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Anticipating the unpredictable
TECHNOLOGICAL
FORECASTING
AND SOCIAL CHANGE
26, 201-205
(1984)
Anticipating the Unpredictable SELWYN ENZER
Issues deemed worthy of societal attention are determined by the way we view “the future.” It makes little sense to be concerned with developments that have virtually no chance of occurring. But if their impacts are great, or their chances of occurring depend upon our actions then we have to be concerned about them. These views of chance and choice affecting outcomes is often in conflict with traditional analysis that supports an image of a future that is essentially predictable-predetermined by past trends and present policies, if not by some version of a manifest destiny. As a result, issues that do not fit into the range of uncertainty defined by extensions of past data are almost always suspect. The image of a predetermined future has been greatly reinforced by the success of empirical research in the social sciences over the past century. As a result, an empirically validated hypothesis has become the only acceptable basis for legitimacy of a forecast. In fact, the greatest successes in the social sciences were achieved during periods of extended social continuity. This is the basis of the well known limitation that forecasts based on empirical data are only valid over the range of variability reflected in the data. This limitation makes traditional forecasting procedures invalid when the pace of change is rapid. But if traditional analytic methods do not provide satisfactory insights into the future, what methods should be used? The futures research community (if there is such a thing) used judgmental information about the impacts of change to supplement the projections obtained from traditional analyses in an attempt to gain a better understanding of the alternative outcomes that may evolve. These forecasts ranged from highly quantitative projections obtained from system dynamics and cross-impact models, to scenarios from such imaginative minds as Herman Kahn [5] and Dennis Gabor [3], to less structured descriptions of change such as were provided by Daniel Bell [l] and Alvin Toffler [8]. This, it turned out, produced a “Catch-22” situation. Supplementing traditional forecasts with judgmental information about future changes not only led to mutually incompatible forecasts, it made the range of possibilities so great that many traditional analysts considered such analyses counter-productive. Since anyone can express an opinion about the future, how was the serious analyst supposed to distinguish forecasts based on expert judgment from pure speculation? As a result, many traditional analysts challenged the use of judgment as unscientific. Since nothing could be more damning to a social scientist than being labeled unscientific, the community became polarized about this issue. In France for example, they even distinguished the nature of analytic organ-
SELWYN ENZER is Associate Director of the Center for Futures Research of the Graduate School of Business Administration of the University of Southern California, Los Angeles, California. Address reprint requests to Dr. Selwyn Enzer, Center for Futures Research, Graduate School of Business Administration, University of Southern California, Los Angeles, California 90089. 0 1984 by Elsevier Science Publishing
Co., Inc.
0040-1625/84/$03.00
202
S. ENZER
izations as being engaged in: “prevision” (scientific forecasting) versus “prospectives” (judgmental scenarios). This polarization not only made important emerging issues analytically off-limits to many social scientists, it actually helped to focus the scientific attack against judgmental analysis. Consider the scientific reception given to “Limits to Growth” [6] or “Global 2000” 141. Even though both of these analyses were presented as possibilities, they were attacked for being scientifically inaccurate. In other words, they were without merit because they are based on subjective relationships and not derived from empirical data. For example, in challenging concerns that higher population growth implied lower per capita economic growth, Julian Simon responded with the following: “Empirical studies find no statistical correlation between countries’ population growth and their per capita economic growth . . .” [ll. Technically this form of criticism says that the issue is not real because it is not reflected in empirical data. The implications of this criticism are inappropriate because it discounts the possibility of a future trend change entirely on the basis of the past trend itself. It is also misleading because it suggests that an issue is not important unless it is going to happen-a perfectly valid argument, if we knew what was going to happen. In the pseudo-scientific world of empirical forecasting, we presume to know what is going to happen-it is written in past data. Some of the important effects of this situation are that it discourages research into low probability, high-impact emerging issues, and discourages public support for programs designed to promote desirable (low probability) changes or to prevent potential problems simply because they appear unlikely. To overcome these constraints it is necessary to analyze and respond to possibilities before they achieve empirical maturity. This requires a social environment in which future possibilities are treated seriously before they occur. This would be much easier to accomplish if we knew how to deal with alternative possibilities in ways that guide rather than immobilize strategic action. Failure to develop methods that public and private organizations can use to deal with the uncertainty inherent in alternative possibilities has been one of the most serious failings of the futures community. From its inception, the futures research movement saw the challenge being not just to forecast what the future will be, but to make it what it ought to be. To accommodate this purpose it was first necessary to shatter the traditional mindset concerning fortune teller-like predictions of a predestined future. This was the primary intent behind such powerful futuristic notions as “alternative futures” and “inventing the future.” The implicit assumption on the part of the futures community was that once the alternatives were made clear, and it was pointed out that society could “choose” from among these alternatives by appropriate use of its resources, better decisions-and hence a better worldwould follow naturally. As it turns out, virtually every forecast depends upon uncertainties that are beyond the control of the decision-making organization involved in the analysis. Since, no organization is truly in a position to create its own future, and few know how to develop a strategy without making some assumptions with regard to the uncertainties, organizations often find frustration to be the most visible consequence of having evaluated their alternative strategic scenarios. Strategic management in the face of alternative scenarios is often seen as an impossibility. The argument given is that an organization needs a single set of assumptions in order for the various units to synchronize their strategic plans, and that this is not possible in the presence of a range of different scenarios. Does this mean that there is no way for an organization to plan for a highly turbulent environment? Hardly! Most entrepreneurs-for example military field commanders (as distinguished from Pentagontype planners), candidates for public office, football coaches, and quarterbacks-manage
ANTICIPATING THE UNPREDICTABLE
203
their affairs in the presence of rapid change without assuming the uncertainties away and without immobilizing their organizations. What do these professionals do that differs from a strategic manager in a public or private organization? They view themselves as being part of a highly dynamic process, whereas the strategic manager analyzes the options as closed-form problems that have unique correct answers. The critical issue confronting modem strategic managers is how to go about analyzing, implementing, and guiding strategies designed to cope with alternative future conditions. A corporate strategist facing a highly uncertain business could learn a great deal by studying the approach used by a battlefield commander in coping with uncertainty. The approach used by a field commander-the type of analysis, the monitoring system, and the continuous stream of modifications required by the changing conditions-is a highspeed version of the approach that is appropriate for the organizational strategist. Both face environments that are highly uncertain, where many possibilities exist but only one actually materializes. The battlefield commander deals with the uncertainty directly, while the corporate manager finds it necessary to try to eliminate the uncertainty by predicting the outcome long in advance. These two approaches can produce fundamentally different results, and they require different analyses, have different information needs, and employ radically different management styles. Because the corporate strategist has eliminated the environmental uncertainty by the use of long-term forecasts, the so-called corporate assumptions, his analysis seeks a fixed strategy that would produce the greatest longterm payoff. A&ed strategy refers to one that can presumably guide a business operation over a three-, five-, or even a 20-year time period. When confronted with alternative business scenarios, the traditional corporate strategist may analyze the strategic differences that would result from the alternatives. This would produce alternative strategies-one strategy for each environment. While this may help develop a better understanding of the consequences of uncertainty and may improve the quality of the strategies, the analysis does not help in deciding which to choose. With this approach, the choice is entirely dependent upon knowing (forecasting) which environment will ultimately occur. The military field commander would analyze the problem differently. He would be less concerned about the probabilities associated with each of the alternative environments and more concerned with his ability to change his strategy if he had anticipated one environment but later found that another environment seemed to be materializing. He would surely want to know how the strategy might be redesigned to facilitate such transitions and at what points strategic adjustments could best be accommodated. The strategy he would pursue would be one that, with proper leadership and management, appears most likely to succeed in meeting the objectives. This would not necessarily be one based on the most likely scenario. It might well be a hybrid strategy that is sufficiently flexible to meet the environmental challenges-a robust strategy. The most important elements in such a strategy are the initial commitments, those that are expected to take the organization to the first branch point. At that time the strategy would be reassessed and revised to reflect actual developments. The military commander’s approach would not end with the initial decision. The next step would be to develop an environmental scanning system that would provide early warning about potential surprises that would cause an early change in the strategy. On the battlefield, this task is the function of the sentries. The decision as to where to look and where to post sentries can be as critical to the management of a corporate strategy as it is to a military campaign. To the military commander, sentries and the information they provide are vital management inputs. Even unlikely possibilities are monitored, and indications of a change in the environment are always quickly acted on. While trend
204
S. ENZER
monitoring systems are becoming more common in public and private organizations, one rarely finds them used in a manner similar to their use on the battlefield. Changing organizational cultures so that they can be effective in managing uncertainty will involve more than just analysis. It will require changes in management procedures, organizational designs, and perhaps most importantly, reward structures. The role of change in an organization must be as firmly accepted as it is on the battlefield, where no one criticizes a commander for frequently changing a battle plan to meet changing conditions. Nor does a commander shoot the sentry who alerts him to enemy movements (his equivalent of changes). However, commanders are blamed for being caught by surprise. Excuses such as “Who could have anticipated such a development?” may console a corporate manager, but they are simply admissions of failure to a military commander. Future-oriented organizations must develop the same attitude toward change. Methodologically speaking, there is considerable hope for progress in analyzing strategic uncertainty. There is much greater awareness regarding future events today than there has been in the past. There are methods like INTERAX [2] and scenario writing that can synthesize these expectations into alternative future environments that are suitable for strategic analysis. The general availability of microcomputers and spreadsheets (e.g., VisiCalc, Lotus l-2-3, and Multiplan), and their use in preparing long-range performance projections and “what-if’ analyses, provide a natural linkage for a strategic analysis that truly takes into account the alternatives. These analyses would not seek to maximize returns as much as they would seek strategic robustness. Such strategic robustness requires that the analysis focus on developing an understanding of the sequence of events that occur as each scenario unfolds, including the ability to test the effectiveness of strategic revision that might be implemented at intermediate time points. The INTERAX method accomplishes this by linking a spread-sheet model with a cross-impact model in a simulation designed to produce individual scenarios, one interval at a time. The cross-impact model contains information about uncertain future events-probability, timing, and impacts-and trend projections. During the simulation, the events either occur or do not occur-just as they might in the real world. The outcome from each simulated period is used to adjust the probabilities of the remaining events in subsequent time intervals and the trend projections. These adjustments are made on the basis of the cross-impact factors. The spreadsheet model contains the strategic issue being explored. This deterministic analysis uses a set of assumed trend forecasts and a strategy to compute other projected trend values. The simplest example of such a model would be a long-term projection of a business unit. Business projections are usually based on an assumed income stream, projected cost patterns, and production and marketing policies. These are then used in conjunction with a series of accounting rules to compute the consequences that would follow if the assumptions are correct and the policies remain unchanged. In a business model, these consequences might reveal sales volume, returns on investments, as well as manpower projections and inventory forecasts. In a well-constructed spread-sheet model, the outputs provide all of the information essential for the user to appreciate the condition being evaluated. With the business example, it would be necessary to generate all of the costs that are important to management. The company may require more detailed information for some areas than for others. If the output information fully satisfies the user’s strategic needs, and if the projections are derived from a set of assumptions and straightforward mathematical relations, then the only conditions that can invalidate the projections are changes that cause deviations in the assumptions or policy changes. In other words, this formulation views long-term
ANTICIPATING
THE UNPREDICTABLE
205
conditions as the consequences of management decisions plus changes that affect the socalled external environment. To use this formulation effectively, it must be possible to include all of the variables that can be affected by environmental changes in the assumptions underlying the projections. In the INTERAX method, the trends in the cross-impact model are the same as the assumed trends in the spreadsheet model. In other words, the endogeneous trends from the cross-impact model are the same as the exogenous trends of the spreadsheet model. This links the uncertainty contained in the future events to the spreadsheet projections, through the assumed trends. As noted above, this procedure is designed to contain all of the external changes that can alter the output information. The linked models are used in an interval by interval simulation that can be stopped at intermediate points, at which time the analysts can play the role of decision maker, to design and introduce alternative strategies. The effectiveness of this approach depends upon many factors, including how accurate the spreadsheet model can be made, the completeness of the set of events included in the cross-impact model, the accuracy of the cross-impact information, and the creativity with which the analyst interprets the outputs and designs robust strategies. Like chess players, not all strategists are equally skilled. If analyses of this type make it feasible to use information about possible changes effectively, without causing organizational paralysis, the role of low probability information about future developments, will be sharply clarified. This may well be the key to changing the way organizations are designed and the way in which they evaluate their management. Perhaps then it will no longer be acceptable for managers to blame narrowly imprudent decisions on the uncertainty of our times, and the analysis ,of low probability, potentially important issues will become a high priority activity with considerable managerial attention. References 1. Bell, D. The Coming of Post-Industrial Society: A Venture in Social Forecasring. New York: Basic Books (1973). 2. Enzer S. INTERAX-An interactive model for studying future business environments: Parts I and II. Technological Forecasting & Social Change 17:141-160, 21 l-242 (1980). 3. Gabor, D. Invenring the Future. New York: Knopf (1964). 4. Global 2ooO Report to the President: Entering the Twenty-Firsr Century. Council on Environmental Quality and Department of State (1980). 5. Kahn, H., and Wiener, A. The Year 2ooO. New York: Macmillan (1967). 6. Meadows, D. H., et al. Limits ro Growth. New York: Universe Books (1972). 7. Simon, Julian L., Resources, population, environment: an oversupply of false bad news. Science, 208 (June 27, 1980). 8. Toffler, A. Future Shock. New York: Random House (1972).
Received 7 May 1984
FORECASTING
AND SOCIAL CHANGE
26, 201-205
(1984)
Anticipating the Unpredictable SELWYN ENZER
Issues deemed worthy of societal attention are determined by the way we view “the future.” It makes little sense to be concerned with developments that have virtually no chance of occurring. But if their impacts are great, or their chances of occurring depend upon our actions then we have to be concerned about them. These views of chance and choice affecting outcomes is often in conflict with traditional analysis that supports an image of a future that is essentially predictable-predetermined by past trends and present policies, if not by some version of a manifest destiny. As a result, issues that do not fit into the range of uncertainty defined by extensions of past data are almost always suspect. The image of a predetermined future has been greatly reinforced by the success of empirical research in the social sciences over the past century. As a result, an empirically validated hypothesis has become the only acceptable basis for legitimacy of a forecast. In fact, the greatest successes in the social sciences were achieved during periods of extended social continuity. This is the basis of the well known limitation that forecasts based on empirical data are only valid over the range of variability reflected in the data. This limitation makes traditional forecasting procedures invalid when the pace of change is rapid. But if traditional analytic methods do not provide satisfactory insights into the future, what methods should be used? The futures research community (if there is such a thing) used judgmental information about the impacts of change to supplement the projections obtained from traditional analyses in an attempt to gain a better understanding of the alternative outcomes that may evolve. These forecasts ranged from highly quantitative projections obtained from system dynamics and cross-impact models, to scenarios from such imaginative minds as Herman Kahn [5] and Dennis Gabor [3], to less structured descriptions of change such as were provided by Daniel Bell [l] and Alvin Toffler [8]. This, it turned out, produced a “Catch-22” situation. Supplementing traditional forecasts with judgmental information about future changes not only led to mutually incompatible forecasts, it made the range of possibilities so great that many traditional analysts considered such analyses counter-productive. Since anyone can express an opinion about the future, how was the serious analyst supposed to distinguish forecasts based on expert judgment from pure speculation? As a result, many traditional analysts challenged the use of judgment as unscientific. Since nothing could be more damning to a social scientist than being labeled unscientific, the community became polarized about this issue. In France for example, they even distinguished the nature of analytic organ-
SELWYN ENZER is Associate Director of the Center for Futures Research of the Graduate School of Business Administration of the University of Southern California, Los Angeles, California. Address reprint requests to Dr. Selwyn Enzer, Center for Futures Research, Graduate School of Business Administration, University of Southern California, Los Angeles, California 90089. 0 1984 by Elsevier Science Publishing
Co., Inc.
0040-1625/84/$03.00
202
S. ENZER
izations as being engaged in: “prevision” (scientific forecasting) versus “prospectives” (judgmental scenarios). This polarization not only made important emerging issues analytically off-limits to many social scientists, it actually helped to focus the scientific attack against judgmental analysis. Consider the scientific reception given to “Limits to Growth” [6] or “Global 2000” 141. Even though both of these analyses were presented as possibilities, they were attacked for being scientifically inaccurate. In other words, they were without merit because they are based on subjective relationships and not derived from empirical data. For example, in challenging concerns that higher population growth implied lower per capita economic growth, Julian Simon responded with the following: “Empirical studies find no statistical correlation between countries’ population growth and their per capita economic growth . . .” [ll. Technically this form of criticism says that the issue is not real because it is not reflected in empirical data. The implications of this criticism are inappropriate because it discounts the possibility of a future trend change entirely on the basis of the past trend itself. It is also misleading because it suggests that an issue is not important unless it is going to happen-a perfectly valid argument, if we knew what was going to happen. In the pseudo-scientific world of empirical forecasting, we presume to know what is going to happen-it is written in past data. Some of the important effects of this situation are that it discourages research into low probability, high-impact emerging issues, and discourages public support for programs designed to promote desirable (low probability) changes or to prevent potential problems simply because they appear unlikely. To overcome these constraints it is necessary to analyze and respond to possibilities before they achieve empirical maturity. This requires a social environment in which future possibilities are treated seriously before they occur. This would be much easier to accomplish if we knew how to deal with alternative possibilities in ways that guide rather than immobilize strategic action. Failure to develop methods that public and private organizations can use to deal with the uncertainty inherent in alternative possibilities has been one of the most serious failings of the futures community. From its inception, the futures research movement saw the challenge being not just to forecast what the future will be, but to make it what it ought to be. To accommodate this purpose it was first necessary to shatter the traditional mindset concerning fortune teller-like predictions of a predestined future. This was the primary intent behind such powerful futuristic notions as “alternative futures” and “inventing the future.” The implicit assumption on the part of the futures community was that once the alternatives were made clear, and it was pointed out that society could “choose” from among these alternatives by appropriate use of its resources, better decisions-and hence a better worldwould follow naturally. As it turns out, virtually every forecast depends upon uncertainties that are beyond the control of the decision-making organization involved in the analysis. Since, no organization is truly in a position to create its own future, and few know how to develop a strategy without making some assumptions with regard to the uncertainties, organizations often find frustration to be the most visible consequence of having evaluated their alternative strategic scenarios. Strategic management in the face of alternative scenarios is often seen as an impossibility. The argument given is that an organization needs a single set of assumptions in order for the various units to synchronize their strategic plans, and that this is not possible in the presence of a range of different scenarios. Does this mean that there is no way for an organization to plan for a highly turbulent environment? Hardly! Most entrepreneurs-for example military field commanders (as distinguished from Pentagontype planners), candidates for public office, football coaches, and quarterbacks-manage
ANTICIPATING THE UNPREDICTABLE
203
their affairs in the presence of rapid change without assuming the uncertainties away and without immobilizing their organizations. What do these professionals do that differs from a strategic manager in a public or private organization? They view themselves as being part of a highly dynamic process, whereas the strategic manager analyzes the options as closed-form problems that have unique correct answers. The critical issue confronting modem strategic managers is how to go about analyzing, implementing, and guiding strategies designed to cope with alternative future conditions. A corporate strategist facing a highly uncertain business could learn a great deal by studying the approach used by a battlefield commander in coping with uncertainty. The approach used by a field commander-the type of analysis, the monitoring system, and the continuous stream of modifications required by the changing conditions-is a highspeed version of the approach that is appropriate for the organizational strategist. Both face environments that are highly uncertain, where many possibilities exist but only one actually materializes. The battlefield commander deals with the uncertainty directly, while the corporate manager finds it necessary to try to eliminate the uncertainty by predicting the outcome long in advance. These two approaches can produce fundamentally different results, and they require different analyses, have different information needs, and employ radically different management styles. Because the corporate strategist has eliminated the environmental uncertainty by the use of long-term forecasts, the so-called corporate assumptions, his analysis seeks a fixed strategy that would produce the greatest longterm payoff. A&ed strategy refers to one that can presumably guide a business operation over a three-, five-, or even a 20-year time period. When confronted with alternative business scenarios, the traditional corporate strategist may analyze the strategic differences that would result from the alternatives. This would produce alternative strategies-one strategy for each environment. While this may help develop a better understanding of the consequences of uncertainty and may improve the quality of the strategies, the analysis does not help in deciding which to choose. With this approach, the choice is entirely dependent upon knowing (forecasting) which environment will ultimately occur. The military field commander would analyze the problem differently. He would be less concerned about the probabilities associated with each of the alternative environments and more concerned with his ability to change his strategy if he had anticipated one environment but later found that another environment seemed to be materializing. He would surely want to know how the strategy might be redesigned to facilitate such transitions and at what points strategic adjustments could best be accommodated. The strategy he would pursue would be one that, with proper leadership and management, appears most likely to succeed in meeting the objectives. This would not necessarily be one based on the most likely scenario. It might well be a hybrid strategy that is sufficiently flexible to meet the environmental challenges-a robust strategy. The most important elements in such a strategy are the initial commitments, those that are expected to take the organization to the first branch point. At that time the strategy would be reassessed and revised to reflect actual developments. The military commander’s approach would not end with the initial decision. The next step would be to develop an environmental scanning system that would provide early warning about potential surprises that would cause an early change in the strategy. On the battlefield, this task is the function of the sentries. The decision as to where to look and where to post sentries can be as critical to the management of a corporate strategy as it is to a military campaign. To the military commander, sentries and the information they provide are vital management inputs. Even unlikely possibilities are monitored, and indications of a change in the environment are always quickly acted on. While trend
204
S. ENZER
monitoring systems are becoming more common in public and private organizations, one rarely finds them used in a manner similar to their use on the battlefield. Changing organizational cultures so that they can be effective in managing uncertainty will involve more than just analysis. It will require changes in management procedures, organizational designs, and perhaps most importantly, reward structures. The role of change in an organization must be as firmly accepted as it is on the battlefield, where no one criticizes a commander for frequently changing a battle plan to meet changing conditions. Nor does a commander shoot the sentry who alerts him to enemy movements (his equivalent of changes). However, commanders are blamed for being caught by surprise. Excuses such as “Who could have anticipated such a development?” may console a corporate manager, but they are simply admissions of failure to a military commander. Future-oriented organizations must develop the same attitude toward change. Methodologically speaking, there is considerable hope for progress in analyzing strategic uncertainty. There is much greater awareness regarding future events today than there has been in the past. There are methods like INTERAX [2] and scenario writing that can synthesize these expectations into alternative future environments that are suitable for strategic analysis. The general availability of microcomputers and spreadsheets (e.g., VisiCalc, Lotus l-2-3, and Multiplan), and their use in preparing long-range performance projections and “what-if’ analyses, provide a natural linkage for a strategic analysis that truly takes into account the alternatives. These analyses would not seek to maximize returns as much as they would seek strategic robustness. Such strategic robustness requires that the analysis focus on developing an understanding of the sequence of events that occur as each scenario unfolds, including the ability to test the effectiveness of strategic revision that might be implemented at intermediate time points. The INTERAX method accomplishes this by linking a spread-sheet model with a cross-impact model in a simulation designed to produce individual scenarios, one interval at a time. The cross-impact model contains information about uncertain future events-probability, timing, and impacts-and trend projections. During the simulation, the events either occur or do not occur-just as they might in the real world. The outcome from each simulated period is used to adjust the probabilities of the remaining events in subsequent time intervals and the trend projections. These adjustments are made on the basis of the cross-impact factors. The spreadsheet model contains the strategic issue being explored. This deterministic analysis uses a set of assumed trend forecasts and a strategy to compute other projected trend values. The simplest example of such a model would be a long-term projection of a business unit. Business projections are usually based on an assumed income stream, projected cost patterns, and production and marketing policies. These are then used in conjunction with a series of accounting rules to compute the consequences that would follow if the assumptions are correct and the policies remain unchanged. In a business model, these consequences might reveal sales volume, returns on investments, as well as manpower projections and inventory forecasts. In a well-constructed spread-sheet model, the outputs provide all of the information essential for the user to appreciate the condition being evaluated. With the business example, it would be necessary to generate all of the costs that are important to management. The company may require more detailed information for some areas than for others. If the output information fully satisfies the user’s strategic needs, and if the projections are derived from a set of assumptions and straightforward mathematical relations, then the only conditions that can invalidate the projections are changes that cause deviations in the assumptions or policy changes. In other words, this formulation views long-term
ANTICIPATING
THE UNPREDICTABLE
205
conditions as the consequences of management decisions plus changes that affect the socalled external environment. To use this formulation effectively, it must be possible to include all of the variables that can be affected by environmental changes in the assumptions underlying the projections. In the INTERAX method, the trends in the cross-impact model are the same as the assumed trends in the spreadsheet model. In other words, the endogeneous trends from the cross-impact model are the same as the exogenous trends of the spreadsheet model. This links the uncertainty contained in the future events to the spreadsheet projections, through the assumed trends. As noted above, this procedure is designed to contain all of the external changes that can alter the output information. The linked models are used in an interval by interval simulation that can be stopped at intermediate points, at which time the analysts can play the role of decision maker, to design and introduce alternative strategies. The effectiveness of this approach depends upon many factors, including how accurate the spreadsheet model can be made, the completeness of the set of events included in the cross-impact model, the accuracy of the cross-impact information, and the creativity with which the analyst interprets the outputs and designs robust strategies. Like chess players, not all strategists are equally skilled. If analyses of this type make it feasible to use information about possible changes effectively, without causing organizational paralysis, the role of low probability information about future developments, will be sharply clarified. This may well be the key to changing the way organizations are designed and the way in which they evaluate their management. Perhaps then it will no longer be acceptable for managers to blame narrowly imprudent decisions on the uncertainty of our times, and the analysis ,of low probability, potentially important issues will become a high priority activity with considerable managerial attention. References 1. Bell, D. The Coming of Post-Industrial Society: A Venture in Social Forecasring. New York: Basic Books (1973). 2. Enzer S. INTERAX-An interactive model for studying future business environments: Parts I and II. Technological Forecasting & Social Change 17:141-160, 21 l-242 (1980). 3. Gabor, D. Invenring the Future. New York: Knopf (1964). 4. Global 2ooO Report to the President: Entering the Twenty-Firsr Century. Council on Environmental Quality and Department of State (1980). 5. Kahn, H., and Wiener, A. The Year 2ooO. New York: Macmillan (1967). 6. Meadows, D. H., et al. Limits ro Growth. New York: Universe Books (1972). 7. Simon, Julian L., Resources, population, environment: an oversupply of false bad news. Science, 208 (June 27, 1980). 8. Toffler, A. Future Shock. New York: Random House (1972).
Received 7 May 1984