Discovery and Ampliative Inference

1989 ◽  
Vol 56 (3) ◽  
pp. 438-462 ◽  
Author(s):  
James Blachowicz
Keyword(s):  
Ampliative Inference

scholarly journals Dogmatism and Ampliative Inference

2021 ◽  
Vol 66 (1) ◽  
pp. e42186
Author(s):  
Berit Brogaard
Keyword(s):  
Inferential Justification ◽  
Plausible Account ◽  
In Virtue Of ◽  
Ampliative Inference

The evidential role of experience in justifying beliefs has been at the center of debate in philosophy in recent years. One view is that experience, or seeming, can confer immediate (defeasible) justification on belief in virtue of its representational phenomenology. Call this view “representational dogmatism.” Another view is that experience confers immediate justification on belief in virtue of its relational phenomenology. Call this view “relational dogmatism.” The goal of this paper is to pit these two versions of dogmatism against each other in terms of their ability to account for ampliative, or non-deductive, inferential justification. I will argue that only the representational view can provide a plausible account of this type of justification.

scholarly journals A General Framework for Ampliative Inference Patterns

1998 ◽  
Vol 1 (2) ◽  
Author(s):  
Claudio Delrieux
Keyword(s):  
Expert Systems ◽  
Intelligent Systems ◽  
General Framework ◽  
General Pattern ◽  
Nonmonotonic Reasoning ◽  
Reasoning Systems ◽  
Research Programmes ◽  
Complete Procedure ◽  
Information Errors ◽  
Ampliative Inference

Non trivial reasoning from contradictory premises is being acknowledged as one of the most important features in intelligent systems. Expert systems, planners and schedulers, and diagnosers, are almost always faced to potentially fallacious information, errors, uncertainty, and difference of opinions. In thesecases, we expect that the reasoning systems will not collapse. Instead, the rational expected behavior is to isolate the source of contradiction. Several systems for reasoning from contradictory premises have been advanced, usually within acontext of strict, monotonic knowledge. In this work we investigate how defeasible knowledge can be also handled in these systems. The key idea is to represent pieces of defeasible knowledge ordered within anepistemic importance relation. A semantic characterization is provided, and a sound and complete procedure to compute conclusions is also given. Then, we show how nonmonotonic reasoning and other patterns of ampliative inference like abduction and induction can be adequately recast within the general pattern of reasoning from contradiction. We discuss some applications, in particular, a brief formalization of scientific research programmes. 

The Problem of Easy Rationality

2020 ◽  
pp. 173-192
Author(s):  
Juan Comesaña
Keyword(s):  
Easy Knowledge ◽  
History Of ◽  
Ampliative Inference

This chapter discusses a version of Cohen’s problem of easy knowledge, arguing for a novel resolution. A brief history of the problem is presented, and then a systematized version of it is developed. Possible solutions are examined, and denying the existence of ampliative inference is defended as a possible solution. It is explained how this is not as bad as it sounds. The chapter also discusses the relation between this solution and one of the arguments against Factualism presented in chapter 4. It is explained that those arguments against Factualism remain in place even if one rejects the possibility of ampliative inference. Finally, the issue of whether and how the problem of easy rationality applies to Experientialism is discussed.

On the Psychology of Ampliative Inference

1992 ◽  
Vol 3 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Tracy S. Myers ◽  
Daniel N. Osherson
Keyword(s):  
Probability Distribution ◽  
Random Sampling ◽  
Point Of View ◽  
Event Space ◽  
Principle Of Maximum Entropy ◽  
Correct Guess ◽  
Psychological Point ◽  
Familiar Form ◽  
Ampliative Inference ◽  
Principle Of Maximum

It is sometimes necessary to guess which probability distribution governs random sampling over a given event space. When the correct guess cannot be deduced from information available about the space, the problem is said to require ampliative inference. The most familiar form of ampliative inference is represented in the principle of maximum entropy. We examine this principle from a descriptive, psychological point of view.

Meaning, reasoning, and common knowledge

2019 ◽  
Vol 16 (3) ◽  
pp. 289-303 ◽  
Author(s):  
Richard Warner
Keyword(s):  
Information Processing ◽  
Common Knowledge ◽  
Bayesian Reasoning ◽  
Explanatory Role ◽  
The Third ◽  
Ampliative Inference ◽  
Processing Steps ◽  
John Perry ◽  
Relevant Detail ◽  
Do So

Abstract“Pragmatics involves perception augmented by some species of ‘ampliative‘ inference—induction, inference to the best explanation, Bayesian reasoning, or perhaps some special application of general principles special to communication, as conceived by Grice … —but in any case a sort of reasoning” (Korta, Kepa & John Perry. 2015. Pragmatics. In The stanford encyclopedia of philosophy Edward N. Zalta (ed.), Metaphysics research lab, Stanford, CA: Stanford University:1). Pragmatics assumes that one’s competence as speaker is sufficient to allow one to construct, in range of significant cases, plausible accounts of how speakers and audiences reason. The question is that are those who attribute reasoning to speakers and audiences suffering a “curious mental derangement” that prevents them from seeing that reasoning is rare? I consider four responses. (1) Speakers and audiences do reason to the extent pragmatic explanations require; they just typically do not do so consciously. (2) The second reply concedes that speakers and audiences often do not reason even unconsciously in any relevant detail, but it insists that attributions of reasoning can nonetheless be, and often are, explanatory. (3) The third reply is a response to objections to the second. It identifies reasoning with information processing steps. (4) The fourth view is that a speaker’s utterance provides an audience evidence for what the speaker means, but the audience typically does not reason to a conclusion about what the speaker means. I reject the first three replies and embrace the fourth, but I argue that attributions of reasoning in pragmatics can still play a significant explanatory role.

scholarly journals A General Framework for Ampliative Inference Patterns

1998 ◽  
Vol 1 (2) ◽  
Author(s):  
Claudio Delrieux
Keyword(s):  
Expert Systems ◽  
Intelligent Systems ◽  
General Framework ◽  
General Pattern ◽  
Nonmonotonic Reasoning ◽  
Reasoning Systems ◽  
Research Programmes ◽  
Complete Procedure ◽  
Information Errors ◽  
Ampliative Inference

Non trivial reasoning from contradictory premises is being acknowledged as one of the most important features in intelligent systems. Expert systems, planners and schedulers, and diagnosers, are almost always faced to potentially fallacious information, errors, uncertainty, and difference of opinions. In thesecases, we expect that the reasoning systems will not collapse. Instead, the rational expected behavior is to isolate the source of contradiction. Several systems for reasoning from contradictory premises have been advanced, usually within acontext of strict, monotonic knowledge. In this work we investigate how defeasible knowledge can be also handled in these systems. The key idea is to represent pieces of defeasible knowledge ordered within anepistemic importance relation. A semantic characterization is provided, and a sound and complete procedure to compute conclusions is also given. Then, we show how nonmonotonic reasoning and other patterns of ampliative inference like abduction and induction can be adequately recast within the general pattern of reasoning from contradiction. We discuss some applications, in particular, a brief formalization of scientific research programmes. 

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