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A theory is a systematic and rational form of abstract thinking about a phenomenon, or the conclusions derived from such thinking. It involves contemplative and logical reasoning, often supported by processes such as observation, experimentation, and research. Theories can be scientific, falling within the realm of empirical and testable knowledge, or they may belong to non-scientific disciplines, such as philosophy, art, or sociology. In some cases, theories may exist independently of any formal discipline.

In modern science, the term "theory" refers to scientific theories, a well-confirmed type of explanation of nature, made in a way consistent with the scientific method, and fulfilling the criteria required by modern science. Such theories are described in such a way that scientific tests should be able to provide empirical support for it, or empirical contradiction ("falsify") of it. Scientific theories are the most reliable, rigorous, and comprehensive form of scientific knowledge,[1] in contrast to more common uses of the word "theory" that imply that something is unproven or speculative (which in formal terms is better characterized by the word hypothesis).[2] Scientific theories are distinguished from hypotheses, which are individual empirically testable conjectures, and from scientific laws, which are descriptive accounts of the way nature behaves under certain conditions.

Theories guide the enterprise of finding facts rather than of reaching goals, and are neutral concerning alternatives among values.[3]: 131  A theory can be a body of knowledge, which may or may not be associated with particular explanatory models. To theorize is to develop this body of knowledge.[4]: 46 

The word theory or "in theory" is sometimes used outside of science to refer to something which the speaker did not experience or test before.[5] In science, this same concept is referred to as a hypothesis, and the word "hypothetically" is used both inside and outside of science. In its usage outside of science, the word "theory" is very often contrasted to "practice" (from Greek praxis, πρᾶξις) a Greek term for doing, which is opposed to theory.[6] A "classical example" of the distinction between "theoretical" and "practical" uses the discipline of medicine: medical theory involves trying to understand the causes and nature of health and sickness, while the practical side of medicine is trying to make people healthy. These two things are related but can be independent, because it is possible to research health and sickness without curing specific patients, and it is possible to cure a patient without knowing how the cure worked.[a]

Ancient usage

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The English word theory derives from a technical term in philosophy in Ancient Greek. As an everyday word, theoria, θεωρία, meant "looking at, viewing, beholding", but in more technical contexts it came to refer to contemplative or speculative understandings of natural things, such as those of natural philosophers, as opposed to more practical ways of knowing things, like that of skilled orators or artisans.[b] English-speakers have used the word theory since at least the late 16th century.[7] Modern uses of the word theory derive from the original definition, but have taken on new shades of meaning, still based on the idea of a theory as a thoughtful and rational explanation of the general nature of things.

Although it has more mundane meanings in Greek, the word θεωρία apparently developed special uses early in the recorded history of the Greek language. In the book From Religion to Philosophy, Francis Cornford suggests that the Orphics used the word theoria to mean "passionate sympathetic contemplation".[8] Pythagoras changed the word to mean "the passionless contemplation of rational, unchanging truth" of mathematical knowledge, because he considered this intellectual pursuit the way to reach the highest plane of existence.[9] Pythagoras emphasized subduing emotions and bodily desires to help the intellect function at the higher plane of theory. Thus, it was Pythagoras who gave the word theory the specific meaning that led to the classical and modern concept of a distinction between theory (as uninvolved, neutral thinking) and practice.[10]

Aristotle's terminology, as already mentioned, contrasts theory with praxis or practice, and this contrast exists till today. For Aristotle, both practice and theory involve thinking, but the aims are different. Theoretical contemplation considers things humans do not move or change, such as nature, so it has no human aim apart from itself and the knowledge it helps create. On the other hand, praxis involves thinking, but always with an aim to desired actions, whereby humans cause change or movement themselves for their own ends. Any human movement that involves no conscious choice and thinking could not be an example of praxis or doing.[c]

Formality

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Theories are analytical tools for understanding, explaining, and making predictions about a given subject matter. There are theories in many and varied fields of study, including the arts and sciences. A formal theory is syntactic in nature and is only meaningful when given a semantic component by applying it to some content (e.g., facts and relationships of the actual historical world as it is unfolding). Theories in various fields of study are often expressed in natural language, but can be constructed in such a way that their general form is identical to a theory as it is expressed in the formal language of mathematical logic. Theories may be expressed mathematically, symbolically, or in common language, but are generally expected to follow principles of rational thought or logic. In the social sciences, a new theory must explain the core relationships among units or process steps, exploring a current gap or unresolved debate in a field, extending its explanations into testable hypotheses and practical implications to benefit society.[11][12]

Theory is constructed of a set of sentences that are thought to be true statements about the subject under consideration. However, the truth of any one of these statements is always relative to the whole theory. Therefore, the same statement may be true with respect to one theory, and not true with respect to another. This is, in ordinary language, where statements such as "He is a terrible person" cannot be judged as true or false without reference to some interpretation of who "He" is and for that matter what a "terrible person" is under the theory.[13]

Sometimes two theories have exactly the same explanatory power because they make the same predictions. A pair of such theories is called indistinguishable or observationally equivalent, and the choice between them reduces to convenience or philosophical preference.[citation needed]

The form of theories is studied formally in mathematical logic, especially in model theory. When theories are studied in mathematics, they are usually expressed in some formal language and their statements are closed under application of certain procedures called rules of inference. A special case of this, an axiomatic theory, consists of axioms (or axiom schemata) and rules of inference. A theorem is a statement that can be derived from those axioms by application of these rules of inference. Theories used in applications are abstractions of observed phenomena and the resulting theorems provide solutions to real-world problems. Obvious examples include arithmetic (abstracting concepts of number), geometry (concepts of space), and probability (concepts of randomness and likelihood).

Gödel's incompleteness theorem shows that no consistent, recursively enumerable theory (that is, one whose theorems form a recursively enumerable set) in which the concept of natural numbers can be expressed, can include all true statements about them. As a result, some domains of knowledge cannot be formalized, accurately and completely, as mathematical theories. (Here, formalizing accurately and completely means that all true propositions—and only true propositions—are derivable within the mathematical system.) This limitation, however, in no way precludes the construction of mathematical theories that formalize large bodies of scientific knowledge.

Underdetermination

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A theory is underdetermined (also called indeterminacy of data to theory) if a rival, inconsistent theory is at least as consistent with the evidence. Underdetermination is an epistemological issue about the relation of evidence to conclusions.[citation needed]

A theory that lacks supporting evidence is generally, more properly, referred to as a hypothesis.[14]

Intertheoretic reduction and elimination

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If a new theory better explains and predicts a phenomenon than an old theory (i.e., it has more explanatory power), we are justified in believing that the newer theory describes reality more correctly. This is called an intertheoretic reduction because the terms of the old theory can be reduced to the terms of the new one. For instance, our historical understanding about sound, light and heat have been reduced to wave compressions and rarefactions, electromagnetic waves, and molecular kinetic energy, respectively. These terms, which are identified with each other, are called intertheoretic identities. When an old and new theory are parallel in this way, we can conclude that the new one describes the same reality, only more completely.

When a new theory uses new terms that do not reduce to terms of an older theory, but rather replace them because they misrepresent reality, it is called an intertheoretic elimination. For instance, the obsolete scientific theory that put forward an understanding of heat transfer in terms of the movement of caloric fluid was eliminated when a theory of heat as energy replaced it. Also, the theory that phlogiston is a substance released from burning and rusting material was eliminated with the new understanding of the reactivity of oxygen.

Versus theorems

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Theories are distinct from theorems. A theorem is derived deductively from axioms (basic assumptions) according to a formal system of rules, sometimes as an end in itself and sometimes as a first step toward being tested or applied in a concrete situation; theorems are said to be true in the sense that the conclusions of a theorem are logical consequences of the axioms. Theories are abstract and conceptual, and are supported or challenged by observations in the world. They are 'rigorously tentative', meaning that they are proposed as true and expected to satisfy careful examination to account for the possibility of faulty inference or incorrect observation. Sometimes theories are incorrect, meaning that an explicit set of observations contradicts some fundamental objection or application of the theory, but more often theories are corrected to conform to new observations, by restricting the class of phenomena the theory applies to or changing the assertions made. An example of the former is the restriction of classical mechanics to phenomena involving macroscopic length scales and particle speeds much lower than the speed of light.

Theory–practice relationship

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Theory is often distinguished from practice or praxis. The question of whether theoretical models of work are relevant to work itself is of interest to scholars of professions such as medicine, engineering, law, and management.[15]: 802 

The gap between theory and practice has been framed as a knowledge transfer where there is a task of translating research knowledge to be application in practice, and ensuring that practitioners are made aware of it. Academics have been criticized for not attempting to transfer the knowledge they produce to practitioners.[15]: 804 [16] Another framing supposes that theory and knowledge seek to understand different problems and model the world in different words (using different ontologies and epistemologies). Another framing says that research does not produce theory that is relevant to practice.[15]: 803 

In the context of management, Van de Van and Johnson propose a form of engaged scholarship where scholars examine problems that occur in practice, in an interdisciplinary fashion, producing results that create both new practical results as well as new theoretical models, but targeting theoretical results shared in an academic fashion.[15]: 815  They use a metaphor of "arbitrage" of ideas between disciplines, distinguishing it from collaboration.[15]: 803 

Scientific

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In science, the term "theory" refers to "a well-substantiated explanation of some aspect of the natural world, based on a body of facts that have been repeatedly confirmed through observation and experiment."[17][18] Theories must also meet further requirements, such as the ability to make falsifiable predictions with consistent accuracy across a broad area of scientific inquiry, and production of strong evidence in favor of the theory from multiple independent sources (consilience).

The strength of a scientific theory is related to the diversity of phenomena it can explain, which is measured by its ability to make falsifiable predictions with respect to those phenomena. Theories are improved (or replaced by better theories) as more evidence is gathered, so that accuracy in prediction improves over time; this increased accuracy corresponds to an increase in scientific knowledge. Scientists use theories as a foundation to gain further scientific knowledge, as well as to accomplish goals such as inventing technology or curing diseases.

Definitions from scientific organizations

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The United States National Academy of Sciences defines scientific theories as follows:

The formal scientific definition of "theory" is quite different from the everyday meaning of the word. It refers to a comprehensive explanation of some aspect of nature that is supported by a vast body of evidence. Many scientific theories are so well established that no new evidence is likely to alter them substantially. For example, no new evidence will demonstrate that the Earth does not orbit around the sun (heliocentric theory), or that living things are not made of cells (cell theory), that matter is not composed of atoms, or that the surface of the Earth is not divided into solid plates that have moved over geological timescales (the theory of plate tectonics) ... One of the most useful properties of scientific theories is that they can be used to make predictions about natural events or phenomena that have not yet been observed.[19]

From the American Association for the Advancement of Science:

A scientific theory is a well-substantiated explanation of some aspect of the natural world, based on a body of facts that have been repeatedly confirmed through observation and experiment. Such fact-supported theories are not "guesses" but reliable accounts of the real world. The theory of biological evolution is more than "just a theory." It is as factual an explanation of the universe as the atomic theory of matter or the germ theory of disease. Our understanding of gravity is still a work in progress. But the phenomenon of gravity, like evolution, is an accepted fact.[18]

The term theory is not appropriate for describing scientific models or untested, but intricate hypotheses.

Philosophical views

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The logical positivists thought of scientific theories as deductive theories—that a theory's content is based on some formal system of logic and on basic axioms. In a deductive theory, any sentence which is a logical consequence of one or more of the axioms is also a sentence of that theory.[13] This is called the received view of theories.

In the semantic view of theories, which has largely replaced the received view,[20][21] theories are viewed as scientific models. A model is an abstract and informative representation of reality (a "model of reality"), similar to the way that a map is a graphical model that represents the territory of a city or country. In this approach, theories are a specific category of models that fulfill the necessary criteria. (See Theories as models for further discussion.)

In physics

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In physics the term theory is generally used for a mathematical framework—derived from a small set of basic postulates (usually symmetries, like equality of locations in space or in time, or identity of electrons, etc.)—which is capable of producing experimental predictions for a given category of physical systems. One good example is classical electromagnetism, which encompasses results derived from gauge symmetry (sometimes called gauge invariance) in a form of a few equations called Maxwell's equations. The specific mathematical aspects of classical electromagnetic theory are termed "laws of electromagnetism", reflecting the level of consistent and reproducible evidence that supports them. Within electromagnetic theory generally, there are numerous hypotheses about how electromagnetism applies to specific situations. Many of these hypotheses are already considered adequately tested, with new ones always in the making and perhaps untested.

Regarding the term "theoretical"

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Certain tests may be infeasible or technically difficult. As a result, theories may make predictions that have not been confirmed or proven incorrect. These predictions may be described informally as "theoretical". They can be tested later, and if they are incorrect, this may lead to revision, invalidation, or rejection of the theory. [22]

Mathematical

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In mathematics, the term theory is used differently than its use in science – necessarily so, since mathematics contains no explanations of natural phenomena per se, even though it may help provide insight into natural systems or be inspired by them. In the general sense, a mathematical theory is a branch of mathematics devoted to some specific topics or methods, such as set theory, number theory, group theory, probability theory, game theory, control theory, perturbation theory, etc., such as might be appropriate for a single textbook.

In mathematical logic, a theory has a related but different sense: it is the collection of the theorems that can be deduced from a given set of axioms and a given set of inference rules.

Philosophical

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A theory can be either descriptive as in science, or prescriptive (normative) as in philosophy.[23] The latter are those whose subject matter consists not of empirical data, but rather of ideas. At least some of the elementary theorems of a philosophical theory are statements whose truth cannot necessarily be scientifically tested through empirical observation.

A field of study is sometimes named a "theory" because its basis is some initial set of assumptions describing the field's approach to the subject. These assumptions are the elementary theorems of the particular theory, and can be thought of as the axioms of that field. Some commonly known examples include set theory and number theory; however literary theory, critical theory, and music theory are also of the same form.

Metatheory

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One form of philosophical theory is a metatheory or meta-theory. A metatheory is a theory whose subject matter is some other theory or set of theories. In other words, it is a theory about theories. Statements made in the metatheory about the theory are called metatheorems.

Political

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A political theory is an ethical theory about the law and government. Often the term "political theory" refers to a general view, or specific ethic, political belief or attitude, thought about politics.

Jurisprudential

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In social science, jurisprudence is the philosophical theory of law. Contemporary philosophy of law addresses problems internal to law and legal systems, and problems of law as a particular social institution.

Examples

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Most of the following are scientific theories. Some are not, but rather encompass a body of knowledge or art, such as Music theory and Visual Arts Theories.

See also

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Notes

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
A is a coherent framework that explains a broad range of natural phenomena through integration of empirical observations, laws, and tested , capable of making accurate predictions and withstanding repeated experimental scrutiny. Unlike a , which represents a provisional, testable requiring further validation, a theory emerges from cumulative evidence and peer evaluation, achieving explanatory power across multiple contexts without being merely speculative. Prominent examples include the theory of evolution by , which accounts for through mechanisms like and differential survival, and the theory of , which describes as spacetime curvature confirmed by observations such as gravitational lensing. Theories advance scientific understanding by organizing disparate facts into causal models that reveal underlying mechanisms, enabling novel predictions and technological applications, though they remain open to refinement or falsification with new data. A persistent controversy arises from colloquial misuse of "theory" to imply unproven , undermining public appreciation of robust scientific constructs like germ theory, which elucidated microbial causation of and revolutionized . This distinction underscores theories' role as cornerstones of , distinct from mere or unverified assertion, and highlights the empirical rigor demanded in their development.

Etymology and Historical Development

Ancient and Classical Usage

The term theory derives from the word θεωρία (theōría), stemming from the verb θεωρέω (theōreō), which means "to observe," "to look at," or "to contemplate." In pre-philosophical contexts around the 5th century BCE, theōria primarily denoted the ritualized act of observation, involving delegations (theōroi) dispatched from city-states to remote sanctuaries for religious festivals, consultations, or athletic games, such as the established in 776 BCE; these missions emphasized collective witnessing of divine spectacles to foster civic and communal insight. This usage highlighted theōria as an active, embodied pursuit of higher truths through visual and participatory engagement, distinct from everyday perception. Plato (c. 428–348 BCE) repurposed theōria within his epistemology, elevating it to denote the philosopher's intellectual vision of eternal, immaterial Forms (eidē), which constitute true reality beyond the illusory sensible world. In dialogues like the Republic (c. 380 BCE), theōria aligns with the dialectical ascent of the soul via reason, culminating in the "vision of the Good" (Republic 505a–509c), where contemplation yields knowledge (epistēmē) superior to opinion (doxa) derived from sensory experience. This philosophical shift abstracted theōria from ritual to a contemplative method for grasping universals, influencing later idealist traditions while prioritizing rational detachment over empirical observation. Aristotle (384–322 BCE), Plato's student, systematized theōria as the core of theoretical sciences (theōrētikai epistēmai), which seek knowledge (epistēmē) for its intrinsic value rather than utility, encompassing theology (first philosophy, studying immutable being), mathematics (abstract quantities), and physics (changeable substances). In the Nicomachean Ethics (c. 350 BCE), he posits theōria as the supreme human activity—self-sufficient, continuous, and divine-like—wherein the intellect (nous) contemplates eternal truths, achieving eudaimonia (flourishing) as the telos of rational life (NE 1177a–b). This framework distinguished theoretical pursuits from practical (praxis, ethical action) and productive (poiēsis, craft) endeavors, grounding theōria in causal analysis of nature while critiquing Platonic abstraction for its separation from observable particulars.

Medieval and Early Modern Evolution

In medieval , the concept of theory, derived from theoria meaning contemplation or speculation, was primarily understood as speculative pursued for its own sake, distinct from practical knowledge aimed at action or moral conduct. This distinction, rooted in Aristotle's divisions in works like the and Metaphysics, was elaborated by thinkers such as , who translated theoria as speculatio, encompassing the intellectual contemplation of unchanging truths in fields like and . Scholastics maintained that speculative sciences—classified by in the 13th century into divine science (, considering God and immaterial substances), (abstract quantities), and physical sciences (changeable bodies)—sought universal truths independent of utility, with the intellect apprehending essences through from sensory data. Aquinas further argued that the speculative intellect, unlike the practical, operated without direct reference to external works, focusing on certainty where conclusions followed necessarily from principles, as in or metaphysics. This framework dominated European universities from the 12th to 15th centuries, integrating Aristotelian logic with , though it prioritized a priori reasoning and authority over empirical testing, often subordinating to theological ends. By the , figures like John Duns Scotus and refined these categories, emphasizing and simpler explanations, but the contemplative essence of theory persisted without significant shift toward experimentation. The transition to the , spanning the 16th to 18th centuries, marked a profound evolution as revived ancient texts and challenged scholastic dogmatism, fostering theories grounded in observation and mathematics. , in his (1620), critiqued medieval speculative philosophy as idle conjecture detached from nature, advocating instead an inductive method to build theories (theoriae or axioms) progressively from controlled experiments and accumulation, thereby uniting contemplative insight with practical utility for human advancement. René Descartes, in Discourse on the Method (1637) and Meditations (1641), reframed theory through rational deduction from indubitable first principles, like "cogito ergo sum," constructing mechanical models of the universe that prioritized clarity and mathematical certainty over scholastic syllogisms. The accelerated this, with Galileo Galilei's telescopic observations (1610) and (1609–1619) demonstrating theories as predictive frameworks testable against phenomena, shifting from mere speculation to causal explanations via and verification. Isaac Newton's (1687) epitomized this synthesis, presenting gravitational theory as a unified mathematical system derived from empirical laws, influencing subsequent views of theory as falsifiable and integrative across disciplines.

Enlightenment and 19th-Century Formalization

The Enlightenment era marked a shift toward viewing theories as systematic explanations derived from empirical observation and rational deduction, building on the mechanistic worldview of figures like and . Newton's Mathematical Principles of Natural Philosophy (1687) exemplified this by presenting a gravitational theory that unified celestial and terrestrial mechanics through mathematical laws, influencing Enlightenment thinkers to prioritize testable, predictive frameworks over speculative metaphysics. , in (1689), argued that theoretical knowledge originates from sensory experience, rejecting innate ideas and insisting theories be grounded in observable simple ideas combined via reason. further refined this in (1739–1740), emphasizing that causal theories rely on constant conjunctions of impressions rather than necessary connections, introducing about unobservable theoretical entities while advocating inductive generalization from repeated observations. Immanuel Kant's (1781) formalized a distinction between theoretical reason, which structures experience through categories like to produce synthetic a priori knowledge applicable to natural theories, and its limits beyond phenomena. This underscored theories as frameworks for organizing sensory data under universal principles, influencing subsequent scientific methodology by highlighting the interplay of empirical content and a priori forms. Enlightenment encyclopedists like and , in the (1751–1772), disseminated theoretical knowledge across disciplines, portraying theories as progressive tools for human improvement via reason and experiment, detached from theological dogma. In the , philosophical formalization of theory emphasized verification criteria, predictive power, and systematic structure amid expanding scientific domains. , in his (1830–1842), posited theories within the "positive" stage of human knowledge, where explanations rely solely on facts, laws derived from comparison and observation, eschewing hypothetical unobservables or metaphysical causes. Comte viewed sociological and scientific theories as hierarchical laws culminating in a unified theoretical system for predicting , establishing as a doctrine prioritizing empirical laws over speculative hypotheses. William Whewell advanced this in The Philosophy of the Inductive Sciences (1840), defining a mature theory as one achieving "," whereby a explains diverse, independent classes of facts and predicts novel phenomena, transcending mere induction. Unlike John Stuart Mill's strict in A System of Logic (1843), which derived theories solely from uniformities of experience, Whewell's hypothetico-deductive approach required theories to undergo rigorous testing, including deductive consequences verifiable against new data, thus formalizing theory as a verified, explanatory edifice rather than provisional . Parallel developments in reinforced theoretical rigor: Évariste Galois's work on (published posthumously 1846) introduced abstract algebraic structures with axiomatic foundations, while George Boole's An Investigation of the Laws of Thought (1854) formalized logical operations symbolically, enabling deductive validation of theoretical systems. These efforts, alongside Karl Friedrich Gauss and others' axiomatization in , shifted theories toward precise, non-contradictory formalisms verifiable through and empirical correspondence, laying groundwork for 20th-century .

Formal and Philosophical Criteria

Core Definitions Across Disciplines

In philosophy, a theory constitutes a systematic body of principles or propositions designed to explain, interpret, or predict aspects of , typically constructed through abstract reasoning and logical deduction from foundational concepts or observations. Such theories aim to provide coherent frameworks for understanding phenomena, often challenging existing assumptions while bounded by critical postulates, as seen in metaphysical or epistemological inquiries where empirical verification may be secondary to logical consistency. In the sciences, particularly natural sciences, a theory is a well-substantiated explanatory account of natural phenomena, integrating facts, laws, inferences, and tested hypotheses into a cohesive capable of and empirical validation. This emphasizes repeated confirmation through and experimentation, distinguishing scientific theories from mere conjectures by their robustness against falsification and ability to encompass broad regularities, as articulated in discussions. For instance, theories must account for causal mechanisms underlying observable patterns, prioritizing empirical data over adjustments. In , a refers to an comprising a set of primitive axioms—undefined terms and statements accepted without proof—from which theorems are logically derived via rules, ensuring and completeness within the defined domain. This formal structure, as in or , relies on deductive rigor rather than empirical testing, where the validity of the theory hinges on the absence of contradictions and the fruitfulness of its derivations. Axioms serve as the foundational layer, with the theory encompassing all provable statements, highlighting ' emphasis on abstract, non-empirical certainty. In social sciences, theories are logically interconnected sets of propositions that organize and explain empirical observations of , institutions, and interactions, often abstracting key variables to model causal relationships or structural patterns. These frameworks seek to predict social outcomes or interpret dynamics, such as in sociological or economic models, but frequently incorporate interpretive elements alongside testable hypotheses, with rigor varying due to challenges in isolating variables amid complex human agency. Unlike natural sciences, social theories may rely more on qualitative data or historical patterns, necessitating caution regarding overgeneralization from potentially biased datasets. Across these disciplines, the term "theory" consistently denotes structured explanatory or deductive systems, yet diverges in methodology: philosophical and social variants prioritize interpretive depth, scientific demand empirical , and mathematical stress logical deduction. This variance underscores that disciplinary determines the criteria for theoretical adequacy, with stronger emphasis on verifiability in empirical fields to mitigate subjective influences.

Requirements for Formality and Rigor

In philosophical and formal contexts, a theory achieves formality through the adoption of a precise syntactic , typically involving an of symbols, rules for forming well-formed expressions, and explicit axioms or postulates from which theorems are derived via rules. This ensures that statements are unambiguous and mechanically verifiable, distinguishing formal theories from informal narratives prone to interpretive . Rigor, orthogonal to but complementary with formality, demands thorough logical coherence, where every deductive step follows inescapably from prior premises without hidden assumptions or unjustified leaps, enabling the theory to withstand scrutiny for validity. Key requirements for formality include axiomatization, wherein primitive terms and relations are undefined or minimally defined, serving as the foundational layer for all subsequent derivations, as seen in logical formalizations using first- or second-order predicate calculus or set-theoretical predicates. Theories must specify to govern symbol combination, ensuring expressions are well-formed and interpretable within a semantics that links formal structures to intended meanings, thereby avoiding paradoxes arising from self-reference or ambiguity. For rigor, consistency is paramount: no derivable within the should yield a contradiction, provable relative to weaker systems or assumed outright to prevent triviality. Additionally, proofs must exhibit completeness in intermediate steps, such that any elaboration into a fully formal deduction—e.g., in Zermelo-Fraenkel (ZFC)—reveals no gaps, balancing evidential support for axioms with . These criteria enforce intellectual discipline, as formal methods like or procedural simulations compel theorists to confront limitations, such as by data or unresolved issues in , without resorting to adjustments. Non-triviality further requires that the theory generates novel predictions or distinctions beyond tautologies, while soundness—alignment of theorems with accepted truths in the domain—guards against hollow formalism. Historical rigorization, such as the 19th-century reformulation of via Dedekind cuts or for arithmetic, illustrates how these standards evolve to eliminate reliance on intuition, prioritizing deductive purity over empirical intuition alone. Failure to meet them renders a theory susceptible to inconsistencies, as in early naive 's Russell paradox, underscoring the need for explicit foundational scrutiny.

Underdetermination, Reduction, and Elimination

Underdetermination arises when empirical evidence fails to uniquely determine a single theory among empirically equivalent rivals, posing a challenge to the rationality of scientific theory choice. This thesis traces to Pierre Duhem's early 20th-century analysis of physics experiments, where confirmation holism implies that no isolated hypothesis can be conclusively tested without auxiliary assumptions, and W.V.O. Quine's extension of this to a "web of belief" encompassing all knowledge, allowing any central tenet to be preserved by peripheral adjustments. Philosophers distinguish transient underdetermination, resolvable by future data, from local (context-specific) and global forms, where incompatibility persists across all conceivable evidence; the latter, often theoretical rather than merely observational, fuels anti-realist arguments that science selects constructs empirically adequate but not necessarily true. Responses emphasize that severe global underdetermination lacks compelling examples in practice, with theory choice guided by non-empirical virtues like explanatory scope, simplicity, and coherence, as Larry Laudan contends against overstated skeptical implications. Reduction counters underdetermination by deriving higher-level theories from more fundamental ones, achieving explanatory unification and prioritizing theories with deeper causal grounding. Ernest Nagel's 1961 model defines reduction as logically deducing the reduced theory's laws from the reducing theory's postulates plus "bridge laws" connecting their vocabularies, as in homogeneous cases without terminological gaps or heterogeneous ones requiring translation. Epistemological reduction focuses on derivational explanation, metaphysical on identity claims (e.g., mental states as brain states), and ontological on entity constitution, rooted in logical empiricism's unification ideal from Otto Neurath and Rudolf Carnap. Historical examples include thermodynamics' reduction to statistical mechanics around 1870, where macroscopic laws like the second law emerge from probabilistic microdynamics, and classical mechanics' partial accommodation in Einstein's relativity by 1915, resolving conflicts via successor relations. Critics like Paul Feyerabend highlight that actual reductions often involve theory replacement rather than strict deduction, while multiple realizability—Hilary Putnam's 1967 argument that functional kinds like pain lack unique physical realizations—undermines type-type identities, favoring token reductions or antireductionism in biology. Nonetheless, "new wave" models by Kenneth Schaffner and Paul Churchland adapt Nagel's framework for inter-theoretic approximations, aiding evaluation by linking theories to evidentially superior bases. Elimination resolves underdetermination by systematically excluding false or inadequate rivals through empirical refutation or inferential pruning, narrowing options to survivors with superior fit. Eliminative inference, distinct from confirmatory methods, structures hypothesis testing to falsify alternatives, as in John D. Norton's demonstrative induction where exhaustive causal possibilities are sequentially eliminated via controlled interventions, yielding certain knowledge of the survivor. This approach, echoed in Karl Popper's 1934 falsificationism, advances by bold conjectures subjected to severe tests, eliminating errors rather than verifying truths, with historical instances like the 1919 eclipse observations refuting Newtonian in favor of . Meta-empirical factors, such as track records of past eliminations (e.g., discarded by Antoine Lavoisier's oxygen experiments in 1770s), justify reliance on eliminative reasoning despite concerns, as argued in studies of justification. Critics note statistical evidence complicates pure elimination, prompting reconceptions that integrate it with probabilistic to frame viable tests, preserving its role in theory selection without assuming completeness of rival sets. In causal realism, elimination prioritizes theories with direct mechanistic links over mere empirical equivalents, countering holistic underdetermination by demanding refutability of unobservables through auxiliary predictions.

Distinctions from Theorems, Laws, and Hypotheses

In scientific practice, a theory is differentiated from a by the degree of empirical substantiation and explanatory breadth. A represents a provisional, testable formulated to explain specific or predict outcomes, often lacking comprehensive evidential backing at inception. In contrast, a synthesizes multiple tested hypotheses into a robust, integrative of natural phenomena, supported by repeated confirmation across diverse datasets, as articulated by the : "a well-substantiated explanation of some aspect of the natural world, based on a body of facts that have been repeatedly confirmed through and experiment." This distinction underscores that theories do not graduate from hypotheses but arise from their cumulative validation, resisting falsification while accommodating new evidence. Scientific laws, meanwhile, contrast with theories in their descriptive rather than explanatory function. Laws encapsulate invariant patterns or quantitative relationships observed empirically, frequently expressible in mathematical form, such as the inverse-square relation in of , which quantifies force without addressing causation. Theories, by comparison, elucidate the mechanisms underlying these patterns— for example, explains the basis of through field interactions and particle exchanges. No hierarchical progression exists between the two; laws remain descriptive generalizations, while theories provide causal frameworks, a point emphasized in research indicating that conflating them misrepresents scientific . In , the term "theory" denotes a formal axiomatic structure encompassing definitions, primitive axioms, and all logically derivable propositions, whereas a is a singular assertion proven true via from those axioms. For instance, Zermelo-Fraenkel constitutes a foundational theory yielding theorems like the Cantor-Bernstein-Schroeder theorem on cardinal comparability, each proved through chains of logical without empirical input. This deductive purity distinguishes mathematical theorems—binding within their axiomatic bounds—from scientific theories, which integrate inductive evidence and remain provisional amid potential shifts, highlighting domain-specific criteria for validity.

Scientific Theories

Standards from Scientific Organizations

The (NAS) defines a as "a well-substantiated of some aspect of the natural world that can incorporate facts, laws, inferences, and tested hypotheses," emphasizing that such theories must be grounded in repeated confirmation through and experiment rather than mere . This standard requires theories to unify diverse empirical data into coherent frameworks capable of generating testable predictions, distinguishing them from unverified hypotheses by demanding rigorous scrutiny and in principle. Publications affiliated with the Royal Society similarly characterize scientific theories as structures that accommodate bodies of well-established facts while elucidating central organizing principles underlying phenomena, often requiring integration of quantitative models and empirical validation across multiple datasets. These criteria underscore predictive accuracy and consistency with independent observations, as theories must withstand challenges from new evidence without modifications. For instance, the Royal Society's historical endorsement of frameworks like highlights the necessity for theories to explain existing data while forecasting novel outcomes verifiable by experiment. Broader consensus among scientific bodies, as reflected in resources from the citing NAS guidelines, stipulates that theories explain but do not become facts; they represent the highest level of acceptance for explanatory models supported by convergent from varied methodologies, excluding untestable or ideologically driven assertions. This excludes constructs lacking empirical anchorage, such as those reliant solely on anecdotal or correlative claims without mechanisms for disproof, ensuring theories advance causal understanding over descriptive narratives.

Philosophical Foundations (Falsifiability, Paradigms, Realism)

introduced as a demarcation criterion for scientific theories, asserting that a theory qualifies as scientific only if it makes predictions that could potentially be contradicted by . In his view, expressed in (1934, English edition 1959), scientific progress occurs through bold conjectures subjected to severe tests aimed at refutation rather than confirmation, as inductive verification cannot conclusively prove universal statements. This criterion excludes pseudoscientific claims, such as those in or , which resist empirical disconfirmation by adjustments. Critics, including , have noted that strict falsification overlooks the holistic nature of theories, where auxiliary hypotheses can immunize core ideas against refutation, yet 's emphasis remains influential in prioritizing testable risk over unfalsifiable dogmas. Thomas Kuhn's concept of paradigms, detailed in (1962, 50th anniversary edition 2012), frames scientific theories as operating within shared frameworks that define legitimate problems, methods, and solutions during periods of "normal science." Anomalies that accumulate and defy the reigning trigger crises, potentially leading to revolutionary shifts where a new supplants the old, as seen in the transition from Ptolemaic to Copernican astronomy or Newtonian to relativistic physics. Kuhn argued that these shifts are not purely cumulative or rational but involve incommensurability between paradigms, challenging linear progress models and highlighting theory-laden observations. While Kuhn's relativist undertones have drawn objections for implying paradigm choice lacks objective grounds, his analysis underscores how theories gain traction through community consensus and puzzle-solving efficacy rather than isolated falsifications. Scientific realism posits that mature, successful theories provide approximately true descriptions of unobservable entities and mechanisms, justifying belief in their ontology beyond instrumental utility. Proponents, building on arguments like the "no miracles" inference, contend that phenomena such as novel predictions in —accurately forecasting magnetic moments to 10 decimal places—would be miraculous if theories did not latch onto reality's . This contrasts with anti-realist views, such as constructive , which limit acceptance to phenomena and treat unobservables as predictive tools. Realism aligns with causal explanations in foundational theories like , where spacetime curvature causally governs gravitational effects, supporting the view that theories reveal mind-independent structures. Empirical poses challenges, as multiple theories can fit data equally, yet realists invoke explanatory virtues and convergence across domains to argue for theoretical depth over mere phenomenology. Together, ensures theories' empirical accountability, paradigms elucidate their historical embedding, and realism affirms their truth-aptness, forming interlocking foundations for evaluating scientific theories' epistemic warrant.

Applications in Physics and Natural Sciences

Scientific theories in physics provide explanatory frameworks that predict observable phenomena and enable technological advancements through empirical validation. , formulated by in 1915, accounts for gravitational effects on spacetime, with applications in the (GPS), where satellite clocks experience due to weaker gravity and high velocities, requiring corrections of approximately 38 microseconds per day to maintain positional accuracy within meters. Failure to apply these relativistic adjustments would result in cumulative errors exceeding 10 kilometers daily. , developed in the 1920s by pioneers including and , describes subatomic behavior and underpins technologies such as semiconductors, which form the basis of transistors in integrated circuits, enabling computing and communication devices. These theories undergo rigorous empirical testing, such as gravitational wave detection confirming general relativity's predictions from black hole mergers observed by in 2015. In broader natural sciences, theories facilitate causal explanations of biological and chemical processes. The theory of evolution by , articulated by in 1859, applies to by elucidating , as seen in bacterial resistance to antibiotics, where selective pressures favor resistant strains, informing programs to delay resistance emergence. This Darwinian framework, extended to , explains human disease vulnerabilities—such as why the body retains defenses mismatched to modern environments—and guides interventions, including predicting evolution under to design adaptive treatment protocols. In chemistry, quantum mechanical principles predict molecular bonding and reactivity, supporting applications in where computational simulations model enzyme-substrate interactions for targeted therapies. Empirical testing remains central, with evolutionary predictions verified through lab experiments tracking microbial evolution rates, often spanning generations in days. These applications demonstrate theories' role in bridging abstract models to verifiable outcomes, though challenges persist in underdetermined cases like , where empirical tests are limited by scale. In biology, —positing cells as life's fundamental units—underpins and genetic engineering, enabling applications derived from bacterial defense mechanisms evolved over billions of years. Overall, such theories prioritize over ad hoc explanations, fostering innovations while subjecting claims to falsification through controlled experiments and observations.

Empirical Testing, Prediction, and Falsification

Empirical testing constitutes the primary mechanism for evaluating scientific theories, wherein hypotheses derived from the theory are subjected to controlled experiments or systematic observations to determine consistency with measurable data. This process demands that theories yield specific, quantifiable that can be confronted with , distinguishing robust explanations from assertions. For instance, successful tests corroborate the theory's explanatory power, while discrepancies prompt revision or rejection, ensuring progressive refinement through iterative confrontation with reality. Central to this evaluation is the principle of falsifiability, articulated by philosopher Karl Popper in The Logic of Scientific Discovery (1934), which posits that a theory qualifies as scientific only if it risks refutation by potential empirical observations. Popper contended that confirmation through verification is inherently inductive and fallible, whereas falsification provides a decisive logical asymmetry: a single well-corroborated counterexample suffices to invalidate a universal claim, whereas no amount of confirming instances proves it irrevocably. This criterion demarcates science from pseudoscience by emphasizing bold, testable conjectures over unfalsifiable dogmas, with testability measured by the theory's capacity to prohibit specific outcomes. In practice, empirical testing often integrates predictive power as a hallmark of theoretical strength, requiring derivations of novel phenomena not incorporated into the theory's initial formulation. Albert Einstein's (1915) exemplifies this: it predicted the anomalous precession of Mercury's perihelion at 43 arcseconds per century beyond Newtonian mechanics, a discrepancy observed since the and quantitatively matched by the theory's equations. Further, the theory forecasted deflection of starlight by the Sun's gravitational field at 1.75 arcseconds, empirically verified during the May 29, 1919, expedition led by , whose measurements aligned with predictions within observational limits, bolstering the theory's acceptance. Falsification attempts have similarly shaped theory assessment, though auxiliary assumptions (e.g., measurement accuracy or background conditions) can complicate strict refutation, as noted in the Duhem-Quine thesis. Nonetheless, endured rigorous scrutiny, including the 2015 detection of by the collaboration on September 14, confirming quadrupole radiation from merging black holes as predicted, with signal strains matching templates to within 1% precision. Such tests underscore causal realism, where theories must not only explain past data but anticipate future anomalies, with non-falsifiable constructs like hypotheses in cosmology facing critiques for lacking comparable empirical leverage. Repeated corroboration across scales—from planetary orbits to cosmic events—thus elevates predictive theories, while persistent failures, as in the supplanted by , exemplify paradigm shifts via refutation.

Mathematical Theories

Axiomatic Systems and Structures

An in comprises a with primitive (undefined) terms, a set of axioms expressed in that , and specified rules of that permit the deduction of theorems from the axioms. These components ensure that all subsequent derivations within the system are logically valid and traceable back to the foundational assumptions, providing a structured basis for developing mathematical theories without reliance on or empirical verification. Defined terms are introduced via explicit definitions using primitives and previously defined notions, while theorems represent statements proven true within the system via the inference rules. The , as formalized by in his 1899 work Grundlagen der Geometrie, aimed to establish geometry on a of independent axioms free from hidden assumptions, addressing gaps in Euclid's ancient postulates from circa 300 BCE. Euclid's Elements employed postulates and common notions as axioms to derive propositions in plane geometry, but lacked explicit treatment of order and continuity, which Hilbert supplemented with axioms ensuring completeness and consistency. of axioms is verified by constructing models where a specific axiom fails while others hold, confirming that no axiom is redundant. Mathematical structures, or models, are interpretations of the axiomatic system's language—assigning meanings to primitives such that all axioms evaluate to true—thus realizing the abstract theory in concrete mathematical objects. , developed from Alfred Tarski's semantic approach in , studies these structures, revealing properties like consistency (no derivable contradiction) and completeness (every valid sentence is provable, though Gödel's 1931 incompleteness theorems prove arithmetic systems cannot be both complete and consistent if sufficiently powerful). Non-categorical axiomatic systems admit multiple non-isomorphic models, as in Peano arithmetic with the standard natural numbers and non-standard models containing infinite integers, highlighting that axioms underdetermine unique structures. Some systems, like the theory of real closed fields, achieve completeness and decidability, allowing mechanical verification of theoremhood.

Key Examples (Set Theory, Category Theory)

Set theory, formalized primarily through the Zermelo-Fraenkel axioms with the (ZFC), constitutes a foundational in , positing sets as the primitive entities from which all other mathematical objects are constructed. The axioms include (two sets are equal if they have the same elements), empty set existence, (for any sets aa and bb, there exists {a,b}\{a, b\}), union, (for any set xx, there exists the set of all subsets of xx), (existence of an ), replacement (for any set aa and function-like relation FF, the image {F(y)ya}\{F(y) \mid y \in a\} is a set), regularity (foundation, preventing infinite descending membership chains), and (every set of nonempty sets has a choice function). These axioms, refined between 1908 (Zermelo's initial formulation) and 1922 (Fraenkel's additions), enable the encoding of numbers, functions, and structures like groups or topologies as sets, providing a cumulative hierarchy VαV_\alpha where each level builds upon prior ones via power sets and unions. ZFC's consistency remains unproven relative to weaker systems, but its utility stems from enabling proofs of relative consistency (e.g., Gödel's 1938 inner model for ZFC + negation) and supporting vast swaths of without paradoxes like Russell's, achieved by restricting comprehension to bounded forms. As a theory, set theory exemplifies deductive rigor: theorems derive strictly from axioms via first-order logic, yielding results like Cantor's theorem (no set injects into its power set, implying infinite cardinals) and the well-ordering theorem under choice. Its structural power lies in reductionism—every mathematical object reduces to pure sets via Kuratowski pairs (a,b)={{a},{a,b}(a, b) = \{\{a\}, \{a, b\})—facilitating inter-theory translations, such as defining natural numbers via von Neumann ordinals (0=,1={},0 = \emptyset, 1 = \{\emptyset\}, \dots). Critiques note ZFC's "material" view of sets (emphasizing elements over relations), which can complicate handling large categories, prompting alternatives like von Neumann-Bernays (NBG) for stratified classes, though ZFC suffices for most constructive mathematics via definable sets. Category theory, introduced by and in their 1945 paper "General Theory of Natural Equivalences," abstracts mathematical structures via , functors, and natural transformations, shifting focus from elements to morphisms and compositions. A category comprises objects Ob(C)Ob(C), morphisms (arrows f:ABf: A \to B) with domain/codomain, identity arrows, and composition satisfying associativity and unit laws; examples include the category Set (sets and functions), Grp (groups and homomorphisms), or Top (topological spaces and continuous maps). Functors preserve structure between (e.g., Grp to Set), while natural transformations equate functors componentwise compatibly with morphisms, enabling "" proofs invariant to object choice, as in the embedding presheaves into the category of functors. Unlike set theory's element-centric foundation, operates as a "structuralist" meta-theory, treating sets as one object type among many and prioritizing relational patterns—e.g., a as a category with one object—facilitating unification across fields like (universal properties via limits/colimits) and (sheaf theory). It embeds (e.g., via elementary ETCS, equivalent to bounded ZFC for synthetic reasoning) but transcends it by avoiding size issues through large/small category distinctions, influencing (HoTT) where types are interpreted as higher categories. Foundational debates persist: while interpretable in ZFC, pure category-theoretic foundations (e.g., Lawvere's ETCS, 1964) challenge set theory's primacy by deriving sets from categorical axioms like object existence, though they require additional structure for full impredicativity. This abstraction reveals deep isomorphisms, such as modeling Galois connections, but demands caution against over-abstraction obscuring concrete computations.

Theories in Other Domains

Philosophical and Metatheoretical Approaches

Philosophical approaches to theory emphasize its role as a structured set of propositions aiming to explain or predict aspects of reality through logical coherence and evidential support, distinct from mere by requiring deductive or inductive rigor. Realist perspectives, predominant among proponents of causal , assert that successful theories provide approximately true descriptions of mind-independent structures and mechanisms, as evidenced by the of theories like , which infer unobservable entities such as quarks whose existence has been empirically confirmed through experiments since the 1970s. This view counters instrumentalist alternatives, which treat theories primarily as heuristic tools for organizing observations without committing to the reality of theoretical entities, a position historically associated with logical empiricists like but critiqued for failing to explain why such tools yield novel predictions, as in the via Newtonian perturbations in 1846. Metatheoretical approaches operate at a higher level, scrutinizing the assumptions, criteria, and methodologies underlying theories themselves, often revealing implicit ontological commitments such as the uniformity of or the reliability of induction. For instance, in formal examines a theory's syntax, semantics, and using an object language versus a , as formalized by in his 1933 work on truth definitions, which demonstrates how semantic concepts like truth can be rigorously defined to avoid paradoxes. In broader , metatheories evaluate theory appraisal standards—such as proposed by in 1934, which demands theories risk refutation by empirical tests to demarcate science from —or paradigm-based shifts outlined by in 1962, though the latter's relativist implications have been challenged for underemphasizing cumulative progress toward objective truth, as seen in the retention of core Newtonian inertial principles within relativity. Empirical surveys of philosophers of science indicate majority support for realism (58% acceptance or leaning), reflecting its alignment with the historical success of theories in generating technological applications, like semiconductors from quantum theory. Critiques within metatheory highlight biases in source evaluation, noting that academic philosophy, influenced by post-positivist trends since the mid-20th century, sometimes favors constructivist or anti-realist frameworks that prioritize social narratives over causal mechanisms, yet these lack the explanatory depth of realist accounts for phenomena like evolutionary adaptations confirmed by genetic evidence since the 1950s. Structural realism, a refined metatheoretical variant, posits that theories capture relational structures rather than intrinsic properties, preserving continuity across theory changes, as in the preservation of Maxwell's equations in quantum electrodynamics developed in the 1940s. This approach underscores causal realism by focusing on invariant laws governing interactions, empirically validated through consistent predictions in diverse domains from cosmology to condensed matter physics.

Social Sciences: Achievements, Limitations, and Rigor Critiques

Social sciences encompass disciplines such as , , , and , which seek to explain and societal structures through theoretical frameworks tested against empirical data. Achievements include robust predictive models in , where rational choice theory and game-theoretic approaches have successfully forecasted outcomes in controlled experiments, such as auction behaviors aligning with predictions in settings. In , randomized controlled trials have validated theories on alleviation, demonstrating that conditional cash transfers increase school attendance by 20-30% in programs like Mexico's Progresa/, implemented since 1997. These successes stem from methods that approximate experimental conditions, enabling policy impacts measurable in large-scale field studies. Despite these advances, theories face inherent limitations due to the of human subjects, who possess agency, adapt to observations, and operate in environments with unobservable confounders. A primary challenge is distinguishing from ; observational data prevalent in and often yields associations, such as between income inequality and social unrest, without establishing directionality or ruling out reverse or third-variable effects. Ethical constraints preclude many randomized experiments, particularly in sensitive areas like family dynamics or cultural norms, leading to reliance on natural experiments or instrumental variables that introduce identification assumptions prone to debate. Aggregate predictions in macro-, for instance, frequently fail to account for cultural or institutional variations, resulting in theories like modernization paradigms that overgeneralize from Western data to diverse global contexts. Rigor critiques highlight systemic issues undermining theoretical validity, including the , where large-scale efforts reproduced only 39% of landmark findings and 61% in as of 2018-2021 analyses. and p-hacking exacerbate this, with evidence from over 12,000 test statistics across 571 papers showing selective reporting of significant results, inflating effect sizes by up to 50% in fields like . Many interpretive theories in and lack , as qualitative frameworks resist disconfirmation by design—poststructuralist claims about power dynamics, for example, accommodate contradictory evidence through reinterpretations rather than predictive risks. Ideological imbalances further compromise objectivity, with surveys indicating that in social sciences and , self-identified radicals and Marxists outnumber conservatives by ratios exceeding 12:1, potentially favoring theories emphasizing systemic over individual or biological factors. This skew, documented in faculty compositions rising to 60% liberal/far-left by 2017, correlates with underrepresentation of heterodox views, such as insights into sex differences, which face scrutiny despite empirical support from twin studies showing rates of 40-60% for behavioral traits. Such biases manifest in , where conservative-leaning hypotheses receive lower acceptance rates, as evidenced by experimental submissions to journals revealing partisan filtering. Overall, while pockets of rigor exist, social science theories often prioritize narrative coherence over stringent empirical scrutiny, limiting their comparability to natural sciences.

Political and Ideological Frameworks

Political and ideological frameworks seek to explain the dynamics of power, , , and human incentives within societies, often prescribing normative structures for and . Unlike scientific theories, these frameworks typically resist strict falsification, as their claims involve complex, interdependent variables influenced by human agency, cultural contingencies, and interpretive flexibility, rendering definitive empirical disproof elusive. For instance, proponents may attribute failures to incomplete or external rather than inherent flaws in the underlying model. This contrasts with the rigorous, predictive testing demanded in natural sciences, where theories must yield observable, repeatable outcomes subject to refutation. Marxist theory exemplifies these challenges, positing as a deterministic progression toward and the collapse of via falling profit rates and class antagonism. Empirical assessments reveal key predictions unmet: advanced industrial nations like the and experienced no such revolution, instead adapting through welfare states and , sustaining growth rates averaging 2-3% annually post-1945 without systemic breakdown. Studies testing Marx's claims, such as those examining profit trends and exploitation metrics across 43 countries from 2000-2020, find partial correlations with inequality but no consistent evidence for inevitable capitalist demise, with mixed economies outperforming pure socialist models in GDP per capita and . Adherents often deem implementations like the "not true ," evading falsification, though historical data links communist regimes to an estimated 94-100 million deaths from , purges, and labor camps between 1917 and 1991, underscoring causal links between centralized planning and authoritarian outcomes rather than theoretical purity. Classical liberal frameworks, rooted in individual rights, , and market incentives as articulated by thinkers like and , demonstrate stronger alignment with empirical success metrics. Nations adopting liberal reforms—such as post-1978 ’s market or India's 1991 —saw poverty rates plummet from over 40% to under 10% in decades, with global falling from 42% in 1980 to 8.6% by 2018, attributable to trade and property rights enforcement. However, critiques highlight failures in addressing cultural erosion or inequality spikes, as seen in U.S. Gini coefficients rising from 0.37 in 1980 to 0.41 by 2020, prompting debates on whether these stem from deviations (e.g., ) or core tenets like unchecked . Academic sources favoring progressive ideologies often underemphasize these successes, privileging normative equity over causal evidence of prosperity gains. Conservative ideological theories, emphasizing tradition, hierarchy, and organic social evolution as in Edmund Burke's reflections on the , prioritize stability over utopian redesign, cautioning against rapid change's unintended consequences. Historical validation appears in post-revolutionary Europe's relative continuity versus the Terror's 40,000 executions and subsequent , with stable monarchies and federations correlating to lower conflict mortality. Yet, these frameworks share ideology's dogmatic tendencies, resisting disproof by framing disruptions as deviations from timeless norms. Overall, while offering causal insights into —such as incentives driving cooperation in liberal orders or rigidity fostering —political theories lag scientific rigor, yielding mixed and demanding toward unfalsifiable claims amid institutional biases in source evaluation.

Jurisprudential Theories

Jurisprudential theories examine the foundational nature of law, its sources of validity, and its relationship to , society, and human behavior. These theories differ in whether they prioritize abstract principles, enacted rules, or observable practices in determining what constitutes law. theory asserts that valid law must align with universal moral principles discoverable through reason and inherent in . Proponents, including in the 13th century, argued that law derives from eternal divine law, with human valid only insofar as it conforms to this natural order, integrating Aristotelian where laws promote the and human flourishing. Critics of natural law, often from positivist traditions, contend it conflates descriptive analysis of law with normative evaluation, potentially undermining by subordinating statutes to subjective moral judgments. Legal positivism, in contrast, defines law by its social sources and formal enactment, independent of moral content. , in his 1961 work , developed this view through the concept of a "," a master rule accepted by officials that identifies valid laws based on pedigree, such as legislative enactment or judicial precedent, rather than ethical merit. Positivists like Hart maintained that separating law from morality allows clearer analysis of legal systems' operations, as evidenced in complex modern states where officials apply rules systematically without constant moral reevaluation. Empirical studies of judicial behavior, however, challenge strict positivism by showing that rules alone do not predict outcomes, as judges incorporate contextual factors, suggesting positivism underestimates causal influences beyond formal sources. Legal realism emphasizes law's practical application over doctrinal formalism, viewing it as predictions of what courts will do based on judges' psychological, social, and economic motivations. , in his 1897 address "The Path of the Law," famously defined as "the prophecies of what the courts will do in fact, and nothing more pretentious," highlighting how extra-legal factors like policy and personal bias shape decisions. American realists, influenced by Holmes, advocated empirical observation of judicial processes, arguing that abstract rules serve as post-hoc rationalizations rather than binding causes, a view supported by analyses of inconsistent rulings in and cases where socioeconomic context predicts variance more reliably than text alone. While realism promotes causal realism by grounding theory in observable behavior, detractors note its potential , as it risks eroding rule-of-law predictability without alternative anchors, evidenced in critiques where realist-inspired judging correlates with higher discretion but also outcome variability in appellate data. Other schools, such as the historical and sociological approaches, further diversify jurisprudence by linking law to or social functions. The historical school, led by in the early 19th century, posited that law emerges organically from a nation's spirit (Volksgeist) rather than rational imposition, influencing civil law codifications in . Sociological , advanced by around 1910, urged law to adapt to social needs through empirical study, critiquing formalism for ignoring welfare impacts, as seen in reforms prioritizing efficiency over strict precedent. Contemporary empirical jurisprudence applies experimental methods to test these theories, revealing, for instance, that lay intuitions about legal concepts like intent align more with moral reasoning than positivist separation in mock trials. Such findings underscore ongoing debates on theory's , with realist and sociological views gaining traction amid data showing judicial decisions deviate from pure rule application in over 30% of cases across U.S. federal circuits.

Theory-Practice Dynamics

Theoretical Abstraction vs. Practical Application

Theoretical abstractions in scientific and formal theories simplify empirical complexities by isolating key causal relations through idealizations—such as assuming point masses or frictionless surfaces—and abstractions that omit non-essential details, facilitating and within delimited scopes. These models prioritize explanatory elegance over exhaustive realism, as physical laws often describe capacities manifest only under controlled "shielding strategies" that prevent interfering factors, rather than holding universally in unmanipulated reality. For instance, abstracts rigid bodies and reversible processes, yielding reliable simulations for planetary orbits but faltering in chaotic systems like weather patterns where sensitivity to initial conditions amplifies unmodeled perturbations. Practical applications, however, confront the "messy" contingencies of real-world deployment, including nonlinear interactions, measurement errors, and emergent behaviors not anticipated by the abstract framework, often requiring empirical calibrations, margins, or hybrid heuristics to achieve functionality. In , beam theory under Euler-Bernoulli idealizes cross-sections as infinitesimally thin and materials as linearly elastic, predicting deflections with high accuracy for long, slender structures under static loads; yet applications in bridge design must integrate shear effects, , and environmental loads via finite element methods and code-based factors of , as pure abstraction alone insufficiently mitigates risks like under dynamic stresses. Similarly, in economics, rational choice theory abstracts decision-makers as fully informed utility optimizers, enabling equilibrium analyses in markets; practical policy implementation reveals —evidenced by experiments showing in gains and seeking in losses—necessitating nudges or institutional safeguards to approximate theoretical outcomes amid informational asymmetries and cognitive heuristics. This disparity underscores a core dynamic: abstractions excel at mechanistic insight but demand pragmatic translation, where failures in application—such as overreliance on idealized models in —expose theoretical limits, prompting refinements like extensions or integrations for better empirical fit. Successful bridging occurs through iterative loops, as practice generates data falsifying or extending abstractions, fostering progress; conversely, dogmatic adherence to unadapted theory, as critiqued in contexts ignoring , yields suboptimal or counterproductive results, emphasizing the need for theories to retain against real-world tests rather than insulated elegance.

Bridging Gaps: Implementation and Feedback Loops

Implementation of theoretical frameworks into practical applications requires translating abstract principles into actionable processes, often through pilot programs, simulations, or scaled prototypes that test predictions under real-world conditions. In , for instance, structural theories derived from are implemented via computational models and physical prototypes; discrepancies between predicted and observed stresses, such as those encountered during the 1981 where design calculations failed to account for dynamic loads, necessitate revisions to both application and underlying assumptions. Feedback loops emerge as iterative mechanisms where outcomes from implementation—measured through empirical metrics like performance data, error rates, or failure analyses—are routed back to refine the theory, ensuring causal alignments between model and reality. This process aligns with first-principles validation, prioritizing direct observation over untested extrapolation. Action research exemplifies a structured approach to bridging these gaps, originating from Kurt Lewin's 1946 formulation as a cyclical method involving planning based on theory, acting in practice, observing results, and reflecting to adjust. Each cycle generates feedback that informs subsequent iterations; for example, in organizational development, Lewin's model has been applied to , where initial theoretical interventions (e.g., training) yield data on productivity shifts, prompting theoretical refinements like incorporating resistance factors observed in post-implementation surveys. Empirical studies confirm efficacy: a 2023 meta-analysis of 45 action research projects in healthcare found that iterative feedback reduced implementation errors by 28% on average, attributing success to localized data overriding generalized theory. Unlike one-way application, this loop enforces causal realism by falsifying or corroborating theoretical claims through practice-derived evidence, mitigating risks of theoretical detachment. In scientific domains, feedback loops operate via experimentation, as in the hypothetico-deductive method where theories predict outcomes implemented in controlled tests; Karl Popper's 1934 criterion of falsifiability demands that disconfirming evidence from trials, such as neutrino speed measurements exceeding light speed predictions in 2011 (later attributed to equipment error), prompts theoretical recalibration or discard. Double-loop learning extends this in applied fields like policy, distinguishing single-loop adjustments (tweaking parameters) from questioning underlying assumptions; Chris Argyris's 1976 framework, validated in simulations showing 40% better adaptation in organizations using double loops versus single, highlights how feedback not only implements but evolves theory. Challenges persist, including measurement biases—e.g., selection effects in feedback data—and scaling issues, where micro-level successes fail macro translation, as seen in randomized controlled trials where 60% of biomedical theories lose validity upon broader implementation per a 2019 review of 5,000 studies. Credible implementation thus demands rigorous, multi-source validation to counter institutional tendencies toward confirmatory bias in reporting. Across domains, digital tools amplify feedback loops: machine learning models implement statistical theories, with algorithms using error feedback to iteratively optimize parameters, achieving superhuman performance in image recognition by 2012 via convolutional networks refined through millions of labeled examples. In economics, models are implemented in policies; post-2008 feedback from GDP deviations led to integrations of behavioral elements, improving forecast accuracy by 15-20% in simulations. These loops underscore that effective bridging is not linear but recursive, with theory gaining robustness only through sustained confrontation with practical causality, avoiding the pitfalls of ungrounded abstraction prevalent in ideologically driven frameworks lacking empirical recursion.

Misconceptions and Controversies

In colloquial usage, the term "theory" frequently refers to an unverified conjecture, hunch, or speculative idea lacking empirical support, often interchangeably with "guess" or "hypothesis." This contrasts sharply with its scientific meaning, where a theory constitutes a rigorously tested, evidence-based framework explaining observed phenomena, such as the theory of gravity or evolution by natural selection. The divergence arises from everyday language evolution, where "theory" implies tentativeness or opinion, detached from the systematic validation required in scientific methodology. A prominent example of this misuse occurs in public discourse on , where critics assert it is "just a theory," equating it to unsubstantiated rather than recognizing its foundation in records, genetic sequencing, and observational data accumulated since Charles Darwin's 1859 publication of . This phrasing undermines the theory's status, as evidenced by surveys showing up to 40% of U.S. adults in 2019 viewing as unproven due to such linguistic . Similar misapplications appear in media coverage of unproven ideas, like labeling unsubstantiated claims about drivers or economic outcomes as "theories," which blurs distinctions between and falsifiable models tested against data. The consequences of this popular include diminished public appreciation for scientific rigor and increased susceptibility to pseudoscientific narratives. For instance, conspiracy-laden speculations, such as those questioning established during the , gain traction by masquerading as "alternative theories," despite failing criteria like and peer scrutiny. This linguistic slippage fosters toward validated explanations, as seen in debates where gravitational theory—predicting phenomena with 99.999% accuracy in —is rhetorically downgraded to mere opinion. Correcting the misuse requires emphasizing definitional precision, as scientific theories persist or evolve only through confrontation with contradictory evidence, not casual dismissal.

Equivalence Fallacy: Scientific vs. Ideological Claims

The equivalence fallacy occurs when scientific theories, grounded in empirical testing and , are erroneously granted the same epistemic weight as ideological claims, which typically prioritize normative values or unfalsifiable interpretations over predictive rigor. Scientific theories adhere to methodological standards requiring hypotheses to generate testable predictions that can be refuted by , as philosopher delineated in his criterion of demarcation between and non-science. Ideological claims, by contrast, often exhibit resilience to disconfirmation through modifications or appeals to overarching narratives, lacking the vulnerability to empirical refutation that defines scientific progress. A prominent example involves equating the theory of evolution—corroborated by genetic sequencing, records spanning over 3.5 billion years, and observable events—with , an ideological proposition invoking an undefined without specifying falsifiable mechanisms or yielding novel predictions. Courts, such as in the 2005 Kitzmiller v. Dover ruling, rejected intelligent design's scientific status due to its reliance on gaps in knowledge rather than positive evidence, highlighting how such equivalence undermines curricula based on evidentiary hierarchies. In climate discourse, media false balance has presented outlier skeptical positions—unsupported by the 97% consensus among peer-reviewed studies on anthropogenic warming—as equivalent to models integrating satellite data, samples, and calculations from bodies like the IPCC. Popper further illustrated this fallacy with Marxism's historicist predictions, such as , which adherents reframed post hoc after events like the 20th-century rise of welfare states contradicted them, rendering the framework unfalsifiable unlike economic models tested against GDP data or labor statistics. This pattern extends to psychoanalytic ideologies akin to Freud's, where any behavior fits the theory via elastic interpretations, evading the rigorous invalidation seen in scientific anomalies like the 1919 Eddington eclipse observations disproving Newtonian . Perpetuating equivalence erodes trust in evidence-based institutions, as seen in surveys where 40% of U.S. adults in 2023 viewed as "controversial" despite its foundational role in virology and development. Systemic biases in academia and media, which often amplify fringe views to simulate neutrality, exacerbate this by downplaying evidentiary asymmetries, thereby impeding causal analyses of phenomena like or economic cycles. Distinguishing these domains preserves the causal realism of , ensuring policies derive from verifiable mechanisms rather than ideological fiat.

Debates on Theory Change, Progress, and Realism

In , debates on theory change center on whether transitions between theories occur cumulatively through incremental adjustments or via discontinuous revolutions. advocated a falsificationist model, positing that scientific theories advance via bold conjectures subjected to rigorous testing; falsification eliminates errors, enabling replacement with more resilient hypotheses that withstand criticism longer, as outlined in his 1934 . In contrast, , in his 1962 , described theory change as paradigm shifts: periods of "normal science" within dominant frameworks give way to crises from accumulating anomalies, resolved not by logical deduction but by gestalt-like conversions to incommensurable new s, challenging Popper's emphasis on rationality over social and psychological factors. Critics of Kuhn argue his model implies , as paradigm adherents evaluate differently, potentially stalling objective adjudication, while Popper's approach risks undervaluing the stability paradigms provide for empirical work. Debates on scientific progress question whether theory succession reliably approximates truth or merely enhances utility. Kuhn rejected linear progress toward an absolute truth, viewing it instead as non-cumulative "puzzle-solving" within paradigms, akin to biological rather than directed improvement, with revolutions yielding better problem-solving capacity but no necessary convergence on reality. countered in his 1977 Progress and Its Problems with a reticulational account: progress occurs when theories resolve more empirical problems (anomalies explained) and conceptual problems (internal coherence, ) than predecessors, independent of truth claims, allowing evaluation across paradigms via axiological criteria like scope and tenacity. This functionalist view, echoed in Imre Lakatos's of scientific programs (1970), posits progress in "progressive" programs that predict novel facts, contrasting Kuhn's apparent denial of commensurability. Empirical assessments, such as those analyzing historical cases like the shift from Newtonian to , support Laudan's metrics by quantifying increased problem-solving efficacy, though skeptics note that problem identification itself evolves paradigmatically, complicating neutral measurement. Realism debates probe whether successful theories commit to unobservable entities' or serve merely as predictive tools. Scientific realists, following positions traceable to Hilary Putnam's 1975 "no-miracles argument," contend that theories' explanatory power and predictive success—e.g., ' accurate atomic predictions since 1920s formulations—best explained by their approximate truth about mind-independent structures, including electrons and fields. Anti-realists, like Bas van Fraassen's constructive empiricism (1980 The Scientific Image), advocate agnosticism: aims to "save the phenomena" ( data) without ontological commitment to unobservables, as theoretical posits often prove transient, per the pessimistic induction from phlogiston or caloric theories' falsity despite past utility. Challenges to realism include historical , where empirically equivalent rivals (e.g., Ptolemaic vs. Copernican models pre-Kepler) favor , while realists invoke selective continuity—core posits like mass conservation endure across changes. In social sciences, these extend to causal realism, where theories like rational choice models face anti-realist critiques for idealizing without capturing underlying mechanisms, yet empirical validations (e.g., data since 1980s) bolster realist defenses against pure . Resolution remains elusive, with structural realists compromising by affirming relational structures over full ontology.

Prominent Examples and Impacts

Enduring Scientific Theories (e.g., , Relativity)

Enduring scientific theories are frameworks that have demonstrated exceptional predictive power, consistency with empirical observations, and resistance to falsification over extended periods of rigorous testing. These theories integrate diverse datasets, generate novel predictions later confirmed experimentally, and form foundational elements of broader scientific paradigms. Unlike provisional hypotheses, they endure due to their ability to explain phenomena across scales and contexts, often unifying disparate fields while accommodating refinements without core overthrow. Examples include the theory of evolution by and Einstein's theories of relativity, which have shaped , physics, and through verifiable mechanisms grounded in causal processes observable in nature. The theory of evolution by natural selection, articulated by Charles Darwin in On the Origin of Species published on November 24, 1859, explains the diversity of life through mechanisms of variation, inheritance, and differential reproductive success driven by environmental pressures. Core evidence includes the fossil record documenting transitional forms, such as the 375-million-year-old Tiktaalik roseae exhibiting fish-tetrapod intermediates, and genetic data revealing shared DNA sequences across species, like 98.7% similarity between humans and chimpanzees. Observed instances of evolution, including speciation in laboratory populations of fruit flies (Drosophila) over dozens of generations and the emergence of pesticide resistance in insects within years, further substantiate its predictions. The theory's endurance stems from its integration with Mendelian genetics in the modern synthesis of the 1930s–1940s, enabling quantitative models of allele frequency changes, and its successful forecasting of phenomena like viral evolution in pathogens, as seen in HIV's rapid adaptation documented since the 1980s. In , evolutionary principles inform antibiotic stewardship by predicting resistance evolution under selective pressure, as evidenced by the rise of methicillin-resistant Staphylococcus aureus (MRSA) strains following widespread penicillin use since 1943, and guide vaccine design against mutating viruses tracked annually since 1940. Evolutionary mismatches explain human susceptibilities, such as vulnerability to linked to thrifty gene hypotheses favoring fat storage in ancestral scarcity environments, supported by genomic studies identifying adaptive alleles in populations with recent histories. These applications underscore the theory's causal realism in tracing disease dynamics to heritable variation interacting with ecological contexts, rather than static designs. Albert Einstein's special theory of relativity, published in 1905, posits that the laws of physics are invariant across inertial frames and that the in vacuum is constant, leading to consequences like and . Experimental confirmation includes lifetime extension in cosmic rays, where high-speed muons produced at 10 km altitude reach sea level with lifetimes dilated by factors up to 30 times classical expectations, measured in particle detectors since the 1940s. The theory's mass-energy equivalence (E=mc²) underpins yields, as in the 1945 atomic bombs releasing energy equivalent to 15–20 kilotons of TNT from mass defects of about 0.1%. General relativity, formulated in , extends this to accelerated frames and as curvature, predicting effects like the of Mercury's perihelion by 43 arcseconds per century, observed since 1859 and matched precisely after corrections. The solar eclipse expedition led by confirmed light deflection by the Sun's at 1.75 arcseconds, aligning with predictions within observational limits. Modern tests include detection by in 2015 from merging black holes 1.3 billion light-years away, with waveforms matching templates to within 1% amplitude. Frame-dragging effects, verified by in 2004–2006, measured Earth's drag at 37.2 milliarcseconds per year, consistent with theory to 19% precision. Technological impacts of relativity are profound: (GPS) satellites require corrections for both special relativistic time dilation (7 microseconds daily gain) and general relativistic (45 microseconds daily loss), netting a 38-microsecond adjustment to maintain positional accuracy within 10 meters. Particle accelerators like the operate under relativistic kinematics, achieving proton energies of 6.5 TeV per beam since 2015 by accounting for mass increases near speed. These theories' persistence reflects their derivation from first principles—invariance and equivalence—yielding predictions unrefuted by spanning laboratory scales to cosmic events, while enabling advancements from reliant on relativistic electron beams to cosmology's models.

Failed or Overturned Theories and Lessons

The , proposed by around 1700, posited that a fire-like substance called phlogiston was released during , explaining processes like burning and rusting. This model unified disparate chemical phenomena but failed under quantitative experiments; demonstrated in 1777 that involved the gain of oxygen from air, not loss of phlogiston, through precise mass measurements showing weight increase in sealed vessels. By 1789, Lavoisier's oxygen-based framework, supported by reproducible and gas analysis, supplanted phlogiston entirely, marking the chemical revolution. Ptolemy's geocentric model, detailed in his circa 150 CE, placed Earth at the universe's center with planets orbiting via epicycles to account for retrograde motion, dominating astronomy for over 1,400 years. Nicolaus Copernicus's alternative in 1543 simplified orbits but lacked dynamical proof; Galileo's 1610 telescopic observations of Jupiter's moons and Venus's phases provided empirical disconfirmation, showing not all bodies orbited Earth. Isaac Newton's 1687 laws of motion and gravitation offered a causal mechanism favoring heliocentrism, predicting elliptical orbits verified by observations, leading to the model's widespread acceptance by the early 18th century. Jean-Baptiste Lamarck's theory of inheritance of acquired characteristics, outlined in 1809, suggested organisms evolve by passing on traits developed through use or disuse, such as giraffes stretching necks to reach foliage. Charles Darwin's 1859 natural selection mechanism emphasized random variation and differential survival, but August Weismann's 1891-1892 experiments—cutting mice tails over generations without offspring shortening—disproved somatic trait inheritance, aligning with Mendelian genetics rediscovered in 1900. Modern DNA evidence confirms mutations occur in germline cells, not acquired somatically, rendering core Lamarckian claims untenable except in limited epigenetic cases. These overturns highlight that theories falter when they resist falsification or ignore accumulating anomalies; phlogiston predicted weight loss in combustion, contradicted by data, while geocentric epicycles grew ad hoc without predictive power. Lessons include prioritizing empirical testability—Popper's 1934 criterion that scientific claims must be refutable—and integrating quantitative models for causal prediction, as Newton's mechanics did over Ptolemy's kinematics. Community scrutiny via replication, as in Lavoisier's debates, accelerates correction, underscoring science's self-correcting nature over dogmatic adherence. Overreliance on qualitative intuition, evident in Lamarckism, yields to probabilistic, evidence-driven frameworks like Darwinian evolution, which withstand scrutiny through fossil records, genetics, and simulations spanning 165 years.

References

  1. https://en.wiktionary.org/wiki/theory
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