Abduction — beyond deduction and induction
from Lars Syll
Science is made possible by the fact that there are structures that are durable and independent of our knowledge or beliefs about them. There exists a reality beyond our theories and concepts of it. Contrary to positivism, yours truly would as a critical realist argue that the main task of science is not to detect event-regularities between observed facts, but rather to identify and explain the underlying structures/forces/powers/ mechanisms that produce the observed events.
Given that what we are looking for is to be able to explain what is going on in the world we live in, it would — instead of building models based on logic-axiomatic, topic-neutral, context-insensitive, and non-ampliative deductive reasoning, as in mainstream economic theory — be so much more fruitful and relevant to apply inference to the best explanation.
In science we standardly use a logically non-valid inference — the fallacy of affirming the consequent — of the following form:
(1) p => q
(2) q
————-
p
or, in instantiated form
(1) ∀x (Gx => Px)
(2) Pa
————
Ga
Although logically invalid, it is nonetheless a kind of inference — abduction — that may be factually strongly warranted and truth-producing.
Following the general pattern ‘Evidence => Explanation => Inference’ we infer something based on what would be the best explanation given the law-like rule (premise 1) and an observation (premise 2). The truth of the conclusion (explanation) is nothing that is logically given, but something we have to justify, argue for, and test in different ways to possibly establish with any certainty or degree. And as always when we deal with explanations, what is considered best is relative to what we know of the world. In the real world, all evidence is relational (e only counts as evidence in relation to a specific hypothesis H) and has an irreducible holistic aspect. We never conclude that evidence follows from a hypothesis simpliciter, but always given some more or less explicitly stated contextual background assumptions. All non-deductive inferences and explanations are necessarily context-dependent.
If we extend the abductive scheme to incorporate the demand that the explanation has to be the best among a set of plausible competing potential and satisfactory explanations, we have what is nowadays usually referred to as inference to the best explanation.
In inference to the best explanation, we start with a body of (purported) data/facts/evidence and search for explanations that can account for these data/facts/evidence. Having the best explanation means that you, given the context-dependent background assumptions, have a satisfactory explanation that can explain the evidence better than any other competing explanation — and so it is reasonable to consider the hypothesis to be true. Even if we (inevitably) do not have deductive certainty, our reasoning gives us a license to consider our belief in the hypothesis as reasonable.
Accepting a hypothesis means that you believe it does explain the available evidence better than any other competing hypothesis. Knowing that we — after having earnestly considered and analyzed the other available potential explanations — have been able to eliminate the competing potential explanations, warrants and enhances the confidence we have that our preferred explanation is the best explanation, i. e., the explanation that provides us (given it is true) with the greatest understanding.
This, of course, does not in any way mean that we cannot be wrong. Of course, we can. Inferences to the best explanation are fallible inferences — since the premises do not logically entail the conclusion — so from a logical point of view, inference to the best explanation is a weak mode of inference. But if the arguments put forward are strong enough, they can be warranted and give us justified true belief, and hence, knowledge, even though they are fallible inferences. As scientists we sometimes — much like Sherlock Holmes and other detectives that use inference to the best explanation reasoning — experience disillusion. We thought that we had reached a strong conclusion by ruling out the alternatives in the set of contrasting explanations. But — what we thought was true turned out to be false.
That does not necessarily mean that we had no good reasons for believing what we believed. If we cannot live with that contingency and uncertainty, well, then we are in the wrong business. If it is deductive certainty you are after — rather than the ampliative and defeasible reasoning in inference to the best explanation — well, then get into math or logic, not science.
What I do not believe — and this has been suggested — is that we can usefully lay down some hard-and-fast rules of evidence that must be obeyed before we accept cause and effect. None of my viewpoints can bring indisputable evidence for or against the cause-and-effect hypothesis and none can be required as a sine qua non. What they can do, with greater or less strength, is to help us to make up our minds on the fundamental question — is there any other way of explaining the set of facts before us, is there any other answer equally, or more, likely than cause and effect?
A lot of talk and no actual example. Does it apply to anything at all? I don’t know. I do know it is not the essence of the scientific method.
“Cause” and “explanation” are in the eye of the beholder. Consider the force of gravity between two bodies. It is given by multiplying the two masses and dividing by the square of the distance between them. F=G.m1.m2/d/d (G is a constant).
It applies to two bodies which are perfectly spherical and of uniform density and which are not influenced by any other bodies. These conditions exist nowhere. This is science. A theory (hypothesis, law, model) expresses a theoretical relationship between theoretical entities.
It is not deduced, abduced or inferred from reality. The starting point for science is a hypothesis and where the hypothesis came from is irrelevant. Yes, it is invented with a view to explaining reality, and yes, with a view to working out causes. But does the formula actually explain gravity? Does it tell you the cause of gravity? Really? I don’t see that it does.
What the theory tells you is what the relationship between those concepts would be if the universe were pure, perfect and one thing at a time—how things would be if the universe conformed to the theory. That is science.
And that’s economics—theoretical relationships between theoretical concepts. It is not a bug; it’s a feature. No other social science adopts this process. Economics is the only successful social science.
The problem with economics is evidently not its method. I think its problem is that it has exhausted its premises (like, 50 years ago). That would mean it needs to generate new hypotheses based on new premises.
I believe that the economics build on “perfect knowledge, instant market clearing and approximating aggregate behaviour with unrealistically heroic assumptions of representative actors, just will not do.” (Lars Syll 2010 What is (wrong with) economics. Real-World Economics Review 54, p.53) I also believe the very phenomena we want to study are “uncertainty, disequilibrium, structural instability and problems of aggregation and coordination between different individuals and groups.” (ibidem.)
However, is Lars Syll right in demanding to oust mathematics from economics? As he knows well, John Maynard Keynes stated in his Obituary of Alfred Marshall: 1842-1924, Economic Journal Vol. 34, No. 135 (Sep., 1924), pp. 311-372:
Although he knew that
,
Keynes did not think it necessary to oust mathematics, but thought that, to be a mathematician in some degree is a necessary talent or capability to be a good economist.
I made a mistake in putting an end tag. The first two paragraphs must be set as follows:
I believe that the economics build on “perfect knowledge, instant market clearing and approximating aggregate behaviour with unrealistically heroic assumptions of representative actors, just will not do.” (Lars Syll 2010 What is (wrong with) economics. Real-World Economics Review 54, p.53.) I also believe the very phenomena we want to study are “uncertainty, disequilibrium, structural instability and problems of aggregation and coordination between different individuals and groups.” (ibidem.)
However, is Lars Syll right in demanding to oust mathematics from economics? As he knows well, John Maynard Keynes stated in his Obituary of Alfred Marshall: 1842-1924, Economic Journal Vol. 34, No. 135 (Sep., 1924), pp. 311-372:
Functions and significance of mathematical and logical reasoning in economics
I thank Lars Syll for indicating now a classical paper on detecting causation in epidemiology: The Environment and Disease: Association or Causation? (1965)by y Sir Austin Bradford Hill (The presidential address at the first meeting of the Section of Occupational Medicine of the Royal Soceiety of Medicine.) Proceedings of the Royal Society of Medicine 58(5): 295-300[7-12], which is now considered as “public health classics“.
Here are three paragraphs from Bradford Hill (1965):
Here are the list of nine viewpoints (features, characteristics) that Hill believes it necessary to take into account and that is now considered as “Bradford Hill criteria of causality”:
(1) Strength
(2) Consistency
(3) Specificity
(4) Temporality
(5) Biological gradient
(6) Plausibility
(7) Coherence
(8) Experiment
(9) Analogy
Among nine criteria (or guidelines after Lucas and McMichael), Coherence deserves special attention:
(6) Plausibility plays a similar role to Coherence. But this cannot be used in the case of economics, because the whole status of economics (mainstream economics in particular) is now put into question.
Lars Syll emphasized the importance of abduction in all scientific research, i.e. efforts to find causal structure and mechanism. I agree with him on this point, but a more important point in scientific research is how to confirm (or determine) an hypothesis (which was found by an abduction) truly reflect causal structure-mechanism that is hidden under our perspective.
The criterion Coherence means we can find various facts (observed facts or hitherto established theories and hypotheses) that do not contradict the examined hypothesis, but preferably no fact that contradicts hitherto established theories. On this phase of scientific research, logical or mathematical reasoning (using or without using models) become crucial. It is necessary to test whether a hypothesis is compatible with the observation, facts, or theories.
At any stage of a science, what we have is a set of various knowledge which may be consistent or contradictory. We cannot know exactly the logical structure of the actual system of our knowledge. Mathematics and logic can contribute to elucidate this logical structure. Discarding logical and mathematical efforts to marshal this complicated and confusing set of knowledge is to abandon the coherent theory. With this research program, we may gather a large collections of (uncertain) facts, like that of Aristotelian natural history. But it is not a way to modern science.
Mathematics a language by which science can measure material reality. Reason and logic are indispensable to science, but science is not reducible to reason, logic let alone mathematics. The belief that science is reducible to mathematics alone is blind dogmatism aka scientism.
Shiozawa reduces mathematics to logic while disingenuously parroting argumentum ad nauseam his straw man argument that Lars is discarding mathematics or logic.
When Shiozawa persistently ignores the real argument instead choosing to make fallacious straw men and then attack his own false straw man it is indicative of his integrity and trolling intentions on this blog.