Liebman 1976

Liebman, Jon (1976) “Some Simple-Minded Observations on the Role of Optimization in Public Systems Decision-Making,” Interfaces 6(4) pp. 103-108.

Summary:
Public-sector decision-making requires a different approach than private-sector decision-making. One example of successful problem-solving is with the improvement of urban firefighting organizations. A second area of applied modeling is with river basin quality management. The first example was successful and helpful to the public system, while the second did not reach useful enough conclusions to make improvements in the system.

In most initial applications of operations research, it was easy to find solutions since the problems had clear cut objectives. Nowadays, as techniques in operations research become more advanced, more complex problems can be addressed and things are no longer simple. Especially in public-sector decision-making, the goals, desired results, intended and unintended side-effects are difficult to determine. Goals are usually set by congress regardless of the many differences in public opinion, but it is uncertain if the goals set by congress always applicable and best to model. Another thing to consider is the fact that models do not have the complexities of a system built into them the way humans do, for instance complexities of morality and human values. Private-sector decision-making can avoid some of this difficulty because the goals in private sectors are often more clearly defined and shared by all members of the organization. This isn’t so in public sectors. In the case of the firefighting operations optimization, the goals are clear-cut with little argument. In the case of the river basin quality management, the goals are far from clear and the case is complicated.

With complicated problems and complicated goals, these new optimization tools are best used when they identify a group of alternatives for the decision maker to select from rather than choosing a single alternative as best. There is a certain amount of understanding gained by building a model and playing around with its parameters. This understanding might be communicated through a sensitivity analysis, but in order to gain the full understanding of the system it is best for a decision-maker to get in and work with the model him or herself. Since a model has unclear goals, different modelers might build different models for the same problem based on their own assumptions and biases. It is better to supply a decision maker with results from a number of models instead of just one. When a modeler builds a complex model, a lot of knowledge can be gained through that development, but the complexities do not benefit the decision maker much. It is better that the developer takes what he or she learns from the process of building a complex model and uses that to build a more simple model to give to the decision maker. The process of building a model helps the modeler understand the situation much better than if the modeler were to try to pick up on a model that was built by another person. Since this is the case, it is not a bad idea for a new analyst to rebuild a model from scratch rather than build upon a model previously built.

In conclusion, the article wishes that modelers are aware of when and how modeling tools can appropriately be applied to public-sector decision-making.

Discussion:
This paper does the important work of informing modelers to be wary of trying to model complex public-sector problems with the expectation that they can find a single optimum solution. This paper was fairly short in length and did not cover in depth actual cases where public-sector modeling struggled to find answers. The article would’ve been strengthened if the author had delved into the details of the river basin quality management issues that modelers have faced in the past.

If this were my research, it would be interesting to experiment with giving a complicated model to different modelers and see the differences in the models they produce. Also, as further research I may outline in a paper the methodology behind providing a range of alternatives rather than selecting an optimum, since this is in line with my interests in systems research.