Management Intelligence Systems

Management Intelligence Systems MANFRED KOCHEN School of Medicine (Mental Health Research Institute) and School of Business Administration (Computer & Information Systems) University of Michigan Ann Arbor. Michigan 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Background . . . . . . . . . . . . . . . . . . . . . . .

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1.2 Six Aspects/Kinds of Technology. . . . . . . . 1.3 Need for a MINTS . . . . . . . . . . . . . . 1.4 An Effectiveness Condition . . . . . . . . . . 1.5 Organization of the Chapter . . . . . . . . . On the Nature of Intelligence . . . . . . . . . . . 2.1 Intelligence as in Organizational Intelligence . . . . 2.2 Natural Intelligence . . . . . . . . . . . . . 2.3 Intelligence as Computation (AI) . . . . . . . . What is a MINTS: Requirements and Uses . . . . . . 3.1 Market Intelligence. . . . . . . . . . . . . . 3.2 Technology Intelligence . . . . . . . . . . . 3.3 Financial Intelligence . . . . . . . . . . . . . 3.4 Organizational Intelligence . . . . . . . . . . 3.5 Environmental Intelligence . . . . . . . . . . 3.6 Requirements for Intelligence in General . . . . . Analysis, Design and Maintenance of MlNTSs . . . . . 4.1 Architecture of a MINTS . . . . . . . . . . 4.2 MINTS Development Lifecycles . . . . . . . . 4.3 The Effectiveness Condition . . . . . . . . . . 4.4 A Model for Relating Natural and Artificial Intelligence Managerial Issues . . . . . . . . . . . . . . . . 5.1 Management OF a MINTS . . . . . . . . . . 5.2 Management WITH a MINTS . . . . . . . . . 5.3 Management IN a MINTS Environment . . . . . 5.4 Communication, Competition and Cooperation . . . 5.5 Emergent Properties and Systemic Intelligence . . . Conclusion . . . . . . . . . . . . . . . . . . Acknowledgement . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . .

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Introduction

A management intelligence system (MINTS) is intended by the organization it serves to scan the organization's environment so that management can better assess its position with a view to enhancing the value of the 'DeQased

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organization and its services. Simple versions of such systems have existed for a long time. The Bible states that Joshua sent from his position east of the Jordan two men to scan the environment on the West Bank and Jericho. Returning from this covert mission, they reported that “the Lord has delivered all the land into our hands, and, moreover, all the inhabitants of the country do melt away because of us” (Joshua 2:24). Contemporary intelligence systems rely far more on overt, public sources than on covert espionage missions. They must screen, evaluate, correlate, interpret, analyze and synthesize vastly more information. These activities require judgment, hypothesis-formation, reasoning, and a great deal of knowledge, understanding, and intelligence. They are performed by persons with the help of computers. But it takes them a long time. It often takes more talented people than can be mobilized and paid to produce high-quality intelligence. The timing and complexity of what an organization must do to identify what is in its interest is rapidly changing. If the capabilities of human intelligence analysts and managers in one organization can be amplified to produce higher quality intelligence much more quickly, and in the face of vastly more information, uncertainty and ambiguity, then competitive organizations will also seek to amplify their capabilities whenever possible. Therefore, the management intelligence systems required by competing organizations in government and business will be the best they can obtain. The best are likely to make good use of advanced technology, such as artificial intelligence. The purpose of such man-machine MINTSs is to support professional strategists, planners, and researchers as would a good semiautomated research assistant, enabling the strategists to produce better plans, more quickly and at lower cost. The requirements of such MINTSs can provide needed direction to research in artificial intelligence and its relation to natural intelligence because, as will be argued, both are necessary for a MINTS to be effective. They also challenge researchers to investigate some basic scientific questions about the nature of intelligence. The purpose of this chapter is to emphasize the importance of Management Intelligence Systems and to encourage studies involuing them. The remainder of this introduction provides some background for the claim that MINTS constructed with some artificial intelligence and used by managers with natural intelligence and competence above a certain threshold will meet important needs. It is followed by an elaboration of why formal, computerized management intelligent systems are needed and why they are feasible. The introduction concludes with a condition for the effectiveness of a MINTS and it presents the logical structure for the remainder of this chapter. The reader interested primarily in more technical or artificial intelligence aspects might wish to skip to Section 4 and Fig. 2.

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1.1 Background The question of whether computers can be programmed to exhibit intelligence and thought has been seriously considered since at least the seminal work of Turing (1950). The early realization that computers are general-purpose symbol processors, steered by a coded program of stored instructions that can be processed just like data, with the computer being able not only to carry out the instructions but to change them and to carry out these changes right away as well, was one of those deep insights central to the intellectual development of computer science. Examples of symbol processing include not only numerical computations but derivations using algebra and the calculus, logical deductions, syntactic analysis, synthesis of music, art and chemical compounds, and self-modifying programs. Presumably any process that can be represented symbolically could be programmed for execution by a computer. I t has long been an intellectual challenge to discover what could not, in principle, be so represented. First, non-computable numbers were found (Turing, 1937). These corresponded to undecidable propositions. Next, the bounds of algorithmic complexity were found (Solomonoff, 1960, 1978, 1988; Kolmogorov, 1965,1968; Chaitin, 1974,1975a, 1975b). Many of the ideas that seem so contemporary, such as indexing, high-level languages, evolutionary programming, genetic algorithms, etc., were conceived in the early days of computing (the 1950s). There may have been much more creative and fundamental activity in the development of algorithms and exciting concepts before the explosive growth of technology than after it. The absence of powerful technologies may have contributed to the intellectual ferment because imagination could be exercised with fewer constraints, because the most intellectually creative persons were attracted to the field (e.g., mathematicians, etc.), and because they could devote all their time to this effort rather than to mastering use of the technologies and exploiting them. Nonetheless, the revolutionary impact of the transistor and of integrated circuits took even the most farsighted and imaginative thinkers by surprise. Reality proved to be far more astounding than the products of the most fertile minds. The seemingly limitless capabilities of the rapidly developing technology stimulated new and different ideas. While the earliest advances were idea-driven and hardware-limited, this was soon reversed. I n the later 1950s and 1960s, advances were hardware-driven and demand-limited. Soon, however, advances became demand-driven and software-, data-, and algorithm-limited. One of the earliest demands for computer use came from the intelligence community and from decisionmakers who felt that computers should help them make more informed decisions. The latter spawned interest in “Management Information Systems” or MISS. Because they tended to be prescriptilie, building on methods and

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results of operations research and management sciences, MISS were not widely and effectively used, and emphasis shifted toward systems that support managers, with the term “Decision Support Systems” (DSS)replacing MIS, to be followed by numerous other acronyms such as SIS (Strategic Informations Systems; Wiseman, 1988).What we call a “Management Intelligence System,” or MINTS is an executive support system, i.e., a decision support system for high-level managers responsible for strategic decision-making and planning, supplying them with intelligence rather than information, and based on advanced information technologies, notably artificial intelligence, AI. In recent years, the business community has become aware of the need for formal MINTS, as has the military intelligence community before (Burke, 1984; Rothschild, 1984; Levite, 1987; Miller, 1987; Porter, 1980, 1985; Sammon, et al., 1987; Wagers, 1986; Ljungberg, 1983; Lancaster, 1978).This is in large part the result of increasing global competition, with newer firms entering the market that have rapidly commercialized technology and recently grown quite rapidly. Moreover, they have used technologies in strategic ways. For example, product development and delivery schedules have been shortened. Which firms can deliver a differentiated product first is often a horse race; the winner may lead by a few days. This makes the availability of intelligence more important. 1.2 Six AspectdKinds of Technology

It is useful in explaining management intelligence systems to distinguish six kinds, or aspects, of technology. The most familiar is what I call material technology. This is the hardware aspect of computer information systems. It involves all aspects of design, manufacturing and maintenance of components, of circuits, cards and boards. The performance of this aspect of computer technology (and, to a slightly lesser extent, of communications technologies) has been doubling per dollar per year, and this trend is likely to continue for at least a decade. The second kind of technology, sofcwure, includes programs of all kinds. These, too, have a material aspect in the sense that some are embodied in diskettes, for example. Documentation is part of such programs, whether they are operating systems, languages, application generators, applications, etc., and it appears in hard copy or online as programs. The third kind of technology is in the form of data and associated forms that make it possible to transform data into information and knowledge. The distinction between these three concepts is important. We regard knowledge to be represented by forms such as sentences, propositions, and formulas, in which there may be blanks to be filled in. For example, the sentence, “The melting point of ice under standard conditions is OOC,” represents knowledge.

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If “0°C” is deleted and replaced by a blank, the “sentence” is a form to be filled in; the string of symbols, “ O T , ” as given, illustrates data. If it is designated for insertion in the blank of the appropriate form, as illustrated here, it becomes ir$irmation, and the completed form is knowledge. The structuring of data, information and knowledge so that it can be easily updated, accessed by a variety of programs and users without introducing inconsistencies or unnecessary redundancy, and so that deductive and inductive inferences and be easily made, comprises this third aspect of technology. These are data/ knowledge-base management systems. The fourth aspect or kind of technology, model-base management systems, consists of models, algorithms, rules and techniques of inference, methods and concepts and ways of organizing and accessing them. It includes the architecture of hardware and software. This aspect of computers is what occupied the attention of pioneers prior to the hardware revolution. It is intellectual technology, though pioneers such as Turing and von Neumann not only developed algorithms but built machines for making them operational. Today, spreadsheet technologies permit anyone to construct simple operational models. The organization of all these models and algorithms, implemented as operational programs or not, so that the collection can be updated to maintain its integrity, accessed and fully used, comprises this fourth type of technology. The fifth kind of technology conists of operating procedures. Both it and the fourth kind are to some extent documented in publications. But procedures and operational principles exist largely in the memories and habits of practitioners. It is the stuff of expertise. It is what a knowledge engineer tries to capture when he interviews and observes experts while he builds an expert system. It includes such expertise as what hardware and what software to recommend, what data to get and where to obtain it, and what existing concepts, principles, models, methods, heuristics, and algorithms to use. It includes not only know-what (i.e., substantive knowledge) but know-how, know-who (whom to turn or refer to when local expertise or resources are inadequate), know-when, know-where, know-why (justification), and knowhow-much. We call this tacit knowledge know-X. The sixth, and probably the most important kind of technology consists of leadership, organizational and managerial structures. It has been called “heartware” by the Japanese, “orgware” by some Russians (i.e., Dobrov) and “peopleware” by some Americans. We shall call it the sociotechnological aspect. The problems it is to help solve are those of strategic planning, of formulating and prioritizing missions, objectives, and goals. It is to support execution of plans by helping in the selection of suitable persons, structuring their interrelations, and, above all, stimulating the awareness of values. It includes incentives and mechanisms for enforcement. In a sense, it differs in

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kind from the other five aspects of technology in that they support the sixth. Actually, each aspect supports the other five in varying degrees. Management takes place in the environment pervaded by these six aspects of technology. A few managers must manage with the help of these six kinds of technologies as tools. They must use them wisely. Even fewer managers really control these six technologies. This sixth kind of technology is used on itself. The very procedures and organizational decisions and designs are called into question and affected by the introduction of technology. That is the meaning of “high” in “high technology,” when technology advances to the level at which it modifies itself, calling for new management perspectives. Each of these six kinds or aspects of technology is a codified, communicable way of solving problems. They were present in a computer information system even during World War I1 before the first electronic computers. T o help solve the decision problem in mathematics and logic, the concept of computability and the abstract model of a universal Turing Machine (Aspect 4) was invented and used. Turing and his team at Bletchley during World War I1 needed hardware and software as well as data and ideas (Aspects 1-4) to solve problems of deciphering ENIGMA codes. Of course the procedural and organizational aspects ( 5 and 6 )were necessary for coping with such problems as secrecy, security and keeping up with German changes. This forerunner to the system developed at Manchester was really a first modern MINTS. All six kinds of technology should be integrated and brought to bear in a coherent way on the strategic planning of an organization. Information, knowledge, understanding, intelligence, and wisdom are now necessary for organizations (and individuals) to cope. We will focus here primarily on intelligence. One aim of this chapter is to show that these six kinds of technologies are emerging in ways that, if integrated and properly directed, could meet the growing needs for MINTS.

1.3 Need for a MINTS A military unit that is responsible for carrying out the will of its commander in the presence of an adversary clearly requires information about the intentions, capabilities and commitments of that adversary. It also requires information about environmental conditions, such as terrain, weather, etc., to which intentions and commitments cannot be attributed. Thus, the unit needs intelligence about the barriers to the tasks facing both it and its adversaries, about the technologies to overcome these barriers, about the resources, etc. It needs such intelligence in time for effective plans and actions to be taken; obviously, the intelligence must also be reliably accurate, appropriately precise, comprehensible and credible to those receiving it. A business organization also needs intelligence. A firm in a rapidly changing market, in an industry undergoing fast technological changes, in an increas-

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ingly global and competitive climate with dramatic changes in labor, capital, land and knowledge must plan in the face of increasing uncertainty as well as in the face of more exacting timing and expertise requirements (Haruurd Business Rruiew, 1987). It needs intelligence about: its markets, such as probable changes in customer preferences, spending patterns, etc.; the technologies for improving processes of production, distribution, servicing, as well as for new products and services; the availability of capital, property, labor; and the intentions, capabilities and commitments of adversaries. Other firms in more atypical situations may not need intelligence as much. Without such intelligence, a vulnerable firm is less likely to make a sound plan that will further its interests, or that will keep it from being moved into intolerable positions, such as bankruptcy, hostile takeover, etc. Some large firms have begun to recognize this need for corporate intelligence functions to support strategic management (Gilad and Gilad, 1986; Sammon, er ul., 1987; Fahey, et ul., 1981; Burke, 1984). But such functions are interpreted narrowly and assigned to a corporate (human) library staff that is among the first to be reduced if cost savings must go into effect. Yet the first serious proposal for a Business Intelligence System (Luhn, 1958) involved computerized content analysis of documents and led to the invention of “Selective Dissemination of Information Systems.” Long-range strategic planning in the 1950s was practiced to a greater extent by what were then developing or recuperating countries (e.g., Japan, Korea, and West Germany) than in the United States. The relative positions of U.S. and East Asian firms is to a large extent due to this discrepancy in the emphasis on intelligent, intelligence-based long-term planning decades ago. For example, the Japanese in the late 1940s realized the trend toward an information society that they, having no natural resources but only brains, had to adapt to this trend, that they would face a labor shortage within the three or four decades i t would take them to lead in these forthcoming knowledge industries so that they must automate and develop robots, and that to capture world markets by reversing the quality image of their products, they must develop a highly educated and motivated workforce. A few organizations in the United States are beginning to realize that competitiveness and survival can only be attained with a planning horizon of decades rather than years. That will require long-range strategic planning on the part of U S . firms as well, with appropriate intelligence capabilities to support it. Fortunately, a scholarly literature in this area has begun to emerge (Porter, 1980, 1985; Rothschild, 1984; Ansoff, 1979).

1.4

An Effectiveness Condition

Assuming that MINTSs will be needed because the preconditions for such needs and for their technological realization are emerging, it is important to know conditions under which a MINTS is effective. I argue in Section 4.3, that

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the users of a MINTS must have sufficient competence if it is to be effective. If these levels are too low, the system will amplify their errors and decrease effectiveness below what it was in the organization before a MINTS was used. If these levels are high enough the MINTS will amplify their users’ performance and greatly increase the organization’s effectiveness and efficiency. Effectiveness refers to the production of timely and high-quality intelligence that is likely to be used as well as to prove useful to a strategic planner. It is the value of the product. Efficiency refers to the value of the resources and effort that are expended in the production of output of a given value. Effectiveness should be as high as needed, and inefficiency as low as available resources can tolerate; if it is lower, unused resources can be reallocated to other productive activities. 1.5 Organization of the Chapter

The purpose of this chapter is to suggest the emergence of an important new field of inquiry in computer science, particularly in managing the impact of computer technologies on organizations. MINTS are already in demand and in operation, though without the required capacities. Methods of artificial intelligence, which are pervading all six aspects of technology, are likely to help meet these requirements. In addition to demand for MINTS by commercial and government organizations, availability of technology to meet the demand, the third factor necessary for making MINTS viable and important in practice (and as objects of scholarly inquiry) is the availability of managers responsible for strategic planning who use MINTS with sufficient com petence. To show the above, the following will be presented: (a) Clarification of the nature of intelligence in its three meanings for subsequent use in specifying what a MINTS is and does, and how well. (b) Description of a MINTS in terms of its requirements and uses, including examples. (c) Issues in the analysis, design and maintenance of MINTSs. (d) Issues in management and organization related to MINTSs. (e) Other issues, such as basic computer science aspects (why this is a major new development in computer science); ethics, competitiveness/ cooperation, new business perspectives, employment.

2.

On the Nature of Intelligence

Modern usage ascribes at least three meanings to “intelligence” of interest in

this study. These are: intelligence as in business, military or political

intelligence (Wilensky, 1967; Montgomery and Weinberg, 1979); intelligence

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as in natural intelligence exhibited by people and animals (Woon, 1980; Fancher. 1985; Fischler, 1987; Cattell, 1987); intelligence as in artificial intelligence (Harmon and King, 1985; Winston and Prendergast, 1984; Silverman, 1987). Each meaning admits of four ways of analyzing intelligence: (1) as a kind of behavioral process or performance, ( 2 )as a set of capabilities, competencies or functionalities, (3) as a product, and (4) as a property. We argue that these reduce to just (1) and (2). Intelligence as a process is often observed by sampling intelligence products at various times. Intelligence analysts will furnish their clients from time to time with intelligence reports, briefings, gaming simulations and other products of the intelligence process. Intelligence as a product is the intelligence report. This resembles the scholarly paper in that i t is creative, adds to knowledge and contributes something new. In both, the scholar must carefully document and check his sources. He much check the validity of his claims, perhaps using several methods of justification. His presentation must be lucid and well organized. He generally brings years of intensive study in one or more specialties and in general fields to his work. He must use judgment in deciding when to stop research and to write. He has a chance to produce a work that is good enough or one that is a source of pride, a work of artistic and/or scientific distinction. But the intelligence paper differs basically from a scholarly paper in that the former must be useful to the executive who must base his decisions on the content of the report. Usefulness means timeliness-not later than needed for the strategic time at which a decision must be made, nor sooner. Usefulness also means that it can be quickly understood and used by a busy executive. Timeliness also refers to the age of information. The value of tactical intelligence-the kind used in military combat or in a business negotiationdepreciates at about lo"(, per day, at least; strategic intelligence in an active dispute ( e g , wartime) depreciates at l0nO per month, at least; strategic intelligence in peacetime depreciates at 20?,, per year; intelligence about semipermanent features, such as roads, etc., depreciates at 10% per year (Platt, 1957). Intelligence as a functionality is ;I potential that may be only partially realized. The greater the repertoire of viable strategies to choose from, combined with strategies for organization of this repertoire and selecting from it, the higher the level of intelligence. The various functional capabilities can be regarded as attributes or properties. It sufices to consider only two rather than four ways of analyzing intelligence: as a performance and as competence, similar to Chomsky's distinction. These two ways are complementary. Competence is necessary for performance. But competence cannot be observed except by observing samples of competence-limited and competence-driven performance. But observing performance samples cannot establish that general-purpose abilities and principles are at work.

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As a behavioral process, all three meanings of intelligence have a common core: rapid and appropriate zooming (expansion or contraction) over levels of specialized knowledge and understanding. This is the primary thesis of this chapter, and the focus of this section. It will be justified by analyzing the three meanings. This explication fills an important need in the conceptual repertoire of computer science. It is also needed to show that business intelligence can be effectively and efficiently provided.

2.1

Intelligence as in Organizational Intelligence

An organization is seen to occupy a position in its environment, and that position is assigned a utility. People differ both in their perception of such positions and in how they value the positions. Scholars of corporate strategy attempt to objectify and render scientific their perceptions of a firm’s position, generally in the various markets in which the firm competes. That often takes the form of simplistic models in which a market is depicted as a linear space with two dimensions, such as market share and profitability (Fig. 1). To this might be added a family of indifference curves, the curve in Fig. 1 being one such indifference curve. Here firm A likes being in at any point on that curve as much as any other point on that curve. That is, firm A is indifferent between low market share and high profitability on the one hand and low profitability but high market share on the other hand. This is for a market dominated by two firms that share the total market between them. Of course the market could expand or change and new firms can enter; relative positions of existing players can change. But a firm also occupies a position in the use and development of technology, in various communities, or constituent groups such as its current

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and prospective employees, customers, suppliers, owners, regulators, and affected non-participants. It also occupies a position in financial markets, such as its standing on various stock exchanges, and this may or may not be related to its position in its markets or its value. It also occupies a position in the technological environment that may enable it to control the supply, demand, or shaping of technological advances, apart from its use of technology as a strategic weapon. 2.1 . l . Organizational Intelligence as a Process

Organizational intelligence is not solely the concern of the intelligence community. There is intelligence implicit in the internal mental maps of decision-makers and in their use of these maps to relate their assessment of the organization’s current positions to the positions they intend the organization to move into. Decision-makers base their decisions on imperfect internal maps and on local information. An intelligence decision-maker learns from the consequences of these actions by improving his internal map, which in turn enables him to obtain and assimilate better as well as more information and knowledge. In doing so, he uses a MINTS. The more intelligent organization will appear to the observer to be clear about positions its values, to learn quickly and to move deliberately toward these. The less intelligent organization will appear to respond to local conditions rather than plan, to be unclear about the positions it values, to move more randomly. This is, of course, a rational view, but it is useful in defining intelligence. According to a less rational but more organic view, an organization can not only muddle through but can attain great success without being clear about positions it values or moving deliberately toward them. It has an “intuitive” understanding of these, recognizing them when it encounters them. I t may be lucky and appear wise afterwards. But we would call this at best understanding rather than intelligent. High levels of intelligence imply understanding. Useful rationality transcends the purely organic. Yet a higher form of intelligence stresses pursuit of interests rather than positions (Fisher and Ury, 1981). This involves the search for win-win situations, in which a firm can cooperate with its competitors, so that all gain something, even if not as much as one firm might gain if it did not cooperate. “Who should we pick a fight with in the industry and with what sequence of moves’?’’(Porter, 1980) is no longer the most intelligent question to pose. 2.1.2

Organizational Intelligence as a Capability or Competence

What properties of an organization can an observer note that enable him to infer that it will behave intelligently? The more intelligent organization will

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have in key positions managers with richer internal models that reflect goals, interests, assumptions, capabilities, and response options, and who constantly improve and use these models. They are generalists who can specialize (or call upon specialists) appropriately as needed. They have the “profound knowledge” (E. Deming’s term) to improve quality, to innovate, and to manage technology. The analysis of intelligence as a competence resembles a selectionistic, Darwinian, approach rather than an instructional one (Edelman and Mountcastle, 1978). New patterns are not generated but discovered among a pre-existing repertoire of all useful possibilities and selected from them. Intelligent organizations, then, are those that survive competitive contests in an environment that requires accurate knowledge about current, and values future, interests and positions and about how to get there. Their managers are selected from a pre-existing population of competent ones, and these continually improve their models. According to this view, MINTSs serving surviving organizations are the products of a process of natural selection.

2.2 Natural Intelligence In Section 1.2 a distinction between data, information and knowledge was introduced. A fair test of a person’s knowledge is a set of questions that he should answer by producing or filling in the forms that we assumed to represent knowledge. This can be explicit or implicit when the respondent replies with “true” or “false” to given statements plus instructions to do so. Thus, multiple-choice, fill-in and essay examinations all test for knowledge.

2.2.1 Distinctions between Information, Knowledge and Understanding

Information adds value to a datum in that it specifies in which blanks and forms the datum is to be inserted. Thus, tabulated data is information if the tables are properly annotated. Knowledge adds value to information in that it provides a complete and integrated form. Thus, the datum, “0°C” becomes information when inserted into the blank in the form “The melting point of ice under standard conditions is -,” and the completed form becomes knowledge. This definition information is consistent with the one offered by Yovits (Yovits and Ernst, 1969; Yovits et al., 1977; Yovits and Foulk, 1985; Yovits rf ul., 1987). A person (or animal or machine) with mastery of the content of a gazetteer, part of a telephone book or all of human skeletal anatomy has a vast amount of knowledge. But he may have little understanding. That is, he may be unable

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to readily comprehend or integrate a new item of knowledge into his knowledge repertoire other than adjoining it to a list or just concatenating it. To comprehend a new knowledge item is to discover or construct a blank in the structured assembly of knowledge attained so far, into which the new item fits. Awareness of such blanks or gaps, for which new knowledge items are sought, manifests itself in question-asking. Thus, a person’s understanding of a domain could be assessed by the questions he asks. A unit of understanding could be modeled as a structured set of knowledge units, somewhat analogously to a knowledge form, by u(x), where x denotes the location of a possible gap in the structure. A person (or machine) can broadly comprehend a domain of knowledge and he can have deep understanding of several specialized subdomains, without being able to switch from the perspective of a generalist to that of an appropriate specialist. We propose to conceptualize natural intelligence as that ability. It does not mean that an intelligent person must command one or more specialties. But he must recognize when such specialized knowledge is needed and how to obtain it and bring it together with other items from other specialties. If u denotes a domain that is understood broadly from a generalist perspective (e.g.. the principle of relativity, that the laws of physics should not change with a transformation of coordinates), and u 1 denotes a specialized subdomain (e.g., the tensor calculus), then intelligence depends on recognizing relations R(u, u , ; s), R ’ ( u , , u ; s) between u, u 1 and a state of the world s in which it is appropriate to switch from u to u , and from u t to u, respectively. Actually, intelligence would need to be specified by a set of such relations for each possible pair or n-tuples of domains, which are either immediately accessible or rapidly computed. Such a set could comprise a unit of intelligence. Intelligence as intellectual zooming (Kochen, 1972) is related to, but goes far deeper than, what has been called “navigation of a conceptual hierarchy” in the use of Menus, such as in Smalltalk, the Macintosh or LISP machines. The latter requires intelligence of the user. In general, intelligence requires knowledge as well as understanding. 2.2.2

Natural Intelligence as a ProcesslPerformance

The behavior of an organism indicative of such switching would enable an observer to call the organism intelligent. Polya (1954) has shown how finding analogies, generalizing and specializing serve as powerful principles for making mathematical discoveries, and these have been further explicated in a pilot program (Kochen and Resnick, 1987) for making discoveries in plane geometry, such as the Pythagorean theorem. There are no simple discovery algorithms despite programs claimed to simulate processes of scientific discovery using heuristics (Grabiner, 1986). Formulating and solving

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problems-not only mathematical ones- that were previously unencountered, inductive and deducting reasoning, comprehending new patterns and new linguistic expressions and improving in the performance of all these tasks are indicators of intelligence. They all require the above-mentioned switching. But there is more to intelligence than problem-solving. For example, given the problem of adding 1 + 2 + 3 + ... + 100, intelligent behavior consists of trying to add different pairs, such as 1 100,2 99, etc., in the hope of finding a pattern. Less intelligent behavior is to add 1 2 to get 3, then 3 + 3, then 6 + 4, etc. More intelligent behavior, motivated by the idea that there might be a less tedious way to find this sum, is to try adding the numbers in a different order, e.g., the first and last. After noting that both 1 + 100 and 2 + 99 sum to 101, comes an inductive leap that all 50 such pairs add to 101, so that the sum is 50 x 101. Moreover, this method of pairing (or structuring the procedure) might be transferred to an entire domain of analogous problems when any of these arise. Another problem that can discriminate between more or less intelligent behavior is that of determining the number of pairs of winning tennis players that must play in matches (assuming no ties) until a final winner from a pool of 1024 contestants emerges. A less intelligent way is to divide the pool into half, with 512 matches played in the first round; then divide the pool of 512 winners in half, with 128 matches played in the second round, etc. Then add up all the matches: 512 128 64 ... = 1023. A more intelligent way is to notice that every player except the final winner must lose in exactly one match, where he is eliminated, since only winners play each other. Thus, the number of matches is equal to the number of players less 1, the final winner, or 1023. To determine whether an organism is intelligent according to this view, then, is to observe how it solves problems, how it reasons, how it learns. The observer will look for a cognitive strategy, for how the organism uses its cognitive processes to plan, to encode stimuli, to switch between the general and the specific, and to analogize. (Sternberg, 1986, 1988; Sternberg and Wagner, 1986). The criteria are the use of level-switching strategies just described and illustrated with the tennis tournament problem, even if the problem is solved faster in a few special cases without such strategies.

+

+

+ +

2.3

2.3.1

+ +

Intelligence as Computation (Al)

Al as a Process

How can an observer of two artifacts discriminate between one that behaves more intelligently than the other? This is a variant of the Turing imitation game, in which an observer is required to distinguish between a machine and a

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person who tries his best to confuse the observer. Assume that both artifacts have the same hardware, the same knowledge bases, and are subjected to the same operating procedures (e.g., same instructions) under identical organizational conditions. Only some of the architectures, the computational algorithms, and their implementations as programs differ. To be specific, suppose both have programs for playing chess and checkers. But one has a program that knows about board games in general and can decide when it should play chess or checkers on the basis of its opponent’s behavior. The other does not. The first automation is more intelligent, in our view, though this is a very simple task. It becomes more interesting if the more intelligent machine must learn the rules of a variety of games by playing them and inferring them from the opponent’s behavior as well as from consequences of its own rule violations, and discovering that there are such games as chess and checkers and forming its own programs for playing the games, possibly transferring ideas from one board game to the other. Again, the observer looks for appropriate switching between generalist and specialist perspectives. The organization theory literature distinguishes (Scott, 1985; El Sawy, 1985) three ways to identify effectiveness that is applicable to identifying intelligence. The first way is via structure: the capacity to perform well and the size of the knowledge base. Size may not, however, be important. The program CHAOS, which had attained the world championship in computer chess was also the smallest; its rules were carefully chosen. It embodies intelligence transferred from its designers. But i t is not intelligent. The second way is via process (e.g., ability to retrieve knowledge). The third way is via outcome (e.g., are actions intelligent).

2.3.2

A1 as Functionality

or Competence

Here the observer does not evaluate the machine on the basis of its inputs and outputs but by analyzing the documentation that describes its functionality. He asks whether a machine can, not whether it does, switch from a generalist perspective to appropriately specialized ones, and vice versa, in situations requiring that. Hardware components organized into massively parallel architectures, such as neural nets, and supporting software with non-deterministic algorithms may give rise to different functions and to newly emerging properties. Selforganization and evolutionary programming (Kochen, 1988) may give the machine new, unpredictable functionalities exhibiting greater adaptivity and intelligence. Our primary concern here is with AI in strategic management (Holloway, 1983) and particularly in the MlNTSs that support strategic planning.

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3. What is a MINTS: Requirements and Uses In this section, a MINTS will be characterized in terms of its functional requirements from a strategy planner’s point of view. Competent use of a MINTS is intended to amplify his performance. In what follows, a business firm is used as an example of an organization because it has much in common with other organizations requiring intelligence, because of the experience the author gained in teaching a course on this topic in a business school, and because of the growing importance of MINTS for business firms. This chapter is based on a new course, with the same name as this paper’s title, offered by the School of Business Administration at the University of Michigan in the Winter of 1987. I t was well received by 45 students, mostly enrolled for the MBA, but also several Ph.D. students in Industrial Engineering, Information Science and other fields. All students, working in teams, produced actual intelligence reports. Some were used by local firms and enterpreneurs in several cases. Each was supplemented by an analysis of how the use of A1 technologies does or would improve the process of producing such reports. Several teams produced operational expert systems. One of these, for example, generated, in good English and online, an analysis and recommendations for a firm’s strategic positioning, given financial data in LOTUS 1-2-3 and based on ratios such as fixed assets to net worth, return on equity, etc. It was demonstrated as a commercial products at the Avignon 1987 Exposition under the name of LEADER. It resembled a prior system using fuzzy sets theory, FAULT (Whalen, et al., 1982). The first general requirement for a MINTS is to help the firm it serves to clarify its map or image of the firm’s environment, to clarify the concept of “position” and interest in that environment, and to discriminate between positions and interests it values highly and ones it values negatively. The MINTS is to help managers with strategic planning and professional strategists by offering research assistance that increases their productivity and performance. As Porter indicated, competitive intelligence begins with the activities of collectors, such as the firm’s sales force, its engineering staff, its suppliers, advertising agencies, security analysts, etc., and scanning of public sources, such as articles, speeches by competitors’ management and several special publications such as The Data Informer by Information U S A , the Corporate 1000 by the Washington Monitor, the Infiwmation Sourcehook f o r Marketers and Strategic Planners by Chilton Book Company, the Handbook ($Strategic Planning by Wiley, the Informution Weapon by W. Synnott, How to Check Out Your Competition by J. W. Kelly, How to Find Out About Companies by Washington Researchers, the Thomas Register of American Manufacturers, etc. Fuld (1985) introduced the “intelligence triangle,” in which the base consists of the technique and the foundation. The middle part comprises the

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basic sources, and at the apex are creative sources. Basic sources include investment manuals, industry directories, government documents, newspapers in the competitors' locations, current industrial reports, financial reports, patent or court records, SEC filings, credit services, state corporate filings, and state industry directories. Creative sources include classified ads, environmental impact statements, trade shows, yellow pages and city directories, visual sightings, such as the number of cars in a competitor's parking lot or expansion of the parking lot, and interviews with people who meet the com petit or. Data from such sources must be compiled, as by clipping services or regular situation reports on competitors. It must be organized, by maintaining files on competitors, for example. Jt must be digested and summarized. It must be communicated to strategists, by means of competitor newsletters or briefings on competitors during planning sessions, for example. But there is more to intelligence than competitor intelligence, as set forth in what follows. 3.1

Market Intelligence

Drucker ( 1 985) proposed the following very simple, yet profound and farreaching assertion, that the purpose of a firm is to create customers. This means creating goods and services of value to certain consumers. It means marketing and innovation. It means enhancing the value of the firm, its suppliers, and the community of which i t is part. To market is to target potential customers, discover their needs, arouse these needs and meet them better than they are being met and at prices they are willing to pay. The added value of a marketing mix lies in a bundle of satisfactions. A MINTS is required to support the following marketing functions.

3.1.1

Market Analysis

Should the firm push a new product or service that its champion feels will certainly meet a latent need? Or should it analyze expressed or established needs for which markets already exist'? The MINTS is required to furnish intelligence on which to base this decision. How large is the market in which a firm is already operating? How rapidly is it growing? What determines its size and growth? What are the market shares of competitors and those offering substitute products and services? How have they been growing, and what factors govern their growth? Answering these questions and those to follow is the primary responsibility of market researchers, not of the MINTS. The MINTS is required to support these researchers, to act as an intelligent research assistant. If market researchers find it difficult to obtain reliable data, the MINTS should suggest

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additional sources, both for verification and for enlarging the supply of data as described in Section 4. They might refer the researchers to external services, such as Washington Researchers (1986, 1987a, 1987b) or to online databases such as that of the Conference Board, a computerized collection of over 800 economic time series. If no source can supply data that reliably answers some questions, the MINTS is required to find reliable data from which answers to the question can be inferred. (This is similar to “backward chaining” in AI.) This applies particularly to information about the market strengths and weaknesses, intentions and commitments of competitors in markets of vital importance. Intelligence is particularly important about competitors in global markets. (Jaffe, 1975; Montgomery and Weinberg, 1979) To repeat, the purpose of a MINTS is to support the professional planners as would an automated research assistant, permitting them to be more productive and at lower costs. 3.1.2

Consumer Analysis

Who uses the firm’s products and services, and how? Who makes the purchasing decisions? What are the customers’ revealed preferences? For the sales force, such questions are usually answered by networking. A good salesman has acquaintances whose acquaintances may be good prospects, and he actively seeks out these acquaintance chains and uses them (Kochen, 1989). The MINTS is required to support this networking process by inferring likely prospects from a knowledge base about a large population by storing a starter’s set of acquaintances, and the sets of acquaintances of all these onceremoved acquaintances, etc., and providing for easy access to all these named persons. Advertisers use the answers to such questions for determining what audiences to try to reach, by what media, with what messages and how to present them. The MINTS is required to help them by synthesizing answers to the questions and by directing them to models likely to help them. Most important, it is required to report changing patterns in demand, in fashion and in taste, with long lead times. 3.1.3

Trade Analysis

What wholesalers and retailers are in place between the firm and the customers? Which ones have and which are likely to leave and which ones to enter the value network? More broadly, what are all the participants in the network who add (or subtract) value in transforming factors of production and materials or goods at various stages of finishing into finished products and services? This includes warehouses, suppliers, transporters, and, of course, government at several levels. How is the structure of the network changing? It

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includes shifting patterns of flows and interactions as well as changes in the network nodes. Here the MINTS may be required to analyze a cluster of firms as an interacting system. 3.1.4 Economic Analysis This includes requirements to estimate the fixed and variable costs, breakeven points of various marketing programs as well as their expected utilities. Some of the variables characterizing a market mix are: the number of different product lines that satisfy the same need-type; the number of different producttypes in each line (differing in color, size, or shape, for example); and the degree of similarity between lines in end use, in production technology, and in distribution channels. The requirement for the MINTS is to bring together knowledge from economics in general with details from highly specialized subfields specific to the product and its technology. All this knowledge is required to be of high quality and transformed into relevant, usable form if i t is not in that form when retrieved. 3.1.5 Market Repositioning

Two kinds of actions are generally considered: adding features to products and services for increased differentiation; and reducing costs and changing prices. (Beatty and Ives, 1986). Here the MINTS is required to provide expected consequences of such changes and of the likely moves of competitors. The MINTS may also be required to generate selected rumors or leaks (e.g., to the press. to financial analysts) in advance of or in place of changes. In general, the MINTS is required to perform two activities at the same time. (a) To monitor selected indicators and alert management if their values fall outside a specified “normal” region. This resembles exception reporting. (b) To search its accumulated knowledge base for emerging and noteworthy patterns that have been seen before very rarely or not at all. Activity (a) may be conducted periodically, continually, at pre-specified times or on an ad hoc basis. Only reasonably current data are acquired, and from several sources, to increase reliability by cross-checking. Leading, concurrent, and lagging indicators are preferred, in that order. Thc MINTS is required to evaluate the costs and utilities of these indicators. Activity (b) may also be conducted at various times, or as a constant background activity when the facilities are available. What makes a pattern

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noteworthy is its ability to trigger understanding: to uncover a blank in a knowledge structure, such as a contradiction, or a gap, and to generate new hypotheses. 3.2

Technology Intelligence

A firm’s position depends in part on its production function. This, in turn, depends on the technology used. Creating customers means marketing and innovation, and innovation means the processes of production, from product development to design to manufacturing to delivery to servicing, and continuing improvements in those processes. A firm can improve its position not only by improving its position in its existing markets and by entering new but existing markets, but also by creating new markets and by changing its production or distribution processes. Innovation can lead to new markets and to changes in process (Diebold, 1984; Tushman and Moore, 1982). A technology is often used by several firms. It may characterize an entire industry. Consider, as an example, the information industry. Two dimensions have been proposed (Harvard University, 1980) to characterize simple technological properties of various goods and services in that industry. One dimension varies from pure form and no substance, such as blank paper or a courier service, to predominantly substance and little form, such as books or professional services. The other dimension ranges from what is primarily a product, such as a file cabinet or a film, to what is primarily a service, such as the U.S. mail or a financial service. A computer is about equally a product and a service and also about equally form and substance. A computer manufacturer may regard itself in the middle of this map of the industry, and may seek to shift toward the service end in the face of competitors entering at the product end. Or it may try to innovate in the product-substance region, in which there is a scarcity of goods and services. 3.2.1

Technology Analysis

A n important role of technology is in the production and distribution process. Here, three of the general technological properties of main contemporary interest are degree of flexibility, labor-amplifying potentials and quality. By flexibility we mean the ease with which a productive unit can be switched from the performance of one function to another (Kochen and Deutsch, 1973). In the context of a flexible manufacturing cell or a CNC machine this could be measured by the setup speed, the inverse of the time it takes to change the settings of a lathe, for example. By labor-amplifying potential, we mean the ratio of person-hours it takes to do a given task with the technology to the number of person-hours it takes to d o the same task

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without it. Quality has perceptual and objective aspects. Customers ultimately choose according to perceived quality. But their perceptions are influenced by ratings, such as those offered in Consumer Reporrs, reports from other customers, advertisements, etc. A firm may position itself according to where on these three dimensions it fits. A n ideal position may be one in which its productive system turns out high-quality products and services, automatically with the least human labor and with the flexibility of a job-shop that can accommodate individual customers’ requirements very quickly and reliably. A technology found increasingly important for high-quality competitive production is sociotechnology. New or better ways to organize and motive people have been found to be more critical than the use of Flexible Manufacturing Systems (FMS), for example. The requirements for the MINTS are to anticipate technological advances likely to affect these and other variables. This includes the assessment of existing technologies for applicability to the firm. It means sifting through masses of articles and reports for a few that could indicate promising technological advances. Whether these are likely to be commercialized depends on many other factors, and reliable documents pertaining to all these must be brought together for a valid assessment to be made. Predicting the success of attempts at commercialization is even harder. It is less predictable and controllable than breakthroughs in science. In any case, the MINTS is required to screen, evaluate, interpret and synthesize all these items into a coherent report. In the near future this will be done exclusively by human intelligence scholars, but these may count on increasing degrees of support from technology. 3.2.2

Technology Management

The use of advanced technology in production systems does not guarantee faster and more reliable delivery, better quality, lower inventories, higher throughput, greater flexibility and lower cost. The purpose of FMSs is to combine the ability of a job shop to custom-tailor products and services to clients and the ability of an assembly line to mass produce standard commodities. The former takes advantage of economies of scope, stressing the production of a variety of goods and services, with the unit cost of producing the last variant decreasing with variety. The latter takes advantage of economies of scale, stressing large batch runs, with the unit cost of producing the last item in a batch run decreasing with batch size. If manufacturers use FMSs primarily to exploit economies of scale while their competitors use them to exploit economies of scope (Jaikumar, 1986) they are mismanaging the technology and may suffer losses in their relative position and interests. The MINTS of a firm is required to assess the quality of technology

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management, both by competitors and by their own firm. They should also forecast the likelihood of various programs in these firms, including management recruiting strategies, for improving that quality. 3.2.3

Intelligent Production

A most important requirement for a MINTS is that it provide direct support for the “smartness” of the workforce, both management and labor. The task is to advise management about intelligent artifacts, used by sufficiently intelligent operators, for scheduling production, for quality process/quality control, for inventory control, for design/layout, for diagnosis of failures, etc. A variety of expert systems exist and many more are being developed to support these functions. The MINTS should incorporate these into its model base and information about them into its knowledge/database.

3.2.4

Intelligence in Products

Increasingly, products such as cars and appliances have built-in computer/ communication systems that are transparent to their users. Also increasingly, these systems are more intelligent. Cars are likely to be guided by intelligent chips distributed on highways that communicate with onboard computers to let the system know where the car is and the options available to the driver. Intelligence in an appliance captures the user’s intention by eliciting from him instructions in simple, general terms, and it carries them out without requiring him to provide detailed commands or dial settings. The requirement for a MINTS is to make available to the firm state-of-the-art technology options for the introduction of intelligence into products as well as into services, to be discussed next. 3.2.5

Intelligence in Services

Since industry discovered A1 in the 1970s, its impact has been mainly in the services. Across the world the labor force is shifting into services. The first industrial revolution shifted production from material-intensive activities, in which human and animal labor was the main source of energy and intelligence, to energy-intensive activities, in which human labor was the primarily source of intelligence. Machines augmented energy-expending labor. The current industrial revolution is shifting production from energyintensive activities to intelligence-intensive activities. Information machines are augmenting intelligent labor, and displacing some. Experimental expert systems for diagnosis in medicine, for exploration in geology, in research and in engineering are showing considerable potential for increasing productivity and quality of services in these areas.

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Technology Assessment and Anticipation have become major fields of study, as has what is called Technology Transfer. The first is performed by the Office of Technology Assessment of the U S . Congress and by those it supports through research grants. It, like the National Academy of Sciences, Engineering and Medicine and other agencies provide technology intelligence to the U S . Congress. The Executive Branch of the U.S. Federal government has its own MINTS, as do state and some local governments. Computer networks have been used to exchange certain kinds of information. For example, the experience of one township in the use of materials other than salt to control icy road conditions may be shared with other townships with similar problems and conditions. Of course, every major firm does its own technology assessment as well. Technology forecasting, which might better be called anticipation, has a large literature. If it were the case that technology advances are usually preceded by scientific discoveries, then there should be a major science intelligence effort. Scientific breakthroughs are quite unpredictable, but no more so than technological breakthroughs. Yet the publication and emphasis of major discoveries-for example, how the body's own proteins may be the source of the most effective drugs ever, or how metals could be replaced with wood or superplastics (Science 85, 1985)-could stimulate innovation. Imaginative and well-presented projections (e.g., Ishikawa, 1986)-even good science fiction-can help shape the future and serve to enhance self-fulfillment of the vision. 3.3

Financial Intelligence

Creditors need intelligence about potential debtors for use in accurately assessing risk. Individuals in search of mismanaged, undervalued firms that they might take over need intelligence about potential prey. And weak firms on the lookout for potential predators that could threaten hostile take-overs need intelligence. Forecasting and analyzing mergers and acquisitions is a prime function of industrial analysts who provide their reports and services for a fee. Such analysts are of course MINTS. (For examples, see Section 3.6.) It is the requirement of a firm's MINTS to stay abreast of such services and their output, to synthesize tthem into a report tailored for their client firm. Intelligence embodies in services and products of (and to) the financial sector, such as "smart cards" belong in Sections 3.2.5, and 3.2.4. 3.4

Organizational Intelligence

This includes intelligence about people, their qualifications, intentions, capabilities, commitments, associations, moves, and relations to one another and to institutions. In certain industries, a firm's critical success factor is to

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attract and keep key technical personnel. Intelligence about the likelihood of such persons leaving their firms, even about who they are, is of great value. Information about who reports to whom, who is on a fast track and who is not, is of value not only to salesmen in choosing whom to contact but for assessing the strengths of a competitor. A great deal about a company’s intentions, capabilities and commitments can be inferred from its organizational structure. It is required of a MINTS not only to provide complete, accurate and timely characterizations and assessments of its own and its competitors’ organization, but to determine what can be inferred from such knowledge (Dutta and King, 1983; Levite, 1987; Ljungberg, 1983). 3.5

Environmental Intelligence

In its broad sense, a firm’s environment includes the market, technology, the financial world, and organizational aspects. It even includes the firm’s internal environment. It also includes environments in their narrower meanings: the natural environment, such as the terrain where plants are located, climate, amenities, etc.: the political environment, including regulations affecting the firm from various levels of government, the stability of governments, public policies and program affecting the firm; the economic climate in which the firm operates; the social environment; and the ecological environment. The MINTS of a firm is required to provide intelligence about each of these aspects.

3.6

Requirements for Intelligence in General

It is important to stress that intelligence differs from information, knowledge and understanding in that it brings together into a coherent, interpreted whole carefully screened and evaluated elements of what is known and understood in several specialized domains, and in that it brings it to bear on decision-making, policy-making and planning. The intelligence may be of strategic or of tactical importance, but more intelligence is required for effective higher-level, long-range strategic planning. Intelligence is generally understood to be needed in situations of competition and conflict. There, it is vital for each firm to have an accurate assessment of its adversaries’ (a) intentions, especially as they affect the firm; (b)capabilities for carrying out these or other intentions; (b) commitments to a course of action; (d) progress (successes, failures, ability to adapt and learn from experience) in pursuing the chosen course of action. Such assessments are at least as important in non-conflict situations as well. A firm should at all times be looking out first for opportunities, including the opportunities for cooperating with other firms, for discovering win-win situations. The MINTS

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is required to help in this task. Secondarily, a firm should be vigilant in detecting threats or traps, and in this, too, it depends on its MINTS. Capability refers to the quantity and quality of resources of all kinds, their state of readiness and availability, morale and the ability of management to mobilize resources rapidly and sometimes secretly so that they can be brought to bear at a time and place chosen by the firm with the desired effect (e.g., surprise, victory in the case of a contest, etc.) To assess relative capability, of both adversaries and its own firm, a MINTS must combine estimates about the balance of strengths and weaknesses on all relevant factors into a composite judgment of the probabilities and risks of various actions and their consequences. An example of contemporary intelligence of a general kind is the kind of analysis of trends and issues in the computer industry that securities analysts and similar organizations (e.g., Gartner, Bernstein Research, and the Conference Board) frequently produce. It is argued by Marc G. Shulman of Salomon Brothers, for example, that IBM’s introduction of its PC in August 1981-which they thought would result in 250,000 units sold by the end of 1986, but resulted in sales of three million-eventually strengthened the competitive position of DEC at the expense of IBM because it led to the proliferation and legitimization of end-user computing rather than computing by data-processing professionals. The lack of integrative products and services to meet the demand for peer-to-peer networks generated by end-user computing hurt IBM more than it did DEC. DEC had positioned itself in distributed processing, which became end-user computing. DEC can now price its products on the basis of their value rather than on the basis of their cost. Its position depends on the company’s ability to gain widespread acceptance of VAX networks plus the ability to resist the spread of UNIX. The analysis then goes on to examine IBM’s two-pronged strategy (OS2 in relation to Systems Application Architecture (SAA) and Personal System 2 in a key role in SAA) and what DEC needs to do by mid 1988 to counter it: volume shipments of VAX8800-based processors; VAX-Compatible LOTUS 1-2-3 packages; application software not available on IBM PCs; commoditization at physical and logical levels, with DECnet supported by workstation vendors (e.g., SUN microsystems), and supercomputer vendors. DEC must offer more differentiated products to replace what it will lose when its competitors attach their products to DEC networks, which, analysts claim, means that DEC must be the leader in the software of the 1990s-artificial intelligence and expert systems. Other analysts (e.g., Bernstein Research) envision a three-way industry-wide contest. This is an alternative to Shulman’s analysis. One party is a revitalized, market-driven IBM using SAA. Another is DEC, joined by Apple and Cray, using DEC net-based services. The third group includes AT&T, SUN Micro, Xerox, NAS, Stratus, Amdahl with UNIX as a standard and RISC (reduced instruction set based on most frequently used instructions) microprocessors.

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This group is attacking DEC in its bid to be the technology leader, while DEC‘s key target is IBM. There are, of course, other perspectives that take account of significant players, such as the Japanese, the Europeans, Hewlett-Packard, Wang, CPQ, Tandem, Unisys and NCR. Some of these may shift toward IBM as the safe alternative. Some may shift toward the third group epitomized by SUN Micro and the open systems model. There is already an organization in Europe called X/Open, comprising 15 companies that hope to work together to position themselves to provide functionality that is distinctly different from IBM. But they seem to have more diverse intentions in their market strategy than in their political rhetoric. Some may form fourth or fifth new foci to compete with the other three. The Europeans, if they can be grouped, seem to be in the SUN Micro camp already. These are but illustrations of what analysts tell investors. It is not clear that investors, vendors, customers or other decision makers (e.g., in government or independents) will be persuaded by these reports, and if they are, whether their decisions based on such intelligence will be sound. In other words, either these intelligence analyses are not sufficiently sound from the point of view of scientific objectivity and scholarship, or they offer the best that can be done and this is not enough to characterize the risk, ambiguity, and uncertainty in an acceptable way. Because the issues are very complex, enveloped by thick clouds of confusion, and may defy clarification in terms of a few simple variables such as the demand for networking, customer advantage, delivered costs, enhancement of variety, vendor dependence, cost of integration, demand for compatibility, etc., it may not be possible to reduce or at least clarify risks to levels that all users consider acceptable. Are such analysts MINTS? Shulman serves as an intelligence officer-he is part of the MINTS-for Salomon Brothers, and to the extent that the firm offers his products to others, he is a MINTS serving a larger clientele. A firm such as DEC has its own MINTS to support its top management, and it will use the output of other MINTS, such as the above, in its own analyses. A final point that needs emphasis is that a MINTS should not be required to (a) leave no stone untuned; (b) never make a mistake; or (c) never miss an “unanticipated event,” even a fatal, “bolt out of the blue.” Even if it were theoretically possible to do (b) and (c), it would be far too costly, and the surveillance activity may cause more damage, by possible invasions of privacy, than it prevents. A MINTS should be designed for use as a tool to beat statistical odds. Intelligence failures that are statistically very rare or improbable and hence very, very hard to anticipate are more excusable than failures that could quite readily have been anticipated. The latter failures are by far the most common and the most damaging (e.g., the surprise attack on Pearl Harbor or the 1941 invasion of the USSR), and it is toward preventing them that top priority in the design of MINTS should go. (For more examples, see Section 4.3.)

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Analysis, Design and Maintenance of MlNTSs

Developing a MINTS employs all the procedures used in developing any computerized information system, such as requirements analysis, rapid prototyping, feasibility studies and implementation. Because of the opportunity to introduce A1 into MINTS and into the MINTS development process, there arc variants in the procedures as well as new procedures. After describing what a MINTS with A1 features is like, this section will indicate these changes in the conventional methods of systems analysis and design as well as the new procedures. 4.1

Architecture of a MINTS

Figure 2 sketches how a MINTS might look to a system developer and to a user, Raw data streams into the system in response to environmental scanning, as shown at the top of the figure. Two basic types of analysis are shown in box 1. One is monitoring indicators. The other is searching for novel patterns. Only the first is applied to the incoming data stream. The second requires comparing and correlating incoming data with what has been accumulated, which is no longer raw data. It is neither useful nor feasible to keep all incoming data. Hence, it must be screened on the basis of estimated reliability, utility, precision, clarity and novelty. The screening function can be partially automated with the help of an expert system if criteria for data evaluation can be specified and the judgments of experts can be expressed in the form of programmable algorithms. Currently the functions in box 1 are generally performed by persons, whose capabilities for dealing with vast and diverse data and knowledge streams are limited. Monitoring well-defined indicators is easily automated, but the search for novel or rarely encountered patterns is a challenge. It is one that resembles the search for patterns of tracks in a bubble chamber photograph corresponding to experimentally induced nuclear events that have not been observed before. The idea is to screen out patterns that fail to correspond to any known patterns or that are unusual variations of known patterns. This physics problems is far easier because all the patterns to be scanned already exist on the photo. Here, the population consists of all possible patterns that can be formed by several data time series that might be correlated. Only sampling in an a priori restricted universe of possible patterns makes this possible, and at the risk of missing interesting patterns. The trained human mind and eye may be very good at noticing the unusual or unfamiliar, and the computer, in symbiotic partnership with a person, could display data in various ways- using methods of Exploratory Data Analysis (Mosteller and Tukey, 1977; Tukey, 1977)-to enhance this ability. Moreover, much of the data in intelligence analysis is qualitative, in the form of unformatted text and graphics, and this

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I

Seek and evaluate new sources (use an expert system)

. ,-'.

__*

representation (essence or intelligence)

/ "/on\ V

Flow of control FIG. 2.

Architecture of a MINTS

1

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would have to be translated into a canonical language, perhaps one resembling the form of influence diagrams (Howard and Matheson, 1984; Bodily, 1985). The most important function in Fig. 2 is in box 2. Here the intelligence analysis process really begins. I t starts in response to an alert or to a stimulus that motivates hypothesis or idea formation. This motivation may stem from external data. But it may also stem from the reflections or meditations of a human analyst. The knowledge archives are partly in his own long-term memory; they extend it. I t may be an insight on his part that actually triggers the alert. For example, suppose that data about the growth of a Japanese automobile component supplier indicates that growth is very high (knowledge), but it is not in the critical region of some indicator that would cause an alert. Suppose that there is further knowledge that this supplier has an outlet in the United States in a state where cars are produced. That, in itself, is also not remarkable. Suppose, further, that the supplier uses advanced technology very effectively, resultingin deskilling of most jobs, while high-skilled jobs such as design are in Japan. That, too, does not by itself justify an alert to a U S . firm. If all three statements are studied together, they may give rise to the suspicion (idea, hypothesis) that the Japanese firm intends to create high-skilled jobs in Japan with a consequence, perhaps unintended, of further deskilling jobs in the United States. At this point, knowledge is transformed into understanding (box 3). Analysts realize that they need knowledge not available to them. They ask questions. Question-asking programs (e.g., SHRDLU) in well-defined domains of discourse (e.g., stacked blocks) have been constructed. I t may not be possible to construct a domain-independent algorithm that asks good questions. A physicist may have a general understanding of a large domain, such as science, and ask simple questions about it. He is likely to have a deeper understanding of a more specializing domain, such as physics, about which he can ask more profound questions. I n the same way, in box 2, some domain restriction may occur, and a specialized question-asking program is selected (box 4). The questions i t generates define the strategy of the investigation to be conducted by the intelligence analyst. The next phase, question-answering (box 5), draws upon information retrieval ( I R ) and artificial intelligence. The state of the. I R art is such that the questions need to be transformed into a query language, generally Boolean combinations of search terms, directed to one of 3000 online bibliographic databases. The latter consist of indexed references to documents that might contain the answer. Another role for A1 or an expert system is to advise the investigator about which database(s) to use. The questioner (currently a human investigator, in the future, perhaps his automated research assistant) is

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presented first with the number of articles that are retrieved from a database which are indexed with the specified terms, so that he can revise his searchterms and combine them: he can also see a sample of titles that would be retrieved, better to guide him. He finally receives a printout of the titles of and bibliographic information about articles that match his search specification. He must then scan these for answers to his questions. Sometimes the questioner may pose his question in a query language that can automatically search a database or a knowledge base. For example, if he wishes to know for the past five years the number of Americans hired by that Japanese firm in its U.S. plant and the skill levels of those hired, there may be a database in a database management system that provides this data. In response to his general question, he should be informed about the existence of databases of possible use to him so that he (or a surrogate person or program) could, if he wished, formulate requests these database systems can process. Many database management systems (DBMS) are directly coupled with statistical analysis packages (SAP) so that confirmatory statistical analysis (hypothesis testing) can be done in a continuous process (e.g., by multitasking). Ideally, a DBMS and a SAP should also be integrated with simulation systems or modeling packages, such as IFPL (Interactive Financial Programming Language), and also with tutorial systems, so that the investigator can get online guidance about which methods and programs or languages to use when. If the investigator obtains no direct answer to his question, he turns to an artificial intelligence program (box 6) that searches its knowledge base for units of knowledge from which the answer to the question may be inferred. For very restricted domains of discourse, automatic question-answering algorithms for English-like questions have been developed (Kochen, 1969a, 1969b).(This could also be done at various other points in Fig. 2.) If that fails after a reasonable effort, the needed knowledge may be sought from an external source. If that fails, or if the inquiry strategy proves to be fruitless, the line of investigation is abandoned, and replaced by a new one that is expected to do better. This is done in box 7 by zooming back to the general domain and selecting a different set of specialized domains or some other representation shift procedure (Amarel, 1962).I contend that this is the essence of intelligence. If, as a result of a shift that leads to a more fruitful inquiry strategy on the research path, a reliable answer is produced (box 8), then understanding (expressed as the question in box 4) is transformed into intelligence. It should not be inferred that we consider an investigator highly intelligent only if he or his automated research assistant does this general-special switching very rapidly. If he mulls over the shift slowly and deliberately, he should not be excluded from the class of intelligent investigators. But if other factors are the same, the faster switcher is more intelligent.

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The remaining two processes (box 9 and lo), complete a learning loop. Learning by the MINTS is necessary if only to enable it to keep up with a changing environment. For it to improve it must learn faster than required by the changing environment. To meet the requirements stated in general terms in Section 3, a MINTS can be regarded as organized into subsystems. Each subsystems has its own knowledge base and specialized hypothesis generators, question askers, question answerers, and expert systems (boxes 2-6). But there are also hypothesis generators at the system level. The subsystems are further organized into more specialized sub-subsystems, in which there is increasing expertise, as suggested in Section 3. It is the integration of all these subsystems that makes the production of intelligence possible. 4.2

MINTS Development Lifecycles

Some aspects of a MINTS are conventional computer information systems. Some aspects are expert systems. Other aspects are non-computerized research systems. The traditional system development lifecycle for conventional computer information systems consists of overlapping phases such as (a) Anulysis. Given a system, estimate performance under various conditions. This involves determining performance requirements, determining feasibility and writing specifications, and using empirical, mathematical and simulation methods. Some of this is now done with computer-based tools. (b) Design. Given specified performance criteria, determine a system likely to meet them. This may involve rapid prototyping, buy or lease decisions, invention, mathematical computation, and heuristics. (cj Fuhricurion. This involves scaling up of the prototype, writing software, establishing databases and programming, and installing the system. (d) Testing. This is being recognized as a major problem. Some very large systems cannot be tested under realistic conditions such as high-speed conflict, and it is dangerous to place too much faith in the competence or performance of such untested systems. (ej Insrullution or Migrotion. This includes setting up operating procedures and an organizational structure, as well as conversion or migration from a previous system. (f) Operation and Muintenunce. This includes updating databases, improvement of functionality, and correcting imperfections. About two thirds of the effort of most programmers are expended on such maintenance activities (Fox, 1982).

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This is planned obsolescence in the expectation of a new generation of technology. It is also based on projections of unacceptable cost and reliability due to excessive patching on top of patches. It gave rise to the idea of composite information systems (Madnick and Wang, 1988).

(8) Phasing Out und Replacement.

The development of expert systems (ES) can be regarded as having two lifecycles, neither identical to that of a conventional system (Sviokla, 1986), one for the ES, and one for the Knowledge Base. For management of program development, a variety of tools are available. At the level of hardware, devices as inexpensive as personal computers, and also special LISP machines, are in use. Expert systems on PCs generally consist of fewer than 400 rules; on LISP machines, they have between 500 and 1000 rules. At the other extreme in levels is a specified problem domain in which a human expert’s models, heuristics, knowledge and inference strategies are observed and analyzed by a knowledge engineer, who tries to represent this expertise in production rules or frames or some other means. This results in expert systems such as XCON (Waterman, 1986).Expert systems are written with programming tools such as EMYCIN, TIMM, INSIGHT, S.l, M.l, etc. The knowledge engineer may do this in a “knowledge engineering environment,” such as KEE, LOOPS, ART, OPS.5 which automates many processes for him. High-level languages such as PROLOG, INTERLISP-D, LISP, FOCUS, IFPS or C serve as even more general-purpose tools. These run under operating systems such as UNIX, which appears to be emerging as a standard. Expert system development goes through the following phases but with a great deal of looping: (a) Initial definition and system identification. (b) Construct a first prototype. This is done in place of requirement determination for ordinary systems. (c) Formulate a plan for the program, involving the user. (d) Design the documentation. (e) Develop the skill and improve it adaptively. (g) Field test the system under realistic conditions. (h) Operation. (i) Maintenance A common way to represent knowledge in expert systems is by means of “production rules,” or sentences of the form, “If any firm in market A raises its price, then firm A will raise its price to the same level within a week, almost certainly” (Kochen, 1971). Since then, limitations on the applicability of

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production rules for representing knowledge have been discovered, and modifications have been proposed (Kochen and Min, 1987), for example to separate declarative and procedural knowledge (Anderson, 1983) though Heidegger had distinguished action and description long before. Knowledge bases are often measured by the number of such rules, though such a simple numerical count cannot be meaningful. (See the previously mentioned example of CHAOS, a chess program.) Still, it is asserted that the knowledge base of an expert system such as R 1 has grown linearly from about 500 rules in early 1980 to 3250 rules by year end 1983 (Sviokla, 1986). But the utility of a knowledge base does not grow linearly with the number of rules. Consider as an extreme case a knowledge base of propositions for an axiomatic mathematical domain. A minimal knowledge base would comprise the axioms. Fewer than the minimum number are not enough. Restatements of the axioms or minor variants of theorems implied by the axioms would not add as much as would significant, non-obvious theorems or alternative noncontradictory axioms. As the number of propositions in the base grows, the number of ways of combining them for use in proofs of new propositions grows exponentially, but so does the effort required to find fruitful or significant new conjectures and their proofs. It is possible that when a knowledge base becomes large enough, the last item added to it decreases in value with KB size relative to the effort needed to generate and update the knowledge base with it. Thus, the value relative to effort of KB may vary with age, assuming linear growth, as shown in Fig. 3. The time at which this curve attains its maximum could be regarded as its natural lifespan, and M its maximum size. Thereafter, value,'efTort ratio

f

I

I I

K B size

-L

M

( # of

production rules)

FK; 3. A possible relation between the sire of a knowledge base and its effectiveness/cost ratio.

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the domain may divide into specialized domains, with more specialized KBs replacing the original ones. There is now a significant literature about how to gather and use intelligence. (Fuld, 1985; Ghoshal and Kim, 1986; Miller, 1987; Wagers, 1986). Such intelligence can be used to build the knowledge bases, though most of the valuable inputs will be current, as shown in Fig. 2. Because of the long history of experience with intelligence production, some basic principles have energed (Platt, 1957). These are unlikely to become invalid as advanced technologies are introduced into intelligence production.

I . Principle of Purpose. Every intelligence project must be directed by the use to which the results are to be put. This includes the problem chosen for attack, its formulation, and a clear vision of how a solution to the problem would serve as a guide to policy or action. 2. Principle of Exploitation of Sources. Explore and assess all sources that can shed light on the project. Vary the sources and use them to cross check one another. Identify strengths and weaknesses of each source. 3. Principle of Significance. Give meaning to bare data. For example, compare facts at one time with corresponding facts at the same date a year ago. Interpret, explain all facts. 4. Principle of Cause and Effect. Seek causes and effects whenever possible in search for the key factor. 5. Principle of Morale. In assessing a competitor or adversary, or even a potential partner, assess the will-to-win of his leadership and his staff. Is he unusually aggressive or unusually defeatist? 6. Principle of Trends. Estimate the direction of probable change. 7. Principle of Degree of Certainty. Attach reliabilities to statements of fact, degrees of precision to quantitative data, and probabilities or other measures of the weight of evidence to estimates and conclusions. The Bayesian approach is probably the soundest one. 8. Principle of Conclusion. The intelligence project is not complete until it offers conclusions, answers to the question “So what?” 4.3

The Effectiveness Condition

There is a rich history of intelligence failures in the military, politics, criminology and business (Strong, 1969). There are also examples of brilliant successes. Consider examples of such successes and failures in six categories (A- F, defined as follows) to suggest that in simple situations, intelligent management is necessary for success, whether or not advanced technology is used; in complex situations, advanced technology must also be used.

26 1

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Category

Success or Failure'?

Complexity?

A B C D E F

Failure Failure Success Success Failure Success

Simple Simple Simple Simple Complex Complex

Intelligent Management?

Use of Advanced Technology or AI?

No No

No Yes No Yes No Yes

Yes Yes Yes Yes

A good example of A, an industrial intelligence failure, is the introduction of Michelin tires into the American market. U S . tire companies failed to note that Michelin built plants in Canada with six times the capacity of the Canadian market, where, unlike in the United States., they were permitted. Another famous example of A is Sorge's message in 1941 to the Russian government about the exact date and place of the planned German invasion. Stalin had overwhelming corroborating evidence about the impending attack from a variety of reliable sources. Both are relatively simple and straightforward situations, with intelligence from reliable sources, obtained with conventional rather than sophisticated technological means. The failure was not that of intelligence agents or analysts, but of the client. Whether he suffered from paranoia, cognitive dissonance (in this case, ignoring information that was inconsistant with prior beliefs), competing hypotheses to explain the information, lack of appreciation for the importance of the information, etc., it is lack of natural intelligence or competence in the system, as defined here. Examples of B include Pearl Harbor and the Bay of Pigs. The United States had broken the Japanese code prior to Pearl Harbor with the help of such technologies as were available, and had reliable intelligence about the planned attack on Pearl Harbor. Lack of natural intelligence and competence in the chain of command prevented the message from reaching the President. Similar errors occurred in the ill-fated Bay of Pigs invasion in Cuba and several incidents in the Pacific. The research, unaided by advanced technology, by Bernstein and Woodward that uncovered the Watergate affair illustrates C. It was also a relatively simple puzzle. The investigation succeeded all the way because of the natural intelligence of the elected representatives of the American people who became concerned. Lest it be thought that the introduction of technology might detract from success, consider the Cuban missile crisis as an example of D. Detecting the missiles was relatively simple, using photo intelligence; success was due to the natural intelligence of John F. Kennedy and his staff. When the situation is very complex, as is the 1988 revolt of Palestinians

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under Israeli occupation (type E), and if natural intelligence is attributed to Israeli leadership, it would seem that the use of sophisticated technologies might have avoided the failure to estimate the intensity of discontent or the intentions and strategies employed by PLO leaders. Another example of this complex kind was the failure to anticipate the success of Khomeini in ousting the Shah of Iran; that is more likely to be a failure of natural intelligence. An example of F is the deciphering of the German Enigma codes by A. Turing in Project Ultra during World War 11. The total situation was complex. The natural intelligence of all those involved in Ultra, which included very few, such as Winston Churchill, F. D. Roosevelt, and a few they trusted, was high. It could not have been done without using the most advanced computing technology that could be brought to bear. It lead to interception of key messages between General Rommel and the German high command in Berlin, which played an important role in defeating the Germans in North Africa. It contributed greatly to the allied victory in that war by many more instances of this kind. The most important cause of intelligence failure is the “poverty of expectations” (Platt, 1957). This is a routine obsession with a few, familiar dangers and opportunities. Other major causes are, in order of decreasing frequency: insufficient knowledge; general incompetence; biases, such as cognitive dissonance; deception by the competitor or adversary; self-deception; mirror imaging, in which the principal assumes that his competitor will do what he would do were he in the other person’s position; judging new phenomena solely in the light of past experience; misreading of signals and indicators; overload and resulting lack of attention and vigilance; inadequate communications; unclear or ambiguous command and control structures. If a MINTS could help to stimulate and guide imagination, it would meet a major need, help overcome a main cause of intelligence failure, and this is a key requirement. The general proposition advanced here is as follows. If the natural intelligence of users of a MINTS exceeds a certain level, then the introduction of advanced information technologies, such as AI, will increase their performance in leading their organization to success and survival. If the natural intelligence of these users is below that level, these technologies will decrease their effectiveness. It may help them lead their organization to failure or to increase the chaotic aspects of performance. 4.4

A Model for Relating Natural and Artificial Intelligence

We now try to prove this proposition by explicating the concepts of intelligence with an abstract and simplified model. Some informal philosophical preliminaries help to motivate the formalism. We start with “things” and

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“ideas” as two universal constituents. Things are material entities that interest scientific experimenters, and which we know from sensory experience. Brains are things. Platonic ideas have no material counterpart. They are pure “thought.” They are not things. Minds are not things. “Ideas” include values, concepts, beliefs, and hypotheses. Persons are both things and ideas, brain and mind. They can generate and comprehend ideas. Above all, they are governed by values, i.e., preferences, revealed or explicit. Machines, even if produced automatically by other machines, are not governed by values. They do not express preferences except as imputed to or designed into them by persons. They may generate and process concepts, beliefs or hypotheses, but they have no preferences. Persons and machines are two basic constituents of organizations. Natural intelligence applies to persons. Artificial intelligence applies to machines. Both are required for the organizational intelligence needed to cope with complex situations because that requires the making and use of accurate value-maps of the world; natural intelligence is needed to make maps; artificial intelligence helps in using them. The key concept to be explicated is that of a value-map. The idea of a statespace was originated by physicists in the last century; state-transition system concepts were adapted by early automata theorists. Utility-theory concepts were developed by early decision-theorists and economists. They were first combined into a model of organizations as information systems. (Kochen, 1954, Kochen, 1956). Suppose for the sake of discussion that we, as objective observers, can represent the “world” in which an organization (denoted by A for actor or decision-maker) moves, survives and thrives or suffers as an object in a state-transition space, illustrated in Fig. 4. The illustration uses just two

~

FK,. 4. Illustr~ttiiigthe etTects of actions i.e.. shifts in positlon in a state-space (such as that of position5 in the market).

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dimensions (say market share msA and profitability PfA) that we “know” to be relevant for A’s well-being in the sense that if we were to observe A in certain states we would observe A to be alive, alive and prospering, alive and suffering, dead, etc. To be sure, “we” cannot know whether and how A is suffering except by observing and inferring, or asking and believing. The state-space could as easily consist of four dimensions, for example, the market share of his major competitor B as well (say msBand pf,, with msA + msB = 1). Preferred states for A would be toward the upper right of the space, with msAand PfA as high as possible. Generally, there are regions of the space that “we know” A to value positively (perhaps in varying degrees), others that we know A to value negatively (perhaps in varying degrees), and possible paths for A to traverse. Figure 4 shows in a highly simplified way one path from a starting state so to a state in the positively valued region. To traverse it, A must choose actions ao2 rather than sol, which are the two choices “we know” are available to him when he is in state so.This causes a transition to state s,, where he must choose uI3rather than a I z .He would experience a downward gradient, and he could learn from the feedback that he is heading in the wrong direction, at least temporarily. It is possible that the (only) path to the positive state from so must take a downward turn (local minimum), but “we know” that it will eventually get him toward a highly value “goal”-state. Now, A does not know Fig. 4. He does act, experience and evaluate the consequences of his actions. He also observes, communicates and reflects, generating ideas that such variables as ms,, pfA are important for him, i.e., he expresses his values. He encodes in symbols the state he was in, the action he took and the state this led to. He correlates the three, thus forming a local map. If he finds himself to be in the same state again he remembers that, and if the action taken previously led to greatly increased value, he repeats it. He checks whether the same transition rule recurs; if not, he revises it using probabilistic estimates so that it is consistent with all his prior experience; if so, he tries to analogize and generalize to similar states and actions. A cannot build a useful value-map solely by patching together the local maps thus acquired from experience. He must “imagine” how the local maps fit together into a pattern that reflects the continuities and discontinuities (i.e.,the topology) among the states he values positively and negatively. The image is analogous to a landscape with a few mountain ranges and peaks corresponding to highly valued states, a few canyons, chasms and nadirs corresponding to negatively valued states, and mostly mesas, steppes or flat terrain. Superposed on this relief are the branched paths that A imagines are accessible to him from various sites, depending on what he chooses to do from the options he believes are available to him. A has, at any time t , such an imperfect map (incomplete and inaccurate relative to what “we know” to be thecase). What is important is that A has a global (bird’s eye) perspective rejecting his values as he perceives them at t , (he may change these perceptions when he finds that a state he

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expected to value highly causes him great distress and he is in it) as well as the possibility of switching to local (worm’s eye) perspectives at various levels in an apprapriurely flexible way. Internal maps of the kind illustrated by Fig. 4 have been modeled as expert systems. (Kochen and Min, 1987) A MINTS has also modeled as an expert system that does two things: (a) I t speeds up the improvement of the system that represents a strategic planner’s value-map. (b) It integrates two or more such expert systems, for example one denoting his value-map and another denoting his map of his major competitor. A system that performs the above functions (a) or (b) is a meta-expert system. It operates on, modifies, and combines other expert systems. It can be said to facilitate “learning” or adaptive improvement. As such, it represents a high level of flexibility. We propose to define A’s natural intelligence by the rate at which he modifies his map in the direction of increased completeness and accuracy and by 4, the quality of his map in this regard. Both aspects are important, because the world-the “actual terrain” of Fig. 4 as “we” see it-may change, or there may be plateaus in how 4 increases over the long term (e.g., new statevariables may add to or replace old ones, values may change, constraints on and opportunities for action may be added, etc.). This pair of variables characterize only two of several variables that characterize competence. It is the most important in the context of this chapter, and focusing on it will provide more insight than a comprehensive analysis. The other variables are: k,, the number of important and relevant questions a respondent can answer well enough, somewhat as in traditional educational testing for knowledge (this is know-what); (know-how), the number of key tasks and procedures a respondent R can do with a high enough level of skill; k , (know-who), the number of valuable personal contacts a respondent can draw on and think of using for getting help, support, for referral, etc.; k, (knowwhen), R’s sense of timing, awareness of the existence of strategically critical time windows and priorities, as evidenced by the number of good opportunities not missed; k , (know-where), which corresponds to R’s sense of place, such as where to look for and quickly find certain important items, and where to be at appropriate times; k, (know-why), which refers to R’s ability to justify and persuade, to explain and make credible his positions; k, (know-howmuch), which reflects R’s sense of quantity, his ability to make sound quantitative estimates, to express judgments about what is too much or too little; u (understanding) the number of important, incisive, answerable and relevant questions that R is able to formulate and thinks of posing; i (intelligence), which refers to how adeptly and quickly R can switch from

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the perspective of a generalist to that of a team of specialists in appropriate specialties at appropriate levels; and w (wisdom), which reflects R’s ability to bring to bear values and integrate all the other aspects of his know-X on deciding what to do when and how to do it. The role of machines in a MINTS-and A1 technology, in particular-is to help A determine and assess his current state, to apprise A of opportunities and threats in the near and more distant future, to help him select goals (targets of opportunity), to check consistency with his values and to advise A in choosing action sequences. This means map-utilization. Action sequences associated with probabilistically branching paths can be viewed as programs for algorithms accompanied by a claim that they will lead to the most highly preferred or valued states from the current state, with high probability and at a given risk. We propose to measure A1 by the extent to which such automatically formed programs are correct according to A’s map at the time, and by the speed with which they are formed and executed. Such machines support A by telling him the probable consequences of various action courses, but leaving it to his judgment to decide, according to his values, how much risk is acceptable, when and where to seize opportunities, what goals to pursue. If q, the quality of A’s map is below a threshold q,-i.e., insufficient natural intelligence-then there is a high probability that (a) states will be misvalued; (b) paths will be missing; (c) paths will be mislabeled, with incorrect associations between states, actions and the transition states. Using such an imperfect map will either result in random behavior or in downward directions much of the time. There is a very small chance that an imperfect map is biased in a positive direction, because relatively few paths are highly valued and relatively fewer paths lead to them. If the expected number of such flaws in the map is sufficiently large, the probability of random behavior or of dysfunctional strategies, with negatively valued outcomes in either case, will be very high. The use of such maps by A1 will lead to such negative outcomes more rapidly and with much higher probability. To the contrary, if q exceeds q,, the reverse occurs. Thus, q, is the point at which A1 begins to pay off. But qo may increase with time. Thus, though q > qo at one time, that condition may no longer hold due to qualitative changes in the world, such as technological, economic, political or social discontinuities. Then, if the rate at which the map is improved is large enough, it may cause q to increase until it exceeds qo once again. It appears that in most industries only 5-1074 of the firms that start when the industry is launched survive a decade, and those who do are transformed discontinuously every 5- 10 years by changing strategy (products, markets), structure, people (replacing entire top management team), process and possibly even values, (Tushman e t al., 1987) generally in anticipation of such major external discontinuities.

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Hopefully the discussion in this section has stimulated the research-oriented reader to ask many questions to be answered by further research. The above conjecture regarding the relation between natural intelligence, artificial intelligence and the benefit of business or organizational intelligence has the status of an empirically testable hypothesis. Managerial and leadership competencies other than natural intelligence are necessary as well. Social networking is also a necessary competence, at least for the successful adoption of technology (Kochen and Chin, 1989). Generally, informal sources of information will also play a more important role in the future (Compaine and McLaughlin, 1987). The concept of a map to aid in strategic planning needs much more elaboration. In Figs. 1. and 4 constraints operate and make certain regions of the space inaccessible and certain transitions forbidden. Rules in expert systems that are equivalent to state-transition maps should apply to sets of points, perhaps to fuzzy sets, rather than to individual points. The transitions should be regarded as probabilistic and not all applied in the same time interval in a synchronous way. The indifference curves, as in Fig. 1, are better modeled as partial orderings (preference) than as crisp curves. Above all, the repertoire of possible actions by all the players in combination should be extended. These are but a few of the more obvious steps that are needed. On a more fundamental level, it can be argued that the A1 support systems could contain enough intelligence to compensate for the user’s lack of natural intelligence, and, in principle, displace the human analyst or manager altogether, as posited by, say, Fredkin. We do not take this position, because A1 is basically a human construct for which a human-perhaps the designer or the owner-ultimately bears responsibility, unless humans lose control over this technology or become extinct.

5.

Managerial Issues

MINTSs are likely to become important in the decades to come. So will the search for a scientific foundation underlying their design and use. Schools of Business will have to teach management of MINTS, as well as management with MINTS as tools after their faculties learn this. All of us will have to learn how to manage and cope in the environment of MINTS. 5.1

Management of a MINTS

To appreciate the difficulties of managing an intelligence organization, we need only to look at the relation between the CIA and the branches of government to which it reports. The most critical issue is the degree of autonomy, authority, power and responsibility delegated to the MINTS. On

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the one hand, intelligence is so necessary for leadership that those who control it have a great deal of power. By withholding or distorting information reported to a chief executive (or to others), the chief intelligence officer (CINTO) can shape the kinds of policies and decisions made. He can even do this inadvertently by the kind of information collection policy he employs. Yet, to be effective, he must have the complete trust of the chief executive officer (CEO) to support him. If the CEO is open-minded and the CINTO loyal and trustworthy, then the CEO’s management of his MINTS (represented by the CINTO) is likely to be effective. But in politics, as perhaps also in war, sports, business and love, persons in power do best to be kind and cautious. The CEO may be open-minded and ready to change course as long as his power base is not threatened, for which he is likely to remain vigilant. There is good reason for caution, because sudden changes in the environment are likely to call for radical and sudden responses from organizations, and that includes the possibility of his being replaced. (Tushman et ul., 1987; Nadler and Tushman, 1986) The same holds for the CINTO. Hence, the CEO’s open-mindedness has, in practice, limits, as does the CINTO’s loyalty. If the CINTO is subordinate to (and funded by) the CEO, with the latter having the power to discharge or promote the former, the value of services rendered by the MINTS may be compromised. The CEO may always suspect the CINTO of acting in self-interest. In a situation in which providing the CEO with valid intelligence could threaten the CINTO’s position, self-interest would imply that the CINTO would withhold or distort such intelligence. If, on the other hand, the CINTO is independent of the CEO, the latter may doubt the CINTO’s incentives and motivation in supplying intelligence. Intelligence cannot be paid for according to the value of reports to the CEO, or else he will get biased reports and possibly act inconsistently with the organization’s values and policies. A system of checks and balances, similar to that between the three branches of the U.S. government, is probably the best way to manage an independent MINTS. Even the best MINTS, headed by the most competent and loyal CINTO, however, will not help the survival of an organization that lacks leadership. This means vision about what to do, and secondarily how to do it well. (Bennis and Nanus, 1985) It requires of the leader also a strong personality, with connections that he is willing and capable of using. It requires clear intention, ability to mobilize and capabilities, commitment and carrying through. It requires the ability to transform at times of change (Tichy and Devanna, 1986). What does it mean for an organization to “have” an independent MINTS? Can the same MINTS serve more than one client organization? Does it manage and support itself? Does it compete with other MINTSs on a mar-

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ket for intelligence‘? Could there be demand for the services of many small MINTSs which specialize? Not according to our conceptualization of intelligence, which emphasize appropriately flexible switching among various levels of specialization and across specialties. If each organization has its own MINTS, will there not be a common overlapping use of coverage that it might pay all of them to share? Many of these and other issues concerning the management of MINTSs are subordinate to the broader issues of competition and cooperation, discussed in Section 5.4.

5.2

Management with a MINTS

The most important principle that is implied by the main thesis of this chapter is that the CEO responsible for deciding how to use a MINTS as a tool in strategic planning should permit only sufficiently competent people in his organization to use it. He should then ensure that the MINTS aids them proactively as well as responsively. A good MINTS is a very flexible and versatile tool. I t must be used as such. A MINTS is like a combination of private detective, lawyer, accountant, librarian/information analyst, and consultant/adviser, which maintains continual surveillance over everything of importance to the CEO and which is looking out for his interests. How does he manage with its services‘?In its zeal to justify and motivate his payment for its services, the MINTS may overload him with input. Thc essence of useful intelligence is that it is carefully screened and prioritized. At all times the effective manager reminds the MINTS of his values, which are the basis of prioritization, offering concrete feedback about the priorities of what is supplied and specifying beforehand what he prefers, whenever possible, in a form that permits clear determination of whether it is useful or not.

5.3 Management in a MINTS Environment The issues here are too many to cover. Since much of intelligence is about people, issues of privacy, confidentiality and secrecy are primary. If everyone in an organization pervaded by a good MINTS feels that the MINTS maintains a growing and secret record about him or her that is likely to be of value to top management-which may include evaluations of performance and potential, personal and other sensitive matters-then fear may also be pervasive. “Secret” means accessible only to those with a “need to know,” as interpreted by someone empowered by the CEO to make such .judgments. If the person about whom the record is kept is denied access to the complete file, this fear may be greater, perhaps justifiably so, since he has no

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opportunity to check the accuracy of the record. On the other hand, knowing that negative evaluations are in the record may make him less content than not knowing that. Fear also increases with suspicion, sometimes justified, that unauthorized persons can gain access to the record. The secrecy issue also applies to technologies embodied in products and production processes. Insofar as a technology is codified-e.g, embodied in blueprints-it is readily imitated and appropriated by competitors, suppliers, customers, etc. Patents, copyrights and trade secrecy offer limited protection. Even technological know-how that is very tacit, in the expertise of experts that even they cannot articulate and communicate, can be appropriated by hiring those experts. Hardware is perhaps most readily appropriated, by reverse engineering, though highly efficient production processes, resulting from numerous incremental imporvements, are more tacit. Software can be imitated and appropriated with increasing facility, as we move toward greater standardization, among other factors. Knowledge and databases, like TV and recorded performances, can also be copied. Model bases, particularly idiosyncratic heuristics, may be somewhat more tacit, though once they are computerized, they become subject to imitation. Only procedures and sociotechnology remain tacit. How much investment to ensure secrecy, to thwart potential imitators, is justified? A small business firm cannot afford as much to guard its technology as a large imitator firm can afford to appropriate or replicate the technology. Moreover, the larger firm may have the needed cospecialized technologies and complementary assets (e.g., distribution channels, service, competitive manufacturing, a popular brand name) in place when the innovative small firm does not. 5.4

Communication, Competition and Cooperation

We generally think of using a MINTS in a world of competing adversaries, often as if they were in a zero-sum conflict situation. Clearly, an increase in one firm’s share of a given market comes at the expense of decreases in the shares of some competitors. In many sports contests or some cultural competitions, gains by one contestant are losses by the other@).In an armed conflict, too, gains by one side are losses by the other, at least on the surface. In a love triangle, too, success of one suitor spells failure for the other. Yet, to compete, adversaries must communicate, if only by widely available products and services at known prices or other transactions open to public scrutiny. (In the love triangle, communication among competing suitors occurs at least through the object of their love). Success in conflict often depends on one party’s ability to conceal (or reveal deceptively) its own intentions, capabilities, commitments and actions, even if that party’s

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capabilities are weaker than its adversaries’ but compensated for by surprise (Levite, 1987),stealth, concentration and speed. Such fighting to win, in which communication is at best unintentional, may be beyond the “rules of the game.” Even war, and certainly competition in business, sports, culture and perhaps in the love triangle, is governed by some rules of “fair play.” Extreme forms of deception may be considered unfair. Industrial espionage is as frowned upon as fraud and crime (Eels and Nehemkis, 1984) and not to be identified with business intelligence. But communication can pave the way for reciprocity, said to be a way of life in the U.S. Senate (Matthews, 1960; Mayhew, 1975) which, in turn, illustrates the emergence of cooperation (Axelrod, 1984). There are few situations in which the interests of all (both)parties are completely opposed to one another. There are always some win-win opportunities, in which each party gains something of value. This kind of situation is better modeled by the non-zerosum prisoner’s dilemma game than by a zero-sum game. Here, the reward to each of two players if both cooperate is greater, say 3 (the pair of numbers in the upper left cell), than if they both compete, in which case both get, say, 1 (the pair of numbers in the lower right cell). But if one offers to cooperate while the other intends to compete, the former gets the “sucker’s payoff,” say 0, and the latter gets rich quick with, say, 5 . This is shown in the off-diagonal cells of the table in Fig. 5. Cooperation is interpreted in the case of two burglars imprisoned for a crime as not testifying to the partner’s guilt, while competing means betraying the other in the hope of gaining release. Could MINTSs be used to find those opportunities in which every part wins sornerkiny of value to it. even if not as much as it would gain if it were the only or the # I winner‘? Could it help parties in conflict switch from interactions based on concern for position to interactions based on concern for their interests (Fisher and Ury, 1981)? Or, if the Tit-for-Tat strategy submitted by A. Rapoport, which consistently won in the tournament staged by Axelrod, or an even better strategy claimed to have been found by use of Holland’s generic algorithm, leads to cooperation, is a MINTS unnecessary‘? A MINTS is useful, if only to make

Orp I

3. 3

0. 5

Compete

FK, S. I’risoncr‘s dilemma payoll’ matrix. The lirsl number in each cell is the payolr to organimlion I . T h e second number I\ the payolT to organiiation 2. F o r example, the benefit to orgiiniLation 1 by ii unilateral move to compete when organiailion 2 seeks 10 cooperate is 5. while the benelil to orgaiiimtion 7 I S 0. a s i n the lower left cell.

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parties aware of the payoff matrix, the nature of the game and the optimality of the Tit-for-Tat or reciprocity strategy; also, the evolution of cooperation can take very long, and the use of MINTS can accelerate its pace. Generally, the assumption of a completely informed, rational person, which underlies much of economic theory, requires either a learning approach based on continual improvement (Kochen, 1971) or a MINTS or both. The use of shared knowledge base items that each of several MINTS in competing organizations collects, and which they know their competitors collect, could be a step in the direction of cooperation while competing. All would gain. But the most valuable kind of intelligence is still that which no competitor has or knows that the organization in question has, and which supports strategically advantageous moves by it. 5.5

Emergent Properties and Systemic Intelligence

A society comprises many living organizations and institutions, each directed by persons with intentions, capabilities (for mobilizing resources), commitments and courses of action in various stages of completion. Some, such as IBM, DEC and Apple, are business firms competing in one or more markets. Others, such as the members of MCC, have begun to cooperate on certain dimensions, such as Research and Development u p to the development of prototypes. Advanced information technologies are pervading nearly all organizations in developed societies, and may soon pervade those in less developed societies as well. As they affect effectively managed manufacturing firms by increasing productivity, quality, and throughput, and lowering product delivery time, costs, variabilities and uncertainties, these technologies increase the wealth generated and the value added. But they do so by displacing and transforming the requirements for human labor (OTA, 1988). Yet, it is people who must meet the demands for greater creativity, to invent new products and services likely to be in demand (assuming insatiability), so that they can acquire the purchasing power to enjoy this share of the increased wealth. Otherwise, societies may become fragmented into two tiers. In one tier are the few who have the competence to manage the advancing, wealthproducing technologies and who thus own or control most of the wealth that is generated. In the other tier are the many without enough means. The arguments against this scenario are based on the assumption that societies such as nation-states will continue to perceive our world of limited resources as a zero-sum game (Cyert and Mowery, 1987). It is argued that job losses in the United States are due to the loss to foreign competition rather than due to labor-displacing technologies in the United States, and that the introduction of automation would, by increasing quality and productivity and by lowering costs, expand our market share sufficiently to create many more jobs than are

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lost. But this should mean lost jobs in competing societies. If proper use of the technology enables one highly skilled person to do the work of a dozen less skilled persons without the technology, some low-skilled jobs will be lost somewhere in the world. This is an example of an emergent or systemic property. Another example is the realization, before long, that it is against the national interest of a society such as the United States to invest in research and development that leads to public knowledge, hence easily appropriated by competitors better positioned to commercialize it than firms in the United States. Yet investment in basic R&D has a very high rate of social return on a world-wide basis. Suppose that a MINTS could discover a win-win strategy, such as a Tit-for-Tat strategy by the United States, to use against a competitor who commercializes U S . innovations but fails to equitably share the rents from this combination of innovation and commercialization. Should such a strategy work, the United States could continue to do what it does best-e.g., research, development, and innovation- while, say, Japan does what it is best at, e.g., commercialization, with both gaining, though not as much as if either were the sole winner. The MINTS might generate systemic intelligence to the effect that the most survivable future is one in which there is no # 1, #2, in a linear hierarchy of players, but every player gains something of value to him.

6.

Conclusion

A Management Intelligence System is a stable structure with the function of screening, evaluating, and synthesizing information, based on knowledge and understanding of its environment to help the intelligent managers it supports in setting goals, assessing their organization’s position, selecting appropriate strategies, tactics and actions, and in carrying these out in appropriate ways. Business firms that face shorter product cycles, easily imitated technologies, and intense global competition for high-quality, low-priced, customized, rapidly delivered and reliably supported products and services are beginning to recognize the need for a MINTS. They are learning the management of MINTS, with these as tools and in environments pervaded by them. System professionals are being challenged to develop such MINTS and system scientists have an opportunity to create scientific underpinnings for the analysis, design and use of MINTS. Some of the main concepts and issues toward a theoretical foundation for these scientific underpinnings were presented here. Principles based on experience and research with other computerized information systems were brought together and applied to MINTS. The resulting analyses and designs were found to be sound and likely to have a significant impact on strategic business planning.

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A MINTS will help an organization improve or maintain its interest with the help of a MINTS only if its leaders, the MINTS users, are sufficiently competent. Competence includes natural intelligence, which is defined as the ability to shift rapidly and appropriately among different levels of specialization in domains of knowledge and understanding needed for making useful cognitive value-maps. A MINTS with sufficient functionality to help an organization facing opportunities and threats in complex situations requires artificial intelligence. The latter is defined as the ability for, and performance in, reading and using cognitive value-maps. A MINTS is necessary but not sufficient to ensure competitiveness. Foreign competition has been characterized by Gomory (1988):(a) tight ties between manufacturing and development; (b) an emphasis on quality; (c) the rapid introduction of incremental improvements; (d) great effort by those in the production process to be educated in the relevant technologies, in the competitors’ products, and in events in the world. An organization may fail because it does not have or use a MINTS when one is necessary to detect opportunities and threats. It may fail even if it uses a MINTS because: the MINTS provided inaccurate, incomplete or ambiguous intelligence, or because it presented it unpersuasively; the valid output of the MINTS was not paid attention to; valid output was attended to but used erroneously. It may succeed without a MINTS by chance. It may also succeed because of the experience-based exceptional intuition, talent or competence of its leaders or because of the simplicity of the situation. With situations becoming more complex, or competitive, high-performance MINTS will be needed more and more. Persons who can manage, build and operate them will be in greater demand. The scientific foundation for their professional use must be created. Some cornerstones for this foundation were laid in this chapter. ACKNOWLEDGEMENT Thanks are due to Professors R. Copper, J. Fry and M. Gordon of the University of Michigan Business School for their helpful comments on an earlier draft, to Ph.D. students Moonkee Min and Choon Lee, who helped prepare the “course pack” used as a text for thecourse/seminar based on the MINTS concept, and to all the students in the Business School of the University of Michigan, many of whom will be leaders, who participated in the course. The excellent work of Betty Wolverton in typing this manuscript is also greatly appreciated.

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