part 3: philosophy of science: scientific explanation

AN INTRODUCTION TO PHILOSOPHY OF SCIENCE

PART 3John Ostrowick

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SO FAR…• So far…

• In part 1, we saw how formal logic works

• We’ve seen some fallacies.

• We’ve seen the difference between induction, deduction and abduction.

• In part 2, we identified that induction is fallacious (logically)

• We saw that there’s a problem of induction which says that we can’t generalise (past to future, token to type, etc)

• Yet science generalises.

SO FAR…• So far…

• We saw that we can instead argue that science uses probability claims

• We saw how Bayes’ Theorem works

• And we saw some problems with it, like how to fix the prior probabilities.

• We saw that we can fix the priors with frequentism or propensity.

• We saw that background knowledge can influence believability.

• We saw that there’s a problem with deciding what evidence is relevant.

INTRODUCTION

• In this part, we ask: So if science succeeds, how does it explain?

• If we can understand how science can explain, we can also determine what counts as science.

SCIENTIFIC EXPLANATION

• The trouble with “cause”

• Hume’s problem: a series of events with no visible necessitation.

• Correlation is not causation. Causation = metaphysical necessity. Is there even such a thing?


• The Curve-Fitting Problem and Evidence

• If a theory has a formula and we graph it, and it fits the data points, we assume that the theory explains the data (P(e|h) > P(e)).

• But we can at most claim a correlation. Especially in social sciences.

• We assume that simpler graphs (and hence theories) are preferred.


• The Curve-Fitting Problem and Evidence

• But what is “simpler” ? We’ll get onto this shortly.

SCIENTIFIC EXPLANATION• Underdetermination of theory by evidence

• A related problem: evidence does not entail a particular theory.

• Ideally we’d like one theory selected.

• We might have two or more plausible theories to explain the evidence, and they’re equally good, as far as we can see, or,

• Even worse, there might be a large range of theories, and we can’t even pinpoint one particular theory as suitable because they all have problematic issues.

SCIENTIFIC EXPLANATION• Underdetermination of theory by evidence

• A related problem: evidence does not entail a particular theory.

• Ideally we’d like one theory selected.

• We might have two or more plausible theories to explain the evidence, and they’re equally good, as far as we can see, or,

• Even worse, there might be a large range of theories, and we can’t even pinpoint one particular theory as suitable because they all have problematic issues.

• Popper: we can’t tell if our theory is true. We can only tell if it is false.


• The Pessimistic Induction

• It’s not just that science continues to succeed, and therefore we expect it to continue to succeed. It’s worse. We also know that most theories thus far have proven false and been discarded, so what if science continues to make false theories?

• There might be theories we’ve not yet thought of which are even better, and which better explain the observations. So whatever we take as “true” now in science, may turn out to be false later on.


• Responses to underdetermination

• The first is the “wait and see” attitude. In this response, one simply says that in practice, we don’t know yet which theory is correct, so it is not that the truth is indeterminate (forever undeterminable), but rather it’s practically undeterminable. E.g. String Theory.

• Another response is “abduction”. If one particular theory is the best explanation for the phenomenon, then we can isolate that theory as the most likely true. So, theories are not underdetermined; we just need to decide which theory is best, based on a range of criteria. We might choose coherence, consistency, simplicity, fit with background knowledge, scope or ambition, and other criteria.

SCIENTIFIC EXPLANATION• Occam’s Razor and Simplicity

• Non sunt multiplicanda entia praeter necessitatem: Do not multiply entities unnecessarily.

• Qualitative vs Quantitative simplicity (Sober/Lewis).

• Qualitative: simple theory. Good. Graph lines.

• Quantitative: few entities, steps, etc. Not really required. Periodic table.

• Explanation of the two types with examples.*

SCIENTIFIC EXPLANATION• Other measures of scientific explanation

• Explanatory Power and Predictive Power. How well does the theory explain, predict, extrapolate?

• Scope or Ambition: the less ambitious, the better. However, the more ambitious and broader, the more likely true. “I have stuff ” (broad, true). “I have a lava lamp” (unambitious, narrow, possibly false).

SCIENTIFIC EXPLANATION• Question-begging: The Theory-Observation problem

• Confirmation bias and tabula rasa. Ideally neutral, but never really.

• The influence of background knowledge on assumptions when observing.

• No-one experiments “out of the blue”. Phenomenon is observed first, explanation comes after. Thus, theorist enters further observations with a theory in mind; a theory she wants to confirm

• Social relativists like to argue that it is impossible to make an observation which is not theory-laden.

SCIENTIFIC EXPLANATION• The Theory-Observation problem

• Logical connections between theories and observations. An observation cannot be made neutrally, and indeed, should not be made neutrally, if the observation has to be contextualised within a framework that seeks to use the observation to support a hypothesis. Hard to tell whether the observation is in any true sense of the word independent of the background theory. Observations are not objective, but rather are made in terms of what we expect to see.

• Salience. What we believe, and the theories we hold prior to observation, will lead us to focus on salient features of the observation that relate to what we’re observing.

SCIENTIFIC EXPLANATION• The Theory-Observation problem

• Objectivity in assessing cultural truth claims.

• Interference with data by instrumentation. A difficult case of this is represented by quantum mechanics.

• Our connectedness, via our senses, to observations; that is, how direct they are, versus how inferential. What counts as an observation? Optical microscopes? Electron microscopes? Particle accelerators? Voltmeter needles? fMRI?

• Limitations of language. Our language may not be adequate to finely distinguish observations, or, to express our own experiences of them. Consciousness, colour blindness, etc.


• Hempel

• Hempel says that science explains by deducing observations from laws. We’ll discuss him further in the ‘demarcation’ section.

part 3: philosophy of science: scientific explanation

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