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Problem of induction
Problem of induction
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Usually inferred from repeated observations: "The sun always rises in the east."
Usually not inferred from repeated observations: "When someone dies, it's never me."

The problem of induction is a philosophical problem that questions the rationality of predictions about unobserved things based on previous observations. These inferences from the observed to the unobserved are known as "inductive inferences". David Hume, who first formulated the problem in 1739,[1] argued that there is no non-circular way to justify inductive inferences, while he acknowledged that everyone does and must make such inferences.[2]

The traditional inductivist view is that all claimed empirical laws, either in everyday life or through the scientific method, can be justified through some form of reasoning. The problem is that many philosophers tried to find such a justification but their proposals were not accepted by others. Identifying the inductivist view as the scientific view, C. D. Broad once said that induction is "the glory of science and the scandal of philosophy".[3] In contrast, Karl Popper's critical rationalism claimed that inductive justifications are never used in science and proposed instead that science is based on the procedure of conjecturing hypotheses, deductively calculating consequences, and then empirically attempting to falsify them.

Formulation of the problem

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In inductive reasoning, one makes a series of observations and infers a claim based on them. For instance, from a series of observations that a woman walks her dog by the market at 8 am on Monday, it seems valid to infer that next Monday she will do the same, or that, in general, the woman walks her dog by the market every Monday. That next Monday the woman walks by the market merely adds to the series of observations, but it does not prove she will walk by the market every Monday. First of all, it is not certain, regardless of the number of observations, that the woman always walks by the market at 8 am on Monday. In fact, David Hume even argued that we cannot claim it is "more probable", since this still requires the assumption that the past predicts the future.

Second, the observations themselves do not establish the validity of inductive reasoning, except inductively. Bertrand Russell illustrated this point in The Problems of Philosophy:

Domestic animals expect food when they see the person who usually feeds them. We know that all these rather crude expectations of uniformity are liable to be misleading. The man who has fed the chicken every day throughout its life at last wrings its neck instead, showing that more refined views as to the uniformity of nature would have been useful to the chicken.

Ancient and early modern origins

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Pyrrhonism

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The works of the Pyrrhonist philosopher Sextus Empiricus contain the oldest surviving questioning of the validity of inductive reasoning. He wrote:[4]

It is also easy, I consider, to set aside the method of induction. For, when they propose to establish the universal from the particulars by means of induction, they will effect this by a review either of all or of some of the particular instances. But if they review some, the induction will be insecure, since some of the particulars omitted in the induction may contravene the universal; while if they are to review all, they will be toiling at the impossible, since the particulars are infinite and indefinite. Thus on both grounds, as I think, the consequence is that induction is invalidated.

The focus upon the gap between the premises and conclusion present in the above passage appears different from Hume's focus upon the circular reasoning of induction. However, Weintraub claims in The Philosophical Quarterly[5] that although Sextus's approach to the problem appears different, Hume's approach was actually an application of another argument raised by Sextus:[6]

Those who claim for themselves to judge the truth are bound to possess a criterion of truth. This criterion, then, either is without a judge's approval or has been approved. But if it is without approval, whence comes it that it is truthworthy? For no matter of dispute is to be trusted without judging. And, if it has been approved, that which approves it, in turn, either has been approved or has not been approved, and so on ad infinitum.

Although the criterion argument applies to both deduction and induction, Weintraub believes that Sextus's argument "is precisely the strategy Hume invokes against induction: it cannot be justified, because the purported justification, being inductive, is circular." She concludes that "Hume's most important legacy is the supposition that the justification of induction is not analogous to that of deduction." She ends with a discussion of Hume's implicit sanction of the validity of deduction, which Hume describes as intuitive in a manner analogous to modern foundationalism.

Indian philosophy

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The Cārvāka, a materialist and skeptic school of Indian philosophy, used the problem of induction to point out the flaws in using inference as a way to gain valid knowledge. They held that since inference needed an invariable connection between the middle term and the predicate, and further, that since there was no way to establish this invariable connection, that the efficacy of inference as a means of valid knowledge could never be stated.[7][8]

The 9th century Indian skeptic, Jayarasi Bhatta, also made an attack on inference, along with all means of knowledge, and showed by a type of reductio argument that there was no way to conclude universal relations from the observation of particular instances.[9][10]

Medieval philosophy

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Medieval writers such as al-Ghazali and William of Ockham connected the problem with God's absolute power, asking how we can be certain that the world will continue behaving as expected when God could at any moment miraculously cause the opposite.[11] Duns Scotus, however, argued that inductive inference from a finite number of particulars to a universal generalization was justified by "a proposition reposing in the soul, 'Whatever occurs in a great many instances by a cause that is not free, is the natural effect of that cause.'"[12] Some 17th-century Jesuits argued that although God could create the end of the world at any moment, it was necessarily a rare event and hence our confidence that it would not happen very soon was largely justified.[13]

David Hume

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David Hume, a Scottish thinker of the Enlightenment era, is the philosopher most often associated with induction. His formulation of the problem of induction can be found in An Enquiry concerning Human Understanding, §4. Here, Hume introduces his famous distinction between "relations of ideas" and "matters of fact". Relations of ideas are propositions which can be derived from deductive logic, which can be found in fields such as geometry and algebra. Matters of fact, meanwhile, are not verified through the workings of deductive logic but by experience. Specifically, matters of fact are established by making an inference about causes and effects from repeatedly observed experience. While relations of ideas are supported by reason alone, matters of fact must rely on the connection of a cause and effect through experience. Causes of effects cannot be linked through a priori reasoning, but by positing a "necessary connection" that depends on the "uniformity of nature".

Hume situates his introduction to the problem of induction in A Treatise of Human Nature within his larger discussion on the nature of causes and effects (Book I, Part III, Section VI). He writes that reasoning alone cannot establish the grounds of causation. Instead, the human mind imputes causation to phenomena after repeatedly observing a connection between two objects. For Hume, establishing the link between causes and effects relies not on reasoning alone, but the observation of "constant conjunction" throughout one's sensory experience. From this discussion, Hume goes on to present his formulation of the problem of induction in A Treatise of Human Nature, writing "there can be no demonstrative arguments to prove, that those instances, of which we have had no experience, resemble those, of which we have had experience."

In other words, the problem of induction can be framed in the following way: we cannot apply a conclusion about a particular set of observations to a more general set of observations. While deductive logic allows one to arrive at a conclusion with certainty, inductive logic can only provide a conclusion that is probably true.[non-primary source needed] It is mistaken to frame the difference between deductive and inductive logic as one between general to specific reasoning and specific to general reasoning. This is a common misperception about the difference between inductive and deductive thinking. According to the literal standards of logic, deductive reasoning arrives at certain conclusions while inductive reasoning arrives at probable conclusions. [non-primary source needed] Hume's treatment of induction helps to establish the grounds for probability, as he writes in A Treatise of Human Nature that "probability is founded on the presumption of a resemblance betwixt those objects, of which we have had experience, and those, of which we have had none" (Book I, Part III, Section VI).[non-primary source needed]

Therefore, Hume establishes induction as the very grounds for attributing causation. There might be many effects which stem from a single cause. Over repeated observation, one establishes that a certain set of effects are linked to a certain set of causes. However, the future resemblance of these connections to connections observed in the past depends on induction. Induction allows one to conclude that "Effect A2" was caused by "Cause A2" because a connection between "Effect A1" and "Cause A1" was observed repeatedly in the past. Given that reason alone can not be sufficient to establish the grounds of induction, Hume implies that induction must be accomplished through imagination. One does not make an inductive reference through a priori reasoning, but through an imaginative step automatically taken by the mind.

Hume does not challenge that induction is performed by the human mind automatically, but rather hopes to show more clearly how much human inference depends on inductive—not a priori—reasoning. He does not deny future uses of induction, but shows that it is distinct from deductive reasoning, helps to ground causation, and wants to inquire more deeply into its validity. Hume offers no solution to the problem of induction himself. He prompts other thinkers and logicians to argue for the validity of induction as an ongoing dilemma for philosophy. A key issue with establishing the validity of induction is that one is tempted to use an inductive inference as a form of justification itself. This is because people commonly justify the validity of induction by pointing to the many instances in the past when induction proved to be accurate. For example, one might argue that it is valid to use inductive inference in the future because this type of reasoning has yielded accurate results in the past. However, this argument relies on an inductive premise itself—that past observations of induction being valid will mean that future observations of induction will also be valid. Thus, many solutions to the problem of induction tend to be circular.

Nelson Goodman's new riddle of induction

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Nelson Goodman's Fact, Fiction, and Forecast (1955) presented a different description of the problem of induction in the chapter entitled "The New Riddle of Induction". Goodman proposed the new predicate "grue". Something is grue if and only if it has been (or will be, according to a scientific, general hypothesis[14][15]) observed to be green before a certain time t, and blue if observed after that time. The "new" problem of induction is, since all emeralds we have ever seen are both green and grue, why do we suppose that after time t we will find green but not grue emeralds? The problem here raised is that two different inductions will be true and false under the same conditions. In other words:

  • Given the observations of a lot of green emeralds, someone using a common language will inductively infer that all emeralds are green (therefore, he will believe that any emerald he will ever find will be green, even after time t).
  • Given the same set of observations of green emeralds, someone using the predicate "grue" will inductively infer that all emeralds, which will be observed after t, will be blue, despite the fact that he observed only green emeralds so far.

One could argue, using Occam's razor, that greenness is more likely than grueness because the concept of grueness is more complex than that of greenness. Goodman, however, points out that the predicate "grue" only appears more complex than the predicate "green" because we have defined grue in terms of blue and green. If we had always been brought up to think in terms of "grue" and "bleen" (where bleen is blue before time t, and green thereafter), we would intuitively consider "green" to be a crazy and complicated predicate. Goodman believed that which scientific hypotheses we favour depend on which predicates are "entrenched" in our language.[original research?]

Willard Van Orman Quine offers a practical solution to this problem[16] by making the metaphysical claim that only predicates that identify a "natural kind" (i.e. a real property of real things) can be legitimately used in a scientific hypothesis. R. Bhaskar also offers a practical solution to the problem. He argues that the problem of induction only arises if we deny the possibility of a reason for the predicate, located in the enduring nature of something.[17] For example, we know that all emeralds are green, not because we have only ever seen green emeralds, but because the chemical make-up of emeralds insists that they must be green. If we were to change that structure, they would not be green. For instance, emeralds are a kind of green beryl, made green by trace amounts of chromium and sometimes vanadium. Without these trace elements, the gems would be colourless.

Notable interpretations

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Hume

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Although induction is not made by reason, Hume observes that we nonetheless perform it and improve from it. He proposes a descriptive explanation for the nature of induction in §5 of the Enquiry, titled "Skeptical solution of these doubts". It is by custom or habit that one draws the inductive connection described above, and "without the influence of custom we would be entirely ignorant of every matter of fact beyond what is immediately present to the memory and senses".[18] The result of custom is belief, which is instinctual and much stronger than imagination alone.[19]

John Maynard Keynes

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In his Treatise on Probability, John Maynard Keynes notes:

An inductive argument affirms, not that a certain matter of fact is so, but that relative to certain evidence there is a probability in its favour. The validity of the induction, relative to the original evidence, is not upset, therefore, if, as a fact, the truth turns out to be otherwise.[20]

This approach was endorsed by Bertrand Russell.[21]

David Stove and Donald Williams

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David Stove's argument for induction, based on the statistical syllogism, was presented in the Rationality of Induction and was developed from an argument put forward by one of Stove's heroes, the late Donald Cary Williams (formerly Professor at Harvard) in his book The Ground of Induction.[22] Stove argued that it is a statistical truth that the great majority of the possible subsets of specified size (as long as this size is not too small) are similar to the larger population to which they belong. For example, the majority of the subsets which contain 3000 ravens which you can form from the raven population are similar to the population itself (and this applies no matter how large the raven population is, as long as it is not infinite). Consequently, Stove argued that if you find yourself with such a subset then the chances are that this subset is one of the ones that are similar to the population, and so you are justified in concluding that it is likely that this subset "matches" the population reasonably closely. The situation would be analogous to drawing a ball out of a barrel of balls, 99% of which are red. In such a case you have a 99% chance of drawing a red ball. Similarly, when getting a sample of ravens the probability is very high that the sample is one of the matching or "representative" ones. So as long as you have no reason to think that your sample is an unrepresentative one, you are justified in thinking that probably (although not certainly) that it is.[23]

Biting the bullet: Keith Campbell and Claudio Costa

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An intuitive answer to Hume would be to say that a world inaccessible to any inductive procedure would simply not be conceivable. This intuition was taken into account by Keith Campbell by considering that, to be built, a concept must be reapplied, which demands a certain continuity in its object of application and consequently some openness to induction.[24] Claudio Costa has noted that a future can only be a future of its own past if it holds some identity with it. Moreover, the nearer a future is to the point of junction with its past, the greater are the similarities tendentially involved. Consequently – contra Hume – some form of principle of homogeneity (causal or structural) between future and past must be warranted, which would make some inductive procedure always possible.[25]

Karl Popper

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Karl Popper, a philosopher of science, sought to solve the problem of induction.[26][27] He argued that science does not use induction, and induction is in fact a myth.[28] Instead, knowledge is created by conjecture and criticism.[29] The main role of observations and experiments in science, he argued, is in attempts to criticize and refute existing theories.[30]

According to Popper, the problem of induction as usually conceived is asking the wrong question: it is asking how to justify theories given they cannot be justified by induction. Popper argued that justification is not needed at all, and seeking justification "begs for an authoritarian answer". Instead, Popper said, what should be done is to look to find and correct errors.[31] Popper regarded theories that have survived criticism as better corroborated in proportion to the amount and stringency of the criticism, but, in sharp contrast to the inductivist theories of knowledge, emphatically as less likely to be true.[clarification needed][32] Popper held that seeking for theories with a high probability of being true was a false goal that is in conflict with the search for knowledge. Science should seek for theories that are most probably false on the one hand (which is the same as saying that they are highly falsifiable and so there are many ways that they could turn out to be wrong), but still all actual attempts to falsify them have failed so far (that they are highly corroborated).

Wesley C. Salmon criticizes Popper on the grounds that predictions need to be made both for practical purposes and in order to test theories. That means Popperians need to make a selection from the number of unfalsified theories available to them, which is generally more than one. Popperians would wish to choose well-corroborated theories, in their sense of corroboration, but face a dilemma: either they are making the essentially inductive claim that a theory's having survived criticism in the past means it will be a reliable predictor in the future; or Popperian corroboration is no indicator of predictive power at all, so there is no rational motivation for their preferred selection principle.[33]

David Miller has criticized this kind of criticism by Salmon and others because it makes inductivist assumptions.[34] Popper does not say that corroboration is an indicator of predictive power. The predictive power[according to whom?] is in the theory itself, not in its corroboration. The rational motivation for choosing a well-corroborated theory is that it is simply easier to falsify: Well-corroborated means that at least one kind of experiment (already conducted at least once) could have falsified (but did not actually falsify) the one theory, while the same kind of experiment, regardless of its outcome, could not have falsified the other. So it is rational to choose the well-corroborated theory: It may not be more likely to be true, but if it is actually false, it is easier to get rid of when confronted with the conflicting evidence that will eventually turn up. Accordingly, it is wrong to consider corroboration as a reason, a justification for believing in a theory or as an argument in favor of a theory to convince someone who objects to it.[35]

See also

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Notes

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
The problem of induction is a central challenge in and the that questions the rational justification for inductive inferences, which generalize from specific observations to broader conclusions about unobserved events, such as predicting future outcomes based on past patterns. First systematically formulated by the Scottish philosopher in the , it highlights the apparent circularity in attempting to validate through either a priori demonstration or , as both approaches presuppose the very uniformity of nature that induction seeks to establish. Hume argued that all inferences concerning matters of fact rely on the relation of cause and effect, yet no necessary connection between causes and effects can be discerned through reason alone, nor does experience reveal more than constant conjunctions without binding necessity, leaving inductive conclusions resting on habit rather than logic. Hume's formulation, detailed in An Enquiry Concerning Human Understanding (Section IV), posits that "all reasonings concerning matter of fact seem to be founded on the relation of Cause and Effect," but "even after we have of the operations of cause and effect... our conclusions from that are not founded on reasoning." This implies that scientific laws and everyday predictions lack a non-circular foundation, threatening the reliability of empirical . The problem gained renewed attention in the through Bertrand Russell's vivid analogy that failing to solve it would make the difference between sanity and insanity indistinguishable, emphasizing its implications for distinguishing reliable from . Philosophers have proposed various responses to address Hume's challenge, though none is universally accepted. Karl Popper's falsificationism rejects the need for inductive confirmation altogether, arguing that scientific theories gain support through rigorous testing and potential refutation rather than probabilistic generalization, thereby sidestepping the problem by denying induction's central role in science. Bayesian approaches, drawing on , offer a framework where inductive updates occur via conditionalization on , with prior beliefs revised to posteriors, though critics contend this merely formalizes the issue without resolving the justification of initial priors or the uniformity assumption. More recent material theories, such as John D. Norton's, dissolve the problem by rejecting universal rules of induction in favor of domain-specific factual postulates that license inferences locally, avoiding both circularity and regress. These debates underscore the problem's enduring influence on understandings of , prediction, and rational belief formation across and science.

Historical Origins

Ancient Skepticism

The earliest skeptical challenges to inductive reasoning emerged in ancient Greek and Indian philosophical traditions, where thinkers questioned the reliability of generalizing from observed particulars to unobserved cases. In the Pyrrhonian school of skepticism, as articulated by Sextus Empiricus around 200 CE, induction was critiqued through what is known as the "mode of induction," highlighting the inability of past uniformities to guarantee future instances without falling into circularity or infinite regress. Sextus argued in his Outlines of Pyrrhonism that to justify an inductive generalization—such as expecting the sun to rise tomorrow based on prior observations—one must either assume the uniformity of nature indefinitely into the past (leading to regress, as each justification requires further evidence) or presuppose that past patterns will hold in the future, which begs the question by relying on the very inductive principle under scrutiny (PH I.166–177). This dilemma rendered induction dogmatic, as it could not be non-circularly validated, prompting the Pyrrhonist practice of epoché—suspension of judgment—to achieve mental tranquility by avoiding unsubstantiated beliefs. In parallel, ancient Indian philosophy featured skeptical challenges to induction from the Cārvāka (Materialist) school, which denied the possibility of establishing universal connections (vyāpti) from particular observations, arguing that unobserved counterinstances could always undermine generalizations—for instance, inferring that all fires are hot from observed flames risks failure if a cold fire exists beyond experience. The Nyāya Sūtras, composed around the 2nd century BCE by Akṣapāda Gautama, formalized anumāna (inference) as a means of knowledge involving the inference of universals from observed instances and absence of counterexamples, such as concluding that a hill has fire because it has smoke, based on established pervasion. Nyāya thinkers like Udayana (10th century CE) defended anumāna as yielding apodictic certainty when vyāpti is rigorously verified through repeated positive and negative observations and logical scrutiny (tarka), countering Cārvāka skepticism by emphasizing methods to exclude exceptions and ensure reliability. These ancient skeptical perspectives framed induction as presumptuous and unreliable, viewing reliance on it as a form of intellectual dogmatism that invites doubt and restraint in belief formation. By exposing the circularity and incompleteness of inductive justifications, Pyrrhonists and Cārvāka philosophers alike advocated suspending firm commitments to inductive conclusions, laying groundwork for later, more systematic inquiries into the problem.

Medieval and Early Modern Precursors

In the medieval period, inductive skepticism emerged within scholastic philosophy, particularly through the works of Nicholas of Autrecourt (c. 1299–after 1350), who challenged the inference of causal necessity from observed correlations. Autrecourt argued that repeated observations of events succeeding one another do not demonstrate a necessary connection between them, as such inferences rely on probabilistic assumptions rather than demonstrative certainty. He proposed occasionalism as a resolution, positing that God directly causes all events, rendering natural causation illusory and aligning empirical observations with divine omnipotence without committing to human-derived inductive necessities. This view attempted to reconcile theological determinism with skeptical doubts about causation, though it left unresolved the circularity of assuming divine regularity to justify observations. Transitioning to the early , (1561–1626) advanced induction as a cornerstone of scientific inquiry in his (1620), advocating a systematic ascent from particular observations to general axioms through controlled experiments and exclusion of biases. Yet Bacon implicitly acknowledged the limitations of even refined induction, critiquing simple enumeration—relying solely on positive instances—as "precarious and exposed to peril from a contradictory instance," which could undermine generalizations without exhaustive verification. His method sought to harmonize empirical progress with a providential , portraying nature's laws as discoverable through induction while attributing ultimate order to God's design, thus bridging theological faith and nascent without eliminating inductive vulnerabilities. John Locke (1632–1704) further developed empiricist foundations for induction in An Essay Concerning Human Understanding (1689), asserting that ideas of causation derive from sensory experience of repeated conjunctions between events, such as fire consistently heating objects. However, Locke maintained that these observations yield no perception of a "necessary connection" binding cause to effect, limiting causal knowledge to probable expectations rather than demonstrative truth. This empiricism endeavored to integrate inductive reasoning with Christian theology by grounding human understanding in God's created order, evident through experience, yet it perpetuated tensions between empirical reliability and the unverifiable necessity underlying generalizations.

Core Formulations

David Hume's Classic Problem

articulated the problem of induction in his seminal works (1739–1740) and An Enquiry Concerning Human Understanding (1748), where he argued that , which extrapolates general principles from specific observations, fundamentally relies on an unproven assumption about the uniformity of nature. Specifically, induction presupposes that "the future will be conformable to the past" or that unobserved instances will resemble those previously experienced, yet this principle cannot be established through reason or observation without . emphasized that all experimental conclusions proceed upon this supposition, but no can justify it, as any such evidence would itself depend on inductive inference. The logical structure of Hume's argument highlights a deep circularity in attempts to justify induction. If one seeks to prove the uniformity principle through induction—by citing past uniformities to predict future ones—this merely assumes the very principle in question, rendering the justification circular. Alternatively, , which Hume distinguished as concerning "relations of ideas" (such as mathematical truths that are intuitively or demonstratively certain), cannot apply here, as inductive inferences pertain to "matters of fact" about the , where the contrary is always conceivable without contradiction. In causation, for instance, we observe constant conjunctions between events but have no rational basis for expecting their continuation, as the necessary connection arises not from the objects themselves but from our mental associations. This circularity leads to profound skeptical implications for knowledge justification, as reason alone provides no foundation for inductive beliefs. Instead, Hume contended that such beliefs stem from custom and habit, which incline the mind to associate ideas based on repeated experiences, forming the psychological basis for expecting uniformity without rational warrant. "Custom, then, is the great guide of human life," Hume wrote, underscoring that while this mechanism renders experience useful, it does not elevate induction to a justified form of knowledge. Thus, Hume's formulation challenges the epistemic status of scientific and everyday predictions, revealing induction as a non-rational propensity rather than a demonstrable method.

Nelson Goodman's New Riddle

In his 1955 book Fact, Fiction, and Forecast, philosopher introduced a reformulation of the problem of induction, shifting focus from the justification of inductive inferences to the selection of appropriate predicates for prediction. Goodman argued that traditional accounts, which assume evidence uniformly supports generalizations, fail to distinguish between projectible predicates—those that legitimately extend to unobserved cases—and non-projectible ones that lead to absurd predictions. This "new riddle" arises because the same evidence can confirm incompatible hypotheses depending on how predicates are defined, challenging the very basis of scientific forecasting. Central to Goodman's paradox is the predicate grue, defined as the property of an object being if observed before a specific future time t (such as the year 2000) and blue otherwise. Suppose all emeralds examined before t are ; this evidence confirms both the hypothesis "all emeralds are " and "all emeralds are grue," since the observed emeralds satisfy the grue condition by being before t. Yet, after t, the first hypothesis predicts emeralds, while the second predicts blue ones, rendering the grue hypothesis unprojectible despite equal evidential support. This demonstrates that induction does not merely rely on observed uniformity, as in Hume's classic problem, but requires criteria for which predicates are confirmable by their instances. An analogous example illustrates the issue with other observations: all crows observed to date are black, confirming "all crows are black" but also potentially "all crows are grackles," where grackles are black if observed before and white thereafter. Here, "black" is projectible due to its established role in natural laws, whereas "grackle" is not, as it introduces an arbitrary temporal cutoff that disrupts predictive consistency. Goodman's new riddle thus refines Hume's concern with temporal uniformity by emphasizing the qualitative problem of predicate choice: alone cannot determine which generalizations are lawlike without additional rules. To resolve this, Goodman proposed that projectibility depends on the entrenchment of predicates, where a predicate is entrenched if it or similar terms have been successfully projected in past confirmed hypotheses, building a linguistic and historical foundation for induction. He also invoked similarity among instances, suggesting that projectible predicates group objects in ways that resemble established scientific categories rather than contrived ones like grue. However, these rules face for their inductive basis: entrenchment relies on prior successful projections, risking circularity by presupposing the validity of the very inductions it seeks to justify. Despite this, Goodman's framework highlights the entrenched nature of everyday predicates like "" or "" in scientific practice, distinguishing them from alternatives.

Philosophical Responses

Inductivist Defenses

, while formulating the problem of induction, offered a partial resolution by acknowledging that inductive inferences cannot be justified by reason but are instead rooted in custom or , an instinctive psychological mechanism that drives human belief formation despite its irrationality. This , Hume argued, is psychologically inevitable and practically indispensable, as it underpins expectations about the future uniformity of and supports by enabling reliable predictions based on past experiences. John Maynard Keynes, in his 1921 work A Treatise on Probability, sought to justify induction through a logical interpretation of probability, positing that inductive inferences can be rationally supported by degrees of partial calibrated to , without requiring absolute . Keynes emphasized weighted analogies and the limits of probability as tools for induction, arguing that such methods provide a non-circular evidential basis for generalizing from observed instances to unobserved ones, thereby rendering induction a defensible epistemic practice. Philosophers Keith Campbell and Claudio Costa have advocated a "biting the bullet" strategy, accepting basic inductive postulates as self-evident axioms necessary for rational , rather than demanding further justification. Campbell, in particular, contends that the apparent circularity in justifying induction via induction itself is non-vicious, as the reliability of inductive rules need not be presupposed in advance but emerges as inherent to the practice of empirical reasoning. Costa extends this by proposing a semantic framework where inductive principles are foundational to meaningful discourse about the world, treating them as primitive assumptions akin to logical axioms. David Stove defended induction against skeptical pessimism by arguing that the historical success of inductive methods provides non-circular evidence for their future reliability, as the track record of accurate predictions from past observations demonstrates a probabilistic alignment between samples and populations. In The Rationality of Induction (1986), Stove critiqued overly pessimistic views of induction's justification, asserting that the cumulative evidence from successful inductions outweighs hypothetical doomsday scenarios, thereby establishing induction as rationally preferable. Donald Williams, in The Ground of Induction (1947), portrayed induction as a fundamental logical relation grounded in probabilistic principles, prioritizing the "straight rule" of induction— which infers the proportion of a from a representative sample—over inverse probability inferences. Williams maintained that this rule is logically sound because, in a combinatorial sense, observed frequencies reliably approximate overall distributions, providing a self-correcting mechanism that justifies inductive generalization without vicious circularity.

Falsificationist Critiques

, in his seminal work originally published in 1934, fundamentally rejected the role of induction in scientific , arguing instead that progresses through the formulation of bold conjectures followed by rigorous attempts at refutation. He contended that inductive inference, which seeks to generalize from specific observations to universal laws, cannot provide a logical foundation for scientific knowledge, as it leads to an or reliance on unproven assumptions. Popper's approach emphasized deductive testing, where hypotheses are subjected to empirical scrutiny not to confirm them but to potentially falsify them. Popper viewed the problem of induction—famously articulated by —as ultimately unsolvable, since no amount of confirmatory evidence can logically verify a universal theory, whereas a single contradictory instance can deductively refute it. In this framework, scientific theories are never proven true but gain temporary support through surviving severe tests aimed at falsification. This deductive asymmetry resolves the inductivist dilemma by eliminating the need for inductive justification altogether, rendering the traditional problem irrelevant to the logic of . A key element of Popper's critique is his criterion of demarcation, which posits that serves as the distinguishing feature between scientific theories and pseudoscientific or metaphysical claims, thereby circumventing the circularity inherent in inductivist justifications of . Theories must be empirically testable in principle, meaning they make predictions that could be contradicted by , unlike non-falsifiable assertions that evade refutation. This standard avoids Hume's challenge by grounding scientific rationality in the potential for criticism and error elimination rather than uncritical acceptance of inductive patterns. Popper's falsificationism directly critiques verificationist approaches, which rely on accumulating positive instances to support generalizations. For instance, repeated observations of white swans do not confirm the universal statement "all swans are white," as such induction lacks logical force; however, the discovery of a single definitively falsifies the through strict deduction. This example underscores the one-sided nature of empirical testing: is illusory and psychological, while falsification is objective and logical. The broader implications of Popper's views portray induction as a methodological perpetuated by inductivists, with scientific corroboration being provisional and always open to future refutation based on the theory's resilience against attempted falsifications. Theories that withstand repeated severe tests earn higher degrees of corroboration, but this does not equate to inductive verification; instead, it reflects the critical growth of through . Thus, Popper's framework shifts the emphasis from seeking confirmatory evidence to designing experiments that maximize the risk of refutation, fostering scientific progress without reliance on problematic inductive logic.

Probabilistic and Bayesian Approaches

Probabilistic and Bayesian approaches to the problem of induction reframe the challenge by treating inductive inferences not as guarantees of certainty, but as rational updates to degrees of based on , thereby providing a framework for measuring confirmation incrementally rather than all-or-nothing. In this view, induction becomes a process of where hypotheses are evaluated in terms of their probabilistic support from observed data, avoiding Hume's demand for deductive justification by embracing uncertainty as inherent to empirical reasoning. Central to Bayesianism is the application of Bayes' theorem, which formalizes how prior probabilities for hypotheses are updated with new evidence to yield posterior probabilities. The theorem states: P(HE)=P(EH)P(H)P(E)P(H|E) = \frac{P(E|H) \cdot P(H)}{P(E)} where P(H)P(H) is the prior probability of hypothesis HH, P(EH)P(E|H) is the likelihood of evidence EE given HH, and P(E)P(E) is the marginal probability of EE. This update rule allows induction to proceed as a coherent process of incrementally confirming or disconfirming hypotheses, with repeated applications leading to convergence on beliefs that reflect the true structure of the world under suitable conditions. Rudolf Carnap developed a foundational system of logical probability in the , aiming to construct an inductive logic as a formal for assigning degrees of to hypotheses relative to evidence. In his framework, confirmation functions c(h,e)c(h, e) quantify how much evidence ee supports hypothesis hh, drawing on symmetric logical relations between linguistic structures to derive objective-like probabilities within a subjectivist envelope. Carnap's approach sought to systematize inductive support in a way that parallels deductive logic, providing a continuum of confirmation values from 0 to 1. Bayesian responses to Hume's problem treat the principle of uniformity of nature as an implicit prior assumption in the probability assignments, justified not a priori but pragmatically through the coherence of rational . Bruno de Finetti's theory of subjective probability argues that probabilities represent personal degrees of , which must satisfy the axioms of probability to avoid "Dutch book" arguments—situations where inconsistent beliefs lead to guaranteed losses in fair bets. This coherence condition ensures that inductive practices converge to truth in the long run, as agents with proper priors will asymptotically align their posteriors with objective frequencies via repeated Bayesian updates. Regarding Goodman's new riddle, Bayesian models address projectibility by incorporating similarity metrics or simplicity priors that favor hypotheses with predicates like "green" over contrived ones like "grue," assigning higher initial probabilities to more natural or entrenched concepts based on their and predictive success. For instance, Elliott Sober's analysis shows that without a background model specifying relevant similarities, grue-like hypotheses lack the evidential support to compete with standard ones in Bayesian confirmation. Modern extensions of these ideas, such as those by , integrate Bayesian with inductive logic to handle formation and selection under , emphasizing how priors can be refined through empirical feedback in scientific contexts. Suppes' work builds on Carnap and de Finetti by exploring probabilistic models for empirical laws, where induction justifies beliefs via their utility in guiding actions and predictions.

Contemporary Implications

In Scientific Methodology

In scientific methodology, the problem of induction manifests prominently through underdetermination, where multiple competing theories can accommodate the same empirical data equally well, preventing inductive inference from uniquely selecting one theory over others. This issue is central to the Duhem-Quine thesis, which posits that scientific hypotheses are tested not in isolation but as part of holistic systems involving auxiliary assumptions, such that any observation confirming or disconfirming a prediction implicates the entire network rather than a single hypothesis. As articulated by Pierre Duhem in his analysis of physical theory, empirical evidence underdetermines theoretical choices because background assumptions about instruments, conditions, and interpretations can always be adjusted to preserve a favored hypothesis. Willard Van Orman Quine extended this holism to all empirical knowledge, arguing that no statement is empirically testable in isolation due to the interconnected web of beliefs facing the "tribunal of experience" as a collective. A key challenge to inductive confirmation in scientific practice is illustrated by the raven paradox, which highlights counterintuitive implications of standard logical accounts of support. Formulated by Carl Hempel, the paradox arises from the "all ravens are black," which, by , is confirmed not only by observing black ravens but also by non-black non-ravens, such as a white shoe; yet, intuitively, the latter observation seems irrelevant to the . Hempel's analysis in the showed that this follows from the equivalence condition in confirmation theory, where evidence supporting a logically equivalent statement must support the original, straining the inductive intuition that confirmation should derive from relevant instances alone. This paradox underscores how inductive methods in testing can lead to paradoxical outcomes, complicating the assessment of theoretical support in empirical sciences. Historical episodes, such as the development of in the early , exemplify the limits of induction when anomalies disrupt uniform expectations derived from past observations. , built on inductive generalizations from macroscopic phenomena, failed to predict phenomena like and the , where experimental results defied expectations of continuous energy distribution and wave-particle duality. These crises revealed induction's vulnerability to unforeseen regularities, as accumulating data did not incrementally refine theories but instead necessitated abandoning inductive projections from classical precedents, leading to revolutionary shifts rather than gradual confirmation. Despite these challenges, modern scientific methodology relies on induction through strategies like auxiliary hypotheses and systematic error mitigation to approximate causal inferences. John Stuart Mill's methods of agreement and difference, for instance, facilitate by identifying common factors across instances (agreement) or isolating variables present only in outcomes (difference), thereby strengthening causal claims amid . Scientists routinely employ such techniques, supplemented by auxiliary assumptions about experimental controls, to mitigate inductive fallibility and advance choice. However, induction remains essential yet inherently fallible in scientific practice, often culminating in paradigm shifts when accumulated anomalies render existing frameworks untenable, as described in his account of scientific revolutions.

In Epistemology and AI

The problem of induction poses significant challenges in contemporary , particularly for theories of justified such as , which posits that a is justified if it results from a reliable process that tends to produce true . Alvin Goldman's seminal 1979 formulation of process emphasizes the reliability of belief-forming mechanisms, yet the inductive step—extrapolating from observed instances to unobserved ones—lacks a non-circular justification, as any appeal to past reliability itself relies on induction. This circularity undermines reliabilist accounts of inductive knowledge, raising about whether beliefs based on inductive inference can be deemed reliably truth-tracking without . In , particularly , the problem of induction manifests as , where models trained on finite datasets capture noise or idiosyncratic patterns rather than generalizable truths, leading to poor performance on unseen data. This echoes Hume's classic concern, as algorithms induce hypotheses from training examples but cannot guarantee extrapolation without assuming the uniformity of nature. For instance, neural networks can exhibit biases akin to Nelson Goodman's "grue" paradox, where adversarial examples—subtle perturbations that fool models—reveal how inductive learning favors projectible predicates like "green" over contrived ones like "grue," yet still succumbs to hidden distributional shifts that invalidate generalizations. Contemporary debates in AI epistemology draw on Solomonoff induction, a formal approach from the that uses algorithmic complexity as a prior to select simpler hypotheses, approximating an ideal solution to the induction problem by assigning higher probabilities to shorter programs generating observed data. However, this method remains computationally intractable, as it requires enumerating all possible Turing machines, limiting its practical application in AI systems. The no-free-lunch theorems, introduced in the , underscore inherent limitations in learning from finite data, proving that no algorithm outperforms others on average across all possible distributions without inductive biases. Responses include methods, such as random forests that aggregate multiple models to mitigate , and regularization techniques like L2 penalties or dropout in neural networks, which impose simplicity constraints to enhance generalization. Emerging links to AI ethics highlight how unresolved inductive issues perpetuate societal harms; for example, inductive biases in algorithms, trained on historical , amplify racial disparities by overgeneralizing from biased samples, leading to discriminatory outcomes. Similarly, in climate modeling, models' reliance on inductive from limited observational can embed erroneous assumptions about future patterns, exacerbating uncertainties in decisions. These applications underscore the need for ethical frameworks that address inductive beyond technical fixes, ensuring AI systems do not entrench flawed generalizations.

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