It might be subtitled, “Better Lies,” a reference to modeling as the pursuit of better stories about the world, which remain never quite true (a variation on the famous Box quote, “All models are wrong but some are useful.”). A few nice points that I picked out along the way,
- All thinking, even about the future, is retrospective.
- Big Data is Big Dumb, because we’re collecting more and more detail about a limited subset of reality, and thus suffer from sampling and “if your only tool is a hammer …” bias.
- A crucial component of a modeling approach is a “bullshit detector” – reality checks that identify problems at various levels on the ladder of inference.
- Model design is more than software engineering.
- Often the modeling process is a source of key insights, and you don’t even need to run the model.
- Modeling is a social process.
Coming back to the comment,
I think one of the greatest values of a model is that it can bring you to the point where you say “There isn’t any way to build a model within this methodology that is not self-contradicting. Therefore everyone in this room is contradicting themselves before they even open their mouths.”
I think that’s close to what Dana Meadows was talking about when she placed paradigms and transcendence of paradigms on the list of places to intervene in systems.
It reminds me of Gödel’s incompleteness theorems. With that as a model, I’d argue that one can construct fairly trivial models that aren’t self-contradictory. They might contradict a lot of things we think we know about the world, but by virtue of their limited expressiveness remain at least true to themselves.
Going back to the elasticity example, if I assert that oilConsumption = oilPrice^epsilon, there’s no internal contradiction as long as I use the same value of epsilon for each proposition I consider. I’m not even sure what an internal contradiction would look like in such a simple framework. However, I could come up with a long list of external consistency problems with the model: dimensional inconsistency, lack of dynamics, omission of unobserved structure, failure to conform to data ….
In the same way, I would tend to argue that general equilibrium is an internally consistent modeling paradigm that just happens to have relatively little to do with reality, yet is sometimes useful. I suppose that Frank Ackerman might disagree with me, on the grounds that equilibria are not necessarily unique or stable, which could raise an internal contradiction by violating the premise of the modeling exercise (welfare maximization).
Once you step beyond models with algorithmically simple decision making (like CGE), the plot thickens. There’s Condorcet’s paradox and Arrow’s impossibility theorem, the indeterminacy of Arthur’s El Farol bar problem, and paradoxes of zero discount rates on welfare.
It’s not clear to me that all interesting models of phenomena that give rise to self-contradictions must be self-contradicting though. For example, I suspect that Sterman & Wittenberg’s model of Kuhnian scientific paradigm succession is internally consistent.
Maybe the challenge is that the universe is self-referential and full of paradoxes and irreconcilable paradigms. Therefore as soon as we attempt to formalize our understanding of such a mess, either with nontrivial models, or trivial models assisting complex arguments, we are dragged into the quagmire of self-contradiction.
Personally, I’m not looking for the cellular automaton that runs the universe. I’m just hoping for a little feedback control on things that might make life on earth a little better. Maybe that’s a paradoxical quest in itself.