Falsifiability, String Theory, and Policy

An interesting post by Noah Theory vs. Data in economics. This is related to the long-standing debate over falsifiability and science, and this ties into Popper’s demarcation problem:

Logically, no number of positive outcomes at the level of experimental testing can confirm a scientific theory, but a single counterexample is logically decisive: it shows the theory, from which the implication is derived, to be false. The term “falsifiable” does not mean something is made false, but rather that, if it is false, it can be shown by observation or experiment. Popper’s account of the logical asymmetry between verification and falsifiability lies at the heart of his philosophy of science. It also inspired him to take falsifiability as his criterion of demarcation between what is, and is not, genuinely scientific: a theory should be considered scientific if, and only if, it is falsifiable. This led him to attack the claims of both psychoanalysis and contemporary Marxism to scientific status, on the basis that their theories are not falsifiable.

This is the major criticism of String Theory – not that String Theory is wrong, but because it cannot even be shown that it is wrong. So the debate is, if something or some theory cannot be readily falsified, it is still science?

String Theory succeeds because it offers a means that is logically consistent to bridge gravity and quantum mechanics, and the strings themselves are building blocks for all particles. If someone can come up with a logically consistent theory that is as encompassing as string theory and can be tested and verified with existing technology, the physics community is all ears.

The tyranny of Popperism impedes the progression of knowledge and theory. The Graviton may never be detected, but that doesn’t render String Theory useless or invalid. Theoretical physicists and economists have the tools to dismiss theories that are nonsense prima facie, so what remain are theories that are mathematically and logically consistent even if they cannot be adequately falsified.

Nicholas Nasisim Taleb, for example, is a major critic of science that uses abstract, difficult-to-test models, instead preferring simple heuristics instead of theory.

The models, while sometimes having limitations, are good enough for the vast majority of situations. Even a non-deterministic system like stock market can often be adequately approximated with models. Black Scholes option pricing works in most instances, and then more complicated modifications like stochastic volatility, barriers, and jumps can be added for improved accuracy. Eventually the model is very comprehensive for all but very, very few outliers, but presence of these outliers doesn’t mean we throw away the model. For Taleb to imply that statisticians are clueless about the limitations of their models, when in fact statisticians are well aware of them, is pedantic nitpicking on his part, an example of how Taleb intentionally misconstrues the views of his ideological opponents to advance his own personal biases. A complete option table for a stock, with all expirations and strikes, typically has thousands of entries, all of which are updated continuously as the underlying stock rises and falls throughout the day. To make these updates with heuristic methods would be impractical, hence the use of mathematical option pricing models to perform all these updates instantly.

But public policy, on the other hand, is a different matter. String Theory is likely to remain confined to laboratories, but economic policy can have ramifications for everyday life, with individuals who may bear the consequences of bad economic policy that is based on a faulty theoretical model. So the questions is, what is sufficient burden of proof for public policy? The problem with economics, unlike the hard sciences, is that it’s almost always possible to find counterexamples. Even fundamental concepts like Comparative Advantage are subject to debate among economists, which would be like the equivalent of physicists debating Newton’s Second Law. I can cite studies where tax cuts help the economy, but others can probably find studies that show the opposite. I can show studies that raising the minimum wage hurts job growth; others can find counter-evidence. And so on. In almost every economics study, there are counterexamples.

Liberal policies like the Obama Stimulus and Cash for Clunkers failed because the results did not align with the economic models that underpinned those policies, but one could try to make the same argument against Bush tax cuts as also being an example of policy that did not meet the expectations of the model, specifically the Laffer Curve. My idea of ‘good policy’ biased in favor of private policy that combines low taxes and low regulation of Reaganomics with HBD-based public policy, and liberals will have a different idea of ‘good policy’.