Assignment #1(a)

The article in review is by Dr. John C. Liebman, Professor of Environmental Engineering, University of Illinois Urbana-Champaign published in the "INTERFACES" journal published by INFORMS (Institute For Operations Research and the Management Sciences) in August, 1976.

Title: SOME SIMPLE-MINDED OBSERVATIONS ON THE ROLE OF OPTIMIZATION IN PUBLIC SYSTEMS DECISION-MAKING

This paper tries to address the suitability and role of quantitative modeling methods like optimization as a tool in public-systems decision making. The author illustrates various problems encountered in modeling by using two examples where public sector decision making is involved. The application of optimization techniques for improvements of effectiveness of urban firefighting organizations and also the problem of river basin quality management are discussed. It was indicated that the use of optimization techniques has contributed in the betterment of one and hasn't been so effective in the other, and this inspired the author to investigate further and put this paper together. The author emphasizes on the character of the public applications as a major factor in the success of the application of these techniques. The relative simplicity and clarity in the formation of objective functions, clear-cut and non-controversial goals and easily identifiable constraints would produce a better model and reliable and accurate results whereas, the more complex the problem becomes the tougher to model it and hence that even effects the reliability of the results. Though the nature of the problems do not change much but the employment of a complex technique requires availability of computing power and technical skills. In olden days, the method of linear programming was used for solving all sorts of optimization problems and a lot about the systems was assumed to be able to use LP. Nowadays, problems are considered to be more multi-objective and with fuzzy constraints. Methods like dynamic programming and meta-heuristics are at our disposal and computational power has been improved exponentially and is relatively inexpensive. Even after being able to model complex physical systems, there are human factors which are very tough to model. Public systems are highly interconnected and a simplified causal model is not always possible. In fact, no single optimal scenario is possible leading to controversy. Such complex problems are termed as "wicked" problems.The author also suggests the following to be kept in mind while modeling systems: a) Modeling is thinking made public. Its a way in which specialized knowledge is published in order to be reviewed, understood, critiqued by public; b) A model is not unique. Any systems can be modeled in several different ways according to what factors have been accounted for, the modelers/analysts perspective on the processes and the utility of the model or the end user. It is very well possible that models may have contradictory results on comparison and hence a better picture of the system can be obtained through putting all these model and their results together and to have a holistic view; c) The model is the message. The more complex the model the more tough it is to be understood by the non-modelers. Hence, it is advisable to have a set of simpler models which break down the more complex models and are easier to understand by the decision makers; d) Reinventing the wheel is not always bad. Its not necessary to use complex models to be used for a relatively simple problem/use and hence its advisable to have a simpler model of one's own if required which is more suited to the needs of the user. In conclusion, the author states that optimization methods have been applied to the private sector fairly successfully but they fail to perform when applied public systems due to their inherent "wickedness". Its very tough to be able to come to consensus in terms of objective functions and form tangible constraints with a lot of non-cooperation and non-conformity in the public systems which lead to conflicts. Optimization may not be applied to these problems unless these conflicts are resolved. It may be noted that even without such a resolution it is possible to apply optimization techniques which may illuminate these conflicts and present scenarios of decision making hence providing insights into the problem. Though finding answers may not be feasible when dealing with these wicked problems.

My observations:

I find this paper to be very objective and as it deals with the general idea of modeling problems it does illuminate some common and important points which are to be kept in mind while modeling. Though I believe that the context of the paper which dates back to the mid 70's is not fairly relevant to the present day as we dont only boast having the brute computational power and effective optimization methods at our disposal but also the advances in public policy, politics, social sciences etc which provide us with not only a better understanding of the wickedness but also a host of performing models which can be quantified and integrated in our system model for better results.

A possible future work on this paper would be a review of these advancements which occurred after its publishing and the coming up with another paper on similar lines.