A probabilistic analysis of argument cogency

Slides:



Advertisements
Similar presentations
Reasons for (prior) belief in Bayesian epistemology
Advertisements

The Basics of Logical Argument Two Kinds of Argument The Deductive argument: true premises guarantee a true conclusion. e.g. All men are mortal. Socrates.
The aim of this tutorial is to help you learn to identify the types of fallacious reasoning discussed in Chapter 6. Chapter 6 discusses fallacies of insufficient.
Ambiguous contents? Arvid Båve, Higher seminar in Theoretical Philosophy, FLoV, Gothenburg University, 8 May 2013.
When is an argument a good one? A cogent argument is an argument in which the premises are rationally acceptable and provide rational support for the conclusion.
Deduction and Induction
1 Chapter 12 Probabilistic Reasoning and Bayesian Belief Networks.
Evaluating Hypotheses Chapter 9 Homework: 1-9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics ~
Henry Prakken August 23, 2013 NorMas 2013 Argumentation about Norms.
De Finetti’s ultimate failure Krzysztof Burdzy University of Washington.
CHAPTER 4 Research in Psychology: Methods & Design
Sociology 5811: Lecture 10: Hypothesis Tests Copyright © 2005 by Evan Schofer Do not copy or distribute without permission.
How to Improve Your Communication of Ideas in an Essay.
LOGIC AND ONTOLOGY Both logic and ontology are important areas of philosophy covering large, diverse, and active research projects. These two areas overlap.
A Checklist for Reasoning & Questions Using the Elements of Thought
LECTURE 22 THE FINE-TUNING ARGUMENT FOR DESIGN. THE INITIAL COMPETITORS NATURALISTIC (SINGLE WORLD) HYPOTHESIS (NH 1 ): Reality consists of a single material,
1 Chapter 12 Probabilistic Reasoning and Bayesian Belief Networks.
An Inquiry Concerning Human Understanding
Reasoning Under Uncertainty. 2 Objectives Learn the meaning of uncertainty and explore some theories designed to deal with it Find out what types of errors.
Robert Trapp, Willamette University Yang Ge, Dalian Nationalities University 2010 BFSU Tournament International Debate Education Association and Willamette.
Methods of Presenting and Interpreting Information Class 9.
RESEARCH METHODOLOGY Research and Development Research Approach Research Methodology Research Objectives Engr. Hassan Mehmood Khan.
A Guide to Critical Thinking Concepts and Tools
Logic of Hypothesis Testing
Coming to Grips With General Conclusion/ Concluding Chapter
IE 102 Lecture 6 Critical Thinking.
Writing a sound proposal
Regression Testing with its types
Coming to Grips With General Conclusion/ Concluding Chapter
Knowledge Empiricism 2.
Writing Research Proposals
Argumentation and Critical Thinking.
Glossary of Terms Used in Science Papers AS
To learn about David Hume’s famous critique of Miracles.
Understanding Results
Sixth Annual Middle East
Probability and Counting Rules
Workshop for Debate Teachers
Workshop for Debate Teachers
Hidden Markov Models - Training
Knowledge Representation
Chapter 5 Logical Fallacies I Fallacies of Relevance
Introduction to aesthetics
Enquiry Based Learning (EBL) and Human Rights
Points of information.
THE COSMOLOGICAL ARGUMENT.
Logic, Philosophical Tools Quiz Review…20 minutes 10/31
Building Argument and Integrating Evidence
Reasoning in Psychology Using Statistics
Argumentation Strategies
Making Sense of Arguments
The discursive essay.
Fallacies.
THE COSMOLOGICAL ARGUMENT.
Parts of an Essay Ms. Ruttgaizer.
Assignments For Tuesday, read Feinberg and Levenbook, ”Abortion” in the text. On Thursday, we will talk about Don Marquis, “Why Abortion is Immoral” and.
Reasoning in Psychology Using Statistics
Comparative Reasoning Think “This is Like That”
Parts of an Essay.
Verification and meaning
Zimbabwe 2008 Critical Thinking.
Critical Thinking Lecture 2 Arguments
Testing Hypotheses I Lesson 9.
Task Criteria – Text-based Argument Rubric
Debate Basics Review.
Odds vs. Probabilities Odds ratio in SPSS (Exp(B)) is an odds rather than a probability Odds = success/failure Probability = Likelihood of success for.
Evaluating Deductive Arguments
Certainty Factor Model
Chapter 3 Hernán & Robins Observational Studies
Avoiding Ungrounded Assumptions
Presentation transcript:

A probabilistic analysis of argument cogency DAVID GODDEN Michigan State FRANK ZENKER Lund/Konstanz/Bratislava Bochum, 2 DEC 2016

Pascalian probabilistic treatment of the conditions for cogent argu-ment in informal logic: relevance, sufficiency, acceptability (RSA). Argument-as-product (vs. process) Aim: to specify content features of defeasible argument on which the RSA conditions (should) depend. Why care? Making the RSA condit-ions more precise shows how formal and informal approaches (can) align.

Overview Terms & definitions The impact term, i Interpret sensitivity and selectivity Strongest vs. weakest reason Specify RSA conditions Open question: update on weak reasons Upshot

PPC-view: premise, premise; ergo conclusion RRC-view: reason, reason; ergo claim

Terms & Definitions Pascalian P.: 0P()=1P()1 ARG: R=R1, …, Rn1, Rn; ergo C P(R1), P(C), P(C|R1): commitment in R1, C, C given R1 [not belief!] Pf(C)=P(C|R1, …, Rn1, Rn) __ P(C) ts, ta: threshold(-value) Pf(C)=P(C|R)__ ts__P(C) >, <, = : “R (sufficiently) supports, undermines, or is irrelevant to, C.”

‘makes a difference’ = ‘has impact’ Basic Idea (Receiving) the reason, R, does or does not make a difference to one’s commitment in the claim, C. ‘makes a difference’ = ‘has impact’

The “oomph” Pf(C)=P(C|R)=P(C)i [i =“impact of reason”]* i=P(R|C)/P(R) [likelihood; 0L<] P(R)=P(C)P(R|C)+P(C)P(R|C) Pf(C)=P(C|R)=P(R|C)P(C) ______________ P(R|C)P(C)+P(R|C)P(C) P(R|C): sensitivity of the reason to the claim. P( R| C)=1−P(R| C): specificity of R to C. * Carnap (1962: 466) calls i the relevance quotient, or the probability ratio; Strevens (2012: 30) calls it the Bayes multiplier (see Joyce, 2009: 5).

Hence: subjective interpretation of probability Sensitivity & specificity are readily meaningful for long run frequencies (e.g., medical test) But: folks do argue for claims about single events such as “Oswald shot Kennedy.” Hence: subjective interpretation of probability

Interpretation proposal Reason R is sensitive to claim C to the extent that R supports C more than R supports any other claim C*, that itself entails ~C, i.e., P(C|R)>.5>P(~C|R). R is specific to C to the extent that R rather than any other reason R*, itself entailing ~R, supports C, i.e., P(C|~R)<.5<P(~C|~R).

Drawing this together, … …the support that R generates for C thus depends: on the extent to which the C-supporting-reason R fails to support ~C, on one hand, and on the extent to which argumentative support for C cannot be generated by reasons besides R, on the other.

Hence, … …in the extremal cases P(C|R)=1 and P(C|R)=0, support is strongest where R is an exclusive and decisive supporting reason-for-C, and weakest where R is a common and indecisive supporting reason-for-C.

…to characterize: relevance, sufficiency (and acceptability) Exploit the i term… …to characterize: relevance, sufficiency (and acceptability) Pf(C)=P(C|R)=P(C)i

Relevance Pf(C)=P(C|R)=P(C)i If … i > 1, R is positively relevant to C i = 1, R is irrelevant to C i < 1, R is negatively relevant to C Compare: Relevance as probability raising, or rather probability-change.

Sufficiency Pf(C)=P(C|R)=P(C)i Pf(C)=P(C|R)≥ts>P(C) [ts: s.-threshold] Inferential sufficiency entails that i1 Note: a necessary reasons is a special case of an insufficient R (cf. Spohn, 2012)

Pf(C)=P(C|R)=P(C)i Pf(R)≥ta [ta: acceptability threshold]

Open question Those who understand offering arguments as the issuing of “invitations to inference” (e.g., Pinto, 2001) can interpret the sufficiency criterion as prohibiting any inferential use of reasons failing the threshold. Sufficiency condition would thus act as an “inference gate,” asking you to “ignore” weak reasons.

Problem case R={R1, R2, R3, R4}; P(R1)=P(R2)=P(R3)=P(R4) P(Rn|C)=.25 [“R is weakly sensitive to C”] P(Rn|~C)=.15 [“R is weakly selective for C”] It follows that P(Rn)=.167 [“weak reason”] Now set P(C)=.17 [“hardly supported C”] But: when successively updating P(C) on R1 to R4, using Bayes’ theorem, P(C|R1)=.1755; P(CR1|R2)=.3625; P(CR1,2|R3)=.4833; and P(CR1,2,3|R4)=.6093. Compare ts=0.5

Upshot RSA conditions depend on: Change in the acceptability of R R’s sensitivity and selectivity to C One’s prior commitment to C Contextually determined thresholds for reasons and claims Particularly: Inferential update may be obligatory rather than permissive. New Q: study linked vs. convergent ARG; compare to probability-model

In sum We suggest a specific understand-ing of the RSA criteria concerning their conceptual (in)dependence, their function as update-thresh-olds, and their status as obligatory rather than permissive norms, which shows how these formal and informal normative approa-ches (can) in fact align.

Forthcoming with Synthese

also on behalf of David frank.zenker@fil.lu.se Thank you! also on behalf of David frank.zenker@fil.lu.se