Abduction^{[1]}
is a form of logical inference that goes from observation to a hypothesis that accounts for the reliable data (observation) and seeks to explain relevant evidence. The American philosopher Charles Sanders Peirce (1839–1914) first introduced the term as "guessing".^{[2]} Peirce said that to abduce a hypothetical explanation $a$ from an observed surprising circumstance $b$ is to surmise that $a$ may be true because then $b$ would be a matter of course.^{[3]} Thus, to abduce $a$ from $b$ involves determining that $a$ is sufficient (or nearly sufficient), but not necessary, for $b$.
For example, the lawn is wet. But if it rained last night, then it would be unsurprising that the lawn is wet. Therefore, by abductive reasoning, the possibility that it rained last night is reasonable. (But note that Peirce did not remain convinced that a single logical form covers all abduction.)^{[4]} Moreover, abducing rain last night from the observation of the wet lawn can lead to a false conclusion. In this example, dew, lawn sprinklers, or some other process may have resulted in the wet lawn, even in the absence of rain.
Peirce argues that good abductive reasoning from P to Q involves not simply a determination that, e.g., Q is sufficient for P, but also that Q is among the most economical explanations for P. Simplification and economy call for the 'leap' of abduction.^{[5]}
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."^{[6]}
The fields of law,^{[7]} computer science, and artificial intelligence research^{[8]} renewed interest in the subject of abduction. Diagnostic expert systems frequently employ abduction.
Deduction, induction, and abduction
 Deductive reasoning (deduction)
 allows deriving $b$ from $a$ only where $b$ is a formal logical consequence of $a$. In other words, deduction derives the consequences of the assumed. Given the truth of the assumptions, a valid deduction guarantees the truth of the conclusion. For example, given that all bachelors are unmarried males, and given that this person is a bachelor, one can deduce that this person is an unmarried male.
 Inductive reasoning (induction)
 allows inferring $b$ from $a$, where $b$ does not follow necessarily from $a$. $a$ might give us very good reason to accept $b$, but it does not ensure $b$. For example, if all swans that we have observed so far are white, we may induce that the possibility that all swans are white is reasonable. We have good reason to believe the conclusion from the premise, but the truth of the conclusion is not guaranteed. (Indeed, it turns out that some swans are black.)
 Abductive reasoning (abduction)
 allows inferring $a$ as an explanation of $b$. Because of this inference, abduction allows the precondition $a$ to be abduced from the consequence $b$. Deductive reasoning and abductive reasoning thus differ in the direction in which a rule like "$a$ entails $b$" is used for inference. As such, abduction is formally equivalent to the logical fallacy of affirming the consequent (or Post hoc ergo propter hoc) because of multiple possible explanations for $b$. For example, after glancing and seeing the eight ball moving towards us, we may abduce that the cue ball struck the eight ball. The strike of the cue ball would account for the movement of the eight ball. It serves as a hypothesis that explains our observation. Given the fact of infinitely many possible explanations for the movement of the eight ball, our abduction does not leave us certain that the cue ball in fact struck the eight ball, but our abduction, still useful, can serve to orient us in our surroundings. Despite infinite possible explanations for any physical process that we observe, we tend to abduce a single explanation (or a few explanations) for this process in the expectation that we can orient better ourselves in our surroundings and disregard some possibilities.
Formalizations of abduction
Logicbased abduction
In logic, explanation is done from a logical theory $T$ representing a domain and a set of observations $O$. Abduction is the process of deriving a set of explanations of $O$ according to $T$ and picking out one of those explanations. For $E$ to be an explanation of $O$ according to $T$, it should satisfy two conditions:
 $O$ follows from $E$ and $T$;
 $E$ is consistent with $T$.
In formal logic, $O$ and $E$ are assumed to be sets of literals. The two conditions for $E$ being an explanation of $O$ according to theory $T$ are formalized as:
 $T\; \backslash cup\; E\; \backslash models\; O$;
 $T\; \backslash cup\; E$ is consistent.
Among the possible explanations $E$ satisfying these two conditions, some other condition of minimality is usually imposed to avoid irrelevant facts (not contributing to the entailment of $O$) being included in the explanations. Abduction is then the process that picks out some member of $E$. Criteria for picking out a member representing "the best" explanation include the simplicity, the prior probability, or the explanatory power of the explanation.
A proof theoretical abduction method for first order classical logic based on the sequent calculus and a dual one, based on semantic tableaux (analytic tableaux) have been proposed (Cialdea Mayer & Pirri 1993). The methods are sound and complete and work for full first order logic, without requiring any preliminary reduction of formulae into normal forms. These methods have also been extended to modal logic.
Abductive logic programming is a computational framework that extends normal logic programming with abduction. It separates the theory $T$ into two components, one of which is a normal logic program, used to generate $E$ by means of backward reasoning, the other of which is a set of integrity constraints, used to filter the set of candidate explanations.
Setcover abduction
A different formalization of abduction is based on inverting the function that calculates the visible effects of the hypotheses. Formally, we are given a set of hypotheses $H$ and a set of manifestations $M$; they are related by the domain knowledge, represented by a function $e$ that takes as an argument a set of hypotheses and gives as a result the corresponding set of manifestations. In other words, for every subset of the hypotheses $H\text{'}\; \backslash subseteq\; H$, their effects are known to be $e(H\text{'})$.
Abduction is performed by finding a set $H\text{'}\; \backslash subseteq\; H$ such that $M\; \backslash subseteq\; e(H\text{'})$. In other words, abduction is performed by finding a set of hypotheses $H\text{'}$ such that their effects $e(H\text{'})$ include all observations $M$.
A common assumption is that the effects of the hypotheses are independent, that is, for every $H\text{'}\; \backslash subseteq\; H$, it holds that $e(H\text{'})\; =\; \backslash bigcup\_\{h\; \backslash in\; H\text{'}\}\; e(\backslash \{h\backslash \})$. If this condition is met, abduction can be seen as a form of set covering.
Abductive validation
Abductive validation is the process of validating a given hypothesis through abductive reasoning. This can also be called reasoning through successive approximation. Under this principle, an explanation is valid if it is the best possible explanation of a set of known data. The best possible explanation is often defined in terms of simplicity and elegance (see Occam's razor). Abductive validation is common practice in hypothesis formation in science; moreover, Peirce argues it is a ubiquitous aspect of thought:
Looking out my window this lovely spring morning, I see an azalea in full bloom. No, no! I don't see that; though that is the only way I can describe what I see. That is a proposition, a sentence, a fact; but what I perceive is not proposition, sentence, fact, but only an image, which I make intelligible in part by means of a statement of fact. This statement is abstract; but what I see is concrete. I perform an abduction when I so much as express in a sentence anything I see. The truth is that the whole fabric of our knowledge is one matted felt of pure hypothesis confirmed and refined by induction. Not the smallest advance can be made in knowledge beyond the stage of vacant staring, without making an abduction at every step.^{[9]}
It was Peirce's own maxim that "Facts cannot be explained by a hypothesis more extraordinary than these facts themselves; and of various hypotheses the least extraordinary must be adopted."^{[10]} After obtaining results from an inference procedure, we may be left with multiple assumptions, some of which may be contradictory. Abductive validation is a method for identifying the assumptions that will lead to your goal.
Probabilistic abduction
Probabilistic abductive reasoning is a form of abductive validation, and is used extensively in areas where conclusions about possible hypotheses need to be derived, such as for making diagnoses from medical tests. For example, a pharmaceutical company that develops a test for a particular infectious disease will typically determine the reliability of the test by hiring a group of infected and a group of noninfected people to undergo the test. Assume the statements $x$: "Positive test", $\backslash overline\{x\}$: "Negative test", $y$: "Infected", and $\backslash overline\{y\}$: "Not infected". The result of these trials will then determine the reliability of the test in terms of its sensitivity $p(xy)$ and false positive rate $p(x\backslash overline\{y\})$. The interpretations of the conditionals are: $p(xy)$: "The probability of positive test given infection", and $p(x\backslash overline\{y\})$: "The probability of positive test in the absence of infection". The problem with applying these conditionals in a practical setting is that they are expressed in the opposite direction to what the practitioner needs. The conditionals needed for making the diagnosis are: $p(yx)$: "The probability of infection given positive test", and $p(y\backslash overline\{x\})$: "The probability of infection given negative test". The probability of infection could then have been conditionally deduced as $p(y\backslash x)\; =\; p(x)p(yx)\; +\; p(\backslash overline\{x\})p(y\backslash overline\{x\})$, where "$\backslash $" denotes conditional deduction. Unfortunately the required conditionals are usually not directly available to the medical practitioner, but they can be obtained if the base rate of the infection in the population is known.
The required conditionals can be correctly derived by inverting the available conditionals using Bayes rule. The inverted conditionals are obtained as follows:
$\backslash begin\{cases\}\; p(xy)\; =\; \backslash frac\{p(x\backslash land\; y)\}\{p(y)\}\backslash \backslash \; p(yx)\; =\; \backslash frac\{p(x\backslash land\; y)\}\{p(x)\}\; \backslash end\{cases\}\; \backslash ;\backslash ;\backslash Rightarrow\; \backslash ;\backslash ;\backslash ;\backslash ;\; p(yx)\; =\; \backslash frac\{p(y)p(xy)\}\{p(x)\}\backslash ;.$
The term $p(y)$ on the right hand side of the equation expresses the base rate of
the infection in the population. Similarly, the term $p(x)$ expresses the default likelihood of
positive test on a random person in the population. In the expressions below
$a(y)$ and $a(\backslash overline\{y\})=1a(y)$ denote the base rates of $y$ and its complement $\backslash overline\{y\}$ respectively, so that e.g. $p(x)\; =\; a(y)p(xy)\; +\; a(\backslash overline\{y\})p(x\backslash overline\{y\})$. The full expression for the required
conditionals $p(yx)$ and $p(y\backslash overline\{x\})$ are then:
$\backslash begin\{cases\}\; p(yx)\; =\; \backslash frac\{a(y)p(xy)\}\{a(y)p(xy)\; +\; a(\backslash overline\{y\})p(x\backslash overline\{y\})\}\backslash \backslash \; p(y\backslash overline\{x\})\; =\; \backslash frac\{a(y)p(\backslash overline\{x\}y)\}\{a(y)p(\backslash overline\{x\}y)\; +\; a(\backslash overline\{y\})p(\backslash overline\{x\}\backslash overline\{y\})\}\; \backslash end\{cases\}$
The full expression for the conditionally abduced probability of infection in a tested person, expressed as $p(y\backslash overline\{\backslash \}x)$, given the outcome of the test, the base rate of the infection, as well as the test's sensitivity and false positive rate, is then given by:
$p(y\backslash overline\{\backslash \}x)\; =\; p(x)\backslash left(\backslash frac\{a(y)p(xy)\}\{a(y)p(xy)\; +\; a(\backslash overline\{y\})p(x\backslash overline\{y\})\}\backslash right)\; +\; p(\backslash overline\{x\})\backslash left(\backslash frac\{a(y)p(\backslash overline\{x\}y)\}\{a(y)p(\backslash overline\{x\}y)\; +\; a(\backslash overline\{y\})p(\backslash overline\{x\}\backslash overline\{y\})\}\backslash right)$.
Probabilistic abduction can thus be described as a method for inverting conditionals in order to apply probabilistic deduction.
A medical test result is typically considered positive or negative, so
when applying the above equation it can be assumed that
either $p(x)\; =\; 1$ (positive) or $p(\backslash overline\{x\})\; =\; 1$ (negative). In
case the patient tests positive, the above equation can be
simplified to $p(y\backslash overline\{\backslash \}x)\; =\; p(yx)$ which
will give the correct likelihood that the patient actually is infected.
The Base rate fallacy in medicine,^{[11]} or the Prosecutor's fallacy^{[12]} in legal reasoning, consists of making the erroneous assumption that $p(yx)\; =\; p(xy)$. While this reasoning error often can produce a relatively good approximation of the correct hypothesis probability value, it can lead to a completely wrong result and wrong conclusion in case the base rate is very low and the reliability of the test is not perfect. An extreme example of the base rate fallacy is to conclude that a male person is pregnant just because he tests positive in a pregnancy test. Obviously, the base rate of male pregnancy is zero, and assuming that the test is not perfect, it would be correct to conclude that the male person is not pregnant.
The expression for probabilistic abduction can be generalised to multinomial cases,^{[13]} i.e., with a state space $X$ of multiple $x\_\{i\}$ and a state space $Y$ of multiple states $y\_\{j\}$.
Subjective logic abduction
Subjective logic generalises probabilistic logic by including parameters for uncertainty in the input arguments. Abduction in subjective logic is thus similar to probabilistic abduction described above.^{[13]} The input arguments in subjective logic are composite functions called subjective opinions which can be binomial when the opinion applies to a single proposition or multinomial when it applies to a set of propositions. A multinomial opinion thus applies to a frame $X\backslash ,\backslash !$ (i.e. a state space of exhaustive and mutually disjoint propositions $x\_i\backslash ,\backslash !$), and is denoted by the composite
function $\backslash omega\_\{X\}=(\backslash vec\{b\},\; u,\; \backslash vec\{a\})\backslash ,\backslash !$, where $\backslash vec\{b\}\backslash ,\backslash !$ is a vector of belief masses over the propositions of $X\backslash ,\backslash !$, $u\backslash ,\backslash !$ is the uncertainty mass, and $\backslash vec\{a\}\backslash ,\backslash !$ is a vector of base rate values over the propositions of $X\backslash ,\backslash !$. These components satisfy $u+\backslash sum\; \backslash vec\{b\}(x\_i)\; =\; 1\backslash ,\backslash !$ and $\backslash sum\; \backslash vec\{a\}(x\_i)\; =\; 1\backslash ,\backslash !$ as well as $\backslash vec\{b\}(x\_i),u,\backslash vec\{a\}(x\_i)\; \backslash in\; [0,1]\backslash ,\backslash !$.
Assume the frames $X$ and $Y$, the sets of conditional opinions $\backslash omega\_\{XY\}$ and $\backslash omega\_\{X\backslash overline\{Y\}\}$, the opinion $\backslash omega\_\{X\}$ on $X$, and the base rate function $a\_\{Y\}$ on $Y$. Based on these parameters, subjective logic provides a method for deriving the set of inverted conditionals $\backslash omega\_\{YX\}$ and $\backslash omega\_\{Y\backslash overline\{X\}\}$. Using these inverted conditionals, subjective logic also provides a method for deduction. Abduction in subjective logic consists of inverting the conditionals and then applying deduction.
The symbolic notation for conditional abduction is "$\backslash overline\{\backslash \}$", and the operator itself is denoted as $\backslash overline\{\backslash circledcirc\}$. The expression for subjective logic abduction is then:^{[13]}
$\backslash omega\_\{Y\backslash overline\{\backslash \}X\}=\backslash omega\_\{X\}\backslash ;\backslash overline\{\backslash circledcirc\}\backslash ;\; (\backslash omega\_\{XY\},\backslash omega\_\{X\backslash overline\{Y\}\},a\_\{Y\})\backslash ,\backslash !$.
The advantage of using subjective logic abduction compared to probabilistic abduction is that uncertainty about the probability values of the input arguments can be explicitly expressed and taken into account during the analysis. It is thus possible to perform abductive analysis in the presence of missing or incomplete input evidence, which normally results in degrees of uncertainty in the output conclusions.
History
The philosopher Charles Sanders Peirce (//; 1839–1914) introduced abduction into modern logic. Over the years he called such inference hypothesis, abduction, presumption, and retroduction. He considered it a topic in logic as a normative field in philosophy, not in purely formal or mathematical logic, and eventually as a topic also in economics of research.
As two stages of the development, extension, etc., of a hypothesis in scientific inquiry, abduction and also induction are often collapsed into one overarching concept — the hypothesis. That is why, in the scientific method pioneered by Galileo and Bacon, the abductive stage of hypothesis formation is conceptualized simply as induction. Thus, in the twentieth century this collapse was reinforced by Karl Popper's explication of the hypotheticodeductive model, where the hypothesis is considered to be just "a guess"^{[14]} (in the spirit of Peirce). However, when the formation of a hypothesis is considered the result of a process it becomes clear that this "guess" has already been tried and made more robust in thought as a necessary stage of its acquiring the status of hypothesis. Indeed many abductions are rejected or heavily modified by subsequent abductions before they ever reach this stage.
Before 1900, Peirce treated abduction as the use of a known rule to explain an observation, e.g., it is a known rule that if it rains the grass is wet; so, to explain the fact that the grass is wet; one infers that it has rained. This remains the common use of the term "abduction" in the social sciences and in artificial intelligence.
Peirce consistently characterized it as the kind of inference that originates a hypothesis by concluding in an explanation, though an unassured one, for some very curious or surprising (anomalous) observation stated in a premise. As early as 1865 he wrote that all conceptions of cause and force are reached through hypothetical inference; in the 1900s he wrote that all explanatory content of theories is reached through abduction. In other respects Peirce revised his view of abduction over the years.^{[15]}
In later years his view came to be:
 Abduction is guessing.^{[2]} It is "very little hampered" by rules of logic.^{[3]} Even a wellprepared mind's individual guesses are oftener wrong than right.^{[16]} But the success of our guesses far exceeds that of random luck and seems born of attunement to nature by instinct^{[17]} (some speak of intuition in such contexts^{[18]}).
 Abduction guesses a new or outside idea so as to account in a plausible, instinctive, economical way for a surprising or very complicated phenomenon. That is its proximate aim.^{[17]}
 Its longer aim is to economize inquiry itself. Its rationale is inductive: it works often enough, is the only source of new ideas, and has no substitute in expediting the discovery of new truths.^{[19]} Its rationale especially involves its role in coordination with other modes of inference in inquiry. It is inference to explanatory hypotheses for selection of those best worth trying.
 Pragmatism is the logic of abduction. Upon the generation of an explanation (which he came to regard as instinctively guided), the pragmatic maxim gives the necessary and sufficient logical rule to abduction in general. The hypothesis, being insecure, needs to have conceivable^{[20]} implications for informed practice, so as to be testable^{[21]}^{[22]} and, through its trials, to expedite and economize inquiry. The economy of research is what calls for abduction and governs its art.^{[5]}
Writing in 1910, Peirce admits that "in almost everything I printed before the beginning of this century I more or less mixed up hypothesis and induction" and he traces the confusion of these two types of reasoning to logicians' too "narrow and formalistic a conception of inference, as necessarily having formulated judgments from its premises."^{[23]}
He started out in the 1860s treating hypothetical inference in a number of ways which he eventually peeled away as inessential or, in some cases, mistaken:
 as inferring the occurrence of a character (a characteristic) from the observed combined occurrence of multiple characters which its occurrence would necessarily involve;^{[24]} for example, if any occurrence of A is known to necessitate occurrence of B, C, D, E, then the observation of B, C, D, E suggests by way of explanation the occurrence of A. (But by 1878 he no longer regarded such multiplicity as common to all hypothetical inference.^{[25]})
 as aiming for a more or less probable hypothesis (in 1867 and 1883 but not in 1878; anyway by 1900 the justification is not probability but the lack of alternatives to guessing and the fact that guessing is fruitful;^{[26]} by 1903 he speaks of the "likely" in the sense of nearing the truth in an "indefinite sense";^{[27]} by 1908 he discusses plausibility as instinctive appeal.^{[17]}) In a paper dated by editors as circa 1901, he discusses "instinct" and "naturalness", along with the kind of considerations (low cost of testing, logical caution, breadth, and incomplexity) that he later calls methodeutical.^{[28]}
 as induction from characters (but as early as 1900 he characterized abduction as guessing^{[26]})
 as citing a known rule in a premise rather than hypothesizing a rule in the conclusion (but by 1903 he allowed either approach^{[3]}^{[29]})
 as basically a transformation of a deductive categorical syllogism^{[25]} (but in 1903 he offered a variation on modus ponens instead,^{[3]} and by 1911 he was unconvinced that any one form covers all hypothetical inference^{[4]}).
1867
In 1867, in "The Natural Classification of Arguments",^{[24]} hypothetical inference always deals with a cluster of characters (call them P′, P′′, P′′′, etc.) known to occur at least whenever a certain character (M) occurs. (Note that categorical syllogisms have elements traditionally called middles, predicates, and subjects. For example: All men [middle] are mortal [predicate]; Socrates [subject] is a man [middle]; ergo Socrates [subject] is mortal [predicate]". Below, 'M' stands for a middle; 'P' for a predicate; 'S' for a subject. Note also that Peirce held that all deduction can be put into the form of the categorical syllogism Barbara (AAA).)
[Deduction].
[Any] M is P
[Any] S is M
$\backslash therefore$ [Any] S is P.

Induction.
S′, S′′, S′′′, &c. are taken at random as M's;
S′, S′′, S′′′, &c. are P:
$\backslash therefore$ Any M is probably P.

Hypothesis.
Any M is, for instance, P′, P′′, P′′′, &c.;
S is P′, P′′, P′′′, &c.:
$\backslash therefore$ S is probably M.

1878
In 1878, in "Deduction, Induction, and Hypothesis",^{[25]} there is no longer a need for multiple characters or predicates in order for an inference to be hypothetical, although it is still helpful. Moreover Peirce no longer poses hypothetical inference as concluding in a probable hypothesis. In the forms themselves, it is understood but not explicit that induction involves random selection and that hypothetical inference involves response to a "very curious circumstance". The forms instead emphasize the modes of inference as rearrangements of one another's propositions (without the bracketed hints shown below).
Deduction.
Rule: All the beans from this bag are white.
Case: These beans are from this bag.
$\backslash therefore$ Result: These beans are white.

Induction.
Case: These beans are [randomly selected] from this bag.
Result: These beans are white.
$\backslash therefore$ Rule: All the beans from this bag are white.

Hypothesis.
Rule: All the beans from this bag are white.
Result: These beans [oddly] are white.
$\backslash therefore$ Case: These beans are from this bag.

1883
Peirce long treated abduction in terms of induction from characters or traits (weighed, not counted like objects), explicitly so in his influential 1883 "A Theory of Probable Inference", in which he returns to involving probability in the hypothetical conclusion.^{[30]} Like "Deduction, Induction, and Hypothesis" in 1878, it was widely read (see the historical books on statistics by Stephen Stigler), unlike his later amendments of his conception of abduction. Today abduction remains most commonly understood as induction from characters and extension of a known rule to cover unexplained circumstances.
1902 and after
In 1902 Peirce wrote that he now regarded the syllogistical forms and the doctrine of extension and comprehension (i.e., objects and characters as referenced by terms), as being less fundamental than he had earlier thought.^{[31]} In 1903 he offered the following form for abduction:^{[3]}
The surprising fact, C, is observed;
 But if A were true, C would be a matter of course,
 Hence, there is reason to suspect that A is true.
The hypothesis is framed, but not asserted, in a premise, then asserted as rationally suspectable in the conclusion. Thus, as in the earlier categorical syllogistic form, the conclusion is formulated from some premise(s). But all the same the hypothesis consists more clearly than ever in a new or outside idea beyond what is known or observed. Induction in a sense goes beyond observations already reported in the premises, but it merely amplifies ideas already known to represent occurrences, or tests an idea supplied by hypothesis; either way it requires previous abductions in order to get such ideas in the first place. Induction seeks facts to test a hypothesis; abduction seeks a hypothesis to account for facts.
Note that the hypothesis ("A") could be of a rule. It need not even be a rule strictly necessitating the surprising observation ("C"), which needs to follow only as a "matter of course"; or the "course" itself could amount to some known rule, merely alluded to, and also not necessarily a rule of strict necessity. In the same year, Peirce wrote that reaching a hypothesis may involve placing a surprising observation under either a newly hypothesized rule or a hypothesized combination of a known rule with a peculiar state of facts, so that the phenomenon would be not surprising but instead either necessarily implied or at least likely.^{[29]}
Peirce did not remain quite convinced about any such form as the categorical syllogistic form or the 1903 form. In 1911, he wrote, "I do not, at present, feel quite convinced that any logical form can be assigned that will cover all "Retroductions". For what I mean by a Retroduction is simply a conjecture which arises in the mind."^{[4]}
Pragmatism
In 1901 Peirce wrote, "There would be no logic in imposing rules, and saying that they ought to be followed, until it is made out that the purpose of hypothesis requires them."^{[32]} In 1903 Peirce called pragmatism "the logic of abduction" and said that the pragmatic maxim gives the necessary and sufficient logical rule to abduction in general.^{[22]} The pragmatic maxim is: "Consider what effects, that might conceivably have practical bearings, we conceive the object of our conception to have. Then, our conception of these effects is the whole of our conception of the object." It is a method for fruitful clarification of conceptions by equating the meaning of a conception with the conceivable practical implications of its object's conceived effects. Peirce held that that is precisely tailored to abduction's purpose in inquiry, the forming of an idea that could conceivably shape informed conduct. In various writings in the 1900s^{[5]}^{[33]} he said that the conduct of abduction (or retroduction) is governed by considerations of economy, belonging in particular to the economics of research. He regarded economics as a normative science whose analytic portion might be part of logical methodeutic (that is, theory of inquiry).^{[34]}
Three levels of logic about abduction
Peirce came over the years to divide (philosophical) logic into three departments:
 Stechiology, or speculative grammar, on the conditions for meaningfulness. Classification of signs (semblances, symptoms, symbols, etc.) and their combinations (as well as their objects and interpretants).
 Logical critic, or logic proper, on validity or justifiability of inference, the conditions for true representation. Critique of arguments in their various modes (deduction, induction, abduction).
 Methodeutic, or speculative rhetoric, on the conditions for determination of interpretations. Methodology of inquiry in its interplay of modes.
Peirce had, from the start, seen the modes of inference as being coordinated together in scientific inquiry and, by the 1900s, held that hypothetical inference in particular is inadequately treated at the level of critique of arguments.^{[21]}^{[22]} To increase the assurance of a hypothetical conclusion, one needs to deduce implications about evidence to be found, predictions which induction can test through observation so as to evaluate the hypothesis. That is Peirce's outline of the scientific method of inquiry, as covered in his inquiry methodology, which includes pragmatism or, as he later called it, pragmaticism, the clarification of ideas in terms of their conceivable implications regarding informed practice.
Classification of signs
As early as 1866,^{[35]} Peirce held that:
1. Hypothesis (abductive inference) is inference through an icon (also called a likeness).
2. Induction is inference through an index (a sign by factual connection); a sample is an index of the totality from which it is drawn.
3. Deduction is inference through a symbol (a sign by interpretive habit irrespective of resemblance or connection to its object).
In 1902, Peirce wrote that, in abduction: "It is recognized that the phenomena are like, i.e. constitute an Icon of, a replica of a general conception, or Symbol."^{[36]}
Critique of arguments
At the critical level Peirce examined the forms of abductive arguments (as discussed above), and came to hold that the hypothesis should economize explanation for plausibility in terms of the feasible and natural. In 1908 Peirce described this plausibility in some detail.^{[17]} It involves not likeliness based on observations (which is instead the inductive evaluation of a hypothesis), but instead optimal simplicity in the sense of the "facile and natural", as by Galileo's natural light of reason and as distinct from "logical simplicity" (Peirce does not dismiss logical simplicity entirely but sees it in a subordinate role; taken to its logical extreme it would favor adding no explanation to the observation at all). Even a wellprepared mind guesses oftener wrong than right, but our guesses succeed better than random luck at reaching the truth or at least advancing the inquiry, and that indicates to Peirce that they are based in instinctive attunement to nature, an affinity between the mind's processes and the processes of the real, which would account for why appealingly "natural" guesses are the ones that oftenest (or least seldom) succeed; to which Peirce added the argument that such guesses are to be preferred since, without "a natural bent like nature's", people would have no hope of understanding nature. In 1910 Peirce made a threeway distinction between probability, verisimilitude, and plausibility, and defined plausibility with a normative "ought": "By plausibility, I mean the degree to which a theory ought to recommend itself to our belief independently of any kind of evidence other than our instinct urging us to regard it favorably."^{[37]} For Peirce, plausibility does not depend on observed frequencies or probabilities, or on verisimilitude, or even on testability, which is not a question of the critique of the hypothetical inference as an inference, but rather a question of the hypothesis's relation to the inquiry process.
The phrase "inference to the best explanation" (not used by Peirce but often applied to hypothetical inference) is not always understood as referring to the most simple and natural. However, in other senses of "best", such as "standing up best to tests", it is hard to know which is the best explanation to form, since one has not tested it yet. Still, for Peirce, any justification of an abductive inference as good is not completed upon its formation as an argument (unlike with induction and deduction) and instead depends also on its methodological role and promise (such as its testability) in advancing inquiry.^{[21]}^{[22]}^{[38]}
Methodology of inquiry
At the methodeutical level Peirce held that a hypothesis is judged and selected^{[21]} for testing because it offers, via its trial, to expedite and economize the inquiry process itself toward new truths, first of all by being testable and also by further economies,^{[5]} in terms of cost, value, and relationships among guesses (hypotheses). Here, considerations such as probability, absent from the treatment of abduction at the critical level, come into play. For examples:
 Cost: A simple but lowodds guess, if low in cost to test for falsity, may belong first in line for testing, to get it out of the way. If surprisingly it stands up to tests, that is worth knowing early in the inquiry, which otherwise might have stayed long on a wrong though seemingly likelier track.
 Value: A guess is intrinsically worth testing if it has instinctual plausibility or reasoned objective probability, while subjective likelihood, though reasoned, can be treacherous.
 Interrelationships: Guesses can be chosen for trial strategically for their
 caution, for which Peirce gave as example the game of Twenty Questions,
 breadth of applicability to explain various phenomena, and
 incomplexity, that of a hypothesis that seems too simple but whose trial "may give a good 'leave,' as the billiardplayers say", and be instructive for the pursuit of various and conflicting hypotheses that are less simple.^{[39]}
Other writers
Norwood Russell Hanson, a philosopher of science, wanted to grasp a logic explaining how scientific discoveries take place. He used Peirce's notion of abduction for this.^{[40]}
Further development of the concept can be found in Peter Lipton's Inference to the Best Explanation (Lipton, 1991).
Applications
Applications in artificial intelligence include fault diagnosis, belief revision, and automated planning. The most direct application of abduction is that of automatically detecting faults in systems: given a theory relating faults with their effects and a set of observed effects, abduction can be used to derive sets of faults that are likely to be the cause of the problem.
Abduction can also be used to model automated planning.^{[41]} Given a logical theory relating action occurrences with their effects (for example, a formula of the event calculus), the problem of finding a plan for reaching a state can be modeled as the problem of abducting a set of literals implying that the final state is the goal state.
In intelligence analysis, Analysis of Competing Hypotheses and Bayesian networks, probabilistic abductive reasoning is used extensively. Similarly in medical diagnosis and legal reasoning, the same methods are being used, although there have been many examples of errors, especially caused by the base rate fallacy and the prosecutor's fallacy.
Belief revision, the process of adapting beliefs in view of new information, is another field in which abduction has been applied. The main problem of belief revision is that the new information may be inconsistent with the corpus of beliefs, while the result of the incorporation cannot be inconsistent. This process can be done by the use of abduction: once an explanation for the observation has been found, integrating it does not generate inconsistency. This use of abduction is not straightforward, as adding propositional formulae to other propositional formulae can only make inconsistencies worse. Instead, abduction is done at the level of the ordering of preference of the possible worlds. Preference models use fuzzy logic or utility models.
In the philosophy of science, abduction has been the key inference method to support scientific realism, and much of the debate about scientific realism is focused on whether abduction is an acceptable method of inference.
In historical linguistics, abduction during language acquisition is often taken to be an essential part of processes of language change such as reanalysis and analogy.^{[42]}
In anthropology, Alfred Gell in his influential book Art and Agency defined abduction, (after Eco^{[43]}) as "a case of synthetic inference 'where we find some very curious circumstances, which would be explained by the supposition that it was a case of some general rule, and thereupon adopt that supposition".^{[44]} Gell criticizes existing 'anthropological' studies of art, for being too preoccupied with aesthetic value and not preoccupied enough with the central anthropological concern of uncovering 'social relationships,' specifically the social contexts in which artworks are produced, circulated, and received.^{[45]} Abduction is used as the mechanism for getting from art to agency. That is, abduction can explain how works of art inspire a sensus communis: the commonlyheld views shared by members that characterize a given society.^{[46]} The question Gell asks in the book is, 'how does it initially 'speak' to people?' He answers by saying that "No reasonable person could suppose that artlike relations between people and things do not involve at least some form of semiosis."^{[44]} However, he rejects any intimation that semiosis can be thought of as a language because then he would have to admit to some preestablished existence of the sensus communis that he wants to claim only emerges afterwards out of art. Abduction is the answer to this conundrum because the tentative nature of the abduction concept (Peirce likened it to guessing) means that not only can it operate outside of any preexisting framework, but moreover, it can actually intimate the existence of a framework. As Gell reasons in his analysis, the physical existence of the artwork prompts the viewer to perform an abduction that imbues the artwork with intentionality. A statue of a goddess, for example, in some senses actually becomes the goddess in the mind of the beholder; and represents not only the form of the deity but also her intentions (which are adduced from the feeling of her very presence). Therefore through abduction, Gell claims that art can have the kind of agency that plants the seeds that grow into cultural myths. The power of agency is the power to motivate actions and inspire ultimately the shared understanding that characterizes any given society.^{[46]}
See also
References
 This article is based on material taken from the Free Online Dictionary of Computing prior to 1 November 2008 and incorporated under the "relicensing" terms of the GFDL, version 1.3 or later.
 Awbrey, Jon, and Awbrey, Susan (1995), "Interpretation as Action: The Risk of Inquiry", Inquiry: Critical Thinking Across the Disciplines, 15, 4052. Eprint
 Cialdea Mayer, Marta and Pirri, Fiora (1993) "First order abduction via tableau and sequent calculi" Logic Jnl IGPL 1993 1: 99117; Oxford Journals
 Cialdea Mayer, Marta and Pirri, Fiora (1995) "Propositional Abduction in Modal Logic", Logic Jnl IGPL 1995 3: 907919; Oxford Journals
 Edwards, Paul (1967, eds.), "The Encyclopedia of Philosophy," Macmillan Publishing Co, Inc. & The Free Press, New York. Collier Macmillan Publishers, London.
 Eiter, T., and Gottlob, G. (1995), "The Complexity of LogicBased Abduction, Journal of the ACM, 42.1, 342.
 Hanson, N. R. (1958). Patterns of Discovery: An Inquiry into the Conceptual Foundations of Science, Cambridge: Cambridge University Press. ISBN 9780521092616.
 Harman, Gilbert (1965). "The Inference to the Best Explanation," The Philosophical Review 74:1, 8895.
 Josephson, John R., and Josephson, Susan G. (1995, eds.), Abductive Inference: Computation, Philosophy, Technology, Cambridge University Press, Cambridge, UK.
 Lipton, Peter. (2001). Inference to the Best Explanation, London: Routledge. ISBN 0415242029.
 McKaughan, Daniel J. (2008), "From Ugly Duckling to Swan: C. S. Peirce, Abduction, and the Pursuit of Scientific Theories", Transactions of the Charles S. Peirce Society, v. 44, no. 3 (summer), 446–468. Abstract.
 Menzies, T. (1996), "Applications of Abduction: KnowledgeLevel Modeling, International Journal of HumanComputer Studies, 45.3, 305335.
 Queiroz, Joao & Merrell, Floyd (guest eds.). (2005). "Abduction  between subjectivity and objectivity". (special issue on abductive inference) Semiotica 153 (1/4). [1].
 Santaella, Lucia (1997) "The Development of Peirce's Three Types of Reasoning: Abduction, Deduction, and Induction", 6th Congress of the Eprint.
 Sebeok, T. (1981) "You Know My Method." In Sebeok, T. "The Play of Musement." Indiana. Bloomington, IA.
 Yu, Chong Ho (1994), "Is There a Logic of Exploratory Data Analysis?", Annual Meeting of American Educational Research Association, New Orleans, LA, April, 1994. Website of Dr. Chong Ho (Alex) Yu
Notes
 R. Josephson, J. & G. Josephson, S. "Abductive Inference: Computation, Philosophy, Technology" Cambridge University Press, New York & Cambridge (U.K.). viii þ 306 pages. Hard cover (1994), ISBN 0521434610, Paperback (1996), ISBN 0521575451.
 Bunt, H. & Black, W. "Abduction, Belief and Context in Dialogue: Studies in Computational Pragmatics" (Natural Language Processing, 1.) John Benjamins, Amsterdam & Philadelphia, 2000. vi þ 471 pages. Hard cover, ISBN 9027249830 (Europe),
1586197942 (U.S.)
 "PAP" ["Prolegomena to an Apology for Pragmatism"], MS 293 c. 1906, New Elements of Mathematics v. 4, pp. 319320.
 A Letter to F. A. Woods (1913), Collected Papers v. 8, paragraphs 385388.
(See under "Retroduction" at Commens Dictionary of Peirce's Terms.)
External links
 Stanford Encyclopedia of Philosophy
 Template:InPho
 Template:PhilPapers
 "Former webpage via the Wayback Machine.)
 "Stanford Encyclopedia of Philosophy.
 "Martin Ryder.
 home page via Google translation.
 "Thomas Sebeok with Jean UmikerSebeok, from The Play of Musement, Thomas Sebeok, Bloomington, Indiana: Indiana University Press, pp. 17–52.
 Commens Dictionary of Peirce's Terms, Mats Bergman and Sami Paavola, editors, Helsinki U. Peirce's own definitions, often many per term across the decades. There, see "Hypothesis [as a form of reasoning]", "Abduction", "Retroduction", and "Presumption [as a form of reasoning]".


 Overview 

 Academic areas  

 Foundations  


                

This article was sourced from Creative Commons AttributionShareAlike License; additional terms may apply. World Heritage Encyclopedia content is assembled from numerous content providers, Open Access Publishing, and in compliance with The Fair Access to Science and Technology Research Act (FASTR), Wikimedia Foundation, Inc., Public Library of Science, The Encyclopedia of Life, Open Book Publishers (OBP), PubMed, U.S. National Library of Medicine, National Center for Biotechnology Information, U.S. National Library of Medicine, National Institutes of Health (NIH), U.S. Department of Health & Human Services, and USA.gov, which sources content from all federal, state, local, tribal, and territorial government publication portals (.gov, .mil, .edu). Funding for USA.gov and content contributors is made possible from the U.S. Congress, EGovernment Act of 2002.
Crowd sourced content that is contributed to World Heritage Encyclopedia is peer reviewed and edited by our editorial staff to ensure quality scholarly research articles.
By using this site, you agree to the Terms of Use and Privacy Policy. World Heritage Encyclopedia™ is a registered trademark of the World Public Library Association, a nonprofit organization.