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Argument from analogy

The argument from analogy is a form of that draws a probable conclusion about an unknown or target subject by comparing it to a known analogue based on shared relevant properties or relations. In this type of argument, similarities between two entities—such as objects, situations, or systems—are used to infer that what is true of one is likely true of the other, though the inference is not guaranteed and depends on the strength of the . Unlike deductive arguments, which yield certain conclusions, or standard inductive generalizations from samples, analogical arguments rely on relational correspondences rather than mere property overlaps. A typical structure of an argument from analogy involves identifying a basic similarity (F) between an analogue (A) and the target (B), then concluding that B shares an additional property (G) with A. For instance, in John Stuart Mill's classic argument for the existence of other minds, human bodies (analogues) exhibit behaviors correlated with internal mental states, so another human-like body (target) likely possesses similar minds. Analogies can occur within the same domain, preserving literal interpretations (e.g., comparing two biological ), or across different domains, often involving metaphorical extensions (e.g., likening a political to a living organism, as in Thomas Hobbes's ). The strength of such arguments is evaluated by criteria including the number and variety of similarities, the of those similarities to the inferred property, the presence of relevant dissimilarities, and the modesty of the conclusion—more analogues and relevant parallels bolster the case, while significant differences undermine it. In and everyday reasoning, arguments from play a central role in problem-solving, ethical , legal precedents, and scientific formation by extending familiar knowledge to novel contexts. They are particularly valuable when is scarce, as in predicting outcomes based on past experiences or defending moral positions through comparisons (e.g., equating the treatment of animals to that of s based on shared ). However, they are vulnerable to weaknesses such as irrelevant similarities, overlooked dissimilarities, or in terms, which can lead to fallacious conclusions if the total condition—requiring that all relevant factors support the inference—is not met. Despite these limitations, analogical reasoning remains a fundamental tool in , influencing fields from metaphysics to .

Definition and Fundamentals

Core Definition

An argument from analogy is a form of reasoning that infers the presence of an unknown property in a entity based on observed similarities between that entity and a entity in certain known respects. Specifically, it posits that if S is similar to T in relevant attributes, and S possesses a further feature Q, then T likely possesses Q or a similar feature Q*. This inference draws on relational resemblances to project properties, making it a common tool in philosophical, scientific, and everyday deliberation. As a type of , the argument from analogy is non-deductive, meaning its conclusions are probable rather than certain, and it amplifies knowledge beyond the premises by extending observed patterns to new cases. Unlike deductive arguments, which guarantee truth preservation, analogical reasoning offers varying degrees of support depending on the strength of the similarities cited, often evaluated probabilistically in frameworks like Bayesian confirmation theory. It differs from other inductive arguments, such as from samples, by emphasizing specific, relational similarities between individual cases rather than statistical patterns across a . For instance, while might extrapolate from multiple observed instances to predict a trend, typically relies on a single or few paradigmatic examples to infer likeness in untested domains. The term "" derives from analogia, meaning "proportion" or "resemblance," originally denoting a of ratios or relations, as used in ancient mathematical and philosophical contexts.

Key Characteristics

Arguments from analogy are fundamentally non-deductive, meaning their conclusions are probable rather than certain, with the strength of the inference depending on the of similarity between the compared entities. Unlike deductive arguments, which guarantee truth preservation from premises to conclusion, analogical reasoning belongs to the category of ampliative reasoning, where conclusions extend beyond the premises but are supported only to varying degrees. This probabilistic nature arises because analogies extrapolate from known similarities to predict unknown ones, without logical necessity. A core characteristic is the reliance on , where the argument's validity hinges on identifying pertinent similarities between the source and target domains while explicitly acknowledging and minimizing the impact of relevant differences. Irrelevant similarities, such as superficial traits, do not contribute to the inference's strength, and unaddressed dissimilarities can undermine it entirely. Thus, effective analogical arguments prioritize attributes that are causally or structurally connected to the conclusion, ensuring the comparison illuminates rather than obscures the issue. The inductive probability of an argument from analogy increases with the number and relevance of shared attributes, as each additional resemblance—unless proven irrelevant—bolsters the likelihood of the inferred similarity. This cumulative effect makes the reasoning persuasive in contexts where direct evidence is scarce, drawing strength from the breadth and depth of parallels. For instance, in a basic form, the structure can be expressed as "A is to B as C is to D," where A and C share key properties with B and D, respectively, suggesting a parallel relationship. A simple example is arguing that since a new novel shares elements and with a previously enjoyed book (A to B), it is likely enjoyable (C to D).

Historical Context

Ancient and Medieval Origins

The argument from analogy emerged in as a method for illustrating abstract concepts through relatable comparisons, particularly in the works of , who lived from approximately 428 to 348 BCE. In his dialogue The Republic, employs the famous to analogize the philosopher's ascent from sensory illusion to true knowledge of the Forms, depicting prisoners chained in a cave mistaking shadows for reality, much like unenlightened souls confuse appearances with eternal truths. This analogy serves not merely as rhetorical flourish but as a core tool for epistemological reasoning, bridging the gap between the material world and ideal realities by drawing parallels between perceptual limitations and intellectual liberation. 's use underscores analogy's role in philosophical , enabling readers to grasp complex metaphysical ideas through vivid, narrative similitudes. Aristotle, born in 384 BCE and dying in 322 BCE, systematized within rhetorical and dialectical frameworks, viewing it as essential for probable reasoning in contexts where certainty is unattainable. In , particularly Book II, Chapter 20, Aristotle classifies metaphors derived from as the most effective type for enthymemes—rhetorical syllogisms that persuade by incomplete but intuitively compelling arguments, such as comparing to autumn to evoke decline without explicit deduction. Complementing this, his Topics integrates into topical reasoning, where likenesses between cases support dialectical inferences, as in arguing from similar situations in or politics to probable conclusions, emphasizing 's utility in enthymematic construction for public discourse. 's contributions elevated from illustrative device to a structured element of persuasive logic, influencing subsequent traditions by linking it to inductive and analogical in non-demonstrative sciences. During the medieval period, arguments from analogy gained prominence in theological discourse, notably through (1225–1274 CE), who adapted Aristotelian methods to reconcile faith and reason. In (I, q. 4, a. 3), Aquinas develops the concept of analogia entis—the analogy of being—to articulate divine attributes, positing that terms like "goodness" apply to and creatures not univocally or equivocally, but analogically, as effects resemble their cause in proportionate yet dissimilar ways. This framework underpins his proofs for 's existence and nature, using analogies from created order to infer transcendent realities, such as likening divine simplicity to the unity of light in illumination. Aquinas's integration of analogy thus transformed it into a cornerstone of scholastic theology, enabling reasoned discourse on ineffable divine matters while preserving the Creator-creation distinction.

Modern Philosophical Developments

In the 18th century, critically examined analogical reasoning, particularly in his (1779), where he scrutinized the argument from design for God's existence as a weak analogy between human artifacts and the , highlighting issues of disanalogies and limited scope that undermine the to a divine creator. Similarly, incorporated analogies into his transcendental philosophy, most notably in the (1781/1787), where the "Analogies of Experience" use analogical principles to establish the categories of substance, , and community as necessary conditions for objective experience, drawing parallels between empirical succession and underlying relational structures. In the 19th century, John Stuart Mill significantly advanced the philosophical understanding of analogical reasoning by integrating it into his framework of inductive logic as a key method of experimental inquiry. In his seminal work A System of Logic (1843), Mill described analogy as a process of inferring probable resemblances between objects or phenomena based on observed similarities, extending knowledge from known cases to unknown ones without the certainty of complete induction. He emphasized that the strength of such inferences depends on the number and relevance of shared attributes versus known differences, positioning analogy as a heuristic tool for hypothesis formation and guiding empirical investigations, such as inferring biological processes in unfamiliar species from those in studied ones. This approach elevated analogy from mere rhetorical flourish to a structured component of scientific reasoning, influencing subsequent empiricist traditions. The 20th century saw further formalization and critique of analogical arguments, particularly through Nelson Goodman's examination of inductive projection in Fact, Fiction, and Forecast (1955). Goodman challenged the uncritical reliance on similarity in analogical reasoning by introducing the "," arguing that predicates like "grue" (green until a certain time, then ) undermine simple resemblance-based projections, as they fit observed data equally well but lead to conflicting predictions. His analysis revealed that analogical inferences depend not just on observable likenesses but on the entrenchment of predicates within a linguistic and , critiquing analogy's role in justifying scientific generalizations and highlighting its vulnerability to in property selection. This work prompted philosophers to scrutinize the logical foundations of , shifting focus toward criteria for valid projection in inductive contexts. Key debates in mid-20th-century centered on analogy's contributions to scientific , as explored by Mary Hesse in Models and Analogies in Science (1966). Hesse defended analogical models as essential for theory construction, proposing three conditions for robust scientific analogies: a material analogy based on similarities, a causal condition linking shared properties through vertical relations, and a no-essential-difference condition ensuring no critical disanalogies undermine the inference. She illustrated this with historical examples, such as the mechanical models in 19th-century physics that facilitated understanding of electromagnetic phenomena by drawing parallels to , arguing that analogies provide and guide empirical testing even when direct evidence is limited. Hesse's framework underscored analogy's ampliative role , bridging theoretical and observational while acknowledging its provisional nature. In the late 20th and early 21st centuries, analogical reasoning received further attention in and . Dedre Gentner's structure-mapping theory (1983) emphasized aligning relational structures between source and target domains, prioritizing systematicity for strong analogies. Keith Holyoak and Paul Thagard's multiconstraint theory (1989) incorporated semantic, structural, and pragmatic factors to model analogy use in problem-solving. Paul Bartha's articulation model (2010) outlined criteria for plausible analogies based on prior associations and generalization potential, while John Norton's material theory (2010, 2019) rejected universal rules in favor of case-specific, local models. These developments have shaped understandings in , such as analogue confirmation in simulations (Dardashti et al., 2017), and cognitive models of reasoning. These developments marked a broader shift in , where analogical reasoning evolved from a primarily in to a rigorous tool for conceptual analysis and scientific inference. Post-Renaissance thinkers, building on Mill's inductive integration, increasingly treated analogies as mechanisms for plausibility assessment in analytic debates, influencing fields like and mind by emphasizing structural mappings over superficial resemblances. This transition highlighted analogy's utility in addressing underdetermined problems, such as model selection in , while demanding explicit evaluation of relational alignments to mitigate fallacious applications.

Structure and Formulation

Basic Components

An argument from analogy fundamentally consists of that articulate relevant similarities between two cases: a source case, where a certain conclusion is known to hold, and a target case, where the conclusion is uncertain. These assert that the source and target share one or more properties or features deemed pertinent to the inference. The conclusion then draws an inference about the target case, positing that it possesses the same further property or outcome as , based on the established parallels. This inductive step relies on the that the shared attributes justify extending the known property from to the target. A critical component involves explicitly acknowledging dissimilarities between the source and target to mitigate risks of overgeneralization. Relevant differences—those that could plausibly affect the inferred property—must be shown to be insignificant or outweighed by the similarities; failure to address them undermines the argument's persuasiveness. For instance, to argue for , one might note that humans learn complex tasks through and trial-and-error, demonstrating understanding and problem-solving abilities, while dogs exhibit similar behaviors in scenarios, such as fetching objects or navigating obstacles; thus, despite physiological differences like brain structure, dogs likely possess comparable cognitive capacities.

Logical Representation

The argument from analogy is commonly represented in an informal schema where two entities, a source S and a target T, are identified as sharing certain properties P_1, P_2, \dots, P_k; the source S is known to possess an additional property P_{k+1}; and thus, it is inferred that the target T likely possesses P_{k+1} as well. This structure highlights the core inductive step, where similarity in observed respects supports a probabilistic extension to unobserved ones. In symbolic form, arguments from analogy can be formalized using predicate logic to capture the similarity relation and inference. For instance, consider premises stating that all instances satisfying a S(x) (defining the source class) possess P(x), that the t satisfies S(t), and that a similarity relation holds between source and target domains; this leads to the probable conclusion P(t), often denoted with a \vdash indicating ampliative rather than strict entailment: \forall x (S(x) \to P(x)) \land S(t) \land \text{Sim}(S, T) \vdash P(t). This representation draws from Aristotelian paradigms, where the generalization \forall x (P(x) \supset Q(x)) is induced from source-target resemblances P(S) \land Q(S) and P(T), yielding Q(T) inductively rather than deductively. Within inductive logic frameworks, Bayesian approaches model analogical reasoning by treating similarities as that updates prior probabilities on the 's properties. Specifically, the degree of confirmation from an analogy is quantified via , where shared properties increase the \Pr(H \mid E \cdot K) > \Pr(H \mid K) for H about the , given E of source similarities and background knowledge K. This framework accommodates varying strengths of analogy by adjusting priors based on the extent of relational mappings between source and , as in models where analogical confirms hypotheses proportionally to the number and relevance of aligned features. Unlike deductive arguments, which provide entailment and , the logical representation of arguments from emphasizes ampliative reasoning, where the conclusion extends beyond the without logical , rendering the fallible and probabilistic.

Evaluation Criteria

Assessing Analogical Strength

Assessing the strength of an analogical argument involves evaluating how well the similarities between the source and target domains support the inferred conclusion, with robustness determined by systematic criteria that emphasize , extent, and contextual support. Philosophers and logicians have developed frameworks to gauge this, focusing on whether the analogy provides plausible inductive support rather than deductive . Key methods include examining the causal or explanatory links in similarities, the breadth of supporting cases, and the overall balance of resemblances versus differences. The relevance criterion requires that the similarities cited between the analogous cases be causally or explanatorily connected to the conclusion, ensuring they are not merely superficial or coincidental. For instance, in arguing that a new will be effective based on its similarity to an existing one, only shared chemical structures relevant to therapeutic strengthen the case, while irrelevant traits like do not. This criterion, rooted in inductive logic, filters out weak analogies by prioritizing features that align with underlying mechanisms or laws of nature. Irrelevant similarities dilute the argument's probative force, as they fail to bridge the inference reliably. Diversity of cases enhances strength when the analogy draws from multiple, varied instances that independently support the conclusion, reducing the risk of overgeneralization from a narrow sample. An argument is more robust if it encompasses analogues across different contexts—such as applying ethical principles from historical legal cases to contemporary dilemmas—rather than relying on identical scenarios. This variety tests the inference's generalizability, as converging evidence from disparate sources approximates the uniformity needed for inductive reliability. Logicians note that greater diversity mitigates biases from singular examples, making the conclusion more defensible. Degrees of similarity are assessed through quantitative and qualitative scales that weigh the number, importance, and type of shared properties against dissimilarities. outlined an approach in his inductive framework, emphasizing that strength increases with numerous and resemblances—those tied to core attributes—while significant differences erode it. For example, Mill's evaluation considers whether the inferred trait appears in many known analogous cases, forming a probabilistic where higher degrees of pertinent overlap yield stronger support. Modern extensions classify similarities as (semantic or causal) versus accidental (incidental), with ones contributing more to inferential power. This scaled assessment avoids binary judgments, allowing nuanced rankings of analogical probity. Philosophical tests, such as John Wisdom's concept of filling a "gap" in understanding, treat as a tool for resolving interpretive disputes where is inconclusive. In Wisdom's , analogical reasoning bridges the divide between facts and deeper interpretations, as in the of the tended garden implying a gardener amid ambiguous . This approach strengthens arguments by highlighting how analogies illuminate contested cases through parallel reasoning, particularly in metaphysics or , where they clarify elusive concepts without empirical closure. Such tests underscore 's role in advancing comprehension beyond literal comparisons.

Common Counterarguments

One prevalent to an argument from analogy involves identifying disanalogies, or relevant differences between the compared cases that weaken the purported . For instance, in evaluating whether a new drug will have similar effects to an existing one based on shared chemical structures, critics may highlight differences in metabolic pathways or patient demographics that could alter outcomes, thereby undermining the analogy's reliability. This approach emphasizes that even strong surface similarities can be overridden by critical dissimilarities, as articulated in criteria for analogical where no essential differences should exist between domains. Another standard rebuttal questions the relevance of the shared traits, arguing that the similarities cited are superficial or incidental rather than predictive of the conclusion. In , this critique posits that analogies often fail when the common properties do not causally or structurally connect to the inferred attribute; for example, comparing two based on color and size might seem apt for predicting speed, but if engine type is the decisive factor, the analogy collapses due to irrelevant parallels. Such challenges underscore the need for similarities to bear directly on the , avoiding overgeneralization where any resemblance justifies broad claims. Critics may also advance alternative explanations for the observed phenomena, proposing non-analogical reasons that account for the conclusion without relying on the comparison. This method shifts the burden by offering rival hypotheses, such as direct causal mechanisms or deductive proofs, that render the analogy unnecessary; in a over outcomes, for instance, socioeconomic might explain results better than drawing parallels to historical precedents. By introducing these alternatives, opponents demonstrate that the analogical is not the most parsimonious or justified path. Finally, a frequent demand in countering analogical arguments is for empirical testing to validate the rather than accepting it on comparative grounds alone. Proponents of this view argue that analogies serve best as hypotheses requiring , as seen in scientific contexts where initial parallels (e.g., between electrical and ) must be experimentally confirmed to hold; without such , the argument remains speculative and vulnerable to revision. This aligns with broader evaluative practices that prioritize observable data over untested resemblances.

Fallacies and Limitations

False Analogy

A false analogy, also known as a faulty analogy, occurs in reasoning when two entities or situations are compared based on superficial or irrelevant similarities while ignoring significant differences that undermine the intended conclusion. This fallacy arises because the analogy assumes that the shared attributes justify transferring properties from one case to the other, but the comparison lacks the necessary to the conclusion being drawn. Philosophers identify it as a specific error in analogical arguments where the basic similarity does not pertain to the inferred trait or where key dissimilarities are overlooked, rendering the inference invalid. Classic examples illustrate how false analogies mislead by emphasizing minor parallels at the expense of critical distinctions. One such case is the comparison of to a ship, often invoked to argue for strong, singular like a captain's ; however, this overlooks the complexities of democratic systems, where involves diverse stakeholders, checks and balances, and collective input rather than hierarchical command alone. Another prominent instance is the , popularized by to suggest that the universe's complexity implies a divine designer akin to a watch's craftsman; critics argue this fails because natural processes like produce complexity through gradual, unguided mechanisms, unlike the deliberate assembly of a mechanical artifact. Detection of a false analogy typically involves testing for failures in or . is assessed by determining whether the similarities cited directly support the conclusion, rather than being incidental; if they do not, the analogy collapses. examines the balance between similarities and differences—if the dissimilarities are substantial and pertinent to the argument's core, the analogy is deemed false, as the scale or nature of the cases renders the comparison inapt. Historical instances of false analogy critiques appear in debates over , where proponents revive the watchmaker argument to infer a creator from biological complexity, but opponents highlight the analogy's flaws by noting that empirical evidence from provides non-design explanations without the need for an external artisan. Such critiques, echoing David Hume's earlier objections to design analogies for their weak empirical basis, underscore how overlooking natural dissimilarities leads to unsubstantiated theological claims.

Analogy Blindness

Analogy blindness refers to the cognitive obstacle in which individuals fail to recognize or apply relevant analogies due to mental fixation on familiar schemas or overlooked features of a problem, thereby hindering innovative problem-solving and reasoning. This bias arises when aspects of a goal or situation are shielded from attention, preventing the transfer of solutions from analogous domains. The psychological basis of analogy blindness is rooted in , particularly Karl Duncker's concept of functional fixedness, which describes how prior experiences rigidly constrain the perception of objects or concepts, limiting their use in novel contexts. Building on this, research by Mary Gick and Keith Holyoak demonstrated the difficulty in spontaneously detecting structural mappings between source analogs and target problems, as seen in experiments where participants struggled to apply a convergence strategy from a general story to a medical radiation dilemma without explicit hints. Schema rigidity further exacerbates this, as entrenched mental models block the identification of obscure but critical relational features needed for analogical insight. In scientific contexts, analogy blindness can manifest as overlooking biological analogies when addressing physical problems. Similarly, in , it contributes to ethical blindness by impeding the recognition of parallels between dilemmas, such as failing to analogize interpersonal harms in personal to broader societal injustices, leading to inconsistent judgments. A classic example from problem-solving illustrates this in the tumor task, where scientists or clinicians often neglect the analogous convergence principle, resulting in suboptimal solutions. To mitigate analogy blindness, training in —such as J.P. Guilford's techniques for generating multiple associations—encourages breaking rigidity and exploring alternative perspectives. Additionally, targeted analogy education, including explicit hinting strategies from Gick and Holyoak's work or the Obscure Features Hypothesis proposed by Tony McCaffrey, promotes the deliberate identification of hidden relational elements to facilitate analogical transfer. These approaches, often implemented in educational or software-based interventions, have shown to enhance recognition rates in both creative and ethical reasoning tasks.

Applications and Examples

In Philosophy and Science

In philosophy, arguments from analogy have played a central role in epistemological debates, particularly in addressing . employed the hypothesis in his (1641) to illustrate radical doubt, positing an all-powerful deceiver that could manipulate perceptions, akin to a malevolent entity orchestrating illusions much like a dream or , thereby challenging the reliability of sensory evidence and foundational beliefs about the external world. This analogy underscores the vulnerability of human cognition to deception, prompting Descartes to seek indubitable foundations through reason alone. Similarly, the argument from analogy for the existence of other minds, famously articulated by in An Examination of Sir William Hamilton's Philosophy (1865), infers mental states in others by comparing observed behaviors to one's own: just as one's internal experiences correlate with external actions, similar actions in others suggest analogous inner lives, providing inductive grounds against despite the privacy of mental phenomena. In scientific contexts, analogies have facilitated breakthroughs by bridging familiar mechanisms to novel phenomena. Charles Darwin's by in (1859) drew a direct between artificial selection—where breeders selectively propagate desirable traits in domesticated plants and animals—and in the wild, where environmental pressures act as an unconscious selector, preserving advantageous variations across generations and explaining without invoking design. This parallel rendered the abstract process of intuitively accessible, transforming it from a speculative idea into a mechanistic explanation supported by observable evidence in breeding practices. A pivotal example in physics is the development of atomic models in the early 20th century. Ernest Rutherford's 1911 gold foil experiment revealed the atom's nuclear structure, prompting Niels Bohr in his 1913 paper "On the Constitution of Atoms and Molecules" to refine it through an analogy to the solar system: electrons orbit a central positive nucleus in stable, quantized paths, much like planets revolve around the sun under gravitational pull, resolving issues of atomic stability and spectral lines by incorporating quantum constraints. This analogy not only visualized the counterintuitive subatomic realm but also predicted empirical observations, marking a foundational shift in quantum theory. Beyond specific cases, arguments from analogy excel in hypothesis generation within , serving as creative tools that illuminate anomalies and guide paradigm shifts. , in (1962), described how scientists deploy analogies drawn from established paradigms to reinterpret puzzling data, fostering innovative conjectures that propel discovery, as seen in the transition from classical to where familiar mechanical models were extended to microscopic scales. Such analogical reasoning, while not deductive proof, provides power essential for theoretical advancement in both and .

In Law and Everyday Reasoning

In systems, arguments from analogy play a pivotal role in judicial decision-making by allowing to extend to new cases based on relevant similarities, particularly when facts do not precisely match prior rulings. This approach relies on identifying shared principles or rationales between a source case (the ) and the target case, such as in disputes where a might analogize a breach of fiduciary duty in a to a similar non-disclosure in a agreement, thereby applying consistent obligations of . For instance, in cases involving implied warranties, judges often draw analogies to established precedents to determine seller responsibilities, ensuring doctrinal coherence without rigid rule application. A notable example of analogical reasoning in U.S. Supreme Court jurisprudence is found in (1973), where the Court extended the established in (1965)—which protected married couples' use of contraceptives—from penumbral constitutional rights to encompass a woman's decision to terminate a . By analogizing to intimate personal decisions shielded from state interference, the majority opinion framed reproductive choice as part of a broader liberty interest under the , influencing subsequent privacy doctrines. This analogy highlighted similarities in bodily autonomy and freedom from unwarranted governmental intrusion, though it has faced criticism for overstretching Griswold's narrower marital context. In everyday reasoning, individuals frequently employ arguments from to navigate decisions, such as comparing a new smartphone's features and reviews to a previously owned model to assess and reliability, thereby inferring satisfaction based on past experiences. Similarly, personal advice often relies on analogies, like counseling a friend on changes by likening their situation to a successful acquaintance's transition, emphasizing transferable skills and outcomes to build persuasive support. These applications demonstrate analogy's utility in inductive for practical choices, though they remain probabilistic rather than deductive. Despite its prevalence, analogical reasoning in law encounters limitations due to variability in , where judges may emphasize different similarities or distinctions, leading to inconsistent outcomes across courts. For example, the strength of an analogy depends on subjective assessments of , potentially resulting in distinguishing precedents on minor factual differences and undermining predictability in evolution. This interpretive flexibility, while enabling adaptability, can introduce bias or error if analogies overlook critical dissimilarities, as noted in analyses of application.