Is the goal of science explanatory power rather than increasing our belief for true theories?

Question

In the article here, David Deutsch argues:

By ‘Bayesian’ philosophy of science I mean the position that (1) the objective of science is, or should be, to increase our ‘credence’ for true theories, and that (2) the credences held by a rational thinker obey the probability calculus. However, if T is an explanatory theory (e.g. ‘the sun is powered by nuclear fusion’), then its negation ~T (‘the sun is not powered by nuclear fusion’) is not an explanation at all. Therefore, suppose (implausibly, for the sake of argument) that one could quantify ‘the property that science strives to maximise’. If T had an amount q of that, then ~T would have none at all, not 1-q as the probability calculus would require if q were a probability.

Also, the conjunction (T₁ & T₂) of two mutually inconsistent explanatory theories T₁ and T₂ (such as quantum theory and relativity) is provably false, and therefore has zero probability. Yet it embodies some understanding of the world and is definitely better than nothing.

Furthermore if we expect, with Popper, that all our best theories of fundamental physics are going to be superseded eventually, and we therefore believe their negations, it is still those false theories, not their true negations, that constitute all our deepest knowledge of physics.

What science really seeks to ‘maximise’ (or rather, create) is explanatory power.

Further quote on Bayesian epistemology:

Prevailing discussions (e.g. Dawid & Thébault, 2014; Greaves & Myrvold, 2010) of the testability of various versions of quantum theory have approached the matter indirectly, in terms of support or confirmation – asking how our credence (degree of belief) for a theory should be changed by experiencing results of experiments. However, experimental confirmation is a philosophically contentious concept. Notably, it is rejected root and branch by Popper (1959). I shall present an account of the nature and methodology of scientific testing that closely follows Popper׳s. It differs from his, if at all,3 by regarding fundamental science as exclusively explanatory. That is to say, I take a scientific theory to be a conjectured explanation (explanatory theory) of some aspects of the physical world – the explicanda of the theory – that is testable (I shall elaborate what that means below) by observation and experiment. A scientific explanation is a statement of what is there in reality, and how it behaves and how that accounts for the explicanda. Neither confirmation nor credence nor ‘inductive reasoning’ (from observations to theories or to justifications of theories as true or probable) appear in this account. So in this view the problem described in Section 1 is about testing theories. This contradicts the ‘Bayesian’ philosophy that rational credences obey the probability calculus and that science is a process of finding theories with high rational credences, given the observations. It also contradicts, for instance, instrumentalism and positivism, which identify a scientific theory with its predictions of the results of experiments, not with its explanations.

The above quote comes from the paper here.

It seems that the gist of his argument is that credences (subjective beliefs about a theory) are merely feelings which are irrelevant to the explanatory power of a theory, and the latter are what we really are after. Is this fairly accurate?

Answer 1

It's both. Scientific endeavour is complex and answers to many different requirements and objectives. Science itself has many different branches that have different paradigms of theories and explanations. This is why attempts to give simple accounts of how science works inevitably oversimplify.

Deutsch is surely correct in saying that science aims at explanation and not just true belief or reliable prediction. But at the same time, believing propositions that are true and avoiding belief in propositions that are false is much better than the other way round. There are many criteria for what constitutes a good theory, including such things as consistency, coherence, clarity, simplicity, comprehensiveness, lack of adhocness, fit with empirical data, consilience, testability, high predictive value, high practical value, and maybe others. Explanatory value is definitely one of the criteria, but hardly the only one.

Also, these criteria are in tension with one another. We are willing to tolerate some degree of adhocness or some complexity or even some inconsistency if a theory performs well overall. We might hope to resolve those issues later when we know more.

But to attempt to describe all of scientific research as a single method such as falsification does not correctly describe what scientists do. In many cases we have a growing body of knowledge that we have established as true. The biochemical mechanism of heritability is accounted for by DNA. That is not some provisional theory that biochemists are actively doing their best to falsify. Smoking causes lung cancer. The Earth's continents move around due to plate tectonics. Atoms are the fundamental units of chemical elements. Sleeping sickness is transmitted by tsetse flies. We can catalogue an immense amount of scientific knowledge that we have established as true and which it would be a waste of resources to attempt to falsify.

The concept of explanation itself is notoriously difficult to explain. There is an SEP article on it which describes some approaches, but there is no concensus on it. Deutsch's, "A scientific explanation is a statement of what is there in reality, and how it behaves and how that accounts for the explicanda," is rather naive. How do we distinguish between a good explanation and a bad one? In the last analysis, how does an explanation extend beyond a collection of predictions? To what extent does quantum field theory constitute an explanation of anything? Does anyone really understand quantum field theory? It's a useful theory and makes reliable predictions, but is it explanatory?

It is fair to say that the scientific method is also not just a matter of Bayesian updating on observations. Though Bayesian methods can be useful in statistics. Bayesian methods help to quantify the relationship between hypotheses and empirical data, but they do not take account of explanatory value and that is an important limitation.