Abductive reasoning

About: Abductive reasoning is a research topic. Over the lifetime, 1917 publications have been published within this topic receiving 44645 citations. The topic is also known as: abduction & abductive inference.

Abductive Generalization and Specialization

Abstract: Abduction and induction both generate hypotheses to explain observed phenomena in an incomplete knowledge base, while they are distinguished in the following aspects. Abduction conjectures specific facts accounting for some particular observation. Those assumptions of facts are extracted using causal relations in the background knowledge base. As there are generally many possible facts which may imply the observation, candidates for hypotheses are usually pre-specified as abducibles. Then, the task is finding the best explanations from those candidates. By contrast, induction seeks regularities underlying the observed phenomena. The goal is not only explaining the current observations but discovering new knowledge for future usage. Hence induced hypotheses are general rules rather than specific facts. In constructing general rules, some constraints called biases are often used but candidates for hypotheses are not usually given in advance. The task is then forming new hypotheses using information in the background knowledge base.

Forensic judgment and decision making

Abstract: This chapter describes the decision making process by forensic experts. Forensic testimony is generally considered more reliable than most other forms of evidence and plays a key role in establishing guilt or innocence. Despite exaggerations in the media, automated, computerized methods are of limited capability and require experts to make judgment calls at various steps. Abductive reasoning is often required, perhaps inviting confirmation bias, particularly with decision making often based on reaching weight-of-evidence thresholds. Forensic scientists are expected to help find a conclusive outcome rather than the probabilistic one more compatible with scientific reasoning, and the need for rapid closure can lead to decisions based on incomplete data. Yet the interpretation of bottom-up information based on top-down information may lead to distortions, as in any type of data-driven decision making. Many of the same biases and difficulties that undermine human reasoning in other contexts reappear here too.

Titel / Title: Models of Abductive Reasoning

Abstract: system-theoretic sense. In this way, the model of a central force system arises, with a central body , peripherical bod-ies , a central force , etc. (160f). Since finding an abductive analogy consists in find-ing the theoretically essential features of the source structure which can be general-ized to other domains, it goes hand-in-hand with forming the corresponding concep-tual abstraction. For example, the analogical transfer of water-waves to sound.-waves makes only sense if the theoretically essential features of (water-) waves have been identified, namely, that waves are produced by coupled oscillators . The abductive conjecture of sound-waves then says that also sound should consist of coupled oscil-lations. Immediately, this gives rise to the further question in what the medium of coupled sound oscillators materially consists. The abductive conjecture says: the me-dium is here the air, the air molecules are moving back and forth, thereby bouncing against each other.

Comment: Implication Analysis as Abductive Inference:

Abstract: It is a pleasure to thank the editor of Sociological Methodology for the kind invitation to comment on the interesting paper on “Implication Analysis: A Pragmatic Proposal for Linking Theory and Data in the Social Science by Lieberson and Horwich (LH; this volume: p. 1). In our opinion, LH ask all the right questions about missing ingredients in the process of weighing evidence in the generation and testing of sociological theories. However, their paper falls substantially short of what it might have achieved in responding to the issues raised. This criticism applies not only to LH, but also to Ni Bhrolchain and Dyson (2007), whose checklist for assessing evidence in demography is highlighted by LH in their outline of how “Implication Analysis” should develop. In our view, the root of the problem lies in the overemphasis through much of the social science literature on a dichotomy in the logic of inference—namely, induction and deduction. Neither of these forms of inference, alone or in combination, provides an adequate basis for the weighing and integration of evidence of disparate types and varying quality that LH would like to see. Missing from the discussion in LH, and throughout the sociological literature, is a treatment of abduction, or “inference to the best explanation.” This form of inference has roots in the 17th century (Hobbs 2006), but was explicitly formalized by Charles Sanders Pierce in the 1870s and further developed by him (Pierce 1955). Abduction represents the reasoning that goes on in much of science— physical, biological, and social—and in everyday life, although it is rarely identified as the mode of inference that is actually being used.