Skip to main content Accessibility help
×
Hostname: page-component-8448b6f56d-c47g7 Total loading time: 0 Render date: 2024-04-18T06:43:07.078Z Has data issue: false hasContentIssue false

Appendix B - Plausibility

Published online by Cambridge University Press:  08 October 2009

John R. Josephson
Affiliation:
Ohio State University
Susan G. Josephson
Affiliation:
Ohio State University
Get access

Summary

Throughout this book we have depended in numerous ways on the idea of plausibility. It appears as “confidence value,” “symbolic plausibility value,” “degree of certainty,” “plausibilities of elementary hypotheses,” and the like.

Just what is this idea of plausibility? Can it be made precise? Is a plausibility a kind of likelihood, a kind of probability? What are the semantics? How much plausibility should be set for a given hypothesis under given circumstances? Is an objective standard possible? Does it even matter for a theory of intelligence, or for epistemology? Unfortunately, we cannot yet give definite answers to these questions.

The plausibility talk in this book is, in the first place, naturalistic. People mention plausibility when they describe their reasoning processes and when they write discussions of results in scientific papers. Plausibility talk seems to come naturally to us and is reflected in our ordinary language. Common usage allows both categorical judgments that something is or is not plausible, and graded comparative judgments, such as that one thing is “a lot” or “a little bit” more plausible than another. Throughout the work that we describe in this book we used our best judgments of when hypotheses were plausible, of how to evaluate evidence, and of how to weigh hypotheses. We were encouraged in our judgments by other people being likewise persuaded by appeals to “because it is the only plausible explanation,” and “more plausible than any other explanation for the data,” and the like. We were encouraged by the systems we built acting mostly according to our expectations, and producing correct answers.

Type
Chapter
Information
Abductive Inference
Computation, Philosophy, Technology
, pp. 266 - 272
Publisher: Cambridge University Press
Print publication year: 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×