Rene F. Reitsma
University of Colorado at Boulder
Center for Advanced Decision Support for Water and Environmental Systems
Campus Box 421
Boulder CO 80309 - 0421
Collaborative spatial decision support. Ok. So there's three aspects to this thing:
Spatially much more interesting are problems of a locational nature; for example, decisions as to where to establish that in-stream flow right, or whether to change temperature regimes below Lake Powell in order to sustain certain fish species.
Does the latter type of problem; the problem where space is to be considered a resource to be allocated among subjects or objectives associated with subjects, constitute an interesting, or better, "rich" enough set of problems to consider them "spatial" problems? Well, perhaps. Certain spatial location and allocation problems seem complex enough from a spatial point of view to warrant special attention, spatial modeling, support and consulting. However, in many cases the spatial aspects seem rather trivial when compared to the other, non-spatial aspects of even spatial problems. For instance, the allocation of a particular space to, say, residential versus open space objectives (hiking, biking, recreation, wildlife, etc.) could, under certain circumstances be considered spatially trivial. It's zoned A or B or, in the most complex case, some of it will be zoned A and some B. What really matters in those circumstances, is how a complex array of objectives, both momentarily and relative to the future (strategic, tactical) interact with a) (perceived) opportunities for satisfying these objectives and b) with each other.
Whether coordination theory is an applicable research topic for I17; we can talk about that. Spatially, applications could be possible in mapping conflict vs. harmony, or degrees of harmony in space. This would be particularly interesting if the utility functions which would compute the valuations of space as a consequence of a series of actions (policy alternatives) could be dynamically linked with their mappings into space. Note that in these cases harmony would not be computed as a simple compensatory model of momentary objectives. Harmony rests in the process and the valuations of its intermediate steps or moves relative to objectives, not the end-result.
From a non-spatial point of view, coordination theory offers at least a means of formalizing collaboration, in terms of harmonious or inharmonious actions and their valuations relative to one or more objectives. Although it remains to be seen how complex these formalizations must be in order to adequately represent the salient features of, for instance, environmental negotiations, it does at least provide a conceptual schema for an attempt to do so.
On this latter point, coordination theory could perhaps be seen as (yet?) another attempt at operationalization of the well-known Brown-Moore model of (spatial) decision-making (Brown and Moore, 1971). One of more fascinating aspects of that model was that valuations of empirical (spatial) situations is the result of a dynamic, complex interaction between
conducted experiments were we had subjects resolve a water resources allocation problem supported by a simulation model of the resource under various policy alternatives. We experimentally manipulated the conditions under which that model could be used, both in terms of frequency and private vs. group access (Reitsma et al. 1996).
conducted a detailed study into the use and role of the Colorado River Simulation Model (CRSM - Schuster, 1987), in the determination of the Colorado River Annual Operating Plan (AOP).
Where the results of the former seem to indicate that making the model more and easier accessible did hardly contribute to the quality of the problem solutions and the resolution process (both objectively and as perceived by subjects), the latter revealed that although the model maintains a central place in the negotiations, the vast majority of negotiations are about aspects of river management which are not in the least represented by the model! Yet, all participants in the AOP process want increased accessibility to the model, and significant system development and modeling are underway to comply with these requests.
Assuming that these observations and generalizations are correct, the only way in which we can explain these results is in stressing:
1. the managerial role of models in environmental management; i.e. the model as a managerial tool, a tool for establishing due process in the negotiations, regardless of the model's correspondence, and
2. reliance of participants common knowledge and reasoning capabilities to evaluate the really relevant aspects of policies. In other words, although the model provides some sort of rational, impartial, formal core to arrange a negotiation around, many of the real objects of negotiation are not modeled at all and are dealt with on an ad hoc basis. Interestingly, in the paper on coordination theory (in preparation) we argue that the latter forms a nice example of harmonious behavior in that the instigation of the AOP process itself changed the context of collaboration such that more harmonious behavior was possible.
Based on these observations, the following research questions could be thought of:
1. Validating physical models in collaborative (or negotiation) settings. Our results show that the value of these models in collaborative and/or multi-objective settings is low, except from the point of view of providing (quasi) rationality to the process. Yet, vast amounts of resources are spent on the development of these models (yes, yes, mea culpa). Question: can this claim be upheld? In other words, what is the role of these models, and how does the rationality of the models correspond with the rationality of negotiations, disputes, or policy making?
2. If it is true that physical models represent so little of the true aspects of many decision problems in, for example, environmental decision making, could those aspects be modeled differently and ought they be modeled? What other modeling paradigms are there which could contribute to the construction of better decision-making models?
3. Is it possible to develop other types of environments (e.g. GDSS, computational planning games (see below) which better support complex decision making?
When Ernest and I started to work out the idea some more, it appeared that one of the things that was more or less missing in all of this, was a theory of why these gameboard planning environments could work, and ideas on how to (experimentally) evaluate that theory and modify the games accordingly. Since we started working on this we have made progress in these areas and hope that soon we will be able to test some of the theory out in experimental settings.
Note that this issue has ties with the issue of the utility of models for decision support; namely the question: does it make a difference, and if so, how so and how much? It is interesting that as geographers or social scientists we spend so much effort evaluating the effects of policy and or the changing landscape of geographical patterns and processes. Yet, at the level of decision support we are often content with simply putting the thing together, throwing it over the wall, and hey presto, another branch on the tree of bleeding (!= leading) edge of technology.
I feel that if research initiatives are developed which are aimed at developing collaborative decision support tools, at least some amount of resources ought to be spent on the systematic evaluation of those tools in practice. This requires the development of evaluation techniques and metrics, as well as the development of data representation schemes which efficiently represent system use information; e.g. in the form of sequences of use. Much of this work has already been addressed in Group Decision Support Systems (GDSS), but it can be argued that in the context of dynamic, physically based systems such as environmental systems, this question needs to be looked at.
Such development of evaluation methodologies also applies to the use of system design methodologies. Although more an issue of software engineering in general, many of us who have developed systems before, have used different methods of designing, developing and implementing (fielding) systems. Some we liked, some we didn't. But as so many writers on software engineering point out, few of us can clearly articulate how we developed our systems, let alone provide clear statistics and measures of the system design and development process.
What we should not forget, however, is that software systems are complicated pieces of (logical) machinery, for which people often pay sizeable amounts of money and which are expected to play important roles in organizations. As such, they deserve scrutiny in design, development and implementation. Although in most engineering and sciences there is a clear distinction between the academic phases of new technology and those were the new technology goes into production; each with their own internal and external metrics of quality assurance and control, in decision support the distinction is often blurred. This leads to an often very messy situation, in which academics see themselves confronted with system development tasks that they really do not want to conduct, the more so since the relationship between effort required to build a software system well and the academic credit received for such efforts is such that many systems are "thrown over the wall" or abandoned in prototype state, a situation which is most annoying for all involved in the project.
One perspective of design and development methodologies is that they should represent the results of some of the research questions formulated here and elsewhere in the position papers. Design methodologies can be seen as the engineering aspects of building collaborative decision support tools. If the science behind is any good at all, it must be possible to translate the scientific results into concrete methodologies for system design and development. Seems like an excellent way "making a difference".
Malone, T.W., Crowston, K (1990) What is Coordination Theory and How Can It Help Design Cooperative Work Systems; ACM CSCW 90 Proceedings; pp.375 - 388.
Reitsma, R.F., Zigurs, I., Lewis, C., Sloane, T., Wilson, V (1996) Experiment With Simulation Models In Water Resources Negotiations: ASCE Journal of Water Resources Management and Planning; forthcoming.
Schuster, R.J. (1987) Colorado River Simulation System Documentation. System Overview; U.S. Department of the Interior, United States Bureau of Reclamation, Denver, CO.