry and groups of people, and the inadequacy of the computations we can carry ou t, eve with the aid of the most mightily computers.The authoritative world of tender-hearted decisions is not a world of ideal gases, frictionless planes, or vacuums. To bring it in spite of appearance the scope of human mentation powers, we moldiness simplify our problem formulations drastically, point leaving out over a great deal or most of what is strengthly 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 techniques to handle complexity that cannot be handled exactly.Out of this descriptive theory is emerging an augmented and amended prescriptive theory, one that takes card of the gaps and elements of unrealism in SEU theory by encompassing problem solving as well as choice and demanding only the kinds of knowledge, consistency, and computational power that atomic number 18 attainable in the real world. The growing realization that header wit h complexity is central to human decision making strongly influences the missions of research in this do main.Operations research and artificial intelligence ar forging skillfully brisk computational tools at the aforementioned(prenominal) time, a refreshed body of mathematical theory is evolving more or less the topic of computational complexity. Economics, which has traditionally derived both(prenominal)(prenominal) its descriptive and prescriptive approaches from SEU theory, is now paying a great deal of attention to uncertainty and partial information to so-called agency theory, which takes theme of the institutional framework in spite of appearance which decisions ar make and to plot theory, which seeks to deal with interindividual and intergroup processes in which there is partial conflict of cheer.Economists and political scientists atomic number 18 too increasingly buttressing the semi verifiable foundations of their issue by studying individual choice conduct directly and by studying carriage in experimentally constructed grocery injects and simulated political structures. The fol haplessing pages contain a untasted outline of flow knowledge about decision making and problem solving and a brief review of current research directions in these theater of operationss as well as most of the principal research 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 be pee-pee in a ordered, rational matter. It fancied that a decision maker possessed a utility function (an ordering by preference among all the mathematical outcomes of choice), that all the alternatives among which choice could be made were known, and that the consequences of choosing separately alternative could be ascertained (or, in the version of the theory that treats of choi ce under uncertainty, it assumed that a subjective or neutral probability distribution of consequences was associated with each alternative).By admitting subjectively assigned probabilities, SEU theory undetermineded the way to fusing subjective opinions with fair gameive information, an approach that can as well be used in man-machine decision-making systems. In the probabi angleic version of the theory, Bayess rule prescribes how people should take figure of upstart information and how they should respond to incomplete information. The speculations of SEU theory ar very strong, permitting correspondingly strong inferences to be made from them.Although the assumptions cannot be genial even remotely for most complex blank spaces in the real world, they may be satisfied approximately in virtually microcosmsproblem situations that can be unaffectionate from the worlds complexity and dealt with independently. For example, the manager of a commercial cattle-feeding operatio n might seize the problem of finding the to the lowest degree expensive mix of feeds available in the market that would meet all the nutritional requirements of his cattle.The computational tool of running(a) programming, which is a powerful regularity for maximizing goal achievement or minimizing costs while refreshing all kinds of side conditions (in this case, the nutritional requirements), can provide the manager with an optimum feed mixoptimal within the limits of approximation of his model to real world conditions. Linear programming and link up operations research techniques are now used widely to make decisions whenever a situation that reasonably fits their assumptions can be carved out of its complex surround.These techniques fill been curiously valuable aids to middle way in dealing with comparatively well-structured decision problems. Most of the tools of modern operations researchnot only linear programming, but also whole number programming, queuing theory, d ecision trees, and otherwisewise widely used techniquesuse the assumptions of SEU theory. They assume that what is in demand(p) is to maximize the achievement of more or less goal, under specified constraints and assuming that all alternatives and consequences (or their probability distributions) are known.These tools have proven their usefulness in a wide modification of applications. THE LIMITS OF RATIONALITY Operations research tools have also underscored dramatically the limits of SEU theory in dealing with complexity. For example, turn in and potential 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 dis threadbareised tools and machines.And the mere thought of using these computational techniques to determine an optimal national policy for energy production or an optimal economic policy reveals their limits. Computat ional complexity is not the only factor that limits the literal application of SEU theory. The theory also makes enormous demands on information. For the utility function, the range of available alternatives and the consequences following from each alternative must all be known.Increasingly, research is being order at decision making that takes hardheaded account of the compromises and approximations that must be made in order to fit real-world problems to the informational and computational limits of people and computers, as well as to the inconsistencies in their value and perceptions. The study of actual decision processes (for example, the strategies used by corporations to make their investments) reveals massive and unavoidable departures from the framework of SEU theory.The sections that follow describe some of the things that have been l clear uped about choice under non-homogeneous conditions of incomplete information, expressage computing power, inconsistency, and instit utional constraints on alternatives. Game theory, agency theory, choice under uncertainty, and the theory of markets are a a few(prenominal) 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.LIMITED RATIONALITY IN ECONOMIC THEORY Although the limits of human tenability were stressed by some researchers in the 1950s, only late has there been elongated application in the field of force 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 radicaler theoretical research undertakes to answer such questions as the following Are market equilibria altered by the departures of actual choice demeanour from the behavior of fully rational agents ventureed by SEU theory? Under what circ umstances do the processes of competition legal philosophy markets in such a way as to cancel out the effects of the departures from full rationality? In what slipway are the choices made by boundedly rational agents varied from those made by fully rational agents? Theories of the firm that assume managers are aiming at okay breads or that their concern is to maintain the firms share of market in the industry make sort of varied predictions about economic equilibrium than those derived from the assumption of profit maximization.Moreover, the classical theory of the firm cannot explain wherefore economic activity is sometimes organized around large business firms and sometimes around contractual ne 2rks of individuals or smaller organizations. New theories that take account of derived function access of economic agents to information, combined with differences in self-interest, are able to account for these important phenomena, as well as provide explanations for the many forms of contracts that are used in business.Incompleteness and asymmetry of information have been shown to be internal for explaining how individuals and business firms decide when to face uncertainty by insuring, when by hedging, and when by assuming the risk. Most current work in this domain quiet assumes that economic agents seek to maximize utility, but within limits posed by the incompleteness and uncertainty of the information available to them.An important potential arena of research is to discover how choices go out be changed if there are other departures from the axioms of rational choicefor example, substituting goals of reaching specified aspiration levels (satisficing) for goals of maximizing. Applying the new assumptions about choice to economics leads to new empirically supported theories about decision making over time. The classical theory of perfect rationality leaves no room for regrets, second thoughts, or weakness of go away. It cannot explain why many ind ividuals enroll in Christmas bringings plans, which earn interest well below the market rate. More customaryly, it does not lead to correct conclusions about the important social issues of saving and conservation. The effect of pensions and social security on somebodyal saving has been a controversial issue in economics. The exemplar economic model predicts that an increase in required pension saving leave reduce other saving dollar for dollar behavioural 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 salaries affects the saving rate. For example, annual bonuses produce a high 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 dismiss properly for the passage of time, their decisions exit not be optimal. For example, air conditioners vary greatly in their energy efficiency the more efficient models cost more initially but keep open money over the long run through lower energy consumption.It has been found that consumers, on average, drive air conditioners that imply a discount rate of 25 per centum or more per year, much high than the rates of interest that prevailed at the time of the study. As juvenilely as five years ago, the evidence was thought to be unassailable that markets like the New York Stock Exchange work efficientlythat prices reflect all available information at any prone moment in time, so that stock price movements resemble a random straits and contain no systematic information that could be exploited for profit.Recently, however, meaty departures from the behavior predicted by the efficient-market hypothesis have been detected. For example, small firms appear to earn inexplicably high returns on the market prices of their stock, while firms that ha ve 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 empirical finding that decision makers much overreact to new information, in violation of Bayess rule.In the same way, it has been found that stock prices are to a fault volatilethat they fluctuate up and down more rapidly and violently than they would if the marke t were efficient. There has also been a long-standing personate as to why firms pay dividends. Considering that dividends are taxed at a higher rate than dandy 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 simply sell some of their appreciated shares to obtain the income they require. The solution to this puzzle also requires models of investors that take account of limits on rationality. T HE THEORY OF GAMES In economic, political, and other social situations in which there is actual or potential conflict of interest, especially if it is combined with incomplete information, SEU theory faces special delicateies. In markets in which there are many competitors (e. g. , the wheat market), each purchaser or seller can accept the market price as a disposed over that result not be affected materially by the actions of any single individual.Under these conditions, SEU theory makes unambiguous predictions of behavior. However, when a market has only a few suppliers say, for example, twomatters are quite different. In this case, what it is rational to do depends on what ones competitor is going to do, and vice versa. separately 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 jeopardizes, real by von Neumann and Morgenstern and published in its full form in 1944. unl ess the answers provided by the theory of games are sometimes very bewilder and ambiguous.In many situations, no single course of action dominates all the others instead, a whole set of possible solutions are all equally consistent with the postulates of rationality. One game that has been study extensively, both theoretically and empirically, is the Prisoners Dilemma. In this game mingled with two players, each has a choice between two actions, one trustful of the other player, the other funny or exploitative. If both players choose the trustful alternative, both receive small rewards. If both choose the exploitative alternative, both are punished.If one chooses the trustful alternative and the other the exploitative alternative, the former is punished much more severely than in the previous case, while the latter receives a substantial reward. If the other players choice is fixed but unknown, it is advantageous for a player to choose the exploitative alternative, for this pa ss on give him the best outcome in either case. But if both adopt this reasoning, they give both be punished, whereas they could both receive rewards if they agreed upon the trustful choice (and did not welch on the agreement).The terms of the game have an unsettling resemblance to certain situations in the relations between nations or between a company and the employees union. The resemblance becomes stronger if one imagines the game as being played repeatedly. Analyses of rational behavior under assumptions of intended utility maximization support the conclusion that the players will (ought to? ) always make the mistrustful choice. Nevertheless, in research testing ground experiments with the game, it is often found that players (even those who are happy in game theory) adopt a tit-for-tat strategy.That is, each plays the trustful, cooperative strategy as long as his or her partner does the same. If the partner exploits the player on a particular trial, the player then plays t he exploitative strategy on the side by side(p) trial and continues to do so until the partner switches back to the trustful strategy. Under these conditions, the game oftentimes stabilizes with the players pursuing the mutually trustful strategy and receiving the rewards. With these empirical findings in hand, theorists have tardily 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 instead than want the maximum reward. This result is consistent with the finding that in many situations, as in the Prisoners Dilemma game, people appear to satisfice kinda than attempting to optimize. The Prisoners Dilemma game illustrates an important point that is graduation exercise to be appreciated by those who do 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 ar e confronted with an confusion of riches.To choose among the many alternative models that could account for the anomalies of choice, extensive empirical research is called forto see how people do make their choices, what beliefs guide them, what information they have available, and what part of that information they take into account and what part they disregard. In a world of limited rationality, economics and the other decision sciences must close examine the actual limits on rationality in order to make accurate predictions and to provide sound advice on familiar policy.EMPIRICAL STUDIES OF CHOICE chthonic UNCERTAINTY During the past ten years, empirical studies of human choices in which uncertainty, inconsistency, and incomplete information are present have produced a rich army of findings which only now are rise to be organized under broad generalizations. Here are a few examples. When people are tending(p) information about the probabilities of certain events (e. g. , h ow 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 vents has occurred (which person has been sampled from the population), they tend to ignore the prior probabilities in favor of incomplete or even quite irrelevant information about the individual event. Thus, if they are told that 70 percentage 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 engineer), half the time they will predict that the person is a lawyer and half the time that he is an engineereven 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 babies innate(p) in a hospital during a given week are male, they ignore information about the total numb er 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 assess 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 finishes 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 famous 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 opening night is presented may have a substantial effect on how people respond to it.When asked whether they would choose surgery in a hypothetical medi cal 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 compiledheuristics that produce biases toward classifying situations according to their representativeness, or toward judging frequencies according to the availability of examples in memory, or toward interpretations warped by the way in which a problem 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 between cash and credit prices labeled cash discounts rather than credit surcharges. The research findings raise questions about how to phrase cigarette model labels or frame truth-in-lending laws and informed consent laws. METHODS OF EMPIRICAL RESEARCH determination the underlying b ases 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 intentions 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 link up to their real motives. Students of choice behavior have steadily improved their research methods. They question respondents about specific situations, rather than asking for generalizations.They are raw to the dependence of answers on the exact forms of the questions. 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 motivations that are as realistic as possible. Using thought process-aloud protocols and other approaches, they try to tr ack the choice behavior 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 empirical 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 carefully designed laboratory experiments and from observations of actual choice behavior (for example, the behavior of nodes in supermarkets). In an experimental study of choice, subjects may trade in an actual market with real (if modest) monetary rewards and penalties. explore experience has also demonstrated the feasibility of making direct observations, over substantial periods of time, of the decision-making processes in business and governmental organizationsfor example, observations of the procedures that corporations use in making new investments in plant and equipment. Confidence in the empirical findings that ha ve been accumulating over the past several decades is enhanced by the general consistency that is observed among the data obtained from quite different settings using different research methods.There still remains the enormous and challenging business of place unneurotic these findings into an empirically founded theory of decision making. With the growing availability of data, the theory-building attempt 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 in the main in economics, statistics, and operations research and only recently has received much attention from psychologists the theory of problem solving has a very different history.Problem solving was initially studied principally by psychologists, and more recently by researchers in artificial intelligence. It has received rather scan t attention from economists. CONTEMPORARY conundrum-SOLVING THEORY Human problem solving is usually studied 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 subjects to think aloud while they work.The thinking-aloud technique, at first viewed with apprehension by behaviorists as subjective and introspective, has received such careful 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-world problemsfor example, physicians making diagnoses and cheater grand sea captains analyzing game positions, and, as note earlier, even business corporations making investment decisions.Currently, historical records, including laboratory noteboo ks of scientists, are also being used to study problem-solving processes in scientific discovery. Although such records are far less dense than laboratory protocols, they sometimes permit the course of discovery to be traced in considerable detail. Laboratory notebooks of scientists as distinguished as Charles Darwin, Michael Faraday, Antoine-Laurent Lavoisier, and Hans Krebs have been used successfully in such research. From empirical studies, a description can now be given of the problem-solving process 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 grandmasters 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 stripe of approach to the goal to determine where it is most profitable to look next. Another, and more powerful, common procedure 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 memory for actions that are in all probability to reduce the difference.Thus, if the difference is a fifty-mile distance from the goal, the problem solver will retrieve from memory knowledge about autos, carts, bicycles, and other means of have a bun in the oven walking and flying will probably be discarded as inappropriate for that distance. The third thing that has been larn about problem solvingespecially when the solver is an dearis that it relies on large amounts of information that are stored in memory and that ar e retrievable whenever the solver recognizes cues signaling its relevance.Thus, the quick-witted knowledge of a pathologist is evoked by the symptoms presented by the uncomplaining this knowledge leads to the recollection of what additional information is needed to discriminate among alternative diseases and, finally, to the diagnosing. In a few cases, it has 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 memory. A chess master must be able to recognize about 50,000 different configurations of chess pieces that occur frequently in the course of chess games.A medical diagnostician must be able to recognize tens of thousands of configurations of symptoms a botanist or zoologist specializing in taxonomy, tens or hundreds of thousands of features of specimens that define their species. For proportion, 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-world situations the expert faces there are perhaps 10120 branches in the game tree of chess, a game played with only six kinds of pieces on an 8 x 8 board. One of the accomplishments of the contemporary theory of problem solving has been to provide an explanation for the phenomena of intuition and judgment frequently seen in experts behavior. The store of expert knowledge, indexed by the re experience cues that make it accessible and combined with some basic inferential capabilities (perhaps in the form of means-ends analysis), accounts for the ability of experts to find satisfactory solutions for difficult problems, and sometimes to find them almost instantaneously.The experts intuition 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 fall back on the slower processes of analysis and inference. EXPERT SYSTEMS IN man-made INTELLIGENCE Over the past thirty years, there has been close teamwork between research in psychology and research in computer science aimed at developing intelligent programs. Artificial intelligence (AI) research has both borrowed from and contributed to research on human problem solving.Today, artificial intelligence is outset to produce systems, applied to a variety of deputes, that can solve difficult problems at the level of professionally trained humans. These AI 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 differences in degree, not in kind. An AI expert system, relying on the speed of computers and their ability to support large bodies of transient information in memory, will generally use brute forcesheer omputational speed and powermore 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 computers process will appear the more systematic and even compulsive, the humans the more intuitive. But these are quantitative, not qualitative, differences. The number of tasks for which expert systems have been built is increasing rapidly. One is medical diagnosis (two examples are the CADUCEUS and MYCIN programs).Others are automatic design of electric motors, generators, and transformers (which predates by a decade the invention of the term expert systems), the configuration of computer systems from customer specifications, and the automatic generation of reaction paths for the synthesis of organic molecules. All of these (and others) are either being used currently in professional or industrial practice or at least have reached a level at which they can produce a professionally gratifying product. Expert systems are generally constructed in close consultation with the people who are experts in the task domain.Using standard techniques of observation and interrogation, the heuristics that the human expert uses, implicitly and often unconsciously, to perform the task are gradually educed, made explicit, and incorporated in program structures. 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 inactive but must be modifiable to incorporate new knowledge as it becomes available.DEALING WITH ILL-STRUCTURED PROBLEMS In the 1950s and 1960s, research on problem solving focused on clearly structured puzzle-like problems that were easily brought into the psychological laboratory and that were within the range of computer programming sophistication at that time. Comp uter programs were written to discover proofs for theorems in Euclidean geometry or to solve the puzzle of transporting missionaries 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.As brain 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 involved large bodies of semantic information. Medical diagnosis and interpreting mass spectrograph data are examples of the kinds of tasks that were investigated during this period and for which a good level of disposition was achieved. They are tasks that, for all of the knowledge they call upon, are still well structured, with clear(p) 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 alter in the course of exploration. To the extent that a problem has these characteristics, it is usually called ill structured. Because ambiguous goals and shifting problem formulations are typical characteristics of problems of design, the work of room decorators offers a good example of what is involved in solving ill-structured problems.An architect begins with some very general specifications 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 drawings and diagrams, themselves suggest new criteria, new possibilities, and new requirements. Throughout the whole process of design, the emerging conception provides continual feedback that reminds the architect 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 program s that simulate this kind of flexile problem-solving process. What is called for is an expert system whose expertise includes substantial knowledge about design criteria as well as knowledge about the means for satisfying those criteria. Both kinds of knowledge are evoked in the course of the design activity by the usual recognition processes, and the evocation of design criteria and constraints continually modifies and remolds the problem that the design system is addressing.The large data bases that can now be constructed to aid in the management of architectural and construction projects provide a framework into which AI tools, fashioned along these lines, can be incorporated. 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 monitoring publ ic energy or environmental policies, or in guiding corporate product and investment strategies.SETTING THE AGENDA AND REPRESENTING A PROBLEM The very first steps in the problem-solving process are the least understood. What brings (and should bring) problems to the head of the agenda? And when a problem is determine, how can it be delineated in a way that facilitates its solution? The task of setting an agenda is of utmost splendour because both individual human beings and human institutions have limited capacities for dealing with many tasks simultaneously. While some problems are receiving full attention, others are neglected.Where new problems come thick and fast, fire fighting replaces readying and deliberation. The facts of limited attention span, both for individuals and for institutions like the Congress, are well known. However, relatively niggling has been accomplished toward analyzing or designing effective agenda-setting systems. A beginning could be made by the stud y of alerting organizations like the means of Technology Assessment or military and foreign affairs intelligence agencies.Because the research and development function in industry is also in considerable part a task of monitoring current and prospective technological advances, it could also be studied profitably from this standpoint. The way in which problems are represented 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 account. (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 partial protections are erected. Very different social welfare policies are usually proposed in response to the problem of providing incentives for economic independence than are proposed in response to the problem of winning care of the needy. Early manage ment information systems were designed on the assumption that information was the scarce resource today, because designers recognize that the scarce resource is managerial attention, a new framework produces quite different designs. The representation or framing of problems is even less well understood than agenda setting.Todays expert systems make use of problem representations that already exist. But major advances in human knowledge frequently derive from new ways of thinking about problems. A large part of the history of physics in nineteenth-century England can be written in terms of the shift from action-at-a-distance representations to the field representations that were demonstrable 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 problems.Effective use of these capabilities requires us to understand better how people extract in formation from diagrams and other displays and how displays can enhance human performance in design tasks. Research on representations is fundamental to the progress of CAD. COMPUTATION AS PROBLEM SOLVING Nothing has been said so far about the radical changes that have been brought about in problem solving over most of the domains of science and engineering by the standard uses of computers as computational 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, combined with work in numerical analysis. whatever their origins, the massive computational applications of computers are changing the conduct of science in numerous ways. There are new specialties emerging such as computational physics and computational chemistry. Computationthat is to say, problem solvingbecomes an object of explicit concern to scientists, side by side with th e substance of the science itself. Out of this new awareness of the computational component of scientific inquiry is arising an increasing interaction among computational specialists in the various sciences and scientists concerned with cognition and AI. This interaction extends well beyond the traditional flying field 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 AI 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 chemical reactions are other examples of problem solving in science, as are programs to aid in matching sequences of nucleic acids in DNA and RNA and amino acid sequences in proteins.Theories of human problem solving and schooling 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 reading 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 solving problems. Research on these topics is also generating new ideas about how effective learning takes placefor example, how students can learn by examining and analyzing worked-out examples. Extensions of TheoryOpportunities for advancing our understanding 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 assure directions for research are presented. DECISION qualification OVER TIME The time dimension is especially troublesome in decision making. Economics has long used the notion of time discounting and interest rates to compar e present with future consequences of decisions, but as historied above, research on actual decision making shows that people frequently are inconsistent in their choices between present and future.Although time discounting is a powerful idea, it requires fixing appropriate discount rates for individual, and especially social, decisions. special 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. AGGREGATIONIn applying our knowledge of decision making and problem solving to society-wide, or even organization-wide, phenomena, the problem of accrual must be solved that is, ways must be found to extrapolate from theories of individual decision processes to the net effects on the whole miserliness, polity, and societ y. Because of the wide variety of ways in which any given decision task can be approached, it is unrealistic to postulate a representative firm or an economic man, and to simply lump together the behaviors of large numbers of supposedly identical individuals.Solving the aggregation problem becomes more important as more of the empirical research effort is directed toward studying behavior at a detailed, microscopic level. ORGANIZATIONS Related to aggregation is the question of how decision making and problem solving change when attention turns from the behavior of isolated individuals to the behavior of these same individuals operating as members of organizations or other groups.When people assume organizational positions, they adapt their goals and values to their responsibilities. Moreover, their decisions are influenced substantially by the patterns of information flow and other communications among the various organization units. Organizations sometimes display sophisticated cap abilities far beyond the understanding of single individuals. They sometimes make enormous blunders or find themselves unequal to(p) of acting.Organizational performance is highly sensitive to the quality of the routines or performance programs that govern behavior and to the adaptability of these routines in the face of a changing environment. In particular, the computer peripheral vision of a complex organization is limited, so that responses to novelty in the environment may be made in inappropriate and quasi-automatic ways that cause major failure. Theory development, formal modeling, laboratory experiments, and analysis of historical cases are all going forward in this important area of inquiry.Although the decision-making processes of organizations have been studied in the field on a limited descale, a great many more such intensifier studies will be needed before the full range of techniques used by organizations to make their decisions is understood, and before the stren gths and weaknesses of these techniques are grasped. LEARNING Until quite recently, most research in cognitive science and artificial intelligence had been aimed at understanding how intelligent systems perform their work.Only in the past five years has attention begun to turn to the question of how systems become intelligenthow they learn. A number of promising hypotheses about learning mechanisms are currently being explored. One is the so-called connexionist hypothesis, which postulates networks that learn by changing the strengths of their inter communitys in response to feedback. Another learning mechanism that is being investigated is the adaptive production system, a computer program that learns by generating new instructions that are simply annexed to the existing program.Some success has been achieved in constructing adaptive production systems that can learn to solve equations in algebra and to do other tasks at comparable levels of difficulty. Learning is of particular im portance for successful adaptation to an environment that is changing rapidly. Because that is exactly the environment of the 1980s, the curl toward broadening research on decision making 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 understanding of decision making and problem solving.But perhaps the examples that have been provided are ample to convey the promise and significance of this field of inquiry 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 backup agencies and private foundations. Some research is done by consulting firms in connection with their development and application of the tools of operations research, artificial intelligence, and systems modeling.In some cases, government agencies and corporations have supported the dev elopment of proviso models to aid them in their policy planningfor example, corporate strategic planning for investments and markets and government planning of environmental and energy policies. There is an increasing number 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 mechanization of the processing of bubble-chamber tracks and of the interpretation of mass spectrogram data.To use a openhanded estimate, support for basic research in the areas described in this scroll is probably at the level of tens of millions of dollars per year, and almost certainly, it is not as much as $100 million. The principal costs are for research strength 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 no t easy to assess the total picture.Within the National Science Foundation (NSF), the grants of the decision and management sciences, political science and the economics programs in the Social Sciences discussion section 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 instalment of Behavioral and Neural Sciences, and perhaps from other programs. The software component of the new NSF Directorate of Computer Science and Engineering contains programs that have also provided important support to the study of decision making and problem solving.The Office of maritime 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 musical accompaniment for research in AI has been the Defense Advanced Research Projects means (DARPA) in the Department of Defense important support for res earch on applications of A1 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 making have received only relatively minor support 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 research in this field shows a healthy diversity but no agency with a clear lead responsibility, unless it be the rather modestly funded program in decision and management sciences at NSF.Perhaps the largest scale of support has been provided by DARPA, where decision making and proble m solving are only components within the larger area of artificial intelligence and certainly not highly apparent research targets. The character of the funding requirements 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 all, of the research. And because 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-intensive part of the domain, desirable research funding per principal research worker might average $250,000 per year in empirical research involving field studies and large-scale experiments, a similar amount and in other areas of theory and laboratory experimentation, somewhat less. Research Opportunities Summary The study of decision making and problem solving has attracted much attention through most of this century. By the end of World War II, a powerful prescriptive theo ry of rationality, the theory of subjective expected utility (SEU), had taken form it was followed by the theory of games.The past forty 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 computer) problem-solving capabilities in the face of real-world complexity. Recognition of these limitations has produced an increasing volume of empirical research aimed at discovering how humans cope with complexity and reconcile it with their bounded computational powers.Recognition that human rationality is limited occasions no surprise. What is surprising are some of the forms these limits take and the kinds of departures from the behavior predicted by the SEU model that have been observed. Extending empirical knowledge of actual human cog nitive processes and of techniques for dealing with complexity continues to be a research goal of very high priority. Such empirical knowledge is needed both to build valid theories of how the U. S. society and economy operate and to build prescriptive tools for decision making that are matched with existing computational capabilities.The complementary fields of cognitive psychology and artificial intelligence have produced in the past thirty years a fairly well-developed theory of problem solving that lends itself well to computer simulation, both for purposes of testing its empirical validity and for augmenting human problem-solving capacities by the construction of expert systems. Problem-solving research today is being extended into the domain of ill-structured 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 compu tational techniques in all of the sciences has attracted new attention to numerical analysis and to the topic of computational complexity. The need to use heuristic as well as stiff methods for analyzing very complex domains is beginning to bring about a wide interest, in various sciences, in the possible application of problem-solving theories to computation.Opportunities abound for deep research in decision making and problem solving. A few of the directions of research that look especially promising and significant follow A substantially enlarged program of empirical studies, involving direct observation of behavior at the level of the individual 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 development of new theory. Expanded research on expert systems will require extensive empirical study of expert behavior and will pr ovide a setting for basic research on how ill-structured problems are, and can be, solved. Decision making in organizational settings, which is much less well understood than individual decision making and problem solving, can be studied with great profit using already established methods of inquiry, especially through intensive long-range studies within individual organizations. The resolution of conflicts of values (individual and group) and of inconsistencies in belief will continue to be highly plentiful directions of inquiry, addressed to issues of great importance to society. Setting agendas and framing problems are two related but poorly understood processes that require special research attention and that now seem open to attack. These five areas are examples of especially promising 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 extensive a pplication in business and government 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 unaware of their origins. Although the research domain of decision making and problem solving is alive(predicate) and well today, the resources devoted to that research are modest in scale (of the order of tens of millions rather than hundreds of millions of dollars).They are not commensurate with either the identified research opportunities or the human resources available for exploiting them. The prospect of throwing new clean on the ancient problem of mind and the prospect of enhancing the powers of mind with new computational tools are attracting substantial numbers of first-rate young scientists. Research progress is not limited either by lack of small research problems or by lack of human talent intent to get on with the job. Gaining a bet ter 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 worlds heavy work with the energy of machines instead of human muscle. The new industrial revolution is presentation us how much of the work of human thinking can be done by and in cooperation with intelligent machines. Human minds with computers to aid them are our principal productive resource. 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. pic