# Abductive reasoning

Abductive reasoning (also called abduction,[1] abductive inference[2] or retroduction[3]) is a form of logical inference that goes from an observation to a hypothesis that accounts for the observation, ideally seeking to find the simplest and most likely explanation. In abductive reasoning, unlike in deductive reasoning, the premises do not guarantee the conclusion. One can understand abductive reasoning as "inference to the best explanation".[4] The fields of law,[5] computer science, and artificial intelligence research[6] renewed interest in the subject of abduction. Diagnostic expert systems frequently employ abduction. History The American philosopher Charles Sanders Peirce (1839–1914) first introduced the term as "guessing".[7] Peirce said that to abduce a hypothetical explanation from an observed circumstance is to surmise that may be true because then would be a matter of course.[8] Thus, to abduce from involves determining that is sufficient, but not necessary, for allows deriving

Deductive reasoning Deductive reasoning links premises with conclusions. If all premises are true, the terms are clear, and the rules of deductive logic are followed, then the conclusion reached is necessarily true. Deductive reasoning (top-down logic) contrasts with inductive reasoning (bottom-up logic) in the following way: In deductive reasoning, a conclusion is reached reductively by applying general rules that hold over the entirety of a closed domain of discourse, narrowing the range under consideration until only the conclusion(s) is left. In inductive reasoning, the conclusion is reached by generalizing or extrapolating from, i.e., there is epistemic uncertainty. Note, however, that the inductive reasoning mentioned here is not the same as induction used in mathematical proofs – mathematical induction is actually a form of deductive reasoning. Simple example An example of a deductive argument: All men are mortal.Socrates is a man.Therefore, Socrates is mortal. Law of detachment P → Q.

Reason Psychologists and cognitive scientists have attempted to study and explain how people reason, e.g. which cognitive and neural processes are engaged, and how cultural factors affect the inferences that people draw. The field of automated reasoning studies how reasoning may or may not be modeled computationally. Animal psychology considers the question of whether animals other than humans can reason. Etymology and related words In the English language and other modern European languages, "reason", and related words, represent words which have always been used to translate Latin and classical Greek terms in the sense of their philosophical usage. The original Greek term was "λόγος" logos, the root of the modern English word "logic" but also a word which could mean for example "speech" or "explanation" or an "account" (of money handled).[7]As a philosophical term logos was translated in its non-linguistic senses in Latin as ratio. Philosophical history Classical philosophy

Inductive reasoning Inductive reasoning (as opposed to deductive reasoning or abductive reasoning) is reasoning in which the premises seek to supply strong evidence for (not absolute proof of) the truth of the conclusion. While the conclusion of a deductive argument is certain, the truth of the conclusion of an inductive argument is probable, based upon the evidence given.[1] The philosophical definition of inductive reasoning is more nuanced than simple progression from particular/individual instances to broader generalizations. Many dictionaries define inductive reasoning as reasoning that derives general principles from specific observations, though some sources disagree with this usage.[2] Description Inductive reasoning is inherently uncertain. An example of an inductive argument: 90% of biological life forms that we know of depend on liquid water to exist. Therefore, if we discover a new biological life form it will probably depend on liquid water to exist. Inductive vs. deductive reasoning

Logical reasoning Informally, two kinds of logical reasoning can be distinguished in addition to formal deduction: induction and abduction. Given a precondition or premise, a conclusion or logical consequence and a rule or material conditional that implies the conclusion given the precondition, one can explain that: Deductive reasoning determines whether the truth of a conclusion can be determined for that rule, based solely on the truth of the premises. Example: "When it rains, things outside get wet. The grass is outside, therefore: when it rains, the grass gets wet." Mathematical logic and philosophical logic are commonly associated with this style of reasoning.Inductive reasoning attempts to support a determination of the rule. See also References T.

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Logically Speaking Graham Priest interviewed by Richard Marshall. Graham Priest is one of the giants of philosophical logic. He has written many books about this, including Doubt Truth to be a Liar, Towards Non-Being: the Logic and Metaphysics of Intentionality, Beyond the Limits of Thought, In Contradiction: A Study of the Transconsistent and Introduction to Non-Classical Logic. He can be found in Melbourne and New York, and sometimes in St. Andrews. 3:AM: You’re famous for denying that propositions have to be either true or false (and not both or neither) but before we get to that, can you start by saying how you became a philosopher? Graham Priest: Well, I was trained as a mathematician. 3:AM: Now, you’re interested in the very basis of how we think. GP: Well, first a clarification. 3:AM: So paraconsistent logic is a logic that tries to work out how we might formally understand treating some propositions as being both true and false at the same time. So for ‘logic’. But more should be said.

Defeasible reasoning Defeasible reasoning is a kind of reasoning that is based on reasons that are defeasible, as opposed to the indefeasible reasons of deductive logic. Defeasible reasoning is a particular kind of non-demonstrative reasoning, where the reasoning does not produce a full, complete, or final demonstration of a claim, i.e., where fallibility and corrigibility of a conclusion are acknowledged. In other words defeasible reasoning produces a contingent statement or claim. Other kinds of non-demonstrative reasoning are probabilistic reasoning, inductive reasoning, statistical reasoning, abductive reasoning, and paraconsistent reasoning. Defeasible reasoning is also a kind of ampliative reasoning because its conclusions reach beyond the pure meanings of the premises. The differences between these kinds of reasoning correspond to differences about the conditional that each kind of reasoning uses, and on what premise (or on what authority) the conditional is adopted: History Specificity

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Logic and Neutrality The Stone is a forum for contemporary philosophers and other thinkers on issues both timely and timeless. Here’s an idea many philosophers and logicians have about the function of logic in our cognitive life, our inquiries and debates. It isn’t a player. The idea that logic is uninformative strikes me as deeply mistaken, and I’m going to explain why. Leif Parsons The power of logic becomes increasingly clear when we chain together such elementary steps into longer and longer chains of reasoning, and the idea of logic as uninformative becomes correspondingly less and less plausible. For instance, Fermat’s Last Theorem was finally proved by Andrew Wiles and others after it had tortured mathematicians as an unsolved problem for more than three centuries. The conception of logic as a neutral umpire of debate also fails to withstand scrutiny, for similar reasons. Another debate in which logical theories are players concerns the ban on contradictions. Read previous posts by Timothy Williamson.

Subjective logic Subjective logic is a type of probabilistic logic that explicitly takes uncertainty and belief ownership into account. In general, subjective logic is suitable for modeling and analysing situations involving uncertainty and incomplete knowledge.[1][2] For example, it can be used for modeling trust networks and for analysing Bayesian networks. Arguments in subjective logic are subjective opinions about propositions. A binomial opinion applies to a single proposition, and can be represented as a Beta distribution. A fundamental aspect of the human condition is that nobody can ever determine with absolute certainty whether a proposition about the world is true or false. Subjective opinions Subjective opinions express subjective beliefs about the truth of propositions with degrees of uncertainty, and can indicate subjective belief ownership whenever required. where is the subject, also called the belief owner, and is the proposition to which the opinion applies. . Binomial opinions

Identifying and Understanding the Fallacies Used in Advertising ReadWriteThink couldn't publish all of this great content without literacy experts to write and review for us. If you've got lessons plans, videos, activities, or other ideas you'd like to contribute, we'd love to hear from you. More Find the latest in professional publications, learn new techniques and strategies, and find out how you can connect with other literacy professionals. More Teacher Resources by Grade Your students can save their work with Student Interactives. More Home › Classroom Resources › Lesson Plans Lesson Plan

Knowledge representation and reasoning Knowledge representation and reasoning (KR) is the field of artificial intelligence (AI) devoted to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets. Examples of knowledge representation formalisms include semantic nets, Frames, Rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers. Overview This hypothesis was not always taken as a given by researchers. History Characteristics

Fallacies A fallacy is a kind of error in reasoning. The list of fallacies contains 209 names of the most common fallacies, and it provides brief explanations and examples of each of them. Fallacies should not be persuasive, but they often are. An informal fallacy is fallacious because of both its form and its content. The discussion that precedes the long alphabetical list of fallacies begins with an account of the ways in which the term "fallacy" is vague. Table of Contents 1. The first known systematic study of fallacies was due to Aristotle in his De Sophisticis Elenchis (Sophistical Refutations), an appendix to the Topics. The more frequent the error within public discussion and debate the more likely it is to have a name. The term "fallacy" is not a precise term. In describing the fallacies below, the custom is followed of not distinguishing between a reasoner using a fallacy and the reasoning itself containing the fallacy. Real arguments are often embedded within a very long discussion.

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