Past exam of the philosophy of science supplementary subject of the University of Oxford / 2017 (A14139W1)

Source paper: www.whatdotheyknow.com/request/oxford_physics_past_papers_2/response/2340696/attach/5/Physics%20Papers%202016.pdf?cookie_passthrough=1

The replacement is philosophically significant because it challenges the inference from a theory's practical success to the existence of its proposed entities. Phlogiston theory interpreted ordinary combustion and metallic calcination as the loss of phlogiston; the oxygen theory of combustion instead treated these processes as combination with oxygen. This changes the account of what matter is doing, not merely the spelling of an agreed substance. Antoine Lavoisier's systematic treatment links the new chemistry with quantitative mass accounting and a revised chemical nomenclature.^[ref]

For scientific realism, the difficulty is that a once-useful theoretical term may fail to refer. If earlier success coexisted with a nonexistent unobservable entity, present success alone cannot guarantee that every central term of our theories refers either. This is the force of the pessimistic induction; it rebuts an unrestricted success-to-truth inference rather than deductively proving every present theory false.^[ref]

Several replies reduce, but do not erase, the problem. A scientific realist can distinguish successful empirical regularities from the particular mechanisms used to explain them, retaining only claims independently supported by varied experiments. Structural realism can emphasize preserved quantitative relations; it must identify the actual relations and their physical interpretation rather than declaring any recurring mathematics a vindication. A causal account of reference may preserve a term across some mistaken descriptions, but it cannot automatically identify phlogiston with oxygen: their proposed roles in the reaction differ.

A defensible conclusion is therefore selective and fallible scientific realism. The historical episode motivates stricter criteria for commitment, including independent manipulation, cross-checks and successful novel tests. It does not force the view that all theoretical entities are mere instruments. Conversely, calling the abandoned theory approximately true without specifying which claims survived simply restates the difficulty.

There are substantial changes in scientific concepts and practice: the Chemical Revolution and the transition from Newtonian mechanics to special relativity are concrete examples. Whether every such episode fits one universal pattern of revolutionary theory change is a further historical claim. Thomas Kuhn contrasts normal science within an accepted scientific paradigm with changes in the standards and exemplary problems that organize a field.^[ref]

Three philosophical difficulties follow. First, scientific progress cannot simply mean retaining every old theoretical assertion: special relativity revises absolute simultaneity, while recovering useful low-speed predictions. Second, incommensurability raises questions about comparison when terms, methods or explanatory priorities change. Third, agreement on a theory may involve judgments about simplicity, explanatory scope and promising future work, so rational choice need not be an algorithm uniquely determined by the current measurements.

None of these difficulties establishes that comparison is impossible. In the mechanical example, the expansion of the Lorentz factor, $\gamma =1+v^2/(2c^2)+O(v^4/c^4)$, displays a controlled regime where the newer account recovers earlier approximations. Shared numerical readings, overlapping experimental techniques and successful predictions beyond the old domain permit assessment even without identical theoretical vocabularies. Incommensurability can mean incomplete translation or changing standards without meaning that every disagreement is beyond argument.

A reasonable position accepts revolutionary theory change while rejecting the stronger inference that all standards are arbitrary. Rational comparison can be plural, historically situated and fallible. It should specify the preserved successes, newly explained phenomena and costs of a proposed change. The existence of dramatic transitions refutes a picture of wholly cumulative truth accumulation; it does not by itself refute evidence-based scientific progress.

Karl Popper's falsificationism treats theories as conjectures to be exposed to serious attempted refutation. A universal claim entails observable restrictions and can conflict with a counterexample, whereas a finite collection of favourable cases does not deductively establish the universal. This emphasizes risky tests and distinguishes surviving criticism from proof. It also avoids pretending that the problem of induction is solved merely by collecting more confirming observations.^[ref]

The main difficulty is moving from this logical asymmetry to a complete account of practice. A prediction usually has the form $(T\wedge A)\Rightarrow E$, where $A$ includes auxiliary hypotheses about apparatus, initial conditions and background physics. Observing $\neg E$ implies $\neg T\vee \neg A$, not specifically $\neg T$. This Duhem problem makes independent measurement calibration and discriminating follow-up experiments essential. An anomalous detector count can reveal a detector fault rather than a new fundamental interaction.

There are also statistical complications. Under a probability model, an unusual observation need not have zero probability; rejection at a chosen significance level is fallible. Repeated severe tests can rationally alter comparative confidence even though they do not prove a theory. A methodology that merely labels all surviving theories unrefuted misses this evidential differentiation.

Falsificationism is therefore valuable as a critical discipline, especially its opposition to adjustments that insulate a theory from every conceivable result. It is incomplete as a rule requiring immediate abandonment after any anomaly, or as a whole theory of warranted acceptance. The better reconstruction combines explicit possible failures, justified auxiliary hypotheses and comparison of competing explanations, while retaining fallibilism about the outcome.

The demarcation problem concerns what distinguishes science from pseudoscience, not merely what distinguishes true claims from false ones. Karl Popper's proposal is falsifiability: a scientific claim must exclude some possible observations, so that a test could in principle count against it. A theory that is compatible with every outcome gains no empirical credit simply by accommodating what occurred.^[ref]

This captures an important methodological defect of pseudoscience: treating every apparent counterexample as further support. But bare falsifiability is not sufficient for good science. A sharply testable lucky-number prediction can be unsupported and methodologically irresponsible. Nor does a mistaken testable theory cease to have been scientific when it is rejected. Scientific status and evidential success are different assessments.

Necessity is also more complicated than a literal one-observation refutation rule. Statistical theories permit rare outcomes, and an instrument-dependent prediction tests auxiliary hypotheses as well as the main theory. Some current research proposals have no feasible immediate test, yet belong to programmes trying to derive discriminating consequences. Their status should depend partly on whether that programme produces such tests, rather than on a permanent exemption from empirical accountability.

Thus falsifiability supplies an important constraint on empirical content but not an adequate stand-alone classification of every scientific activity. Reliable experiments, openness to criticism, comparative explanatory success and correction of failed predictions also matter. This does not make demarcation pointless: it explains why an assessment of a practice over time can be more informative than attaching a binary label to one sentence.

Theory-ladenness can affect the concepts used to describe an observation, the interpretation of an instrument and even which features attract attention. A detector's pulse height is not already a particle's energy: the inference depends on measurement calibration, response assumptions and background subtraction. This defeats a particularly strong empiricism according to which every scientific claim rests on a completely theory-free observational foundation.

It does not follow that observation can never constrain theory. The crucial distinction is between relying on some background account and presupposing the truth of the particular target under test. One can calibrate a detector with independently characterized sources and compare measurements from detectors with different systematic errors. Agreement across these routes is more compelling than repeated use of one disputed reduction procedure. Scientific replication and tests with deliberately varied apparatus can expose shared assumptions rather than merely repeat them.

An empiricist can therefore adopt a fallible, public evidential practice. The observable phenomenon need not be linguistically uninterpreted to have an evidential role. Reports can be translated sufficiently for disagreement, and raw records can be retained so that rival analyses can be compared. Where all purported evidence is defined through the target theory itself, the circularity is a real problem; the reply must supply independent checks, not simply assert objectivity.

Constructive empiricism offers one further distinction: accepting a theory for its empirical adequacy does not require believing its full story about every unobservable entity. Its proponent can use a theoretical account to organize instruments without identifying use with unconditional belief.^[ref] Theory-ladenness therefore undermines an infallible foundation, while leaving room for constrained, corrigible empiricism.

The covering-law model explains a phenomenon by subsuming it under scientific laws and specified circumstances. Its deductive-nomological model requires the event to follow by valid deduction, with a law used essentially, empirical content and true premises. For example, a dynamical law and appropriate initial conditions can explain a body's later trajectory.^[ref]

This captures an important feature of many physical explanations: the event is shown to fit a wider, independently supported pattern. It makes assumptions explicit and connects explanation with prediction. However, derivability alone does not ensure the right explanatory direction. With a pole, a known solar elevation and a measured shadow, geometry lets us calculate either height from shadow or shadow from height. The explanatory asymmetry remains: under the ordinary causal setup, the pole and illumination explain the shadow, rather than its measured length causing the pole's height.

Irrelevant information also exposes a weakness. From a general law plus an unnecessary fact one may still derive the result; logical validity does not show that the added fact helps explain it. Conversely, many accepted explanations are statistical or mechanistic rather than deductions from exceptionless universal laws. Extending the account to the inductive-statistical model helps, but a high conditional probability does not by itself establish causal relevance.

The covering-law model is thus a useful account of one important explanatory structure, not a complete criterion of scientific explanation. A stronger account must consider which assumptions make a difference, what mechanism operates and why the explanatory direction is appropriate. The model's virtues survive when it is treated as a component of explanation rather than an exhaustive definition.

Let $R(x)$ mean that $x$ is a raven and $B(x)$ that it is black. The hypothesisis logically equivalent to $\forall x[\neg B(x)\Rightarrow \neg R(x)]$. If the observed paper is both nonblack and a nonraven, it is a positive instance of the second formulation. Combining an unrestricted positive-instance rule with preservation of scientific confirmation under logical equivalence produces the raven paradox.^[ref]

Whether the observation actually supplies evidence depends on what was already known and how the object was selected. In Bayesian confirmation, with background $K$, support means $P(H\mid E,K)>P(H\mid K)$. If the observer already knows the selected item is a nonraven and its colour is conditionally independent of $H$, then $P(E\mid H,K)=P(E\mid \neg H,K)$: the likelihood ratio is one and there is no confirmation. If all of $E$ was already known, learning it again also gives no update.

Under a different sampling scheme, the same classification can provide slight support. In the finite-population model of raven confirmation, fix $r$ ravens and $M$ nonblack objects. Compare no nonblack raven with exactly one. Sampling uniformly among the nonblack objects gives a nonraven with probabilities $1$ and $1-1/M$, hence likelihood ratio $(1-1/M{)}^{-1}>1$. If $M$ is large this is tiny evidence, not a proof. This example establishes possibility under explicit assumptions, not that every white-paper observation has that evidential role.

The appropriate answer is therefore conditional: mere logical instantiation does not settle the support supplied by observing this paper. State the background and sampling procedure. A familiar paper deliberately selected as a known nonraven normally supplies no new evidence about raven colour; a specified alternative sampling model may yield a small positive update.

Numerous successful tests can justify a fallible belief in a general hypothesis, but do not usually entail its truth. If finitely many tested instances satisfy $F(x)\Rightarrow G(x)$, a further untested instance can still fail. The logical gap is the problem of induction, not a practical complaint that the sample was merely too small. Appealing to the past success of inductive reasoning to establish its universal reliability uses the disputed form of inference itself.^[ref]

The kind of testing matters as much as the count. Repeating one measurement with the same unnoticed systematic bias can add little. Independent apparatus, novel circumstances and predictions on which competing theories disagree can be much more informative. A test deliberately designed to expose a failure is stronger than a loose resemblance noticed after the outcome. Still, auxiliary hypotheses and instrument assumptions must be considered when interpreting either success or failure.

Bayesian confirmation expresses the distinction quantitatively. For independent trials with success probability $p$, $n$ successes have likelihood $p^n$. In a simple comparison between a certain-success hypothesis and a specified alternative $p=q<1$, the Bayes factor is $q^{-n}$. This favours the first hypothesis without proving it. Independence, the alternative and the prior probabilities are substantive assumptions; repeated correlated results cannot automatically be multiplied as independent evidence.

There is also a difference between a universal statement and an adequate approximation within a domain. Many successful low-speed tests of Newtonian mechanics justify its use there, without establishing its unrestricted truth at relativistic speeds. A reasonable conclusion is fallibilism: well-designed and varied successful tests can warrant acceptance or high confidence, while the scope and remaining uncertainty should stay explicit. If “justified” means deductively certain, finite testing generally does not suffice; if it means rationally supported, it often can.

Empirical equivalence must be stronger than agreement with the evidence already collected: relative to a stated domain, empirically equivalent theories yield the same possible observational consequences. If they nevertheless make incompatible claims about unobservable entities, observations in that domain cannot alone determine which ontology is correct. This is a strong form of scientific underdetermination, directed against a unique inference from empirical success to full theoretical truth.

The scientific realist has several responses. One may deny that every formally constructed alternative is a serious physical rival, appeal to independently justified explanatory virtues, or restrict commitment to the structure genuinely shared by the alternatives. If two descriptions are connected by a change of variables or a gauge transformation, apparent difference may be representational rather than a disagreement about the world. Before calling such a pair a counterexample, identify what incompatible physical claims remain.

Actual and merely possible rivals have different evidential force. An actual, comparably developed rival demonstrates that the current inference is nonunique under specified criteria. A merely possible rival refutes a claim of logical entailment if its possibility is established, but does not automatically defeat ordinary fallible justification: otherwise any unexcluded sceptical possibility would defeat almost every empirical belief. A distant conceivable alternative need not deserve the same prior probability or explanatory standing as a well-tested account.

Consequently, empirical equivalence is a serious challenge to unrestricted, uniquely determined theoretical belief. It is not a deductive disproof of all scientific realism. A convincing argument must say whether the target is certainty or defeasible warrant, whether equivalence covers all observations or only present data, and why the rivals are physically distinct. Merely announcing that another story could be invented leaves these crucial questions unanswered.

Instrumentalism treats theoretical descriptions primarily as tools for organizing and predicting observations rather than as a literally true inventory of unobservable entities. Its attraction is epistemic economy: where empirical success is all the evidence establishes, using a successful calculation need not commit us to every mechanism appearing in it. The abandonment of phlogiston illustrates why such caution can be reasonable.

However, distinguish three claims: we can use a theory without believing it; we ought to suspend belief about some of its entities; and those entities do not exist or theoretical sentences are not truth-apt. The first is commonplace and does not establish the last two. Constructive empiricism interprets theoretical claims literally while distinguishing acceptance for empirical adequacy from belief in the whole theory. It therefore offers restraint without treating all theoretical language as a disguised calculation recipe.^[ref]

A scientific realist can answer that independent experimental access, successful interventions and unexpectedly accurate new predictions support some entities more strongly than mere curve fitting. The no-miracles argument treats this convergence as something needing explanation. The reply is not conclusive: explanatory success need not guarantee truth, and selectively retained successful theories can make retrospective success look stronger than it was. Equally, historical failures do not show that every present theoretical commitment is equally fragile.

There is therefore a good argument for instrumental use and selective suspension of belief where evidence does not discriminate ontologies. There is a weaker argument for universal instrumentalism about every scientific entity. A defensible position should compare the actual evidence for particular commitments and explain why empirical adequacy is the right stopping point, rather than infer global agnosticism from the bare possibility of error.

John Locke's primary-secondary quality distinction separates properties such as shape, extension and motion from powers to produce experiences such as colour and taste. On this account a secondary quality is a body's disposition to affect a perceiver through its physical constitution; the disposition should not be confused with the sensation it produces.^[ref]

This distinction can help scientific explanation. A surface's spectral reflectance, the illuminating spectrum, and a visual system's receptor responses jointly affect perceived colour. Altering illumination can change the experience without changing the surface. The lesson is to distinguish an object's physical structure, the surrounding conditions and the observer's response, rather than to assign a colour experience to the object as though no observer or illumination mattered.

A simple absolute contrast between invariant primary properties and observer-dependent secondary properties is less plausible. Special relativity makes measured lengths and durations depend on the inertial frame; appearance of shape also depends on viewing geometry. Dependence on circumstances therefore does not by itself mark a property as unreal. Conversely, a disposition to reflect particular wavelengths or stimulate receptors can be an objective physical relation even though its manifestation requires suitable conditions.

A useful modern version is thus explanatory rather than an unrevisable division of reality. Distinguish independently specified physical variables from response properties defined relative to a system and conditions, then study the mechanism connecting them. The boundary can move with better science. This preserves the insight that experiences require explanation while avoiding the claim that every relational property is merely subjective or that the historical list of primary qualities is final.