2002: Models of Abductive Reasoning

Journal of Logic and Computation, 1991

Synthese, 1988

There never was a sounder logical maxim of scientific procedure than Ockham's razor: Entia non sunt multiplicanda praeter necessitatem. That is to say; before you try a complicated hypothesis, you should make quite sure that no simplification of it will explain the facts equally well. No matter if it takes fifty generations of arduous experimentation to explode the simpler hypothesis, and no matter how incredible it may seem that that simpler hypothesis should suffice, still fifty generations are nothing in the life of science, which has all time before it …. But you will mark the limitation of my approval of Ockham's razor. It is a sound maxim of scientific procedure. If the question be what one ought to believe, the logic of the situation must take other factors into account. Charles Sanders Peirce, Harvard Lectures on Pragmatism, Lecture II, 1903. Abstract I will analyse some properties of abduction that are essential from a logical standpoint. When dealing with the so-called 'inferential problem', I will opt for the more general concepts of input and output instead of those of premisses and conclusions, and show that in this framework two consequences can be derived that help clarify basic logical aspects of abductive reasoning: (i) it is more natural to accept the 'multimodal' and 'context-dependent' character of the inferences involved, (ii) inferences are not merely conceived of in the terms of the process leading to the 'generation of an output' or to the proof of it, as in the traditional and standard view of deductive proofs, but rather, from this perspective abductive inferences can be seen as related to logical processes in which input and output fail to hold each other in an expected relation, with the solution involving the modification of inputs, not that of outputs. I will also describe that if we wish to naturalize the logic of the abductive processes and its special consequence relation, we should refer to the following main aspects: 'optimization of situatedness', 'maximization of changeability' of both input and output, and high 'information-sensitiveness'. 1 Abductive Inferences: Input and Output vs. Premisses and Conclusions From the perspective of the EC-Model of abduction, 1 the available logical views concerning abduction have to be reconsidered. By now this fact is acknowledged by some logicians. Estrada-González [10, p. 182], e.g. by referring to the properties called consistency, minimality, etc. stated in the AKM-schema 2 usefully notes: We think that overemphasizing the characteristics of abduction as it occurs in scientific practices and daily life scenarios has led to overlook some features that abduction in those circumstances shares with other phenomena in which some given outputs fail to stand in a certain relation with some given inputs and thus a modification on those inputs is in order. Indeed, abduction in an eco-cognitive perspective is not circumscribed by indicating particular requirements of each particular field of application, such as scientific discovery, diagnosis, machine 1 I have described my eco-cognitive model of abduction (EC-model) in the recent [21, 23]. 2 I have illustrated the classical and so-called AKM-model of abduction in [19].

Peirce's novel, post-1903 interrogative construal of abductive logic is studied in terms of a dynamic approach. The information flow in abduction is interpreted by a dynamic mechanism over ranges. An abductive logic is proposed that analyzes conjecture-making in scientific discovery. It explains how the updates on models representing the inquirers' information proceed when they are faced with new surprising facts. The formal semantics for this logic is given by neighborhood models. We apply our abductive logic to an analysis of some examples of scientific discovery. We thank the two anonymous reviewers of this journal for the abductive feedback that has contributed so much to the improvement of the present paper. We also thank the audience of the 15th Congress of Logic, Methodology and Philosophy of Science held in Helsinki, August 2015, in which a part of the present paper was presented in a special symposium on Pragmati(ci)st Philosophy of Science: Old and New organized by Chiara Ambrosio. * This work is supported by China National Fund of Humanities and Social Sciences (grant no. 14ZDB016).

Teorema Revista Internacional De Filosofia, 2014

RESUMEN Jaakko Hintikka es uno de los más importantes filósofos analíticos de la segunda mitad del siglo XX. Su impacto se percibe fácilmente en muchas ramas de la epistemología actual. Este artículo analiza su distinguida concepción de la información en la lógica deductiva (información 1). También se describe el modo en que articula su distinción entre información 1 de superficie e información 1 de profundidad. Mi objetivo es arrojar luz sobre un dilema que el propio Hintikka provocativamente denomina "un escándalo de la deducción". El enfoque que se presenta se centra en el concepto de prueba epistémica y en un concepto diferente de información (información 2) postulado por la lógica info-teorética tradicional. De este modo se distinguen dos sentidos de 'información' presentes en los estudios lógicos señalándose su naturaleza básicamente complementaria en la explicación de los diversos aspectos involucrados en la práctica deductiva.

Artificial Intelligence in Medicine, 2002

Researchers in arti®cial intelligence (AI) should be aware of Philosophy of Science as a sub-discipline of modern academic philosophy that has deep traditions and lively ongoing research in analyzing Knowledge and its tangled relationships with Experience and Reason. Think``Reason Intelligence''. A lot of good and careful thinking has gone in Philosophy of Science and a general awareness and openness to it may allow AI researcher to avoid the pitfalls of overly simple models of reasoning. Philosophers of science know a lot about the limitations of various formalisms for representing knowledge, inference and scienti®c reasoning. Reciprocally, by this time many modern philosophers of science have been in¯uenced by developments in AI, as amply illustrated by Professor Magnani's recent book Abduction, Reason and Science. The author describes the intent of the book as follows:``this volume explores abduction (inference to explanatory hypotheses), an important but neglected topic in scienti®c reasoning. My aim is to integrate philosophical, cognitive and computational issues, while also discussing some cases of reasoning in science and medicine'' (preface). Accordingly, the book takes the form of an extended discussion, making connections, gathering opinions and organizing opinions according to issues, but not particularly providing solutions, or introducing new theoretical ideas. Professor Magnani has, indeed, succeeded in gathering together good ideas from many places including philosophy, AI, cognitive psychology and the analysis of medical reasoning. The book mainly consists of quotations and paraphrases of the works of many authors, stitched together by the writings of Professor Magnani. By picking relevant ideas from broad sources, Professor Magnani succeeds in showing the centrality of abduction to scienti®c reasoning, as well as to medical reasoning, and at least implicitly, to such areas as accident investigations, perception, language understanding and common sense reasoning about causes and effects. Chapter 1 discusses the``meno problem'' from Plato's dialog of that name. From the mouth of Socrates, the core of the problem is,``for he (a man) cannot inquire about what he knows, because he knows it, and in that case is in no need of inquiry, nor again can he inquire about what he does not know, since he does not know about what he is to inquire'' (as quoted page 7±8). Although, the title of this chapter is Hypothesis Generation, its theme seems rather to concern the impulse to hypothesis generation, and``Doubt'' or``Puzzlement'' might have been a more descriptive title. Chapter 2 introduces the idea of``Theoretical Abduction'' which is contrasted with`M anipulative Abduction'' the topic of Chapter 3. Unfortunately, the distinction never Artificial Intelligence in Medicine 24 (2002) 257±260

FLAP, 2016

This special issue of the IfColog Journal of Logics and their Applications "Frontiers of Abduction" is based on a selection of papers concerning abduction that are situated at the crossroad of logic, epistemology, and cognitive science. The status of abduction-that Hintikka considered the fundamental problem of contemporary epistemology [7]-is very controversial. When dealing with abductive reasoning, misinterpretations and equivocations are common. What are the differences between abduction and induction? What are the differences between abduction and the well-known hypothetico-deductive method? What did Peirce mean when he considered abduction a kind of inference? Does abduction involve only the generation of hypotheses or their evaluation too? Are the criteria for the best explanation in abductive reasoning epistemic, or pragmatic, or both? How many kinds of abduction are there? The papers presented in this issue aim at increasing knowledge-logical, cognitive, epistemological-about creative and expert inferences. Analyzing various reasoning and modeling practices provides a way of specifying the nature of some abductive reasoning processes. Following [12; 14] we have to distinguish between sentential, model-based, and manipulative abduction. In the past decades many attempts have been made to model abduction by developing some formal tools in order to illustrate its computational properties and the relationships with the different forms of deductive reasoning (see, for example, [4]). Some of these formal models of abductive reasoning are based on the theory of the epistemic state of an agent [3], where the epistemic state of an individual is modeled as a consistent set of beliefs that can change by expansion and contraction (belief revision framework). These formal tools, often related to the tradition of nonmonotonic logic, logic programming, semantic tableaux, dynamic and adaptive logic, etc., are usually devoted to illustrating sentential abduction and present some limitations [13]. It may be said that the traditional logical accounts of abduction certainly illustrate much of what is important in abductive reasoning, especially the objective of selecting a set of hypotheses (diagnoses, causes) that are able to dispense good (preferred) explanations of data (observations), but fail in accounting for many cases of explanations occurring in science or in everyday reasoning. The logical papers

In the companion article “The eco-cognitive model of abduction” [66]I illustrated the main features of my eco-cognitive model of abduction (EC-Model). With the aim of delineating further aspects of that “naturalization of logic” recently urged by John Woods [94]I will now set out to further analyze some properties of abduction that are essential from a logical standpoint. When dealing with the so-called “inferential problem”, I will opt for the more general concepts of input and output instead of those of premisses and conclusions, and show that in this framework two consequences can be derived that help clarify basic logical aspects of abductive reasoning: 1) it is more natural to accept the “multimodal” and “context-dependent” character of the inferences involved, 2) inferences are not merely conceived of in the terms of the process leading to the “generation of an output” or to the proof of it, as in the traditional and standard view of deductive proofs, but rather, from this perspective abductive inferences can be seen as related to logical processes in which input and output fail to hold each other in an expected relation, with the solution involving the modification of inputs, not that of outputs. The chance of finding an abductive solution still appears to depend on the Aristotelian concept of “leading away” (ἀπαγωγή) I dealt with in the companion article, that is, on the starting of the application of a supplementary logic implementing an appropriate formal inference engine. An important result I will emphasize is that irrelevance and implausibility are not always offensive to reason. In addition, we cannot be sure, more broadly, that our guessed hypotheses are plausible (even if we know that looking – in advance – for plausibility is a human good and wise heuristic), indeed an implausible hypothesis can later on result plausible. In the last part of the article I will describe that if we wish to naturalize the logic of the abductive processes and its special consequence relation, we should refer to the following main aspects: “optimization of situatedness”, “maximization of changeability” of both input and output, and high “information-sensitiveness”. Furthermore, I will point out that a logic of abduction must acknowledge the importance of keeping record of the “past life” of abductive inferential praxes, contrarily to the fact that traditional demonstrative ideal systems are prototypically characterized by what I call “maximization of memorylessness”. ©

Handbook of Abductive Cognition , 2023

The chapter analyzes the evolution of conceptions of abduction as a logic of discovery. Peirce's original texts have provided the means for various, overlapping interpretations. N. R. Hanson's texts were crucial in defending and developing the idea that abduction can be interpreted as a logic of discovery. Many commentators since Hanson have been more hesitant on this and more in line with the logical empiricists, maintaining that discovery is not an area for logic and justification. Since Hanson it has been typical to discern a middle area in between discovery and justification, that is, the context of pursuit. Many commentators have maintained that abduction is the logic for the context of pursuit, and cannot be the logic for the context of discovery; the latter being an area for psychology or heuristics but not for logic. Others maintain that abduction can be a logic of discovery, which means rational reconstruction of heuristic processes and dynamics involved in discovery. The chapter presents arguments both for the logic of pursuit without discovery interpretation, and for various ways of developing abduction as a logic of discovery, as well as supporting the potential of interpreting abduction as a logic of discovery. Elements for these formulations come from Peirce and Hanson, according to whom abduction is a "weak" form of inference, close to guessing, is based on the use of clue-like signs, and on iconic relationships, and is aimed at reconstructing dynamic inquiry and search processes of discovery.

Studies in Applied Philosophy, Epistemology and Rational Ethics, 2012

The paper starts from the claim that every assertion of belief may be analyzed into an assertion about a counterfactual surprise of the believer in front of a dissonant knowledge. The notion of a counterfactual surprise can be usefully related to the well studied notion of Shackle's potential surprise. In §2 the author stresses the difference between explanation and abduction on one side and interpretation on the other, maintaining that interpretation expresses what the interpreter believes to be the best explanation of the interpreted fact. The analysis which is proposed takes for granted the classical distinction between doxa and episteme, i.e. between a subjective and an objective dimension of the epistemic enterprise. However, in §1 belief is defined by making reference to knowledge, so reversing a relation which has been established by a deeply rooted philosophical tradition. §1. The aim of this note is developing some reflections inspired by a basic intuition about belief statements which may be synthesized as follows : 1) Belief statements have an essentially counterfactual character 2) They presuppose the notion of knowledge and are definable in terms of it 3) They have to do with the notion of surprise To be clearer, the analysis of belief which we want to take as a starting point is provided by the following definition: (Def Be) x believes that A = Df x would be surprised if x were to know that not-A