From optimism to surrender Logical Empiricism From optimism to surrender

The logical part: First order logic First order (quantified) logic combines the resources of truth functional propositional connectives, a simple subject-predicate analysis of sentences and (individual) variables and quantifiers. The result is a formal language with quite a lot of expressive power– enough for us to formalize mathematical theories, for instance. With a language this clear and this powerful, the logical empiricists hoped they could do what Hume and other early empiricists could not: give a full account of all (meaningful) ideas and concepts in terms of experience and logic.

Experience Experience clearly plays a role in observation– in ‘pure’ observation, many philosophers have thought that we simply report what we experience. Because such reports are often regarded as incorrigible and infallible, this sort of pure report of experience is often part of a foundationalist picture of the epistemology of ‘facts about the world’.

Skepticism There are two characteristic forms of skepticism that arise here. The first is about the links between an account of what we experience and the external world: How can the course of our private experience tell us about the public, external world that (we believe) causes most such experiences? The second is about induction and regularities: How can reports about patterns of experience we’ve had provide a basis for projecting those patterns into future experiences?

Patterns of actual and possible sensations One traditional response to the first sort of skepticism is to re-define what an ‘external object’ is, in terms of ‘patterns of actual and possible sensations’. This view is called phenomenalism. But it is widely accepted to be untenable. The problem is, we cannot describe these patterns independently of our understanding of the (familiar) world of physical objects. Kant’s account of our knowledge as combining fixed conceptual structures (including space and time) with ‘raw material’ from sensation solves this problem, if a bit crudely. Other accounts focus on language and language acquisition, which can be more flexible about concepts.

Science By this time, science is a standard example of epistemic success. Einstein’s theory of relativity was a recent example, undermining the ‘fixity’ of fundamental concepts that Kant had assumed. The logical positivists proposed to apply their logical tools together with empiricist ideas about experience as the key to scientific knowledge, to arrive at a new account of science that would fit with Einstein’s work.

Philosophy of Language Analytic-synthetic distinction & knowledge of mathematics. Einstein’s use of non-Euclidean geometry in physics illustrates the view that all mathematical knowledge is analytic (and all synthetic knowledge is empirical). The difference between applied and pure mathematics is central here. The verifiability theory of meaning. The meaning of a sentence is reduced to methods of deciding whether the sentence is true or false. This turns out to be untenable right away, since dispositions are meaningful, but we often aren’t able to say whether an object has a certain disposition or not. So weaker conditions were developed, always maintaining a link between empirical testability and meaningfulness.

Observational vs. Theoretical Language The positivists recognized the importance of theoretical language, even though it seems impossible to directly test theoretical claims against experience. They proposed that theoretical language always depended on a basic observational language for its meaningfulness. The observational language would remain fixed over time, ensuring that we always have an agreed starting point we can apply to evaluating and comparing different scientific theories.

Responding to Hume This requires an account of the logic of induction– somehow, regularities in our observations have to provide evidence that certain observed patterns will persist. The positivists expected that logical work on the relations between observations and the theoretical claims those observations confirm (or disconfirm) would reveal the structure of inductive reasoning.

Limits of philosophy Context of justification: here logic is the key, and we should be able to give an account of our commitments and why we hold them. Context of discovery: here psychology is the key; philosophy does not have to give an account of how (rationally) we should go about developing new hypotheses and theories to test against our observations.

Positivists as bad guys? In some circles today ‘positivist’ is a term of abuse– it implies a narrow, authoritarian commitment to science, and a dismissal of everything that science does not account for. Otto von Neurath’s ship metaphor is a good starting point for appreciating how open-minded and modest the positivists actually were. Better yet, this is an intellectual movement that, in the end, realized that its goals could not be achieved. How many such movements are clear (and honest) enough to achieve such closure?

Holism W.V.O. Quine famously challenged the analytic-synthetic distinction in his classic article, “Two dogmas of empiricism”. Quine’s response (in a move that some other philosophers, including Sellars, also favoured) was a kind of holism: our commitments face the ‘tribunal of experience’ together, rather than individually. Further, the ‘analytic’ truths can also be changed, if that’s the best way to accommodate experience.

Late Logical Empiricism Theories are logically closed sets of sentences. They are connected to observation by ‘bridge rules’ that link observations to the theoretical vocabulary. A successful theory will be confirmed by the combination of observations and bridge rules. If there is trouble (a contradiction between the observations made and the theory’s commitments) then how to respond is decided holistically, by a kind of cost-benefit calculus—any sentence in the theory can be given up, if doing so accommodates experience better. Explanation is a matter of deriving some pattern in our observations from other observations and theoretical principles. (Deductive-nomological explanation)

The theoretician’s dilemma This paper by Hempel proposed (as Carnap also maintained) that the contribution theoretical language makes to our understanding of the world is exhausted by the theory’s implications for patterns expressed in the observation language. Thus, even though we have words like ‘electron’ in our theory that seem to refer to strange, unobservable objects which play a role in explaining what we observe, the meaning of these words lies only in what the theory (which uses them) implies about our observations: science is about the ‘surface’ of things only, not what lies beneath it. (See also Carnap, “The methodological character of theoretical concepts”.)

What is hidden? Are electrons really hidden? Van Fraassen’s example: high energy electrons can be directly detected as flashes of ‘light’ produced when they strike our retinas. Shapere: Observation in science and (traditional) philosophy seem rather different: Consider neutrinos and the interior of the sun.

Puzzled by induction Ampliative reasoning: Induction (from particular cases to generalizations) Projection (from past cases to new cases) Explanatory inference (inference from data to conclusions that explain the data– e.g. the Alvarez hypothesis).

Hypothetical deductivism A theoretical claim is confirmed when consequences of the claim are observed to be true. In a simple version this is clearly wrong. But it fits so many familiar examples of good science that it’s hard to give up altogether.s

A puzzle about some simple rules for induction Nicod’s criterion: An instance of an A that is B confirms all A’s are B’s. Equivalence rule: If some evidence confirms a sentence P, it confirms any logically equivalent sentence. ‘All Ravens are Black’ is equivalent to ‘All non-black things are non-Ravens’. It follows that white sneakers confirm ‘All Ravens are black’.

Goodman’s riddle Grue: Observed before or at t and green or observed after t and blue. Bleen: Observed before or at t and blue or observed after t and green. Let t be midnight, Decmber 31 2009. Then all observed emeralds are grue. Should we infer by simple induction that all emeralds are grue?

Curve-fitting Given just some points on a graph indicating observed relations between some quantities, how do we ‘project’ to arrive at a general pattern or rule relating them? These problems were never satisfactorily solved, although there are many ideas about how we might make some progress with them.