I’m at Stanford today to discuss a paper, Policy Models as Networks of Beliefs. After circulating my draft, I realized that the following is really my argument. …

We use mental models to think about and discuss contested questions of policy. Worthy models typically have these features:

1. They have many components, not just a few. A model might include a causal inference, such as “spending more on x produces better outcomes.” But those two components (the spending and the outcomes) must be part of a much larger model that also explains why certain outcomes are valuable, where the money would come from, what else effects the system, and so on.
2. The components should be connected, and the resulting structure matters. Structures can take various forms (e.g., root-cause analysis, vicious cycles). There is no single best structure.
3. Pieces of models may prove regular. For instance, maybe spending more on x regularly produces better outcomes, all else considered. But such regularities only apply to small aspects of good models. The science-like effort to find regularities can only get us so far.
4. Some components of any worthy model should be values or normative claims. Some normative components have regular significance in all models. However, many value components change their significance depending on the context. Equality, for example, does not consistently mean the same thing and may not always be desirable.
5. If a model proves influential, it can change the world, which can require a new model. For example, arguing that more money should be spent on X could cause more funds to be allocated to X, at which point it would no longer be wise to increase the funding. Models are dynamic in this sense.

I believe this account supports a pluralistic, polycentric, pragmatist, and deliberative approach to policymaking, as opposed to a positivistic one.

See also: choosing models that illuminate issues–on the logic of abduction in the social sciences and policy; different kinds of social models; social education as learning to improve models; etc.