Research Briefings 1986 (1986) / Chapter Skim
Currently Skimming:
Report of the Research Briefing Panel on Decision Making and Problem Solving
Report of the Research Briefing Panel on Decision Making and Problem Solving
Pages 17-36
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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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In psychology, economics, mathematical statistics, operations research, political science, artificial intelligence, 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 issues, large and small, that confront us.
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The expert systems that are now being produced by research on artificial intelligence and applied 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.
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These techniques have been especially valuable aids to middle management in dealing with relatively wellstructured 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 techniques use the assumptions of SEU theory.
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The sections that follow describe some of the things that have been learned about choice under various conditions of incomplete 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.
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asymmetry of information have been shown to be essential 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 still assumes that economic agents seek to maximize utility, but within limits posed by the incompleteness and uncertainty of the information available to them.
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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.
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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 warning labels or
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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 customers in supermarkets)
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The third thing that has been learner! about problem solving especially when the solver 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.
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EXPERT SYSTEMS IN ARTIFICIAL INTELLIGENCE Over the past 30 years, there has been close teamwork between research in psychology and research in computer science aimed at developing intelligent programs. Artificial intelligence (Al)
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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 transformed in the course of exploration. To the extent that a problem has these characteristics, it is usually called ill structured.
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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 taking care of the needy. Early management 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.
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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, polity, and society.
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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, gov ernment agencies and corporations have supported the development of planning models to aid them in their policy planning for example, corporate strategic planning for investments and markets and government planning of environmental and · · ~ · · energy pot uncles.
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program in decision and management sciences at NSF. Perhaps the largest scale of support has been provided by DARPA, where decision making and problem solving are only components within the larger area of artificial intelligence and certainly not highly visible research targets.
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The complementary fields of cognitive psychology and artificial intelligence have produced in the past 30 years a fairly weDdeveloped theory of problem solving that lencts itself wed 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.
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The new industrial revolution is showing 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.
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