S is a state of affairs; a collection of data, facts, observations, givens.
H hypothesis, would, if true, explain S.
No other hypothesis [A, B, C] can explain S as well as H does.
Therefore, it is probable that H is true.
Inferences to the best explanation are common in all fields of inquiry, including scientific, and everyday life. For a more thorough exploration:
We may be choosing the best of a bad lot, and that we have no way of knowing whether the truth is contained in our set to begin with.
Reply: Scientists don't claim to have completely certainty on any scientific fact, no fact from any field of inquiry does. We've gone from Newtonian physics to special and general relativity, and guess what? Einstein's work is likely to only be partially correct. Qualms with IBE on this account are off base. Abductive inferences [IBE] are used in every field of inquiry, including science, history, linguistics, and everyday life,
Explanations help us to understand why something happened, not simply convince us that something happened. However, there is a common kind of inductive argument that takes the best explanation of why x occurred as an argument for the claim that x occurred. For example, suppose that your car window is broken and your iPod (which you left visible in the front seat) is missing.
The immediate inference you would probably make is that someone broke the window of your car and stole your iPod. What makes this a reasonable inference? What makes it a reasonable inference is that this explanation explains all the relevant facts (broken window, missing iPod) and does so better than any other competing explanation. In this case, it is perhaps possible that a stray baseball broke your window, but since (let us suppose) there is no baseball diamond close by, and people normally don’t play catch in the parking garage you are parked in, this seems unlikely.
Moreover, the baseball scenario doesn’t explain why the iPod is gone. Of course, it could be that some inanimate object broke your window and then someone saw the iPod and took it. Or perhaps a dog jumped into the window that was broken by a stray baseball and took your iPod. These are all possibilities, but they are remote and thus much less likely explanations of the facts at hand. The much better explanation is that a thief both broke the window and took the iPod.
This explanation explains all the relevant facts in a simple way (i.e., it was the thief responsible for both things) and this kind of thing is (unfortunately) not uncommon—it happens to other people at other times and places. The baseball-dog scenario is not as plausible because it doesn’t happen in contexts like this one (i.e., in a parking garage) nearly as often, and it is not as simple (i.e., we need to posit two different events that are unconnected to each other—stray baseball, stray dog—rather than just one—the thief). Inference to the best explanation is a form of inductive argument whose premises are a set of observed facts, a hypothesis that explains those observed facts, and a comparison of competing explanations, and whose conclusion is that the hypothesis is true. The example we’ve just been discussing is an inference to the best explanation.
Explanation: The hypothesis that a thief broke the window and stole your iPod provides a reasonable explanation of the observed facts.
Comparison: No other hypothesis provides as reasonable an explanation.
Conclusion: Therefore, a thief broke your car window and stole your iPod.
Notice that this is an inductive argument because the premises could all be true and yet the conclusion false. Just because something is reasonable, doesn’t mean it is true. After all, sometimes things happen in the world that defy our reason. So perhaps the baseball-dog hypothesis was actually true. In that case, the premises of the argument would still be true (after all, the thief hypothesis is still more reasonable than the baseball-dog hypothesis) and yet the conclusion would be false.
Explanation: The hypothesis that a thief broke the window and stole your iPod provides a reasonable explanation of the observed facts.
Comparison: No other hypothesis provides as reasonable an explanation.
Conclusion: Therefore, a thief broke your car window and stole your iPod.
Notice that this is an inductive argument because the premises could all be true and yet the conclusion false. Just because something is reasonable, doesn’t mean it is true. After all, sometimes things happen in the world that defy our reason. So perhaps the baseball-dog hypothesis was actually true. In that case, the premises of the argument would still be true (after all, the thief hypothesis is still more reasonable than the baseball-dog hypothesis) and yet the conclusion would be false.
But the fact that the argument is not a deductive argument isn’t a defect of the argument, because inference to the best explanation arguments are not intended to be deductive arguments, but inductive arguments. That isn’t a defect of an inductive argument, it is simply a definition of what an inductive argument is! As we’ve seen, in order to make a strong inference to the best explanation, the favored explanation must be the best - i.e. the most reasonable.
But what makes an explanation reasonable? There are certain conditions that any good explanation must meet. The more of these conditions are met, the better the explanation. The first, and perhaps most obvious condition, is that the hypothesis proposed must actually explain all the observed facts.
Commonly acknowledged criteria for inference to the best explanation
1. Explanatory scope. The best hypothesis will explain a wider range of data than will rival hypotheses. An explanation has explanatory scope if it
- makes fewer assumptions
- accounts for more facts or observations
- more details of causal relations are provided
- depends less on authorities and more on observations
- is more falsifiable (more testable by observation or experiment
- offers greater predictive power (if it offers more details about what should be expected to be seen and not seen);
2. Explanatory power. The best hypothesis will make the observable data more epistemically probable than rival hypotheses; an epistemic possibility is a statement that may be true given the current state of knowledge about the world. For example, "Given what we know about the weather, it might rain tomorrow" is an epistemic possibility
3. Plausibility. The best hypothesis will be implied by a greater variety of accepted truths, and its negation implied by fewer accepted truths than rival hypotheses.
4. Less ad hoc. The best hypothesis will involve fewer new suppositions not already implied by existing knowledge than rival hypotheses.
5. Accord with accepted beliefs. The best hypothesis, when conjoined with accepted truths, will imply fewer falsehoods than rival hypotheses.
3. Plausibility. The best hypothesis will be implied by a greater variety of accepted truths, and its negation implied by fewer accepted truths than rival hypotheses.
4. Less ad hoc. The best hypothesis will involve fewer new suppositions not already implied by existing knowledge than rival hypotheses.
5. Accord with accepted beliefs. The best hypothesis, when conjoined with accepted truths, will imply fewer falsehoods than rival hypotheses.
6. Consistency: Is the hypothesis consistent with other established facts or theories?
7. Comparative superiority: The best hypothesis will so exceed its rivals in meeting conditions (1) through (6) that there is little chance of a rival hypothesis’s exceeding it in fulfilling those conditions.
7. Comparative superiority: The best hypothesis will so exceed its rivals in meeting conditions (1) through (6) that there is little chance of a rival hypothesis’s exceeding it in fulfilling those conditions.
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