Collaborative exploration of spatial problems

Paul Hendriks
Dirk Vriens

University of Nijmegen
Business School
Email address: p.hendriks@bw.kun

Position paper for the NCGIA I17 Initiative on 'Collaborative Spatial Decision Making'

The complexity of spatial problems

Spatial problems are usually complex problems. If we are to offer adequate support to deal with spatial problems, we have to get to grips with the nature of their complexity. There are a number of elements that contribute to this complexity. The first is the relation between problems and their solution space: getting a sufficient understanding of both the problem space and the associated solution space may suffer from what we might call a 'technical' complexity. It may, for instance, be unclear which criteria are relevant, how these are to be combined, what measures are feasible and what their results may be, how alternative solutions may be scaled as more or less preferable, etcetera. A second source of complexity is introduced when more than one goal is associated with the problem. This will lead to the competitive existence of more than one problem space and more than one solution space at the same time. A next element to be considered is the fact that goals can be stated at various levels of abstraction, in the sense that every solution may be seen as a problem at a lower level of abstraction, and every problem may be seen as an alternative solution at a higher level of abstraction. The situation is further confused by what might be called the social context of problems: the existence of different and possibly conflicting goals is usually associated with the existence of different parties, with different interests, different positions with varying degrees of power within the decision making process, different access to information sources, etcetera. Finally, a fifth complication is made up by the fact that goals may vary over time as may our understanding of alternative solutions, thereby causing a shift in the problems to be solved as well as their solutions. An example of a spatial decision problem borrowed from Reitsma & Behrens (1991) may help clarify these various, what might be called, 'domains of complexity'. The example describes the case of river basin management in the western part of the United States. A multi-faceted setting for water management is defined here by the great variety of factors at play, such as shortage of water supply, the occurrence of flooding, the use of water for such widely diverging purposes as power generation and rafting. Many of the management problems involved fall within the first category, for instance perceiving which technical measures may help control the water flow and what effects they will have: what (and when) is the effect of closing or opening dams up the river for the downstream area, how do effects of measures at individual dams combine, etcetera. The situation is complicated by the fact that apart from water control to prevent floods, the waters also have to be managed for the generation of hydropower, meeting urban and rural demands for water, maintaining an economic viability in fish hatcheries, etcetera (second source of complexity identified before). In the third place we may identify these goals and objectives at various levels of abstraction. For instance: the use of water as a source of power cannot be studied in isolation, but should be related to the fact that the overall goal of power supply can also be attained from alternative sources, and that the more abstract goal of power generation may have competitors (for instance: energy saving) for its own higher order goals. In the fourth place, the decision process in the case of river basin management is, as Reitsma & Behrens (1991, p.33) explain, not something that can be easily pinpointed to a number of clearly identifiable meetings in some management office. There are many parties involved, including local and federal government, environmental pressure groups, individual consumers and consumer groups, firms, etcetera, all spread out over a wide decision network with more or less clearly identifiable cross links. A final complication stems from the fact that neither these parties, nor their goals remain stable over time, thereby making the river basin management problem a highly dynamic one.

The case for collaborative decision support

The case of river basis management is clearly a case in which a group decision support tool may prove fruitful: the complexity of the situation consists among other things in the presence of various interest groups. It will be clear that when looking for tools to support such a complex process of decision making from the multi-party perspective, our main concern should not focus mainly on ways to improve cooperation, but to address the various sources of complexity at the same time. If we fail to do so, and if we instead concentrate on solving the complexities of only one source (for instance the 'technical' source) for the various parties involved, we run the risk of providing the right solutions for the wrong problems. The question may then be asked what the goal is of designing tools in the give situation. Three alternatives have been discerned (Reitsma & Behrens, 1991, p.34):

a. aim at solving the problems, that is design tools in such a way that they will allow the decision makers (DMs) to relate problems to solutions; this approach is, for instance, taken when models (such as MCA) are made available to the DMs; underlying assumption is then that the use of these models, for instance by allowing variations of the model parameters, may then prove helpful to the DMs to find their way through the solution space;

b. aim at satisfying the participants, for instance by exploring ways to reach consensus with other parties involved about alternative solution paths; this second alternative may build on the first, for instance by providing means for participants to have their individual modeling outcomes combined with these of other participants;

c. build the group decision support system as an information generating tool that will help participants to gain more insight into how the proposed decisions will affect their own particular situation. Reitsma & Behrens identify this as 'the informative GDSS'.

Common to these approaches is that, to a different degree, they all converge around problem solutions. The paper tries to elaborate a fourth approach, an approach that concentrates on problem exploration instead. Basic idea is that the combination of different sources of complexity as sketched before should be integrated and addressed by the DM as much as possible. Before thinking in terms of alternative solutions to these various aspects of complexity, it is seen as essential to explore the nature of complexity of the problem at hand as widely as possible. The approach therefore shares with the third alternative described before (the informative GDSS) the concern not to strive for consensus too soon, it differs from this approach mainly by its problem orientation rather than solution orientation.

A formal basis in systems theory

Key issue in a collaborative problem exploration approach is finding a formal representation that will allow all sources of complexity to be represented. In a recent paper (Vriens & Hendriks, 1995) we have indicated that the theory of adaptive systems may serve as a basis that will allow the introduction of dynamic aspects (the fifth source of complexity as described before). An adaptive system is basically a system that can show behaviour aiming at "maintaining the essential variables within [...] limits" (Ashby, 1960, p.58). Systems theory offers the tools to provide a general model for problem situations, both at the conceptual level and at the level of an actual tool to be used to model all relevant aspects. When put in systemic terms, a problem can be said to occur when a system, in the cybernetic sense, does not manage to keep its essential variables within certain limits. At this stage it becomes vital for the system to adapt in order to reach a new state of equilibrium. In order to do so a match has to be found between the variety of the environment causing the problem situation and the variety of possible actions. Here the GDSS comes into play, as it is conceived here as a means to relate as many alternative actions as possible to the perceived goals (a more elaborate description of adaptive systems and how they help address the various sources of complexity shall be given in the proposed contribution). In that paper, however, we did not address the social context of decision making, that is the explicit recognition of the fact that conflicting goals are usually linked to opposing parties in the decision process. There are basically two alternative ways to do so: the first is to introduce a model of the opposing goals into the "single explorer" situation, the second is to model every distinct goal situation as a system in its own right and establish conflicts and overlaps between these individual situations in terms of the actions conceived within each of them. In the contribution this second approach will be elaborated, as it is superior in terms of allowing individual parties to explore their own problem space independently, and identify conflicts and overlaps with other problem spaces as a separate step. Central in the approach is its focus on actions that are feasible within each individual context and the fact that it stimulates the participants to come up with as many alternative actions as they can conceive. The task of the CSDM-tool is both to help participants define their private problem spaces, and to suggest matches between the exploration outcomes of participants with interests that appeared as opposite, as well as to identify situations where no such match has yet been reached. It should be stressed that in the approach as advocated the focus is not on finding consensus or starting negotiations, but on as wide a problem exploration as possible, in order to better the chances for overlaps in actions. A simple example may help clarify this: imagine two people wanting to go to the movies together but having different preferences as to which movie to pick. It may be suggested to each of them to contemplate on what they hope to gain by going to this specific movie, and the outcome for both parties may be something like 'recreation and relaxation'. It may then be suggested to them to seek for alternative ways to satisfy this objective, and at the end of the process we may see them going out to dinner and live happily ever after. Another - classical - example given by Ackoff illustrates the same point: in a multi-storied office building firms occupying the upper floors complained of the long waiting time for the elevators, and three lines of action were suggested to solve the problem. The first was to introduce a computer system to manage the, what we might call, ups and downs of the elevators more intelligently (a sort of decision support tool, though not a collaborative one), the second to increase the number of elevators, and the third to reserve certain elevators for the higher floors. None of these appeared to solve the problem, complaints persisted. These, however, stopped when someone came up with the idea to put mirrors up in the elevator hall, giving the persons waiting the opportunity to check their ties and make-up, and to spy on their fellow waiters. Problem solved. The waiting time in terms of minutes and seconds had not changed, but its perception had. As in the previous example, a creative problem exploration not aimed at consensus but at as wide an search for feasible actions as possible, proved to be far more rewarding than a conventional problem- solution centered approach. Our elaboration of adapted systems theory with collaborative elements may be seen as an attempt to provide this creative problem exploration process with the formal basis necessary for defining collaborative decision tools.


Ashby, W.R. (1960) Design for a Brain. London: Chapman and Hall.

Reitsma, R.F. & Behrend, J.S. (1991) Integrated river basin management: a decision support approach; in: Klosterman, R.E. (ed.) Second International Conference on Computers in Urban Planning and Urban Management, Proceedings; Oxford, July 1991; pp.29-41.

Schrage, M. (1990) Shared minds: the new technologies of collaboration; New York: Random House.

Vriens, D. & Hendriks, P.H.J. (1995) How to define problems: a systemic approach; in: Timmermans, H. (ed.) Decision support systems in architecture and urban planning; London: Chapman & Hall (in press).