Chapter 10 SUMMARY AND CONCLUSIONS

At the end of this study we summarize some of the major issues we have discussed. We began this study with the observation that the world is confronted with many complex interdisciplinary societal problems, which have a great impact on society. These problems are difficult to handle. There are many reasons for this. There may be financial constraints, political constraints or personal goals that are in contradiction with the general goals. It may be that the problems themselves are so complex that they are hard to handle. There may be a lack of experience in handling these problems and/or a lack of adequate methods and tools to analyze these kind of problem. From a wide range of possible causes, and combinations of causes, that might be responsible for not handling these problems adequately, we have focused on three issues, the problem itself, the methods and the tools. Exploring the problem itself is formulated in expectation one:

the character of the complex interdisciplinary societal problems is itself an important cause of the difficulty of handling these problems

1a is handling complex interdisciplinary societal problems so difficult because of to the special character of these problems (expectation 1)?

We assumed that a lack of methods and tools result in an inadequate handling of the problem, and that a good method and a set of adequate tools can improve the problem handling process of people. The main emphasis of this study is on:

what kind of method(s) and tools can support the problem handling skills of humans for analyzing and defining complex interdisciplinary societal problems?

Because there is little known about methods and tools for analyzing and defining complex interdisciplinary societal problems we were not able to formulate hypotheses, but had to be satisfied with exploring the area first. This exploration is formulated in the expectations:

models of complex interdisciplinary societal problems contain so much uncertainty that scenarios based on these models also contain a large degree of uncertainty. This makes it hard to make reliable predictions based on these scenarios for the future development of the problem (expectation 2)

the computer can be a fruitful tool in assisting the human being in the process of problem handling, but it cannot replace the human being (expectation 3)

for analyzing and defining complex interdisciplinary societal problems a method is needed that will support co-operative problem handling (expectation 4)

1b in what way do the problems that are studied in the field of cognitive psychology differ from complex interdisciplinary societal problems and what are the similarities that are relevant for analyzing and defining complex interdisciplinary societal problems (expectation 1)?

2 what is the relation between a scenario of a complex interdisciplinary societal problem and reality (expectation 2)?

3a why can computer tools not replace the human being in the process of handling complex interdisciplinary societal problems (expectation 3)?

3b in what way can the computer assist the human being in analyzing and defining complex interdisciplinary societal problems (expectation 3)?

4 what are the special aspects that a method for supporting the process of analyzing and defining complex interdisciplinary societal problems should possess (expectation 4)?

We approached these research questions by studying the literature. We mainly studied literature from cognitive science, together with literature from computer science. We discussed this literature and on this basis formulated our own ideas. We performed a small pilot study on knowledge based systems and we illustrated some of the theoretical ideas with examples of the Aids problem. Since it is too early to prove the quality of our ideas empirically, we have to be content with 'proof' by logical constructions.

In chapter one we gave an introduction to complex interdisciplinary societal problems, to the expectations, the research questions and the methodology of the study. In chapter two we discussed "What is a problem?", "When is something a problem?", and "What is problem handling?".

a problem is a discrepancy between the actual or (near future) situation and the desired future situation and/or a lack of knowledge and/or a lack of know-how, and/or a lack of relevant data; the problem is often undefined and the desired situation is not always clear

the process of analyzing a problem in order to gain more insight into the problem, whether or not this leads to influencing the problem in order to reach the desired situation. This process can take place actively or passively, consciously or unconsciously, routinely or onceonly, whether it is by circumventing or by forgetting the problem, by shifting the problem to another party or by (partly) changing the problem, whether through thinking, applying tools and/or methods

the character of the complex interdisciplinary societal problems is itself an important cause of the difficulty of handling these problems

We concluded that a combination of several factors make it difficult to handle these kinds of problems. The complexity and the interdisciplinary nature are two reasons that make it hard to handle those problems. The combination with other complicating factors makes it even more difficult to handle them. These further factors include the fact that often some of the relevant knowledge and data are missing, incomplete or in contradiction with each other. The problems have seldom been 'solved' before, and can mostly only be temporarily changed rather than solved, because they are embedded in, and in interaction with, a continuously changing environment. These problems are too complex, too important and too large to be handled by one person. However, analyzing the problems co-operatively also gives rise to complications such as 'group think' and hidden agendas. There is a close connection between defining the problem and suggesting interventions. A wrong, or partly wrong, or too limited a definition of the problem makes it difficult to suggest the correct interventions.

All these aspects make it difficult to handle the problems sufficiently well. We have concluded that one of the reasons for not handling the problem adequately lies in the problem itself.

However, given the nature of these problems we still wanted to explore how these problems can be analyzed optimally. Therefore we looked for methods and tools that can support the problem handling process. We turned for support to the discipline that analyzes human problem solving activities: the domain of cognitive psychology. We began by exploring what knowledge about human problem solving had been produced by research in the field of cognitive psychology. We started by exploring what is known in this field about human problem handling in order to answer the research question 1b:

in what way do the problems that are studied in the field of cognitive psychology differ from complex interdisciplinary societal problems and what are the similarities which are relevant for analyzing and defining complex interdisciplinary societal problems?

We gave a brief historical review of some important contributions to human problem handling, in the course of which the research of Selz (1922), who used the thinking-aloud method to discover how human beings think, and of De Groot (1969) on problem solving in the chess game, and some of the ideas of Newell & Simon (1972) are discussed. Newell & Simon's ideas have influenced many researchers[1]. We discussed the concept of 'problem space' and their 'state-space-search' paradigm. These concepts are discussed from the viewpoint of handling complex interdisciplinary societal problems. We also discussed some research on undefined problems: the research of Crombag (1984) on how judges and physicians handle problems and the recent research by Wierda (1991) on developing interdisciplinary information systems. We noted that most contemporary researchers on problem solving in cognitive science regard the human being as an information processing system.

We noticed that the problems on which Newell & Simon (1972) based their theoretical ideas, are small artificial domain related problems that have already been defined and solved, and which can be solved within a short period of time by one person. These problems differ greatly from the undefined, complex, societal, interdisciplinary, hard to handle problems we focus on in this study. In our opinion the state-space-search paradigm can only be applied fruitfully for artificial and rather small problems such as Newell & Simon (1972) used in their research on human problem solving and for the problems Artificial Intelligence focuses on. For handling complex interdisciplinary problems the state-space-search paradigm is not only too limited but also not correct. It suggests that it is possible to find a solution and that the solution can be found in the problem space. Finding a solution for an interdisciplinary societal problem is not always possible. For handling complex interdisciplinary societal problems fruitfully one needs knowledge of the different fields. As it cannot be assumed that one person has all the knowledge that is needed, these kind of problems need to be handled co-operatively. Co-operative problem handling complicates the problem handling process.

We concluded that there are important differences between problems used for studying problem solving in cognitive science and the problems that we focus on.

The following are some of the differences between problems cognitive psychology deals with and complex interdisciplinary societal problems.

Most problems that cognitive psychology focuses on are small artificial problems, seldom real life problems, whereas complex interdisciplinary societal problems, studied here, mainly involve real life problems.

In contrast with the research problems cognitive psychology focuses on, which are clearly described, and whose solution is known, at least to the researcher, with most of the complex interdisciplinary societal problems there is uncertainty about the beginning, the process, and the ending of the problem. For complex interdisciplinary societal problems it is often very hard to know what the solution can be. Often the problem cannot be solved, only changed.

3 The data, the description and the knowledge of the problems studied by cognitive psychology are almost always correct and complete, also the problem handling techniques are often known. With complex interdisciplinary societal problems, on the other hand, many things are unknown, or there is uncertainty about them.

The problem has already been defined for the problem cognitive psychology researchers to deal with. The problem is not defined for complex interdisciplinary societal problems.

Research problems that cognitive psychology addresses are problems that have been solved many times before. Complex interdisciplinary societal problems are often unique, and the experience of handling these problems is often lacking.

Another difference concerning the solution of the problem is the implementation of the solution. Most research problems in contrast with complex interdisciplinary societal problems do not have to be implemented.

The research problems in cognitive psychology can often be solved within a short period of time. Handling complex interdisciplinary societal problems, however, often takes a great amount of time.

Most of the problems cognitive research focuses on can be handled by one person alone, because the problems are small, simple and domain related. However most complex interdisciplinary societal problems need more than one person to be able to handle these adequately.

The two most important differences are the definition of the problem and the number of people needed for handling the problem.

The special nature of complex interdisciplinary societal problems makes it necessary to develop special methods and special tools that can support the problem handling process. This confirmes our fourth expectation.

In addition to the differences, we discussed certain similarities between the problems cognitive science focuses on and complex interdisciplinary societal problems. We found these similarities mainly in the phases of the problem handling process.

We use two terms to describe what is happening in one's mind when one thinks about a problem: the mental idea, which is vague and the conceptual model, which is more elaborate and extended. We discussed how the mental idea of the problem develops into a conceptual model of the problem during the problem handling process. By thinking, discussing and data gathering the often rather vague mental idea, gradually forms hypotheses about the conceptual model of the problem. Data gathering is done following the mental idea one has of the problem. Research by Crombag (1984) on undefined problems indicates that people tend to analyze only a few hypotheses and from that point on, only look for data which pertain their hypotheses.

In a conceptual model the phenomena and the relations between the phenomena are carefully formulated. The conceptual model is formulated on the basis of theory, hypotheses, experience, assumptions, and/or intuition. The conceptual model can be described by many models formulated in different languages, which together form the definition of the problem. In combining different models and languages one can benefit from the strong points of the models and languages while the weak points are compensated for. The conceptual model can be expressed in a seven layer model in which the different models in different languages are combined. In the first sub-cycle, the models will be primarily used as a vehicle for discussion.

The whole problem handling process can be divided into two sub-cycles. In the first sub-cycle the question should be answered what does the problem looks like? In the first sub-cycle the problem will be defined. The emphasis is on thinking, discussing and data gathering. The first sub-cycle begins with awareness of a problem and ends with a conceptual model of the problem. Although the problem handling process is a continuum, several phases can be distinguished in it. The phases in the first sub-cycle, the cycle of defining the problem, are:

The second sub-cycle is a combined process of thinking, discussing, data gathering and acting. In this sub-cycle the emphasis is on interventions. Here the question will be answered which interventions should be carried out and how they should be implemented and evaluated.

Constructing the empirical model is based on the conceptual model. Because of the lack of empirical data and knowledge about these kinds of problems, making an empirical model is difficult and there are always many uncertainties in the model.

Before interventions can be suggested the handling space has to be established. The handling space is a metaphor for the space where interventions in the problem will be sought that might lead in the direction of the desired situation. The handling space limits how, and to what extent the problem can be changed. The 'handling space' is a concept different from the term 'problem space' of Newell & Simon (1972). The handling space as such is indifferent as to whether the change will actually lead to the desired situation; one can only hope that it will be. The handling space can be described in terms of different levels and kinds of constraints. The handling space is narrowed by the constraints. We distinguish four levels of constraints. The first level is the most restrictive, the fourth level is the most permissive. At the first level of constraints the interventions in the problem will be sought within the current situation. The second level of constraints allows some more changes in the contemporary situation, although not too many, but the changes can be greater. The third level of constraints broadens the possibilities as widely possible but still within the 'normal ' possibilities of mankind and nature. The fourth level of constraints abandons the constraints of human possibilities and escapes into the imigination. In addition to levels of constraints, there are different kinds of constraints: financial constraints, political constraints, psychological constraints, geographical constraints, physical constraints, time constraints etc. Each of these constraints can be included in the different levels.

what is the relation between a scenario of a complex interdisciplinary societal problem and reality?

By answering the research question we focused on the use of system dynamic modeling for future prediction. We conclude that it is to the credit of system research, and also of the system dynamic modeling, that a tool is developed for future prediction that is able to include far more and more subtle distinctions than with regression analysis. However, system dynamic modelling has it constraints too which are partly due to the kind of problems that are focused.

We discussed three types of criticism of the use of systems dynamic modeling for scenario building: a criticism based on the theory of complex interdisciplinary societal problems, criticism based on systems theory and criticism deriving from chaos theory.

Criticism of the use of system dynamic models as a scenario for future prediction from the point of view of the theoretical ideas of complex interdisciplinary societal problems points out that the model of a complex interdisciplinary societal problem will be almost always incomplete, that there are blind spots and white spots. Some of the relations between phenomena are unknown or are wrong because of unknown facts. The relevant data are often missing, uncertain, change very fast or contradict each other. The parameters of the variables and relations between variables can often only be estimated. It seems that the more complex the societal problem is, the more this is the case. The effect of interventions is very hard to estimate. Scenarios based on so much uncertainty are also uncertain themselves.

Criticism of the use of system dynamic modeling techniques derives from two kinds of system theoreticians: from hard system scientists and from soft system scientists.

Hard system scientists criticize the methodology, in particular the fact that there is no sufficient information to make the empirical model.

Hard system scientists, further argue an ideological critique. The people who build the model have the idea that they are elite technicians, that the model is theirs, and they allow no involvement by other 'stakeholders'.

Another criticism of the hard system scientists concerns the utility of the model, the fact that the system dynamic model uses poor data.

Soft system thinkers question the underlying assumptions of system dynamic modelers that there is an external world made up of systems, whose structure can be grasped by models based on feedback processes. The soft system scientists state that the model cannot be quantified, and that subjective intentions of human beings cannot be captured in such 'objective' models. A further criticism is that system dynamic modeling offers no point of view to compare the model.

A critique based on the ideas of chaos theory is that there is too much uncertainty in the model, making predictions difficult, and that the models of complex interdisciplinary problems contain many non-linear feedback-loops. There can be moments in which these non-linear feedback loops are unpredictable. Filling the model with empirical data makes one realize there are many uncertainties in the model. This uncertainty makes it very hard or sometimes impossible to predict future developments based on the model. In many cases it will only be possible to a certain extent, to make an empirical model that fits reality.

These criticisms clearly show that for predicting future development of complex interdisciplinary societal problems system dynamic modelling also has its limitations. For complex interdisciplinary societal problems there will always be a large degree of uncertainty in the model, in the effect of the interventions and in the scenarios. Even when the selection of interventions and the comparison of scenarios is carefully conducted with as much knowledge, tools, methodological support and human effort as possible one should be very careful in using scenarios for policy making. However, knowing the limitations and having nothing better, we can use system dynamic modeling, although it should be done only with the greatest caution, and should not be used as an instrument for politicians to justify their politics.

In this third chapter we have also discussed the phases distinguished by other researchers. We noticed that they either skip, or pay little attention to the first sub-cycle of the problem handling process.

The chapter continues by discussion of decisions that are not as rationally made as one might hope they would be. Decisions are often based on a combination of prejudices, rational and irrational arguments, intuition and emotion. We discussed the different kinds of decisions as distinguished by Rosenthal (1984).

The third chapter ends with discussion of different levels of knowledge and some different kinds of problem handling techniques.

In chapter four we explored what tools can assist the problem handling process. There are many tools that can do this. We focused specially on the computer for assisting the analysis of complex interdisciplinary societal problems. The question we explored was: can the computer replace or only assist the human being? This is formulated in expectation three:

the computer can be a useful tool in assisting the human being in the process of problem handling, but it cannot replace the human being

3a why can computer tools not replace the human being in the process of handling complex interdisciplinary societal problems?

3a-1 what kind of problems are handled by programs built according to the paradigm of Artificial Intelligence?

3a-2 how are the problems that Artificial Intelligence programs focus on related to complex interdisciplinary societal problems?

3b in what way can the computer assist the human being in analyzing and defining complex interdisciplinary societal problems?

3b-2 in what way can these programs assist the human being in the process of handling complex interdisciplinary societal problems?

We restricted ourselves to the area of problem solving in Artificial Intelligence, to general problem solvers and to knowledge based systems.

We concluded that general problem solvers perform remarkable feats and can learn from experience. They do solve several small problems and have a general approach to the way they are able to solve problems that they have never solved before. However, until now they have only handled small, artificial and well-defined problems that have already been solved before, in a well-specified domain. Many research evidence in this field is derived from research in chess, and of small problems. One should be very careful in using research evidence, collected from a search in a limited artificial situation, such as a chess game, as evidence for how to 'solve' problems in situations that are far more complicated. Artificial Intelligence problems and chess problems differ enormously from the complex interdisciplinary societal problems that we focus on in this study.

After the general problem solvers, we explored knowledge based systems. We observed that knowledge based systems focus on domain related, small, already defined and already solved problems.

Knowledge based systems cannot handle problems outside their problem space. However, this is often the case with new and unexpected problems, which complex interdisciplinary societal problems are. For management and policy problems, knowledge based systems are not suitable. Complex interdisciplinary societal problems are undefined, too broad, and too complex to be handled by way of a knowledge based system.

We concluded that Artificial Intelligence research does give some interesting insight into the phenomena of intelligence and can handle small artificial problems but the question of whether those programs can replace the human being in handling complex interdisciplinary societal problems must be answered negatively. Reasons for this are:

Artificial Intelligence programs solve domain specific, and until now small problems.

Artificial Intelligence programs solve problems that have already been solved before.

Artificial Intelligence programs imply that the world is static, that the problem and the environment will not change over a certain period of time.

Exploring knowledge based systems made us curious as to how knowledge based systems would be used and developed in real life. Therefore we conducted a small pilot study in which we interviewed several developers of knowledge based systems. We interviewed four large organizations in the Netherlands in order to explore the expectations:

a A knowledge based system only handles a small part of a domain related, already solved problem

b A knowledge based system cannot replace a human being, but can to a certain extent assist a human being

General conclusions cannot be drawn based on such a small pilot study. This kind of pilot study can only provide some indications, some trends. Nevertheless on the basis of this study we concluded that knowledge based systems only support small domain related problems that have already been solved. The knowledge based systems focus on domain specific problems and within that domain on a rather small part of the domain. The knowledge based systems assist the human being in analyzing the problem and support decision making by proffering advice. None of the knowledge based systems replaces the human being.

We interviewed the same organization again four years later and found that only one had been successfully implemented. The other projects had been stopped or significantly changed for a variety of reasons such as organizational troubles, because the field was too broad, or the implementation was not well managed.

Not finding the answer in the field of Artificial Intelligence we looked at computer programs based on conventional programming methods to see if they were able to support analyzing complex interdisciplinary societal problems. We concluded that there are many programs in this area, that can support the human being in handling complex interdisciplinary societal problems. Programs such as databases, spreadsheets and text-writers etc. These are programs which are relatively independent of the kinds of problems support, support only some part(s) of the problem handling process.

By analyzing conventional programs, we focused especially on decision support systems and on group decision support systems, the latter because we are searching for tools that support co-operative problem handling. The research questions are:

3b-2 in what way can these programs assist the human being in the process of handling complex interdisciplinary societal problems?

We concluded that decision support systems are not very useful in assisting the handling of complex interdisciplinary societal problems, because for decision support systems the problem must be more defined and the data more reliable. We also discussed group decision support systems. Although few group decision support system programs have been developed and there is little experience with working with these programs in practice, some ideas of group decision support systems inspired us to some of our ideas for supporting the human being in the process of defining complex interdisciplinary societal problems. In analyzing (group) decision support systems we concluded that group decision support systems can be fruitfully applied in supporting some aspects of co-operative problem handling, for instance with brainstorming, group voting and multiple-criteria analysis. Based on this discussion we conclude that:

the computer can be a useful tool in assisting the human being in the process of problem handling, but it cannot replace the human being

Knowing the limitations and possibilities of the way a computer can support the problem handling process and knowing something of the problem handling process of human beings, we are now able to combine this knowledge in a description of a method in combination with tools for handling complex interdisciplinary societal problems.

Due to the complexity, the interdisciplinarity and the importance, the problem should be defined in co-operation. Co-operative problem handling needs a special method that supports the problem handling process. This is expressed in expectation four:

for analyzing and defining complex interdisciplinary societal problems a special method is needed to support the process

what are the special aspects that a method for supporting the process of analyzing and defining complex interdisciplinary societal problems should have?

We saw that the knowledge of the different aspects of the problem is (partly) possessed by human experts of different domains. Each expert regards the problem from his or her own personal point of view influenced by his or her profession, history, experience and political point of view. In order to gain an adequate insight into the problem the individual domain knowledge of a part of the problem should be combined to a mutual view of the problem which is more complete and which is on a higher level. That is the reason that defining these kinds of problems needs to be co-operatively analyzed by a multi-disciplinary team.

The process of co-operative problem handling can be interfered by all kinds of negative group processes as, for instance, group think, hidden agendas and collective blind spots. In order to support the co-operative problem analyzing a special approach and a special method are required that simulate the information exchange and avoids as far as possible the negative consequences of co-operative problem handling. The points which a method for analyzing and defining of complex interdisciplinary societal problem should contain are:

support attention to selection of a team and to the question "Who should do the selection?";

support alternating written and verbal information, just as individual preparation and group sessions;

support a mutual goal and analyzing the problem handling process in accordance with the different phases of problem definition;

support the participants to confront non supporting data, support them to look for and fill white spots;

support discussion of the aggregation level and demarcation of the scope of the problem;

support modeling of the domain knowledge in addition to that of the whole problem;

support avoiding: group think, collective blind spots and the negative influence of power differences among the participants.

In chapter eight we formulated a method, based on these prescriptions, for analyzing and defining complex interdisciplinary societal problems as far as this is possible: the method COMPRAM. COMPRAM stands for: COMplex PRoblem Analyzing Method. This is an example of a method for supporting the analysis of complex interdisciplinary societal problems. The method gives no algorithm for the solution of a problem, but only guidelines, suggestions and heuristics. The method should be regarded as a framework in which indications for analysis are given. In each step one can add one or more tools and methods. Basically the steps should be approached sequentially.

The method COMPRAM supports the process from an individual mental idea of a complex interdisciplinary societal problem to a mutual conceptual model of the problem by an alternate process of individual preparation and group sessions of a multi-disciplinary team. This is an iterative process of describing the problem in words, defining the concepts and phenomena and explaining the theoretical ideas on which the concepts and the phenomena are related, making a semantic model, a causal model and a system dynamic model supports defining the problem. The process of problem definition is guided by a facilitator and supported by several computer tools. Although defining a problem this way takes much time, we are convinced that taking time to define the problem thoroughly will enhance the chances of a better grasp of the problem. This way of working will in the end save considerable time, effort and money, because an improved analysis increases the chance of better changes to the problem.

We illustrated the method with examples of Aids, an example of a complex interdisciplinary societal problem. We selected Aids because it touches many disciplines and has a huge impact on many aspects of the society.

In chapter nine we compared some of the theoretical statements of the previous chapters to the empirical data of the Aids problem.

In former papers on this study we were of the opinion that the problem space could also function as a good metaphor for handling complex interdisciplinary societal problems (DeTombe, 1989a, b, c, 1990a, b, c). By studying the concept of the problem space in more detail, we realized that the problem space was too specific for the kind of problems we focus on in this study. Another unexpected result was the support we found in chaos theory for explaining certain phenomena. Confronted with uncertainty in the models and in the scenarios of complex interdisciplinary societal problems, we looked for a theory that could describe the uncertainty. We found it in chaos theory.

We analyzed several aspects of the problem handling process in general and of complex interdisciplinary societal problems in particular.

Confronted with complex interdisciplinary societal problems that are difficult to handle we assumed that improved support by a special method and tools could enhance the quality of problem defining, which in turn could enhance the quality of suggesting interventions which in the end could enhance the quality of problem changing. We explored only some of the aspects of the methods and tools, and in doing so we excluded other aspects such as, psychological, political, and financial aspects. We limited ourselves to certain major aspects of the problem handling process. There are still many things we have mentioned only briefly or omitted, because of our own white and blind spots and because of the limitation of this study. We focused primarily on the first sub-cycle of problem handling. We can conclude that handling a complex interdisciplinary societal problem is very difficult, even when supported by the method we developed.

The study is only one step (further) in the exploration of the methods and tools for complex interdisciplinary societal problems. Many issues still have to be explored and empirically tested. We hope that the method of analyzing complex interdisciplinary societal problems will contribute to a better handling of complex societal problems. We are of the opinion that we have made an initial start in the right direction. However, our approach to complex interdisciplinary societal problems is only one step on the road to a theory for problem analyzing of complex interdisciplinary problem solving. We also hope that the study will contribute to the discussion on finding better ways to support handling complex interdisciplinary societal problems. We hope that this study will inspire other researchers to explore this field further.

We began the study with the statement that complex interdisciplinary societal problems are not handled optimally, that humanity is not able to handle some of the major problems adequately. This inability to give an adequate answer to some of the major societal problems gives an uneasy feeling to many people. Particularly in this century when many people have thought that with the availability of so much knowledge, know-how, skills and technology, mankind would be able to handle all kinds of problems adequately. One may wonder what the origin of the idea is that proposes that it must be possible to handle all problems adequeately. There are some scientific ideas to which this idea may be attributed. The idea of being able to know, control and manipulate the world was based on the theoretical ideas of Newton. From that time on science was not something that could be believed, but something that had to be based on experimental evidence. The belief grew that in due time and with much effort human beings would be able to know, to control and to predict everything. If one were able to know how and why things happen, one should also be able to control, guide and even manipulate the world in the future, physical things as well as human beings. This idea was emphasized by the nineteenth century researcher Laplace (1796). The rapid development of technology re-inforced the idea that technology combined with scientific knowledge could solve all kinds of problems. These ideas were especially dominant in the fifties. This belief the ability to control and to manipulate was reinforced by the invention of the computer. The computer was thought to be a tool that could enable human beings to control and manipulate the word. These were high expectations of what a computer could do. In addition to doing accurate and fast calculations the computer could control an organization with an up-to-date information system, replace people in factories by robots, guide aeroplanes and satellites, do translations and equal or even surpass human beings in problem solving. Technologists and scientists were the people that should be able to provide the answers to all kinds of problems, problems in the field of science, technology, society and even, in the seventies and eighties, in the field of emotion and happiness (Achterhuis, 1979). Schön showed that society assumes that professionals will solve all kinds of problems (Schön, 1983 ), including the answers to major societal and technological problems. However, they seemed to have failed. Sometimes advice turned out to be wrong, projects have failed and suddenly, quite unexpectedly, there are changes everywhere that thwart all kinds of plans. Manipulating, controlling or even knowing seems far more difficult than expected. Chaos theory points out that even in systems that can be described exactly by formulae there can be periods when it is impossible to predict with certainty how the system will develop. Chaos theory shows that non-linear processes have some points which cannot accurately be predicted eventhough they are described by mathematical models, making it difficult or sometimes impossible to say how certain phenomena will develop in the future. We should abandon the primacy of control, the primacy of science and technology and the idea of total control over the world. We still have difficulties in explaining why something happens, let alone predicting what it will be like in the future.

The feeling that it may not be possible to solve all kinds of problems, to control and manipulate everything is on the increase. That we are not able or obliged to react at all to complex interdisciplinary societal problems does not mean that we cannot improve our methods and tools for problem solving.

The method as described in this study is a general tool that can be used to support the analysis of a complex interdisciplinary societal problem. The method is neutral in the sense that it says nothing about which problem can be analyzed or which intervention should be applied. Like every neutral tool it can be used for good things, for the benefit of all people and for bad things (for the benefit of some and with negative consequences for the majority). In describing the method we made no ethical statements. However, we sincerely hope that the method will be used exclusively for analyzing complex interdisciplinary societal problems for the benefit of humanity.

At the end of the study, we want to reflect on some future research on this subject. The first thing we plan to do and which we have actually started during the past year, is to experiment with the method in handling real empirical problems[2]. Meanwhile the theoretical part of the research will be extended, in the next four years, to the second sub-cycle of the problem handling process. In this way, the method can be applied to the whole course of problem handling.

We distinguished three kinds of complex interdisciplinary societal problems. Problems of knowledge, problems in which besides a problem of knowledge there are also different parties involved and urgent problems. With urgent problems there is often a combination of a problem of knowledge, a different parties problem and time pressure. Until now the method was limited to problems of knowledge. We would like to extend the method in the coming years in such a way that it can support the problems of different parties as well as urgent problems. We hope to do this in a mutual research program.

[1] The ideas of Newell & Simon are central in formation of theories on problem solving in cognitive psychology and in Artificial Intelligence.

[2] A (part of) the method was for many years applied experimentally by the author at the Delft University of Technology; now it is applied elsewhere by her and many others .

Chair Operational Research Euro Working Group

Complex Societal Problems