A provocative article appeared on the arxiv last month:

## Inflation, evidence and falsifiability

## Giulia Gubitosi, Macarena Lagos, Joao Magueijo, Rupert Allison

(Submitted on 30 Jun 2015)

In this paper we consider the issue of paradigm evaluation by applying Bayes’ theorem along the following nested chain of progressively more complex structures: i) parameter estimation (within a model), ii) model selection and comparison (within a paradigm), iii) paradigm evaluation … Whilst raising no objections to the standard application of the procedure at the two lowest levels, we argue that it should receive an essential modification when evaluating paradigms, in view of the issue of falsifiability. By considering toy models we illustrate how unfalsifiable models and paradigms are always favoured by the Bayes factor … We propose a measure of falsifiability (which we term predictivity), and a prior to be incorporated into the Bayesian framework, suitably penalising unfalsifiability …

(I’ve abbreviated the abstract.)

Ewan Cameron and Peter Coles have good critiques of the article. Cameron notes specific problems with the details, while Coles takes a broader view. Personally, I’m more interested in the sort of issues that Coles raises, although I recommend reading both.

The nub of the paper’s argument is that the method of Bayesian inference does not “suitably penalise” theories that are unfalsifiable. My first reaction, like Coles’s, is not to care much, because the idea that falsifiability is essential to science is largely a fairy tale. As Coles puts it,

In fact, evidence neither confirms nor discounts a theory; it either makes the theory more probable (supports it) or makes it less probable (undermines it). For a theory to be scientific it must be capable having its probability influenced in this way, i.e. amenable to being altered by incoming data “i.e. evidence”. The right criterion for a scientific theory is therefore not falsifiability but testability.

Here’s pretty much the same thing, in my words:

For rhetorical purposes if nothing else, it’s nice to have a clean way of describing what makes a hypothesis scientific, so that we can state succinctly why, say, astrology doesn’t count. Popperian falsifiability nicely meets that need, which is probably part of the reason scientists like it. Since I’m asking you to reject it, I should offer up a replacement. The Bayesian way of looking at things does supply a natural replacement for falsifiability, although I don’t know of a catchy one-word name for it. To me, what makes a hypothesis scientific is that it is

. That just means that we can imagine experiments whose results would drive the probability of the hypothesis arbitrarily close to one, and (possibly different) experiments that would drive the probability arbitrarily close to zero.amenable to evidence

Sean Carroll is also worth reading on this point.

The problem with the Gubitosi *et al.* article is not merely that the emphasis on falsifiability is misplaced, but that the authors reason backwards from the conclusion they want to reach, rather than letting logic guide them to a conclusion. Because Bayesian inference doesn’t “suitably” penalize the theories they want to penalize, it “should” be replaced by something that does.

Bayes’s theorem is undisputedly true (that’s what the word “theorem” means), and conclusions derived from it are therefore also true. (That’s what I mean when use the phrase “Bayesian reasoning, or as I like to call it, ‘reasoning’.) To be precise, Bayesian inference is the provably correct way to draw probabilistic conclusions in cases where your data do not provide a conclusion with 100% logical certainty (i.e., pretty much all cases outside of pure mathematics and logic).

When reading this paper, it’s worthwhile keeping track of all of the places where words like “should” appear, and asking yourself what is meant by those statements. Are they moral statements? Aesthetic ones? And in any case, recall Hume’s famous dictum that you can’t reason from “is” to “ought”: those “should” statements are not, and by their nature cannot be, supported by the reasoning that leads up to them.

In particular, Gubitosi *et al.* are sad that the data don’t sufficiently disfavor the inflationary paradigm, which they regard as unfalsifiable. But their sadness is irrelevant. The Universe may have been born in an inflationary epoch, even if the inflation paradigm does not meet their desired falsifiability criterion. The way you should decide how likely that is is Bayesian inference.

“By considering toy models we illustrate how unfalsifiable models and paradigms are always favoured by the Bayes factor”

This is not correct. An unfalsifiable theory has infinite degree of freedom – as soon as we ask it to compete against a theory with finite degrees of freedom, the unfalsifiable theory will perform maximally badly (P = 0), regardless of the accumulated evidence. (With a large number of degrees of freedom, the prior probability must be spread thinly over a high-dimensional hypothesis space, leading to less prior density in the region picked out by the data – with infinite DOF, the penalty is maximally punishing.)

For this reason, falsifiability is a sound requirement, though it often gives (and was born from) a badly distorted impression of how science works.