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Argumentation scheme
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In argumentation theory, an argumentation scheme or argument scheme is a template that represents a common type of argument used in ordinary conversation. Many different argumentation schemes have been identified. Each one has a name (for example, argument from effect to cause) and presents a type of connection between premises and a conclusion in an argument, and this connection is expressed as a rule of inference. Argumentation schemes can include inferences based on different types of reasoning—deductive, inductive, abductive, probabilistic, etc.

The study of argumentation schemes (under various names) dates back to the time of Aristotle, and today argumentation schemes are used for argument identification, argument analysis, argument evaluation, and argument invention.

Some basic features of argumentation schemes can be seen by examining the scheme called argument from effect to cause, which has the form: "If A occurs, then B will (or might) occur, and in this case B occurred, so in this case A presumably occurred."[1]: 170  This scheme may apply, for example, when someone argues: "Presumably there was a fire, since there was smoke and if there is a fire then there will be smoke." This example looks like the formal fallacy of affirming the consequent ("If A is true then B is also true, and B is true, so A must be true"), but in this example the material conditional logical connective ("A implies B") in the formal fallacy does not account for exactly why the semantic relation between premises and conclusion in the example, namely causality, may be reasonable ("fire causes smoke"), while not all formally valid conditional premises are reasonable (such as in the valid modus ponens argument "If there is a cat then there is smoke, and there is a cat, so there must be smoke"). As in this example, argumentation schemes typically recognize a variety of semantic (or substantive) relations that inference rules in classical logic ignore.[2]: 19  More than one argumentation scheme may apply to the same argument; in this example, the more complex abductive argumentation scheme may also apply.

Overview

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Since the beginning of the discipline called rhetoric,[3] the study of the types of argument has been a central issue.[4][5][6] Knowledge of types of argument allows a speaker to find the argument form that is most suitable to a specific subject matter and situation. For example, arguments based on authority may be common in courts of law but not as frequent in a classroom discussion; arguments based on analogy are often effective in political discourse, but may be problematic in a scientific discussion.

The two interrelated goals of argument identification and analysis were the core of ancient dialectics (similar to debate), and specifically the branch called topics.[7][8][9] In the 20th century, the ancient interest in types of arguments was revived in several academic disciplines, including education, artificial intelligence, legal philosophy, and discourse analysis.[10]

The study of this ancient subject is mostly carried out today in the field of study called argumentation theory under the name of argumentation schemes.[1][11]

An example of an argumentation scheme is the scheme for argument from position to know given below.[12]: 86 

Argument from position to know
Premise: a is in a position to know whether A is true or false.
Assertion premise: a asserts that A is true ([or] false).
Conclusion: A may plausibly be taken to be true ([or] false).

Following the usual convention in argumentation theory, arguments are given as a list of premises followed by a single conclusion. The premises are the grounds given by the speaker or writer for the hearer or reader to accept the conclusion as true or as provisionally true (regarded as true for now). An argumentation scheme's definition is not itself an argument, but represents the structure of an argument of a certain type. The letters in the scheme, lower case a and upper case A, need to be filled in if an argument is to be created from the scheme. Lower case a would be replaced by the name of a person and upper case A by a proposition, which might be true or false.

Argumentation theorist Douglas N. Walton gives the following example of an argument that fits the argument from position to know scheme: "It looks as if this passer-by knows the streets, and she says that City Hall is over that way; therefore, let's go ahead and accept the conclusion that City Hall is that way."[12]: 86 

History

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Among 20th-century authors, Chaïm Perelman and Lucie Olbrechts-Tyteca may have been the first to write at length about argumentation schemes, which they called argumentative schemes.[13]: 9 [14]: 19  They present a long list of schemes together with explanation and examples in part three of The New Rhetoric (1958).[13] The argumentation schemes in The New Rhetoric are not described in terms of their logical structure, as in more recent scholarship on argumentation schemes; instead they are given prose descriptions. The structure of the arguments is, nevertheless, considered important by the authors.[13]: 187 

Perelman and Olbrechts-Tyteca also suggest a link between argumentation schemes and the loci (Latin) or topoi (Greek) of classical writers.[13]: 190  Both words, literally translated, mean "places" in their respective languages. Loci is a Latin translation of the Greek, topoi, used by Aristotle in his work, Topics, about logical argument and reasoning. Perelman and Olbrechts-Tyteca explain loci as: "headings under which arguments can be classified".[13]: 83  And they write, "They are associated with a concern to help a speaker's inventive efforts and involve the grouping of relevant material, so that it can be easily found again when required."[13]: 83  While Aristotle's treatment of topoi is not the same as the modern treatment of argumentation schemes, it is reasonable to consider Aristotle as the first writer in the genre.[1]: 267 

The first contemporary writer to treat argumentation schemes in the way they are treated by current scholars and the way they are described in this article may have been Arthur Hastings in his 1962 Ph.D. dissertation.[15]

Forms of inference

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The study of argument in the field of argumentation theory since Perelman and Olbrechts-Tyteca's The New Rhetoric and Stephen Toulmin's The Uses of Argument,[16] both first published in 1958, has been characterized by a recognition of the defeasible, non-monotonic nature of most ordinary everyday arguments and reasoning.[14]: 615  A defeasible argument is one that can be defeated, and that defeat is achieved when new information is discovered that shows that there was a relevant exception to an argument in the presence of which the conclusion can no longer be accepted. A common example used in textbooks concerns Tweety, a bird that may or may not fly:[12]: 72–73 

(All) birds can fly;
Tweety is a bird;
Therefore, Tweety can fly.

This argument (with the addition of "All", which is shown in parentheses) has the form of a logical syllogism and is, therefore, valid. If the first two statements, the premises, are true, then the third statement, the conclusion, must also be true. However, if it is subsequently learned that Tweety is a penguin or has a broken wing, we can no longer conclude that Tweety can fly. In the context of deductive inference, we would have to conclude that the first premise was simply false. Deductive inference rules are not subject to exceptions. But there can be defeasible generalizations (defeasible inference rules). When we say that birds can fly, we mean that it is generally the case, subject to exceptions. We are justified in making the inference and accepting the conclusion that this particular bird can fly until we find out that an exception applies in this particular case.[17]: 21 

In addition to deductive inference and defeasible inference, there is also probabilistic inference.[12]: 65–69  A probabilistic version of the generalization, "birds can fly", might be: "There is a 75% chance that a bird will be found to be able to fly" or "if something is a bird it probably can fly". The probabilistic version is also capable of being defeated (it is defeasible), but it includes the idea that the uncertainty might be quantifiable according to axioms of probability. (An exact number need not be attached as in the first example.[12]: 67 )

In some theories, argumentation schemes are mostly schemes for arguments with defeasible inference although there could be schemes for specialized areas of discourse using other forms of inference, such as probability in the sciences.[1]: 1–2  For most or all everyday arguments, the schemes are defeasible.[18]

In other theories, the argumentation schemes are deductive or there is an attempt to interpret the schemes in a probabilistic way.[19]

Examples

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Argument from expert opinion

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Main article: Argument from authority

Argument from expert opinion can be considered a sub-type of the argument from position to know presented at the beginning of the article. In this case, the person who is in a position to know is an expert who knows about some field.[20]

Argument from expert opinion[20]
Major premise: Source E is an expert in subject domain S containing proposition A.
Minor premise: E asserts that proposition A is true (false).
Conclusion: A is true (false).

Critical questions

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The schemes of Walton (1996) and Walton, Reed & Macagno (2008) come with critical questions. Critical questions are questions that could be asked to throw doubt on the argument's support for its conclusion. They are targeted toward key assumptions that, if true, make the argument acceptable. The reason these assumptions are presented in the form of questions is that these schemes are a part of a dialectical theory of argumentation.[1]: 15  An argument is dialectical when it is a back and forth of argument and rebuttal or questioning. This can be the case even when there is only one reasoner, presenting arguments, then seeking out new information or sources of doubt, or critically probing their own initial assumptions. Since everyday arguments are typically defeasible, this is an approach to strengthening a case over time, testing each element of the case and discarding those parts that do not stand up to scrutiny.[21]: 47, 60  The critical questions for argument from expert opinion, given in Walton, Reed & Macagno (2008), are shown below.

Critical questions for argument from expert opinion[20]
CQ1: Expertise question: How credible is E as an expert source?
CQ2: Field question: Is E an expert in the field that A is in?
CQ3: Opinion question: What did E assert that implies A?
CQ4: Trustworthiness question: Is E personally reliable as a source?
CQ5: Consistency question: Is A consistent with what other experts assert?
CQ6: Backup evidence question: Is E's assertion based on evidence?

Another version of the scheme argument from expert opinion, given in a textbook by Groarke, Tindale & Little (2013), does not include critical questions. Instead more of the key assumptions are included as additional premises of the argument.[22]

Argument from ignorance

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Main article: Argument from ignorance

Argument from ignorance can be stated in a very informal way as, "if it were true, I would know it".[17]: 112  Walton gives the following example of an argument from ignorance: "The posted train schedule says that train 12 to Amsterdam stops at Haarlem and Amsterdam Central Station. We want to determine whether the train stops at Schipol. We can reason as follows: Since the schedule did not indicate that the train stops at Schipol, we can infer that it does not stop at Schipol."[17]: 112  Examples very much like this are well known in computer science discussions about the closed-world assumption for databases.[citation needed] One can assume that the train operating authority has a policy of maintaining a complete database of all of the stops and of publishing accurate schedules. In such cases it is fairly well assured that the information on the published schedule is correct even though it is possible for information to be missing from the database or not included in some particular schedule posting.

The scheme and its accompanying critical questions are shown below.[1]: 327 

Argument from ignorance[20]
Major premise: If A were true, then A would be known to be true.
Minor premise: It is not the case that A is known to be true.
Conclusion: Therefore, A is not true.
Critical questions for argument from ignorance[20]
CQ1: How far along has the search for evidence progressed?
CQ2: Which side has the burden of proof in the dialogue as a whole? In other words, what is the ultimate probandum [claim that is to be proved] and who is supposed to prove it?
CQ3: How strong does the proof need to be in order for this party to be successful in fulfilling the burden?

These critical questions, CQ2 and CQ3 especially, show the dialectical nature of the theory from which this scheme derives (that is, the scheme is based on a back and forth exchange between different parties). Two dialectical concerns are considered. It might be the case, as in some legal systems, that there is a presumption favoring a certain position—e.g., a presumption of innocence favoring the accused.[1]: 98  In that case, the burden of proof is on the accuser, and it would not be proper to argue in the opposite direction: "If the accused were innocent I would have known about it; I don't know about it; therefore, the accused is not innocent." Even if it were a proper argument, the standard of proof in such a case (as asked in CQ3) is very high, beyond a reasonable doubt, but the argument from ignorance alone might be very weak. When challenged, additional arguments would be needed to build a sufficiently strong case.[1]: 35 

Other schemes

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The following list is a selection of names of argumentation schemes from Walton, Reed & Macagno (2008); other sources may give different names:

  • Argument from witness testimony
  • Argument from popular opinion
  • Argument from popular practice
  • Argument from example
  • Argument from composition
  • Argument from division
  • Argument from oppositions
  • Argument from alternatives
  • Argument from verbal classification
  • Argument from definition to verbal classification
  • Argument from vagueness of a verbal classification
  • Argument from arbitrariness of a verbal classification
  • Argument from interaction of act and person
  • Argument from values
  • Argument from the group and its members
  • Practical reasoning argument
  • Argument from waste
  • Argument from sunk costs
  • Argument from correlation to cause
  • Argument from sign
  • Argument from evidence to a hypothesis
  • Argument from consequences
  • Argument from threat
  • Argument from fear appeal
  • Argument from danger appeal
  • Argument from need for help
  • Argument from distress
  • Argument from commitment
  • Ethotic argument
  • Generic ad hominem argument
  • Pragmatic inconsistency argument
  • Argument from inconsistent commitment
  • Circumstantial ad hominem argument
  • Argument from bias
  • Bias ad hominem argument
  • Argument from gradualism
  • Slippery slope argument

See Practical reason § In argumentation for a description of argumentation schemes for practical reasoning.

Relation to fallacies

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Many of the names of argumentation schemes may be familiar because of their history as names of fallacies and because of the history of the teaching of fallacies in critical thinking and informal logic courses. In his groundbreaking work, Fallacies, C. L. Hamblin challenged the idea that the traditional fallacies are always fallacious.[23][14]: 25  Subsequently, Walton described the fallacies as kinds of arguments; they can be used properly and provide support for conclusions, support which is, however, provisional and the arguments defeasible. When used improperly they can be fallacious.[24]

Uses

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Argumentation schemes are used for argument identification, argument analysis, argument evaluation, and argument invention.[25]

Argument identification

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Argument identification is the identification of arguments in a text or spoken discourse. Many or most of the statements will not be arguments or parts of arguments. But some of those statements might look similar to arguments. Informal logicians have especially noted the similarity between words used to express arguments and those used to express explanations.[26][27] Words like "because" or "since" can be used to introduce reasons that justify argumentative positions, but they can also be used to introduce explanations: e.g., "something is the way it is because of the following explanation". Schemes may aid in argument identification because they describe factors that distinguish the argument type from other text. For example, an argument from expert opinion refers to an expert and a field of expertise, both of which could be identified in a text. Some schemes contain more easily distinguished characteristics than others.

Argument mining

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Argument mining is the automatic identification of arguments in natural language using computing technology.[28] It also includes some of the tasks of argument analysis.[28]: 57  The same benefits from the use of argumentation schemes as described above for identification and analysis are relevant to argument mining. Linguistic features that distinguish specific schemes can be used by computer algorithms to identify instances of those schemes and therefore automatically identify the arguments that are of those kinds.[28]: 109–113  Without the ability to notice such argumentative patterns, only features common to all arguments would be available. Feng & Hirst (2011) proposed using argumentation schemes to automatically help fill in missing (implicit) premises in arguments, and they experimented with detecting instances of such schemes.[29] Similar work was done by Lawrence and Reed, and reported in 2016.[30]

Argument analysis

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Argument analysis is distinguishing the premises and conclusion of an argument and determining their relationships (such as whether they are linked or convergent—see Argument map § Key features for diagrams of such relationships), determining the form of inference, and making explicit any implicit premises or conclusions.[12]: 138–171  (These are the tasks of analysis from a logical perspective. When discourse and rhetorical analyses are considered, there would be additional tasks.)

The logical analysis of arguments is especially made difficult by the presence of implicit elements.[13]: 177 [27]: 208–9  Their being implicit means that they are not present in the text (or spoken discourse) as statements; nevertheless, they are understood by the reader or hearer because of nonverbal elements or because of shared background knowledge from the social, cultural, or other shared, context. The implicit elements are also elements that are needed to make the argument cogent. Arguments containing implicit elements are called enthymemes, which is a term that was used by Aristotle in his works about dialectical reasoning and argument.[14]: 18  If an argument appears to match a scheme but is missing some elements, the scheme could be used as a guide to determining what is implicit in the argument.[1]: 189 [29]: 987  An additional challenge with regard to this task could be that some schemes are easy to confuse. In Perelman and Olbrechts-Tyteca's concept of argumentative scheme, different schemes could apply to the same argument depending on the interpretation of the argument or the argument could be described by multiple schemes.[13]: 187–88  Hansen and Walton also write that arguments may fit multiple schemes.[31]

Argument evaluation

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Argument evaluation is the determination of the goodness of the argument: determining how good the argument is and whether, or with what reservations, it ought to be accepted. As mentioned above, in schemes accompanied by critical questions, a measure of the goodness of the argument is whether the critical questions can be appropriately answered. In other schemes, as in the example of the versions of argument from expert opinion in Groarke, Tindale & Little (2013), only good arguments fit the scheme because the criteria for goodness are included as premises,[32] so if any one of the premises is false, the conclusion should not be accepted.

Argument invention

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Argument invention is making new arguments to suit the occasion. As mentioned above, Perelman and Olbrechts-Tyteca attribute that use to the loci and topoi of the classical argumentation theorists.[13][14]: 20  They form a catalog of argument types from which arguers may draw in constructing their arguments. With argumentation schemes described by their structure with single letter variables as placeholders, constructing such arguments is just a matter of filling in the placeholders. The arguer could use other words that convey the same meaning and embellish the argument in other ways.

See also

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Argumentation scheme

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Argumentation schemes are abstract structures representing the most generic types of argument, constituting the building blocks of those used in everyday reasoning and specialized discourses such as and . These schemes encapsulate stereotypical patterns of inference that combine semantic-ontological relations with logical rules, allowing for the identification, analysis, and evaluation of persuasive arguments in . Unlike deductive logic, argumentation schemes are typically defeasible, meaning they yield presumptive rather than certain conclusions, and are assessed through associated critical questions that probe potential weaknesses. The concept traces its origins to Aristotle's Topics and Rhetoric, where it emerged as topoi—general lines of argument used in dialectical and rhetorical reasoning—and evolved through contributions by , , and medieval scholastics before a modern resurgence in the via scholars like Chaim Perelman and . Douglas Walton played a pivotal role in revitalizing and systematizing the approach, collaborating with Chris Reed and Fabrizio Macagno to compile comprehensive catalogs, including a 2008 compendium of 96 schemes covering categories such as causal arguments, practical reasoning, and arguments from or . These schemes are classified by pragmatic purpose (e.g., justifying actions versus evaluating states of affairs) and structural means (e.g., definitional or causal relations), enabling modular combinations for complex argument reconstruction. In contemporary applications, argumentation schemes facilitate computational modeling in fields like AI and , supporting tools such as the Argumentation System for argument mining, statutory , and . For instance, schemes like argument from precedent or expert opinion are formalized to weigh factors in legal disputes, achieving high accuracy in automated analysis (e.g., 0.64–0.98 in rhetorical classification tasks). This framework underscores their utility in bridging informal human argumentation with , promoting clearer debate and decision-making across disciplines.

Definition and Fundamentals

Core Definition

An argumentation scheme is a stereotypical pattern of reasoning that captures common forms of argument encountered in everyday discourse, linking premises to conclusions in a defeasible manner. These schemes represent abstract structures of generic argument types, serving as building blocks for the more complex arguments used in natural language communication, legal reasoning, and scientific inquiry. Unlike arguments in formal logic, which aim for or validity through strict deductive , argumentation schemes are inherently defeasible, allowing for rebuttals, exceptions, or additional that can undermine the conclusion even if the hold. This presumptive nature distinguishes them from deductive systems, as they support plausible rather than certain reasoning, often evaluated through critical questions that probe potential weaknesses. In the fields of and , argumentation schemes function as essential tools for identifying, evaluating, and constructing arguments in non-formal contexts, bridging the gap between everyday and . They enable a systematic approach to understanding how arguments operate beyond fallacious stereotypes, facilitating better argumentation in and . The basic structure of an argumentation scheme generally consists of a major premise that articulates a general rule or , a minor premise that matches the specific case to that rule, and a conclusion that follows defeasibly from the . This framework allows for the instantiation of schemes in varied contexts while maintaining their stereotypical form.

Key Components

Argumentation schemes are structured templates for common forms of reasoning, typically comprising that lead to a conclusion. These are divided into major and minor types. The major premise provides a general or , such as "If A then B," establishing the inferential rule. The minor premise instantiates this rule with a specific fact, such as "A is the case here," thereby supporting the conclusion that "B is the case." This structure ensures that the argument relies on both a broad pattern of reasoning and particular to justify the claim. Matching conditions refer to the criteria that determine whether an argument appropriately fits a given scheme, ensuring the premises align with the scheme's template without distortion. These conditions verify that the language and logic of the premises correspond directly to the scheme's form, preventing misapplication; for instance, the specific instantiation in the minor premise must genuinely exemplify the generalization in the major premise. Each scheme includes a set of critical questions designed to probe the argument's strength by identifying potential weaknesses in the or their connection to the conclusion. These questions serve as a preliminary tool, highlighting assumptions that require further justification. The declaration of the conclusion explicitly states the claim derived from the , often in a matched form that mirrors the scheme's structure for clarity and directness. This explicitness facilitates assessment by making the argumentative goal unambiguous. Argumentation schemes embody defeasibility, meaning they support presumptive rather than and remain open to exceptions or counter. Rebutting defeaters directly challenge the conclusion by offering an alternative that contradicts it, such as showing B does not follow despite A and the general rule. Undercutting defeaters, in contrast, question the reliability of the inferential link without denying the , for example, by demonstrating that the major premise's fails in this context due to special circumstances. This defeasible nature underscores the schemes' role in everyday , where absolute certainty is rare.

Historical Development

Early Foundations

The roots of argumentation schemes trace back to , particularly in 's works on and . In his Rhetoric and Topics, introduced the concept of topoi (singular: topos), which served as general patterns or "places" for generating arguments based on probable rather than certain . These topoi functioned as proto-schemes, providing structured methods for constructing dialectical and rhetorical arguments in contexts where full demonstration was impossible, such as public or . For instance, common topoi included arguments from consequence, division, or , emphasizing plausible inferences drawn from shared . This framework was adapted and expanded in the Roman period by , who in De Inventione and Topica organized topoi into categories for rhetorical invention, applying them to legal and political discourse to generate persuasive lines of reasoning from probable evidence. In the early 6th century, preserved and refined these ideas through his commentaries In Ciceronis Topica and De topicis differentiis, classifying topics as maximal propositions that bridge particular cases to general principles, ensuring the transmission of dialectical tools to the Latin West. During the medieval period, scholastic logicians built upon Aristotelian foundations, developing frameworks for probable arguments within theological and philosophical discourse. Thomas Aquinas, in his Summa Theologica, distinguished between demonstrative proofs and probable opinions, allowing for reasoned assent to propositions supported by credible evidence but lacking absolute certainty. This approach integrated Aristotelian topoi into scholastic methods, where probable syllogisms—deductions from likely premises—facilitated debates on faith, ethics, and . Other scholastics, such as and , further refined these techniques, emphasizing context-dependent reasoning in disputationes to resolve doctrinal ambiguities without dogmatic finality. In the 19th and early 20th centuries, precursors to modern emerged through inductive methodologies, notably in John Stuart Mill's A System of Logic (1843). Mill outlined five methods of experimental inquiry—agreement, difference, joint method, residues, and concomitant variations—to systematically identify causal relationships via empirical observation, shifting focus from deductive certainty to probabilistic generalizations. These inductive patterns prefigured argumentation schemes by prioritizing evidence-based, fallible inferences applicable to everyday and scientific reasoning, influencing later developments in non-formal argument analysis. A key distinction in these early foundations was the emphasis on plausible, context-dependent over syllogistic deduction, which and subsequent thinkers reserved for necessary truths. Unlike strict syllogisms yielding certain conclusions from universal premises, proto-schemes like topoi and probable syllogisms accommodated enthymemes—arguments with implicit assumptions—tailored to and situation, laying groundwork for flexible argumentative practice.

Modern Advancements

The modern resurgence of argumentation schemes in the mid-20th century was marked by Chaim Perelman and Lucie Olbrechts-Tyteca's The New Rhetoric: A Treatise on Argumentation (1958), which rehabilitated topical reasoning for practical discourse in law and philosophy, emphasizing audience adherence over formal proof, and Stephen Toulmin's The Uses of Argument (1958), which proposed a diagrammatic model of claim, data, warrant, backing, qualifier, and rebuttal to analyze everyday arguments beyond deductive logic. These works shifted focus toward defeasible, context-sensitive patterns, paving the way for systematic catalogs. In the late , Douglas Walton advanced the study of argumentation schemes by systematically cataloging patterns of presumptive reasoning, identifying 25 core schemes in his 1996 work Argumentation Schemes for Presumptive Reasoning, each paired with critical questions for evaluation. Walton's efforts expanded significantly in the 2000s, culminating in the 2008 co-authored book Argumentation Schemes, which compiled 96 schemes and emphasized their role in dialectical analysis across contexts like and . These contributions shifted toward a more formalized, scheme-based approach, influencing subsequent interdisciplinary applications. The integration of argumentation schemes with emerged prominently in the 2000s through computational dialectics, exemplified by the software tool developed by Chris Reed and Glenn Rowe, which enabled diagramming and analysis of arguments using scheme structures to represent linked and convergent reasoning patterns. In the 2020s, advancements in neural argument mining incorporated schemes to detect complex reasoning patterns in texts, with models leveraging contextual embeddings to classify and extract scheme-based inferences from essays and debates, achieving improved accuracy in identifying argumentative components. These developments facilitated automated argument reconstruction, bridging philosophical schemes with techniques. Interdisciplinary expansion of argumentation schemes gained traction in legal reasoning, where Henry Prakken formalized schemes for case-based analysis, incorporating value preferences and hypothetical reasoning to model judicial decisions and defeasible inferences in AI-driven legal systems. Post-2020, schemes extended to AI ethics, particularly for detecting , with argumentative frameworks using scheme-based debates to transparently evaluate local and global biases in decision-making models without requiring internal access to proprietary algorithms. As of 2025, argumentation schemes have been incorporated into large language models for argument generation and verification, enabling the creation of contestable claims through formal reasoning frameworks that align outputs with dialectical schemes. However, critiques highlight limitations in scheme universality, as large models struggle with nuanced scheme identification in zero-shot settings and across diverse cultural contexts, underscoring the need for hybrid neural-symbolic approaches to enhance reasoning fidelity.

Classification of Schemes

Inference-Based Categories

Argumentation schemes are categorized based on the underlying types of they facilitate, providing a framework for analyzing the logical structure of arguments in informal reasoning. This draws from patterns observed in everyday , legal argumentation, and , emphasizing over strict logical necessity. Key categories include deductive, inductive, abductive, conductive, analogical, and presumptive schemes, each corresponding to distinct inferential processes that vary in strength and vulnerability to . Deductive schemes represent inferences where the conclusion is intended to follow necessarily from the premises in formal systems, but in the context of argumentation schemes, they are typically treated as defeasible to accommodate informal reasoning and , such as reframed for conversational exchange. They are rare in everyday settings, where arguments must accommodate uncertainty; these schemes prioritize semantic and ontological relations, like genus-species hierarchies, to support validity while allowing for critical questions. Inductive schemes support generalizations from specific observations to broader conclusions, relying on patterns such as sampling to project properties across a . Their probative force depends on factors like sample size, representativeness, and diversity, as seen in statistical arguments where larger, unbiased samples yield stronger inferences. These schemes are inherently defeasible, allowing for revision based on new evidence. Abductive schemes enable to the best available for observed phenomena, often involving causal hypotheses in diagnostic or explanatory contexts. They are defeasible, as alternative explanations may emerge, but they excel in scenarios requiring practical under incomplete information. Conductive schemes involve the accumulation of multiple, independent reasons that converge to support a conclusion, emphasizing the cumulative weight of rather than a linear . Unlike deductive or inductive forms, they require weighing pros against potential cons, making them suitable for evaluative deliberations where no single premise is conclusive. Blair and Johnson characterize conductive arguments as defeasible, non-conclusive, and distinct from both induction and deduction. Analogical schemes draw inferences based on similarities between cases or entities, positing that what holds in one context likely applies to a parallel one. Presumptive schemes, in turn, operate on default assumptions or reasonable expectations that stand unless challenged, facilitating efficient reasoning in uncertain environments. Both categories are defeasible and integral to presumptive reasoning in dialogue.

Domain-Specific Variations

In the legal domain, argumentation schemes are tailored to address the unique demands of evidence presentation and evaluation, incorporating procedural norms like burdens of proof and standards of . Douglas Walton's work in the 2010s extended general schemes, such as argument from expert opinion, to legal contexts by adapting them for courtroom use, where expert must demonstrate specialized knowledge, reliability, and pertinence to the case at hand. For instance, schemes for witness emphasize factors like , , and sincerity, enabling systematic critique through critical questions that probe potential biases or inconsistencies. These adaptations, as detailed in analyses of Walton's contributions to AI and , facilitate the construction and evaluation of arguments in adversarial settings, ensuring arguments align with evidentiary rules. In scientific domains, causal argumentation schemes support testing by structuring inferences from observed effects to potential causes, often under conditions of incomplete . Schemes like argument from cause to effect are used to build and challenge causal claims during . Post-2020 integrations with Bayesian methods have further refined these schemes, incorporating probabilistic updating to quantify and strength, as seen in Bayesian argumentation-scheme networks that facilitate probabilistic modeling of argument validity using argumentation schemes. Value-based argumentation schemes in political and ethical domains prioritize the of values to resolve disputes, adapting practical reasoning to evaluate actions based on their promotion of ideals like equity or . In debates, these schemes structure arguments by linking proposed policies to value hierarchies, allowing debaters to attack or defend based on value conflicts, as demonstrated in case studies of environmental and public . Recent 2025 extensions apply these schemes to AI , using value comparisons to argue for regulatory measures that balance innovation with ethical concerns like and fairness in AI deployment. Cultural variations manifest in non-Western adaptations of argumentation schemes, particularly relational schemes rooted in Confucian traditions, which emphasize interconnected roles and social over confrontational logic. Unlike Western schemes focused on linear , Confucian approaches frame arguments within relational dynamics, where arises from aligning with moral roles (e.g., or benevolence) to foster consensus rather than refute opponents. Studies of Confucian philosophical argumentation highlight how these relational patterns integrate emotional and contextual elements, adapting schemes to prioritize collective in . Such variations underscore the need for culturally sensitive scheme design in global .

Prominent Examples

Argument from Expert Opinion

The argument from expert opinion is a presumptive argumentation scheme that justifies accepting a based on an assertion made by a qualified in a relevant field. Formulated by Douglas Walton, the scheme consists of the following structure:
  • Major Premise: Source E is an in subject domain S containing A.
  • Minor Premise: E asserts that A is true (or false).
  • Conclusion: A may (plausibly) be true (or false).
This structure draws on the general reliability of expert judgment within their domain, providing a for reasonable in uncertain contexts. For the scheme to apply effectively, the source's expertise must align precisely with the domain encompassing the , ensuring the opinion addresses a matter within the expert's demonstrated competence rather than an adjacent or unrelated area. Furthermore, consensus among multiple experts in the domain enhances the argument's probative , as agreement signals greater reliability, though divided opinions do not necessarily invalidate the appeal if the individual expert's credentials hold. The scheme commonly arises in policy advice scenarios, where experts offer guidance on intricate issues like or fiscal reforms, leveraging their specialized knowledge to inform decision-makers lacking equivalent insight. It also features prominently in scientific , such as in proceedings where specialists elucidate technical on topics ranging from forensic to medical diagnoses. One key strength of the argument from expert opinion lies in its high presumptive weight, enabling efficient acceptance of conclusions in complex domains where lay evaluation is impractical and direct verification is resource-intensive. Nonetheless, a notable limitation is its susceptibility to , as experts may be swayed by personal interests, funding sources, or ideological commitments, which can compromise the objectivity of their assertions.

Argument from Analogy

The argument from analogy is a form of reasoning that infers a conclusion about a target case based on its similarity to a source case where the conclusion is known or plausible. In its standard structure, the scheme consists of three premises: (1) a situation is similar in certain respects to the source case C1, (2) in C1, the conclusion A holds or is plausible, and (3) the target case C2 shares relevant similarities with C1; from these, it follows that A is plausible in C2. This relational mapping relies on identifying shared attributes or relations between the cases, such as structural or causal parallels, to extend from the familiar to the . Douglas Walton distinguishes two variants: a similarity-based scheme emphasizing overall resemblance and a factor-based scheme focusing on shared specific features like attributes f1 through fn, leading to the conclusion that the entities should be treated alike regarding those features. The strength of an argument from analogy depends on the number, relevance, and systematicity of shared features between the source and target, weighed against any disanalogies that undermine the inference. Relevant similarities boost plausibility, particularly when they involve causal or relational properties central to the conclusion, while irrelevant or superficial matches weaken it; conversely, significant differences in essential aspects, such as differing contexts or outcomes, can render the analogy invalid. For instance, in ethical debates, analogies comparing human and animal physiology argue against certain animal testing by highlighting shared pain responses and biological similarities, implying similar moral considerations. In legal contexts, precedents function analogically, as when courts extend rulings from prior cases with comparable facts to new disputes, ensuring consistency in application. Philosophically, the argument from analogy draws from John Stuart Mill's methods of , particularly the methods of agreement and difference, which identify causal links by comparing instances where an effect occurs (agreement) or is absent (difference) amid varying circumstances. Mill viewed analogies as probabilistic tools that increase confidence in conclusions through resemblances, provided the shared properties are causally pertinent and disanalogies are minimized. This basis underscores the scheme's abductive nature, generating explanatory hypotheses from observed parallels.

Argument from Ignorance

The argument from ignorance, also known as argumentum ad ignorantiam, is a presumptive argumentation scheme that infers the truth or falsity of a based on the absence of to the contrary. In its formal structure, as outlined by Douglas Walton, the scheme consists of a major stating that if a proposition A were true, it would be known to be true; a minor premise asserting that A is not known to be true; and a conclusion that A is therefore not true. This represents the negative form, where the lack of supporting evidence is taken to support the negation of the proposition. Conversely, the positive form reverses the : if A were false, it would be known to be false; A is not known to be false; therefore, A is true (or at least plausible). These forms highlight the scheme's reliance on the current state of knowledge within a defined context. The scheme can be legitimately employed in situations where the search for evidence has been exhaustive or the knowledge base is sufficiently closed, allowing the absence of disproof to carry probative weight. For instance, in legal proceedings, the operates under this principle: absent evidence of guilt after thorough investigation, the defendant is deemed not guilty, shifting the burden of proof to the prosecution. Similarly, in scientific inquiry, the scheme aligns with null hypothesis testing, where failure to detect evidence supporting an after rigorous experimentation supports retaining the , provided the test's power and sample size ensure adequate sensitivity. However, this application remains defeasible, as new evidence could overturn the inference. Misuse of the argument from ignorance occurs when the absence of is treated as conclusive proof rather than presumptive support, particularly in domains where exhaustive search is infeasible or evidence might simply be undiscovered. This risks conflating with positive evidence against a claim, leading to overconfident conclusions. For example, asserting that a medical treatment is ineffective solely because no studies have demonstrated its ignores the possibility of under-researched areas or methodological limitations in existing trials. Walton emphasizes that the scheme's validity depends on contextual factors, such as the feasibility of evidence detection, to avoid such pitfalls.

Evaluation Methods

Critical Questions Framework

The Critical Questions Framework, developed by Douglas Walton, serves as a primary method for evaluating argumentation schemes by systematically questioning their premises and underlying conditions. Originating in Walton's 1996 work on presumptive reasoning, the framework assigns a tailored set of critical questions to each scheme, designed to probe the argument's foundational elements and identify potential weaknesses. These questions evolved from earlier tools into a structured evaluative device, later expanded in Walton's collaborative compendium of 96 schemes, which refined their application for broader dialectical analysis. In general structure, the critical questions target key aspects of the scheme, including the truth or of , their to the conclusion, the sufficiency of provided, and potential rebuttals or exceptions that could undermine the argument. For example, questions might assess whether a is factually supported (truth), whether it logically connects to the claim (), whether it alone justifies the conclusion without additional (sufficiency), or if countervailing factors exist (rebuttals). This modular approach ensures that evaluation is scheme-specific yet standardized, allowing for consistent scrutiny across diverse argumentative patterns. The evaluative process involves systematically addressing each question: affirmative ("yes") responses reinforce the argument's strength, while negative ("no") or unresolved answers weaken its presumptive force, potentially defeating it entirely and shifting the burden of proof to the proponent. This dialogical mechanism supports non-monotonic reasoning, where arguments can be revised based on new information without invalidating the entire structure. Recent advancements as of 2025 incorporate large language models (LLMs) for automated identification and resolution of critical questions, improving scheme evaluation in computational argumentation mining tasks. A key advantage of the framework is its promotion of dialectical fairness in argumentative discourse, as it encourages balanced interrogation without presuming bias, thereby fostering rigorous and equitable evaluation in both theoretical and practical contexts. For instance, when applied to the argument from expert opinion, the questions verify the expert's domain credibility and the absence of bias, enhancing the scheme's reliability in scenarios.

Scheme Mismatches and Applicability Failures

Scheme mismatches and applicability failures occur in the evaluation of when an fails to satisfy the structural or contextual prerequisites for the scheme's application, such as when do not align with the scheme's templated form or when necessary background assumptions are absent. These mismatches prevent the scheme from being properly instantiated, distinguishing them from defeasible weaknesses addressed by critical questions. Applicability conditions are prerequisites, including premise alignment and shared epistemic assumptions, required for valid scheme use. Premise mismatches represent one primary type, where individual components of the argument, particularly the minor premise, do not conform to the scheme's requirements, often due to factual inaccuracy or irrelevance. For instance, in the argument from expert opinion scheme—which posits that a statement is true if asserted by an expert in the relevant domain—a mismatch arises if the expert's assertion concerns a topic outside their field of expertise, rendering the minor premise invalid. Similarly, in the argument from analogy, a weak or irrelevant similarity between cases can cause the premise detailing the resemblance to fail, as the scheme requires a substantive, contextually appropriate parallel. Condition failures form another type, involving broader contextual or assumptive elements that undermine the scheme's foundational applicability, such as the absence of consensus on a general rule or . In the argument from , for example, the scheme depends on an accepted conditional linking similar cases, but if disputants lack agreement on this linkage—perhaps due to differing —the condition fails, blocking scheme application. These failures highlight how schemes rely on shared procedural or epistemic norms, without which the inferential structure collapses. Detection of scheme mismatches often employs checklist-based methods integrated into argumentation software, which systematically compare argument components against scheme templates to identify non-conformities. Tools like the system formalize this process by modeling scheme premises as verifiable propositions and exceptions, allowing automated or semi-automated checks for mismatches before proceeding to deeper evaluation. Such approaches facilitate precise identification in complex discourses, reducing reliance on subjective judgment. The consequences of scheme mismatches are significant, as they indicate an invalid application of the scheme, compelling evaluators to discard it and explore alternative schemes or reframe the argument to avoid erroneous inferences. This abandonment preserves argumentative integrity, preventing the propagation of unsupported reasoning patterns in dialogue or analysis. Critical questions may assist in flagging potential mismatches once basic is assumed, but they do not substitute for verification.

Connection to Fallacies

Transformation into Fallacies

Argumentation schemes, as structured patterns of reasoning, can devolve into fallacies when misapplied within dialogical contexts, leading to arguments that appear persuasive but ultimately fail to meet normative standards of correctness. According to Douglas Walton's pragmatic theory, fallacies represent "deceptively bad arguments that impede the progress of dialogue," often arising from breakdowns in the application of these schemes. This perspective frames fallacies not as inherently flawed forms but as failed instantiations of otherwise legitimate schemes, where presumptive obligations are violated. Key mechanisms driving this transformation include overgeneralization, where a scheme is extended beyond its appropriate scope without sufficient justification; ignoring rebuttals, which involves disregarding potential counterarguments or exceptions; and biased premise selection, whereby are cherry-picked to support a preconceived conclusion rather than objectively fitting the scheme's structure. These misuses disrupt the defeasible nature of schemes, which rely on tentative acceptance pending further or questioning. Walton emphasizes that such errors, termed paralogisms, stem from deviations in scheme application that create a semblance of validity while obstructing rational . Central to this relation is the scheme's internal structure, particularly the associated critical questions designed to probe premises and conclusions for robustness. Fallacies emerge when these questions remain unanswered or are evasively handled, undermining the scheme's presumptive strength. For instance, Walton's framework posits that argumentation schemes carry inherent reasoning obligations, and their neglect links directly to fallacious outcomes by failing to uphold dialogical norms. A prominent example is the transformation of the argument from expert opinion into the ad verecundiam fallacy, where an appeal to succeeds as a scheme only if the expert's domain, consistency, and are verified through critical questions. Misuse occurs via biased selection of an unqualified "expert" or overgeneralization to unrelated fields, rendering the argument fallacious by ignoring rebuttals to the authority's credentials. In Walton's view (developed from 1995 onward), this illustrates how a scheme's failure to address its own evaluative criteria turns presumptive reasoning into deceptive error.

Preventive Measures

To prevent scheme-based fallacies, arguers are advised to systematically apply the critical questions associated with each prior to accepting its conclusion as justified. These questions, developed as a core component of , probe the and contextual conditions to identify potential mismatches or weaknesses that could lead to erroneous inferences. For instance, in the argument from expert opinion scheme, critical questions examine the 's domain of expertise, , and consistency with other evidence, thereby averting fallacious reliance on unqualified . In conductive arguments, where multiple independent reasons cumulatively support a conclusion, best practices involve balancing various argumentation schemes to avoid overdependence on any single one, which might obscure flaws if that scheme's conditions are unmet. This approach treats schemes as complementary, weighing their collective strength while addressing counter-considerations to ensure the overall argument remains robust against partial invalidity. Such balancing mitigates fallacies arising from isolated scheme application, as seen in ethical deliberations where schemes from and consequence are integrated for comprehensive support. Educational programs emphasizing argumentation scheme recognition have proliferated since the 2010s, integrating scheme identification into critical thinking curricula to foster skills in detecting and avoiding fallacious uses. These initiatives, often embedded in science and philosophy education, train participants to map arguments to schemes and apply critical questions proactively, enhancing argumentative literacy. For example, post-2010 science education frameworks have incorporated scheme-based activities to develop students' ability to recognize and critique presumptive reasoning patterns. In the 2020s, computational aids powered by have emerged to assist in checking for unmatched conditions in argumentation schemes, automating the detection of potential fallacies through scheme matching and evaluation. Tools leveraging large language models analyze textual arguments to identify instantiated schemes and flag violations of matching conditions, such as unsupported or contextual mismatches. These AI checkers, building on formal models of argumentation, support users in refining arguments before deployment in debates or decision-making.

Practical Applications

Argument Identification and Mining

Argument identification involves manual and automated techniques to detect and extract argumentation schemes from texts, focusing on recognizing the structural patterns that connect to conclusions. Manual identification relies on rhetorical analysis, where analysts apply a systematic process to , such as Douglas Walton's six-stage approach: first locating potential by identifying and conclusions, then matching them to specific schemes like argument from expert opinion, and verifying fit through critical questions. This method emphasizes contextual interpretation in dialogues or texts, enabling human experts to handle nuances that automated systems may overlook. Automated argument mining employs (NLP) techniques to tag and classify argumentation schemes at scale, often using models trained on annotated corpora derived from Walton's framework. Supervised approaches, such as support vector machines (SVMs) and neural networks like , achieve competitive F-scores for identifying scheme components in datasets like the AIFdb corpus annotated with Walton's schemes from broadcast debates. More recent advancements leverage large language models (LLMs), such as Claude-3.5 and GPT-4o, in few-shot prompting setups to classify schemes across 20+ categories, attaining macro F1 scores up to 0.65 on corpora like NLAS (1,893 arguments) and EthiX (686 real-world examples), outperforming traditional classifiers by adapting to scheme families like cause-to-effect or . These models scheme components—premises, conclusions, and critical questions—for tagging, facilitating extraction from diverse texts like political debates or essays. Key challenges in scheme identification stem from ambiguity and context dependency, where (implicit premises) and polysemous terms lead to misclassification, as seen in LLM struggles with expert opinion schemes (notable drops on subsets). Limited annotated data, with corpora often under 2,000 examples per scheme, exacerbates , while varying dialogue contexts require models to generalize from textbook-like to conversational inputs. Tools for scheme annotation include , a for manually applying Walton's schemes to texts, supporting export to AIF formats for further . Recent systems like Argilla enable collaborative NLP annotation workflows, allowing domain experts to tag schemes in datasets for training, integrated with LLMs for semi-automated labeling in argument mining pipelines. Additionally, ARGAEL provides specialized support for evaluating scheme annotations in legal and persuasive texts.

Analysis and Evaluation

Scheme mapping involves identifying the structural components of an and matching them to the and conclusion of a predefined argumentation scheme to determine its fit. This process requires examining the argument's key elements, such as claims and reasons, against the scheme's template, often using tools like argument diagramming to visualize alignments and mismatches. For instance, in the appeal to expert opinion scheme, the major asserts that expertise implies knowledge on a subject, the minor identifies the expert's statement, and the conclusion follows if both hold; mismatches, such as lack of recognized expertise, indicate poor fit. Evaluation metrics for argumentation schemes primarily rely on critical questions, which probe the scheme's premises for vulnerabilities, allowing strength grading based on responses. Each scheme is paired with 3–10 critical questions that test assumptions, such as or bias; affirmative answers bolster the argument's presumptive strength, while negative ones weaken or refute it. In formal systems like , critical questions are modeled as additional with weights (e.g., ordinary or exceptional) and proof standards (e.g., preponderance of ), enabling quantitative assessment where argument graphs compute overall scores from 0 to 1. This weighted approach distinguishes varying degrees of support, prioritizing schemes with robust defenses against counterarguments. Multi-scheme analysis addresses hybrid arguments by decomposing them into chained or nested structures, where the conclusion of one scheme serves as a for another, facilitating of complex reasoning. Frameworks like ASPIC+ formalize this by representing schemes as defeasible rules, with critical questions as undercutting attacks that propagate through the graph to assess overall coherence. In multi-agent systems, agents negotiate scheme applications via , resolving conflicts by instantiating multiple schemes and measuring computational efficiency, such as reasoning time increasing with chain length (e.g., from 1 to 10 premises). This method ensures comprehensive analysis without oversimplifying intertwined arguments. Case studies in AI-mediated discussions post-2020 illustrate scheme mapping and in dynamic environments, such as multi-agent debates on ethical dilemmas. In healthcare deliberations, AI systems mapped arguments for to schemes like practical reasoning, evaluating strength via critical questions on equity and urgency, revealing potential biases through weighted premise assessments. Similarly, in legal AI dialogues, schemes for were chained to analyze precedents, with evaluations showing improved decision accuracy when hybrid structures addressed multi-faceted disputes. These applications highlight schemes' role in enhancing transparency and defeasibility testing in AI-facilitated interactions.

Invention and Construction

Argumentation schemes serve as reusable templates that facilitate the of new arguments by providing structured patterns of reasoning tailored to specific persuasive objectives. Originating from classical , these schemes, such as argument from expert opinion or , allow arguers to identify relevant premises and conclusions that align with goals like establishing or urging action. For instance, in a , an arguer might select the scheme of practical reasoning—whereby a proposed action is justified by its desirable consequences—to persuade stakeholders of its benefits. This selection process draws on the scheme's matching premises to the audience's commitments and the context's exigencies, ensuring the argument's relevance and potential persuasiveness. To construct more robust arguments, strategies often involve combining multiple schemes into interconnected structures, enabling the representation of complex reasoning that no single scheme can fully capture. Walton et al. describe this modular approach, where schemes form "nets" or chains; for example, an analogy scheme might link a novel case to a familiar one, reinforced by an expert opinion scheme to bolster and address potential doubts. In legal argumentation, combining a precedent-based scheme with a consequences scheme can build a multi-layered case, such as arguing that a new mirrors past successful policies while highlighting its benefits. This combination enhances argumentative depth without introducing inconsistencies, as each scheme's critical questions guide integration. Various tools have operationalized schemes for practical and since the . The Rationale software, developed in the mid-, supports users in mapping and generating arguments by incorporating scheme templates to visualize premise-conclusion links and explore alternatives, aiding educational and professional contexts. More advanced systems like , introduced around 2006 and refined through 2017, automate via backward and forward reasoning: starting from a goal proposition, the system instantiates schemes from a to generate supporting arguments, such as applying the scheme to infer credibility from testimony. By 2025, large language models (LLMs) have extended this capability, with models like GPT variants demonstrating proficiency in scheme-based generation; for instance, prompting an LLM with a practical reasoning scheme yields contextually adapted arguments in domains like , where it constructs by chaining precedents and outcomes. Ethical considerations in scheme-based emphasize responsible application to prevent manipulation, aligning with rhetorical ideals of truthful . Walton's framework highlights that while schemes enable creative , misapplying them—such as fabricating in an to mislead—can transform valid patterns into deceptive tactics, underscoring the need for transparency in sourcing and . Tools like incorporate burden-of-proof mechanisms to promote fairness, ensuring invented arguments withstand scrutiny without exploiting vulnerabilities.

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