Past exam of the philosophy of science supplementary subject of the University of Oxford / 2018 (A14139W1)

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Necessary connections would matter if they provided an independently knowable bridge from observed cases to unobserved ones. Their mere existence would not provide that bridge. In David Hume's problem of induction, observations of past regularities do not deductively entail that the same regularities continue. Appealing to the past reliability of inductive reasoning uses the very kind of inference whose justification is at issue.^[ref]

Suppose, for example, that a particular causal power necessarily makes an isolated system respond in a certain way. If we knew independently that the power exists, that it remains present and that the relevant conditions obtain, a prediction could follow by deduction. But repeated agreement with the prediction does not itself establish those premises without further inductive reasoning. The proposal can relocate the epistemic burden rather than discharge it. A guarantee that every cause has some necessary effect also fails to identify which cause and effect our observations instantiate.

This separates metaphysical necessity from access to it. A scientist might posit causal powers because they unify otherwise separate regularities, explain counterfactual responses and guide successful interventions. An experimentally supported molecular mechanism can make the behaviour of a material intelligible across varied conditions. That supplies a comparative scientific reason to prefer a structured causal model over a bare catalogue. It does not demonstrate that the model captures a connection whose violation is impossible in every relevant world, or that its scope extends beyond the tested domain.

Hume's discussion of necessary connection challenges the idea that inspecting one cause discloses such a connection directly; repeated experience instead explains our expectation.^[ref] A defender of causal powers can reject that account, but must explain both the powers and how we know them. Mathematical necessity in a model is insufficient: the deduction is conditional on the model applying to the physical system.

The defensible conclusion is conditional. Independently warranted knowledge of stable necessary connections could justify particular projections. Simply adding them to one's account of nature does not solve the problem of induction. Scientific support for a causal account can remain strong, comparative and fallible without becoming a noncircular deductive foundation for every future inference.

A scientist should take a sceptical scenario seriously when it exposes a credible way in which the evidence could be misleading and there is a useful way to investigate that possibility. A shared instrument offset, a software error copied across analyses, or an unrecognized background signal can survive the appearance of repeated agreement. Such possibilities motivate independent calibration, changes of apparatus and analysis, and deliberately varied experiments. Mere repetition using the same error-prone chain is weaker than agreement among genuinely independent tests.

Consider two detectors that report the same departure from a predicted signal. Their agreement supports a new effect less strongly if both use the same calibration source and processing program. A proposed common failure is not defeated merely by observing that both detectors agree. An independently constructed detector, measurements at several settings, or analysis of deliberately hidden test signals can make the failure scenario and the target hypothesis predict different outcomes. Here scientific doubt generates a better test rather than an unlimited demand for reassurance.

A radically global sceptical scenario is different. If it is stipulated to reproduce every possible observation and experiment, it supplies no empirical contrast to investigate. That does not make it logically contradictory or prove it false. It limits its role in choosing among scientific models. A researcher cannot improve a detector by an experiment whose outcome is stipulated to be identical whether all experience is globally deceptive or not.

The line is not simply between imaginable and unimaginable errors. Limited time and resources require attention to plausibility, consequences and discriminating power. A low-probability fault may deserve a check when it could invalidate an entire result and is inexpensive to test. Conversely, inventing an unconstrained auxiliary hypothesis after every contrary result can insulate a favoured theory from criticism. The Duhem problem explains why a failed prediction does not isolate the guilty assumption; it does not make every rescue equally well evidenced.

A scientist can therefore combine fallibilism with justified confidence. Results can be warranted by calibration, independent methods and successful interventions without excluding every logically possible deception. Productive scientific scepticism identifies concrete vulnerabilities and seeks evidence against them. It neither assumes that every appearance is reliable nor treats the absence of Cartesian certainty as the absence of scientific knowledge.

The covering-law model captures one important feature of scientific explanation: a result should follow from general principles together with the relevant circumstances. In its deductive-nomological model, laws and conditions entail the phenomenon, the laws are needed in the derivation, and the premises must be true and empirically substantive. That is stronger than merely redescribing the event.^[ref]

For example, the ideal gas law, a specified amount of gas and a fixed-volume condition explain why changing its temperature changes its pressure in the model's regime. The derivation identifies a systematic dependence rather than an isolated coincidence. It also makes assumptions visible: a calculation using a law outside its domain is not repaired simply by being logically valid.

Nevertheless, valid subsumption does not by itself determine explanatory direction. A vertical pole of height $h$, a solar elevation $\alpha$ and a shadow of length $l$ satisfy $h=l\tan \alpha$ under suitable geometric assumptions. The same relation allows one to deduce the height from the shadow. In an ordinary intervention on the pole, changing its height changes its shadow; shortening the shadow with a screen does not shorten the pole. The reverse calculation can be excellent evidence for the height without providing the same causal explanation. This is explanatory asymmetry.

Relevance is another difficulty. One can append an irrelevant fact to a sound derivation without invalidating the deduction, but the added fact need not explain anything. Requiring an essential law prevents some vacuous derivations, yet still does not identify which features make a physical difference. A useful explanation should reveal the dependence the question is asking about, not merely contain enough true premises to reach its conclusion.

Nor are all explanations deterministic. A particular radioactive decay can have a good account in terms of a physical decay mechanism and a probability distribution without being entailed by initial conditions. An inductive-statistical model extends the covering-law idea, but requiring a high probability would exclude the explanation of some rare outcomes. Conversely, a high conditional probability produced by irrelevant correlations need not explain a mechanism.

The model is therefore a valuable discipline for organizing some explanations, rather than a sufficient universal analysis. It clarifies derivational structure and testable commitments. An adequate broader account must also address causal relevance, the question's contrast, statistical events and explanations by constraints or unification that are not straightforward causal histories.

An empiricist can distinguish what could be observed by creatures like us from what is posited to account for observations. This is not a distinction between what is already known and what is unknown. A distant ordinary object can be observable in suitable circumstances even if no one has seen it; successful detection of an electron need not make an individual electron directly visible to an unaided human observer. The criterion concerns a capacity, not the history of a particular observation.

Bas van Fraassen's constructive empiricism uses this distinction to separate accepting a theory as empirically adequate from believing every claim it makes about unobservable entities. Instrumental outputs can be treated as further phenomena that the theory must accommodate, without identifying every instrumental detection with unaided perception.^[ref] On this view theoretical language remains literal; agnosticism about an unobservable entity is not the assertion that its name is meaningless.

The difficulty is to make the boundary principled. A microscope, a telescope and a particle detector extend our practical reach in different ways. Instrument use is continuous with ordinary perception in its dependence on lenses, biological processing and learned interpretation. An observer can also be mistaken about a readily visible object. Thus a distinction between direct observation and instrument-assisted inference cannot simply be a distinction between certainty and fallibility.

The empiricist need not claim that the boundary is sharp in every marginal case or fixed independently of science. Information about human perception can help identify what humans could observe. That makes the criterion scientifically informed and relative to an epistemic community; it does not by itself make it inconsistent. But someone who uses accepted science to identify human limitations must explain why such acceptance does not already commit them to all of that science's unobservable ontology.

The distinction is useful as a proposal about the aim and evidential commitment of scientific acceptance. It permits disciplined use of a model while withholding some metaphysical belief, and it makes the difference between fitting appearances and describing hidden structure explicit. Its usefulness does not establish it as the uniquely rational boundary of belief. A scientific realist can argue that independently calibrated instruments and successful interventions justify belief beyond unaided observability. The main dispute is how much belief that success warrants, rather than whether scientists should use the instruments or take their results seriously.

The history of science is relevant because a philosophical account purporting to describe scientific knowledge and rational change must fit more than the finished derivations in current textbooks. Historical episodes reveal what evidence was available, which alternatives were considered and how assumptions about instruments and phenomena were revised. A reconstruction that imports later knowledge can make a reasonable contemporary choice appear irrational.

A simple picture of falsificationism illustrates the point. A discrepancy between an observed orbit and a calculation need not immediately discredit Newtonian mechanics. The prediction also uses estimated masses, initial data and assumptions about other bodies. Investigating an additional perturbation may be more informative than rejecting the entire theory. History can show whether such a proposal was independently constrained and successful, rather than merely a device for avoiding any possible refutation. The philosophical distinction between a testable extension and an ad hoc hypothesis therefore benefits from examining actual evidence and practice.

Thomas Kuhn's introduction to The Structure of Scientific Revolutions by Thomas Kuhn challenges the picture obtained from retrospective textbook accumulation. His distinction between normal science and revolutionary theory change directs attention to changing research problems, standards and exemplars.^[ref] Whether one accepts that particular account, the historical challenge is substantive: an account of progress must explain why some earlier achievements remain useful while broader frameworks change.

Historical explanation is not itself a complete justification. Showing that a social setting or personal commitment caused someone to accept a theory does not show that the theory was well supported. Nor does a single episode automatically refute a normative ideal: actual scientists can make mistakes. The context of discovery and context of justification can be distinguished even when they interact. What matters is identifying the standard being evaluated and the evidence relevant to it.

History is therefore neither dispensable decoration nor an infallible rulebook. It can expose counterexamples to oversimple universal claims, supply scientifically realistic cases and reveal unnoticed alternatives. Philosophical reasoning must then assess the inference, the evidential situation and the claimed norm. A good account should be historically responsible without reducing rational justification to whatever scientists happened to do.

Incommensurability can express several different claims. Rival scientific paradigms may organize terms differently, favour different exemplary problems, or rank standards of explanation differently. None of those claims by itself entails that their users cannot communicate or that no evidence can favour one account. Distinguishing these levels is essential to judging the claim rather than treating it as a slogan.

Semantic change is a serious possibility. In special relativity, distant simultaneity depends on an inertial frame; a Newtonian framework instead admits an absolute temporal ordering. Reusing the word “simultaneous” does not preserve every inference across the two accounts. A translation must identify which concepts and conditions correspond. That is more demanding than matching terms one by one.

Yet difference does not imply total incomparability. Observers can agree on local clock readings and compare predictions for specified experimental arrangements. In the low-speed domain, an appropriate limit relates many relativistic results to their Newtonian counterparts. Such a connection does not make the theories identical: it identifies a domain and an approximation under which particular predictions agree. It supplies a basis for comparison without requiring a complete, context-free translation of every theoretical term.

Methodological differences can also be real without making theory choice arbitrary. One framework may solve a familiar problem especially well, while another opens a wider range of questions. Accuracy, scope, simplicity and coherence can conflict, and applying them may require judgement. But shared measurements and successful novel predictions can still constrain that judgement. A disagreement over their relative weight is not evidence that every outcome is equally acceptable.

A defensible reading of Kuhn's revolutionary theory change therefore involves partial changes of meaning and standards, together with the work needed to understand a rival framework. It need not involve permanent mutual unintelligibility.^[ref] The strongest thesis, that rival frameworks cannot be rationally compared at all, is much less plausible: it would erase the distinction between changing conceptual resources and losing all evidential contact. The moderate thesis usefully warns against assuming that scientific change is always the addition of sentences to an unchanged language.

Logical empiricism seeks to preserve the authority of logic and mathematics while denying that they supply unsupported factual discoveries about nature. The analytic-synthetic distinction offers a division of labour: an analytic truth holds by logical or linguistic rules, whereas a synthetic truth requires information about the world. A logical implication can organize a physical calculation without deciding that its premises describe an actual apparatus.

Without some such account, a strict claim that all genuine knowledge is empirical faces its own treatment of logic and mathematics. A logical tautology is not usually established by sampling cases. Conversely, a measurement of a wire's length does not follow merely from fixing the meaning of “metre”. The distinction lets the empiricist use formal structure while locating factual risk in the empirical application. This does not mean that every historical version of logical empiricism accepted the same sharp boundary.

The central challenge is to explain analyticity without a circle. Calling a statement true by meaning appeals to a notion that must itself be clarified. Defining the relevant meanings by synonymy, then explaining synonymy through analytic equivalence, gives no independent foundation. Willard Van Orman Quine develops this pressure against definitions, substitution tests and rules, and links it to the testing of whole theoretical packages.^[ref]

A useful reply distinguishes describing ordinary language from constructing an explicit formal framework. Rudolf Carnap's meaning postulates restrict the admitted interpretations of a language. One can define a sentence as analytic relative to those rules when it holds under every permitted interpretation.^[ref] That is a precise and usable local classification. It does not, merely by stipulation, prove that a natural-language sentence was already analytic independently of the framework, or that a scientific framework can never be revised.

Thus a modest defence is more convincing than an absolute one. Logical empiricists can justify a distinction relative to stated linguistic and inferential commitments, using it to separate a model's formal consequences from empirical assumptions. They have a harder task if they require a unique, unrevisable division governing all meaningful discourse. The practical value of explicit conventions survives even if there is no universal boundary immune to scientific and linguistic change.

Falsificationism gives an attractive account of one aspect of scientific progress: replace protected dogma by conjectures that make risky claims and expose them to serious tests. From $T\wedge A\Rightarrow E$ and an accepted $\neg E$, deduction rules out the conjunction. A theory that accommodates every possible outcome supplies less opportunity to learn from error. Karl Popper emphasizes exposure to refutation and warns against treating any convenient supporting instance as a demanding test.^[ref]

This captures why a successful novel prediction can be more informative than retrospectively adjusting a model to its original fitting data. It also helps articulate a progressive replacement: the new account should address the earlier successes and the discrepancy, while exposing itself to further independent tests. Merely adding a freely adjustable parameter after each failure need not improve knowledge.

The Duhem problem limits the simple account. A failed prediction is a failure of a package containing initial conditions, instrument assumptions and background theory. It is not a deductive identification of the particular target theory as false. Revising an auxiliary hypothesis can be rational when independently supported, but an unconstrained rescue can frustrate testing. Methodological judgement is needed to distinguish them; the bare logic of falsifiability does not do all the work.

There is also a positive-warrant problem. A theory's survival of past tests does not deductively entail that it will succeed tomorrow. Calling it corroborated records its demanding test history, but does not by itself solve the problem of induction. Nor does the elimination of one theory guarantee that a chosen rival is closer to the truth: many alternatives may remain, and progress can involve improved measurement, new concepts or increased explanatory scope as well as refutation.

A convincing form of falsificationism is therefore a critical methodology within a broader account. It explains the value of error detection, risky commitments and resistance to immunizing manoeuvres. It becomes less convincing as a complete logical algorithm for identifying falsified theories, ranking every successor or deriving confidence about future performance. A more adequate account combines severe criticism with independent checks on assumptions, comparative success and explicit acknowledgement of fallible inference.

Falsifiability is a relation between a claim, testing assumptions and possible outcomes that would conflict with it. It is not the fact that someone has criticized the claim, and not the same property as being false. It gives an important demand for empirical accountability: a theory that is compatible with every conceivable result cannot lose by an observational test. That demand motivates Popper's approach to the demarcation problem.^[ref]

Simply possessing a falsifiable hypothesis is nevertheless insufficient to make an entire activity scientific. A precise prediction can be false, based on unreliable methods, or repeatedly insulated from contrary evidence by new excuses. A practice that makes one testable prediction while disregarding all its failures does not acquire sound scientific methodology merely because a sentence in its vocabulary could have been refuted. How claims are tested and revised matters.

Strict deductive refutability is also a difficult necessary condition for every legitimate scientific claim. A stochastic radioactive decay model may assign nonzero probability to every finite sequence of counts. No individual finite record then logically contradicts it. For example, a Poisson distribution with $\lambda >0$ gives every integer count $k\ge 0$ probability $e^{-\lambda }{\lambda }^k/k!>0$. A sufficiently extreme count can nevertheless give strong evidence against the specified rate. A probability model can be tested through sampling distributions and error-controlled procedures. Extremely unlikely outcomes can count strongly against it without being impossible. Replacing scientific testing by a requirement of zero model probability would misdescribe much of quantitative science.

A further difficulty is the use of auxiliary hypotheses. A highly specific physical prediction becomes a test of the intended account only when instrument behaviour and background conditions are sufficiently controlled. Otherwise failure can be redirected indefinitely. Conversely, designing and calibrating an instrument can be genuine scientific work without itself taking the form of a single dramatic conjecture awaiting refutation.

The plausible conclusion is that falsifiability, or a suitably statistical form of testability, is one important component of a scientific practice. It does not provide a self-sufficient binary label for whole areas of inquiry. Publicly specified commitments, reliable methods, independent scrutiny and responsiveness to evidence are also needed. This preserves the force of the demand that claims risk failure without treating a useful methodological principle as a complete definition of science.

Scientific underdetermination occurs when the relevant evidence does not select a unique theoretical account. Its force depends on the scope of “the evidence”. Several models can agree with a finite data set while disagreeing about new measurements; a suitable experiment can then discriminate them. The stronger claim of empirical equivalence says that rivals agree on every possible observation within a stated domain, so further evidence of that same kind cannot separate them.

The Duhem problem gives a related but distinct source of ambiguity. If a target theory $T$ and auxiliary hypotheses $A$ jointly imply an observation $E$, then observing $\neg E$ shows $\neg (T\wedge A)$, not uniquely $\neg T$. Independent evidence about the apparatus or background assumptions can redistribute support. It does not make the original conjunction logically true; it provides additional information relevant to which revision is warranted.

A finite-data example makes the limits clear. If $p_0$ interpolates observations at distinct $x_1,\dots ,x_n$, then every polynomial $p_c(x)=p_0(x)+c{\prod }_{i=1}^n(x-x_i)$ has the same sampled values. Thus polynomial interpolation does not determine a unique prediction outside the sampled points when the degree is unrestricted. A degree bound of $n-1$ would instead give the familiar uniqueness result, so the allowed model class matters. That does not show that every interpolant is equally credible for extrapolation. Smoothness assumptions, independently motivated mechanisms and further measurements can matter. But those extra commitments must be stated rather than smuggled into the claim that the original data uniquely determined a model.

In a probabilistic comparison, Bayes' theorem giveswhen the quantities are defined. Equal likelihoods leave the prior odds unchanged by this evidence. Neither equality nor unequal priors follows merely from both theories being logically compatible with $E$. Thus compatibility, equal fit in a particular measure and full empirical equivalence should not be conflated.

The philosophical significance is a constraint on inference, not a universal proof of irrationality. An empiricist may use it to motivate restraint about hidden structure; a scientific realist may defend further explanatory or intervention-based reasons for belief. A convincing sceptical argument must identify genuine rivals and the relevant equivalence, while a convincing realist reply must explain why its additional virtues track the claims at issue. Scientific knowledge can be selective and fallible even when evidence does not deductively choose a unique complete theory.

It is plausible that some claims in current science will be revised, and much stronger to say that every successful part of present science is probably false in every relevant respect. The first is compatible with warranted knowledge. The second needs a substantive argument about the particular claims, their evidence and what “false” means for an approximate or idealized model.

Pessimistic induction points to theories once successful and accepted that were later rejected. Larry Laudan uses historical cases to challenge the inference from predictive success to the truth or successful reference of all theoretical commitments.^[ref] The lesson is especially strong against an indiscriminate realism that treats every productive representation as a literal inventory of nature.

But a historical list is not automatically a representative statistical sample. One must ask which successes were substantial, which theoretical components generated them, what survived and whether current evidence is comparable. The eventual rejection of an entire ontology does not entail that every measurement, mathematical relation or limited-domain prediction associated with it was worthless. Nor does the chance that at least one claim in a large conjunction is false imply a high error probability for each member separately.

Newtonian mechanics illustrates a useful distinction. It is not a universally exact account across all speeds and gravitational regimes, but controlled low-speed and weak-field approximations can remain extremely accurate. That is an empirical and mathematical claim about a domain, not permission to call any false account approximately true. Approximate truth needs interpretable quantities, a specified regime and some account of relevant error. Structural realism similarly needs preserved physical relations rather than merely a shared abstract notation.

The possibility of error therefore matters. It should discipline extrapolation, encourage independent testing and prevent practical success from being turned into unconditional metaphysical certainty. It does not require abandoning the best-supported models within tested domains. An empiricist can retain justified confidence in empirical adequacy, while a selective scientific realist may defend stronger commitments where evidence and intervention support them. Both positions must distinguish fallible confidence from certainty and carefully bounded reliability from unrestricted literal truth.