Summary and Discussion of Liebman (1976)

Reference
Liebman, Jon (1976) “Some simple-minded observations on the role of optimization in public systems decision making,” Interfaces 6(4) pp. 102-108.

Summary
Liebman addresses the efficacy of applying optimization tools to public decision making regarding “wicked” problems. He explains that optimization developed to solve problems that were simple, not controversial, the constraints were apparent, the client was clear, and when the result was in place gained, the client benefit and no one was hurt. He does not think that a decision-making paradigm, designed under these conditions, is applicable to public policy where there is little agreement on public goals, constraints, and evaluation criteria. Additionally, Liebman views decision making as conflict resolution, in which a moral choice on who should be benefitted, and who should be harmed, has to be made. To him, this choice must always be a human choice, no matter how flawed or irrational.
Liebman makes three “simple-minded” recommendations for how to improve our used of modeling:
1) That decision makers utilize more fully the “understanding of interrelationships within the modeled system” that the modeler gains from developing the model. This places the emphasis less on the outcome of the model, and more of the experience gained by the person designing the model.
2) Since problems can be modeled in multiple ways, decision makers should have access to multiple models, based on diverse points of view.
3) Models should be simplified and used as tools to increase public understanding and awareness.


Discussion
Liebman presents several very interesting and insightful distinctions. I thought it was interesting to compare the decision making hierarchy of private and public entities. I also liked the examples he compared of wicked and non-wicked problems. It was interesting that optimization analysis evolved in the military during WWII to solve very specific, clear objectives. It was then applied to the private sector, where goals are fairly uniform (e.x. maximizing profit) and decisions are often authoritarian (e.x. owner or CEO). It makes sense that optimization would be very effective in these situations.
I agree with several of his points. I think that a modeler should use her understanding of a system to develop simple representations of complex systems that can be used for public education. I also agree that an important function of modeling can be in determining policy alternatives.
However, there is one significant point of contention I have with Liebman’s position. Liebman states that models should be used to identify alternatives, but not to make decisions regarding wicked problems. He advocates this because wicked problems are complex, controversial, and supposedly there is no consensus on what criteria define the “best” solution. He advocates instead that policy makers should decide, no matter how “imperfect and irrational” they may be.
I think that if we should not rely on modeling, the author should propose a better tool. Decision makers are often elected officials, such as lawyers or politicians, with little to no technical expertise. I do not think their judgment is more highly qualified than the analyst, or even the computer that models the problem. Additionally, I think that it is possible to objectively reach a state of “highest benefit.” Highest benefit to me means greatest utility. The philosophy of utilitarianism states that a moral choice is one that provides the greatest good for the greatest number. By using multi-objective analysis, and defining the greatest good as “minimum cost,” “minimum environmental disruption,” and “equity” (or some combination thereof), I think it is feasible to say that an optimization model could provide, to a better degree than a politician, the “best” solution to a water resources problem.

Future work…?
I think that it would be interesting to examine several policy decision case studies. It would be especially interesting to find case studies solved under various circumstances, such as with or without models, and by degree of public participation. By modeling these case studies retroactively, I would like to compare how the chosen alternative measures up to what the model projects as the optimal solution. It would be very interesting to see if one type of decision making strategy appeared to be more effective than the others.