Summary : Water Resources Management: The Myth, The Wicked, & The Future

Reed, P. M, and Kasprzyk, J. R., 2009, “Water Resources Management: The Myth, The Wicked, & The Future,” ASCE Journal of Water Resources Planning & Management, v135, n6, pp. 411-413.

Water management science has to explore the backgrounds, legacies, and deficiencies of the problem-solving frameworks about water resource management field. The main purpose in this article is here that water management field is necessary to clarify what this field is now and has to be in the future.

The Optimality Myth

In the past years, optimization in water management field has showed a lot of limitations to resolve water resource problem, because single-criterion optimality is not fit to resolve complex water resource system environment. Climaco (2004) tried to reinforce this limitation, getting rid of using narrow scientific definition of optimality in water resource management. However, it is still difficult for analyst to define balancing technological innovation and its concomitant societal risks, using single-criterion. Therefore, hidden consequences, compromises, and hypotheses from stake holders and decision makers have to be avoided in complex public systems planning. Various approaches in modeling can also enhance decision making, and we can also avoid locality (or myopia) in water resource management.

Water: A Wicked Class of Problems

“Wicked” problem is a still challenge in water resource management when we try to resolve social value problem. First, it is hard to get exact formula, because of people’s different tastes. Second, it cannot be simply true and fault. Third, it is exclusive and not decomposable. Fourth, some of the values can be irreversible. Fifth, sometimes it is difficult to predict a range of the results or the impacts. These could be future works in a water management field.

The Future Requires Constructive Modeling

In water resource management, there are critical methodological limitations in traditional methods for many years. As water-cycle science have grown, the gap between water management and water-cycle science becomes significant. For executing successful water management modeling, diversity of hypothesis and broadly knowledge have to be provided to stakeholders, decision makers, engineers, and scientists. Moreover, by augmenting observations and estimates to social value and by build a wide range of alternatives to help water resource decision making, the gap between the water management and science have to be reduced for the future of water resource management.

a.       Why is the paper interesting or significant? This article provides conceptually what kind of myth analysts in water management have to have and what “wicked” problem is in this field and what they have to do for the future.

b.      What are the faults or limitation of this work? It is too conceptual.

c.       What is the possible work extending from this work? If this were your research, what would be your next steps to fix the work, apply ideas to other applications, or start new work from these ideas? There are no specific solutions and detailed examples about the matters. If it was my research, I try to provide some examples for reader’s understanding about problem and solution. For next research, I like to figure out how this conception can change into a methodology in detail for being able to apply real world such as constructing dams, navigations which are issue politically and economic.

Summary : Some simple-minded observations on the role of optimization in public systems decision making

Liebman, J., 1976, “Some simple-minded observations on the role of optimization in public systems decision making,” Interfaces, v6, n4, pp. 102-108.

Previous optimization has been successfully applied to resolve private sector problem, but in public sector problems there are many defects to resolve.

In the past, using linear programming method was so useful and successful for optimization in private sector matter and the optimization skills also have grown in many ways, even though there was not so much understanding between decision-maker and analyst. However, when analysts have attempted to resolve public matters, the results of the optimization have showed many failing cases due to some reasons. First, public and social system is vague and difficult to find clear interconnection between cause and effect. Second, there is no perfect agreement in public due to the difference of human tastes. Third, analyst cannot fully comprehend problem of decision-makers, which easily results in failing optimization. This public problems call “wicked problem”.

Therefore, a suggestion is here that optimization should not be used to resolve conflicts which are diversity of problem perceptions, objectives, goals, measures of effectiveness, and constraints.

Optimization tools seem that we cannot use the tools to get the perfect result of optimization in public matters due to the too huge range of elements, which have to be considered by analyst. However, the optimization can still play pivot role in decision. In large degree, analyst cannot provide the best answer but can offer various alternatives for decision-makers to be able to select one.

a.       Why is the paper interesting or significant? I think people who used optimization to resolve public sector problem at that time could not know what was wrong when the modeling and optimization failed, because the results of optimization were usually right. However, this article showed that what was wrong and promoted further study.

b.      What are the faults or limitation of this work? It is too conceptual and mostly about problems.

c.       What is the possible work extending from this work? If this were your research, what would be your next steps to fix the work, apply ideas to other applications, or start new work from these ideas? I would like to select several good examples about failing optimization, and I will analysis them and try to figure out which elements are significant in public sector cases. Then, I will try to compare prior models and new models until I will get some good results.