Paul D. Thorn. HHU Düsseldorf, DCLPS, DFG SPP 1516

Size: px
Start display at page:

Download "Paul D. Thorn. HHU Düsseldorf, DCLPS, DFG SPP 1516"

Transcription

1 Paul D. Thorn HHU Düsseldorf, DCLPS, DFG SPP 1516

2 High rational personal probability (0.5 < r < 1) is a necessary condition for rational belief. Degree of probability is not generally preserved when one aggregates propositions. 2

3 Leitgeb (2013, 2014) demonstrated the formal possibility of relating rational personal probability to rational belief, in such a way that: (LT ) having a rational personal probability of at least r (0.5 < r < 1) is a necessary condition for rational belief, and (DC) rational belief sets are closed under deductive consequences. In light of Leitgeb s result, I here endeavor to illustrate another problem with deductive closure. 3

4 Discounting inappropriate applications of (LT ) and some other extreme views, the combination of (LT ) and (DC) leads to violations of a highly plausible principle concerning rational belief, which I ll call the relevant factors principle. Since (LT ) is obviously correct, we have good reason to think that rational belief sets are not closed under deductive consequences. Note that Leitgeb s theory is not the primary or exclusive target of the argument. 4

5 The following factors are sufficient to determine whether a respective agent s belief in a given proposition,, is rational: (I) the agent s relevant evidence bearing on, (II) the process that generated the agent s belief that, (III) the agent s degree of doxastic cautiousness, as represented by a probability threshold, s, (IV) the features of the agent s practical situation to which belief in are relevant, and (V) the evidential standards applicable to the agent s belief that, deriving the social context in which the agent entertains. 5

6 It is intended that (I) through (V) outline a range of factors upon which facts about rational belief supervene. To be slightly more precise, I propose (for each proposition ) that: For any two possible agents who both believe, no difference in factors (I) through (V) implies no difference in the status of the respective beliefs, as rational or not.

7 I regard (DC) as expressing the following claim: For all possible agents, A, with unlimited deductive abilities, the set of propositions that it is rational for A to believe is closed under deductive consequences. I adopt the convention of saying that it is rational for an agent, A, to believe a proposition,, just in case there are grounds immediately available to A such that if A were to believe that and base her belief on those grounds, then A s belief that would be rational. 7

8 One may consistently hold that having a rational personal probability of at least r (r < 1) is a necessary condition for rational belief, while also holding that a rational personal probability of one is a necessary condition for rational belief. In order to exclude the preceding possibility, I propose to treat the application of (LT ) in characterizing a theory of rational belief as appropriate just in case the theory admits cases where the rational personal probability for some proposition is r, and it is rational to believe that proposition. 8

9 For reductio, assume (LT ) and (DC). Consider two agents A 1 and A 2, whose total evidence, and rational personal probability functions, PROB 1 and PROB 2, exclusively concern two disjoint domains D 1 and D 2, describable by the following propositional atoms: p 1,, p n for D 1, and q 1,, q m for D 2. Suppose that all of A 1 s and A 2 s beliefs are rational. Let 1 be the strongest proposition believed by A 1, and 2 be the strongest proposition believed by A 2. Assume that PROB 1 ( 1 ) = PROB 2 ( 2 ) = r, and r is the minimum cautiousness threshold for A 1 and A 2. 9

10 Suppose that A 1 and A 2 will proceed within the respective domains D 1 and D 2 via actions that are specific to each domain, and the result of performing various actions are immediate payoffs in units of utility. Suppose that both A 1 and A 2 engage in appropriate practical deliberations. Finally, suppose that contexts in which A 1 and A 2 entertain 1 and 2 are thoroughly asocial.

11 Now observe that it is possible to form a probability function, PROB 1 2, which is defined over truth functional combinations of p 1,, p n, q 1,, q m, which: (i) agrees with PROB 1 regarding propositions that exclusively concern D 1, (ii) agrees with PROB 2 regarding propositions that exclusively concern D 2, and (iii) assigns probabilities to other propositions by treating propositions that exclusively concern D 1 as being probabilistically independent of propositions that exclusively concern D 2. 11

12 Consider the sets of possible worlds with respect to D 1 and D 2, respectively, which may be identified with propositions of the form ( )p 1 ( )p n for D 1, and ( )q 1 ( )q m for D 2. These possible worlds may be listed as: w P1,, w P2 n, and w Q1,, w Q2 m. The set of possible worlds with respect to the joint domain of D 1 and D 2 may be identified with the set propositions of the form: w Pi w Qk. Let PROB 1 2(w Pi w Qk ) = PROB 1 (w Pi ) PROB 2 (w Qk ), for all such combinations. 12

13 It is apparent that adopting the probability function PROB 1 2 would be a rational response (though perhaps not uniquely so) for an agent whose total evidence is the aggregate of A 1 s and A 2 s evidence (but see below). Consider an agent, A 1 2, whose total evidence is the aggregate of A 1 s and A 2 s, and who rationally adopts PROB 1 2.

14 Suppose that A 1 2 believes exactly the same propositions concerning domain D 1 as A 1, forming them by type identical processes to the ones that produced A 1 s beliefs (and similarly for D 2 and A 2 ) Suppose that A 1 2 s degree of doxastic cautiousness is identical to that of A 1 and A 2.

15 Suppose that the language of D 1 and D 2 concern mutually remote parts of A 1 2 environment, and this fact is transparent to A 1 2. Suppose that the actions available to A 1 2 with respect to D 1 are identical to the ones available to A 1, where A 1 2 s payoff for performing respective actions under various D 1 conditions are identical to the payoffs for A 1 (and similarly for D 2 and A 2 ).

16 Given the preceding, it would be appropriate (though perhaps not uniquely so) for A 1 2 to negotiate D 1 by engaging in deliberations that are identical to the ones employed by A 1 (and similarly for D 2 and A 2 ). Assume that A 1 2 proceeds in this manner. In that case, it s clear that the features of A 1 2 s practical situation to which her belief that 1 is relevant are identical to the features of the A 1 s practical situation to which her belief that 1 is relevant (and similarly for A 2 and 2 ). As with A 1 and A 2, assume that the context in which A 1 2 entertains 1 and 2 is thoroughly asocial.

17 There is no difference in factors (I) through (V) regarding A 1 2 s and A 1 s belief that 1. So A 1 2 s belief that 1 is rational [by the relevant factors principle]. Similarly, A 1 2 s belief that 2 is rational. So it is rational for A 1 2 to believe 1 2 (by (DC)). But it is not rational for A 1 2 to believe 1 2 (by (LT ), since PROB 1 2 ( 1 2 ) = r 2 < r). Thus, by reductio, not (LT ) or not (DC). So not (DC), since (LT ).

18 (a) One can modify the above example so that PROB 1 and PROB 2 are both defined over the joint domain of D 1 and D 2, assuming the rational permissibility of suspending belief regarding propositions about which one has no evidence, or the rational permissibility of imprecise personal probabilities. (b) One need not accept that PROB 1 2 is rational given the aggregate of A 1 s and A 2 s evidence.

19 The End. Thanks for your attention.

ELEONORA CRESTO. CONICET (Argentina) FEW 2011 USC

ELEONORA CRESTO. CONICET (Argentina) FEW 2011 USC ELEONORA CRESTO CONICET (Argentina) FEW 2011 USC If S knows that p, S knows that she knows that p: KK Principle: Kp KKp Knowledge reflexivity Positive introspection Self-knowledge Transparency Luminosity

More information

Doxastic Logic. Michael Caie

Doxastic Logic. Michael Caie Doxastic Logic Michael Caie There are at least three natural ways of interpreting the object of study of doxastic logic. On one construal, doxastic logic studies certain general features of the doxastic

More information

Comment on Leitgeb s Stability Theory of Belief

Comment on Leitgeb s Stability Theory of Belief Comment on Leitgeb s Stability Theory of Belief Hanti Lin Kevin T. Kelly Carnegie Mellon University {hantil, kk3n}@andrew.cmu.edu Hannes Leitgeb s stability theory of belief provides three synchronic constraints

More information

COMP310 MultiAgent Systems. Chapter 16 - Argumentation

COMP310 MultiAgent Systems. Chapter 16 - Argumentation COMP310 MultiAgent Systems Chapter 16 - Argumentation Argumentation Argumentation is the process of attempting to agree about what to believe. Only a question when information or beliefs are contradictory.

More information

Desire-as-belief revisited

Desire-as-belief revisited Desire-as-belief revisited Richard Bradley and Christian List June 30, 2008 1 Introduction On Hume s account of motivation, beliefs and desires are very di erent kinds of propositional attitudes. Beliefs

More information

A Little Deductive Logic

A Little Deductive Logic A Little Deductive Logic In propositional or sentential deductive logic, we begin by specifying that we will use capital letters (like A, B, C, D, and so on) to stand in for sentences, and we assume that

More information

For True Conditionalizers Weisberg s Paradox is a False Alarm

For True Conditionalizers Weisberg s Paradox is a False Alarm For True Conditionalizers Weisberg s Paradox is a False Alarm Franz Huber Department of Philosophy University of Toronto franz.huber@utoronto.ca http://huber.blogs.chass.utoronto.ca/ July 7, 2014; final

More information

For True Conditionalizers Weisberg s Paradox is a False Alarm

For True Conditionalizers Weisberg s Paradox is a False Alarm For True Conditionalizers Weisberg s Paradox is a False Alarm Franz Huber Abstract: Weisberg (2009) introduces a phenomenon he terms perceptual undermining He argues that it poses a problem for Jeffrey

More information

In Defense of Jeffrey Conditionalization

In Defense of Jeffrey Conditionalization In Defense of Jeffrey Conditionalization Franz Huber Department of Philosophy University of Toronto Please do not cite! December 31, 2013 Contents 1 Introduction 2 2 Weisberg s Paradox 3 3 Jeffrey Conditionalization

More information

Lecture 1: The Humean Thesis on Belief

Lecture 1: The Humean Thesis on Belief Lecture 1: The Humean Thesis on Belief Hannes Leitgeb LMU Munich October 2014 What do a perfectly rational agent s beliefs and degrees of belief have to be like in order for them to cohere with each other?

More information

Thoughts on Decision Making with Imprecise Probabilities

Thoughts on Decision Making with Imprecise Probabilities THE BASIC IDEAS OF BAYESIAN EPISTEMOLOGY Believing is not an all-or-nothing matter. Opinions come in varying gradations of strength ranging from full certainty of truth to complete certainty of falsehood.

More information

COMP310 Multi-Agent Systems Chapter 16 - Argumentation. Dr Terry R. Payne Department of Computer Science

COMP310 Multi-Agent Systems Chapter 16 - Argumentation. Dr Terry R. Payne Department of Computer Science COMP310 Multi-Agent Systems Chapter 16 - Argumentation Dr Terry R. Payne Department of Computer Science Overview How do agents agree on what to believe? In a court of law, barristers present a rationally

More information

Your quiz in recitation on Tuesday will cover 3.1: Arguments and inference. Your also have an online quiz, covering 3.1, due by 11:59 p.m., Tuesday.

Your quiz in recitation on Tuesday will cover 3.1: Arguments and inference. Your also have an online quiz, covering 3.1, due by 11:59 p.m., Tuesday. Friday, February 15 Today we will begin Course Notes 3.2: Methods of Proof. Your quiz in recitation on Tuesday will cover 3.1: Arguments and inference. Your also have an online quiz, covering 3.1, due

More information

Belief Revision in Social Networks

Belief Revision in Social Networks Tsinghua University and University of Amsterdam 8-10 July 2015, IRIT, Université Paul Sabatier, Toulouse Outline 1 2 Doxastic influence Finite state automaton Stability and flux Dynamics in the community

More information

Imprint ACCURACY AND THE CREDENCE-BELIEF CONNECTION. Richard Pettigrew. volume 15, no. 16 june, 2015

Imprint ACCURACY AND THE CREDENCE-BELIEF CONNECTION. Richard Pettigrew. volume 15, no. 16 june, 2015 Philosophers Imprint volume 15, no. 16 june, 2015 ACCURACY AND THE CREDENCE-BELIEF CONNECTION Richard Pettigrew Department of Philosophy University of Bristol, UK 2015, Richard Pettigrew This work is licensed

More information

The Lottery Paradox. Atriya Sen & Selmer Bringsjord

The Lottery Paradox. Atriya Sen & Selmer Bringsjord The Lottery Paradox Atriya Sen & Selmer Bringsjord Paradoxes Type 1: Single body of knowledge from which it is possible to deduce a contradiction (logical paradox) e.g. Russell s paradox Type 2: Counter-intuitive

More information

Part II A Reexamination of Contemporary Utilitarianism

Part II A Reexamination of Contemporary Utilitarianism Part II A Reexamination of Contemporary Utilitarianism In Part II of this book, we will turn to contemporary moral philosophers by this I mean twentieth-century philosophers who have reconstructed modern

More information

I I I I I I I I I I I I I I I I I I I

I I I I I I I I I I I I I I I I I I I STRONG AND WEAK METHODS: A LOGCAL VEW OF UNCERTANTY John Fox mperial Cancer Research Fund Laboratories, London AAA, Los Angeles, August 1985 Before about 1660 when the modern Pascalian concept of probability

More information

CL16, 12th September 2016

CL16, 12th September 2016 CL16, 12th September 2016 for - Soft Outline for - Soft for - Soft The Problem of Anyone who utters: (S) Sherlock Holmes lives in Baker Street would not be objected against by non-philosophers. However:

More information

A Little Deductive Logic

A Little Deductive Logic A Little Deductive Logic In propositional or sentential deductive logic, we begin by specifying that we will use capital letters (like A, B, C, D, and so on) to stand in for sentences, and we assume that

More information

Review CHAPTER. 2.1 Definitions in Chapter Sample Exam Questions. 2.1 Set; Element; Member; Universal Set Partition. 2.

Review CHAPTER. 2.1 Definitions in Chapter Sample Exam Questions. 2.1 Set; Element; Member; Universal Set Partition. 2. CHAPTER 2 Review 2.1 Definitions in Chapter 2 2.1 Set; Element; Member; Universal Set 2.2 Subset 2.3 Proper Subset 2.4 The Empty Set, 2.5 Set Equality 2.6 Cardinality; Infinite Set 2.7 Complement 2.8 Intersection

More information

Introduction to Metalogic

Introduction to Metalogic Introduction to Metalogic Hans Halvorson September 21, 2016 Logical grammar Definition. A propositional signature Σ is a collection of items, which we call propositional constants. Sometimes these propositional

More information

Philosophy 148 Announcements & Such

Philosophy 148 Announcements & Such Branden Fitelson Philosophy 148 Lecture 1 Philosophy 148 Announcements & Such Overall, people did very well on the mid-term (µ = 90, σ = 16). HW #2 graded will be posted very soon. Raul won t be able to

More information

Favoring, Likelihoodism, and Bayesianism

Favoring, Likelihoodism, and Bayesianism Favoring, Likelihoodism, and Bayesianism BRANDEN FITELSON Rutgers University In Chapter 1 of Evidence and Evolution, Sober (2008) defends a Likelihodist account of favoring. The main tenet of Likelihoodism

More information

Philosophy 148 Announcements & Such

Philosophy 148 Announcements & Such Branden Fitelson Philosophy 148 Lecture 1 Philosophy 148 Announcements & Such The mid-term is Thursday. I discussed it last night. These are fair game: True/False/short calculation (quiz-like) questions

More information

A Note on the Existence of Ratifiable Acts

A Note on the Existence of Ratifiable Acts A Note on the Existence of Ratifiable Acts Joseph Y. Halpern Cornell University Computer Science Department Ithaca, NY 14853 halpern@cs.cornell.edu http://www.cs.cornell.edu/home/halpern August 15, 2018

More information

How Serious Is the Paradox of Serious Possibility?

How Serious Is the Paradox of Serious Possibility? How Serious Is the Paradox of Serious Possibility? Simone Duca Ruhr University Bochum simone.duca@gmail.com Hannes Leitgeb Ludwig Maximilian University hannes.leitgeb@lmu.de The so-called Paradox of Serious

More information

What is DEL good for? Alexandru Baltag. Oxford University

What is DEL good for? Alexandru Baltag. Oxford University Copenhagen 2010 ESSLLI 1 What is DEL good for? Alexandru Baltag Oxford University Copenhagen 2010 ESSLLI 2 DEL is a Method, Not a Logic! I take Dynamic Epistemic Logic () to refer to a general type of

More information

Probabilistic Justification Logic

Probabilistic Justification Logic Ioannis Kokkinis Logic and Theory Group Institute of Computer Science University of Bern joint work with Petar Maksimović, Zoran Ognjanović and Thomas Studer Logic and Applications Dubrovnik September

More information

ELEMENTARY NUMBER THEORY AND METHODS OF PROOF

ELEMENTARY NUMBER THEORY AND METHODS OF PROOF CHAPTER 4 ELEMENTARY NUMBER THEORY AND METHODS OF PROOF Copyright Cengage Learning. All rights reserved. SECTION 4.6 Indirect Argument: Contradiction and Contraposition Copyright Cengage Learning. All

More information

Chapter 11: Automated Proof Systems (1)

Chapter 11: Automated Proof Systems (1) Chapter 11: Automated Proof Systems (1) SYSTEM RS OVERVIEW Hilbert style systems are easy to define and admit a simple proof of the Completeness Theorem but they are difficult to use. Automated systems

More information

Logic and Artificial Intelligence Lecture 13

Logic and Artificial Intelligence Lecture 13 Logic and Artificial Intelligence Lecture 13 Eric Pacuit Currently Visiting the Center for Formal Epistemology, CMU Center for Logic and Philosophy of Science Tilburg University ai.stanford.edu/ epacuit

More information

THE SURE-THING PRINCIPLE AND P2

THE SURE-THING PRINCIPLE AND P2 Economics Letters, 159: 221 223, 2017 DOI 10.1016/j.econlet.2017.07.027 THE SURE-THING PRINCIPLE AND P2 YANG LIU Abstract. This paper offers a fine analysis of different versions of the well known sure-thing

More information

Argumentation-Based Models of Agent Reasoning and Communication

Argumentation-Based Models of Agent Reasoning and Communication Argumentation-Based Models of Agent Reasoning and Communication Sanjay Modgil Department of Informatics, King s College London Outline Logic and Argumentation - Dung s Theory of Argumentation - The Added

More information

THE SURE-THING PRINCIPLE AND P2

THE SURE-THING PRINCIPLE AND P2 Economics Letters, 159: 221 223, 2017. Doi: 10.1016/j.econlet.2017.07.027 THE SURE-THING PRINCIPLE AND P2 YANG LIU Abstract. This paper offers a fine analysis of different versions of the well known sure-thing

More information

Mutual Optimism in the Bargaining Model of War

Mutual Optimism in the Bargaining Model of War Mutual Optimism in the Bargaining Model of War Mark Fey Kristopher W. Ramsay December 19, 2016 Abstract Mutual optimism is often considered an explanation for war, but recent research has challenged that

More information

Dr. Truthlove, or How I Learned to Stop Worrying and Love Bayesian Probabilities

Dr. Truthlove, or How I Learned to Stop Worrying and Love Bayesian Probabilities Dr. Truthlove, or How I Learned to Stop Worrying and Love Bayesian Probabilities Kenny Easwaran 10/15/2014 1 Setup 1.1 The Preface Paradox Dr. Truthlove loves believing things that are true, and hates

More information

Bayesian Reasoning. Adapted from slides by Tim Finin and Marie desjardins.

Bayesian Reasoning. Adapted from slides by Tim Finin and Marie desjardins. Bayesian Reasoning Adapted from slides by Tim Finin and Marie desjardins. 1 Outline Probability theory Bayesian inference From the joint distribution Using independence/factoring From sources of evidence

More information

Propositional Logic: Part II - Syntax & Proofs 0-0

Propositional Logic: Part II - Syntax & Proofs 0-0 Propositional Logic: Part II - Syntax & Proofs 0-0 Outline Syntax of Propositional Formulas Motivating Proofs Syntactic Entailment and Proofs Proof Rules for Natural Deduction Axioms, theories and theorems

More information

An Inquisitive Formalization of Interrogative Inquiry

An Inquisitive Formalization of Interrogative Inquiry An Inquisitive Formalization of Interrogative Inquiry Yacin Hamami 1 Introduction and motivation The notion of interrogative inquiry refers to the process of knowledge-seeking by questioning [5, 6]. As

More information

Argumentation and rules with exceptions

Argumentation and rules with exceptions Argumentation and rules with exceptions Bart VERHEIJ Artificial Intelligence, University of Groningen Abstract. Models of argumentation often take a given set of rules or conditionals as a starting point.

More information

Introduction to Metalogic

Introduction to Metalogic Philosophy 135 Spring 2008 Tony Martin Introduction to Metalogic 1 The semantics of sentential logic. The language L of sentential logic. Symbols of L: Remarks: (i) sentence letters p 0, p 1, p 2,... (ii)

More information

Joyce s Argument for Probabilism

Joyce s Argument for Probabilism Joyce s Argument for Probabilism Patrick Maher (p-maher@uiuc.edu) Department of Philosophy, University of Illinois at Urbana-Champaign Abstract. James Joyce s Nonpragmatic Vindication of Probabilism gives

More information

Robust Knowledge and Rationality

Robust Knowledge and Rationality Robust Knowledge and Rationality Sergei Artemov The CUNY Graduate Center 365 Fifth Avenue, 4319 New York City, NY 10016, USA sartemov@gc.cuny.edu November 22, 2010 Abstract In 1995, Aumann proved that

More information

Knowledge Based Obligations RUC-ILLC Workshop on Deontic Logic

Knowledge Based Obligations RUC-ILLC Workshop on Deontic Logic Knowledge Based Obligations RUC-ILLC Workshop on Deontic Logic Eric Pacuit Stanford University November 9, 2007 Eric Pacuit: Knowledge Based Obligations, RUC-ILLC Workshop on Deontic Logic 1 The Kitty

More information

ESSENCE 2014: Argumentation-Based Models of Agent Reasoning and Communication

ESSENCE 2014: Argumentation-Based Models of Agent Reasoning and Communication ESSENCE 2014: Argumentation-Based Models of Agent Reasoning and Communication Sanjay Modgil Department of Informatics, King s College London Outline Logic, Argumentation and Reasoning - Dung s Theory of

More information

Preliminary statistics

Preliminary statistics 1 Preliminary statistics The solution of a geophysical inverse problem can be obtained by a combination of information from observed data, the theoretical relation between data and earth parameters (models),

More information

PROBLEMS OF CAUSAL ANALYSIS IN THE SOCIAL SCIENCES

PROBLEMS OF CAUSAL ANALYSIS IN THE SOCIAL SCIENCES Patrick Suppes PROBLEMS OF CAUSAL ANALYSIS IN THE SOCIAL SCIENCES This article is concerned with the prospects and problems of causal analysis in the social sciences. On the one hand, over the past 40

More information

RELATION OF WHITEHEAD AND RUSSELL'S THEORY OF DEDUCTION TO THE BOOLEAN LOGIC OF PROPOSITIONS*

RELATION OF WHITEHEAD AND RUSSELL'S THEORY OF DEDUCTION TO THE BOOLEAN LOGIC OF PROPOSITIONS* 932.] BOOLEAN LOGIC OF PROPOSITIONS 589 RELATION OF WHITEHEAD AND RUSSELL'S THEORY OF DEDUCTION TO THE BOOLEAN LOGIC OF PROPOSITIONS* BY B. A. BERNSTEIN. Introduction. Whitehead and Russell's theory of

More information

Accuracy, conditionalization, and probabilism

Accuracy, conditionalization, and probabilism Accuracy, conditionalization, and probabilism Peter J. Lewis, University of Miami Don Fallis, University of Arizona March 3, 2016 Abstract Accuracy-based arguments for conditionalization and probabilism

More information

-1- THE PROBABILITY THAT TWEETY IS ABLE TO FLY Giangiacomo Gerla Dipartimento di Matematica e Fisica, Università di Camerino ITALY.

-1- THE PROBABILITY THAT TWEETY IS ABLE TO FLY Giangiacomo Gerla Dipartimento di Matematica e Fisica, Università di Camerino ITALY. -1- THE PROBABILITY THAT TWEETY IS ABLE TO FLY Giangiacomo Gerla Dipartimento di Matematica e Fisica, Università di Camerino ITALY. Abstract. Consider the question of assigning a probabilistic valuation

More information

Commentary on Guarini

Commentary on Guarini University of Windsor Scholarship at UWindsor OSSA Conference Archive OSSA 5 May 14th, 9:00 AM - May 17th, 5:00 PM Commentary on Guarini Andrew Bailey Follow this and additional works at: http://scholar.uwindsor.ca/ossaarchive

More information

Beliefs, Desires And Intentions

Beliefs, Desires And Intentions Jordy Oldenkamp Erwin Scholtens Jesse van den Kieboom Pieter de Bie Michiel Holtkamp March 27, 2007 1 The BDI System 2 Agents within BDI-Architecture 3 Collective Intentions 4 Formalization 5 Problems

More information

A Note On Comparative Probability

A Note On Comparative Probability A Note On Comparative Probability Nick Haverkamp and Moritz Schulz Penultimate draft. Please quote from the published version (Erkenntnis 2012). Abstract A possible event always seems to be more probable

More information

Modal Dependence Logic

Modal Dependence Logic Modal Dependence Logic Jouko Väänänen Institute for Logic, Language and Computation Universiteit van Amsterdam Plantage Muidergracht 24 1018 TV Amsterdam, The Netherlands J.A.Vaananen@uva.nl Abstract We

More information

Mechanism Design for Argumentation-based Information-seeking and Inquiry

Mechanism Design for Argumentation-based Information-seeking and Inquiry Mechanism Design for Argumentation-based Information-seeking and Inquiry Xiuyi Fan and Francesca Toni Imperial College London, London, United Kingdom, {xf309,ft}@imperial.ac.uk Abstract. Formal argumentation-based

More information

In Newcomb s problem, an agent is faced with a choice between acts that

In Newcomb s problem, an agent is faced with a choice between acts that Aporia vol. 23 no. 2 2013 Counterfactuals and Causal Decision Theory Kevin Dorst In Newcomb s problem, an agent is faced with a choice between acts that are highly correlated with certain outcomes, but

More information

Full Surplus Extraction and Costless Information Revelation in Dynamic Environments. Shunya NODA (University of Tokyo)

Full Surplus Extraction and Costless Information Revelation in Dynamic Environments. Shunya NODA (University of Tokyo) Full Surplus Extraction and Costless Information Revelation in Dynamic Environments Shunya NODA (University of Tokyo) Outline 1. Introduction. Two-Period Example 3. Three-Period Example 4. Model 5. Main

More information

Beliefs, we will assume, come in degrees. As a shorthand, we will refer to these. Syracuse University

Beliefs, we will assume, come in degrees. As a shorthand, we will refer to these. Syracuse University AN OPEN ACCESS Ergo JOURNAL OF PHILOSOPHY Calibration and Probabilism MICHAEL CAIE Syracuse University In this paper, I consider an argument due to Bas van Fraassen that attempts to show that considerations

More information

First Prev Next Last Go Back Full Screen Close Quit. Game Theory. Giorgio Fagiolo

First Prev Next Last Go Back Full Screen Close Quit. Game Theory. Giorgio Fagiolo Game Theory Giorgio Fagiolo giorgio.fagiolo@univr.it https://mail.sssup.it/ fagiolo/welcome.html Academic Year 2005-2006 University of Verona Summary 1. Why Game Theory? 2. Cooperative vs. Noncooperative

More information

Introduction to Intuitionistic Logic

Introduction to Intuitionistic Logic Introduction to Intuitionistic Logic August 31, 2016 We deal exclusively with propositional intuitionistic logic. The language is defined as follows. φ := p φ ψ φ ψ φ ψ φ := φ and φ ψ := (φ ψ) (ψ φ). A

More information

15414/614 Optional Lecture 1: Propositional Logic

15414/614 Optional Lecture 1: Propositional Logic 15414/614 Optional Lecture 1: Propositional Logic Qinsi Wang Logic is the study of information encoded in the form of logical sentences. We use the language of Logic to state observations, to define concepts,

More information

Multi-Agent Systems and Social Influence

Multi-Agent Systems and Social Influence Multi-Agent Systems and Social Influence Part 2 Marija Slavkovik Truls Pedersen University of Bergen Slavkovik & Pedersen Social Influence 1 / 58 (Only apparently overwhelming) plan 1 Fenrong Liu, Jeremy

More information

Evidence with Uncertain Likelihoods

Evidence with Uncertain Likelihoods Evidence with Uncertain Likelihoods Joseph Y. Halpern Cornell University Ithaca, NY 14853 USA halpern@cs.cornell.edu Riccardo Pucella Cornell University Ithaca, NY 14853 USA riccardo@cs.cornell.edu Abstract

More information

Proof strategies, or, a manual of logical style

Proof strategies, or, a manual of logical style Proof strategies, or, a manual of logical style Dr Holmes September 27, 2017 This is yet another version of the manual of logical style I have been working on for many years This semester, instead of posting

More information

Defeasible Conditionalization 1

Defeasible Conditionalization 1 Defeasible Conditionalization 1 Abstract The applicability of Bayesian conditionalization in setting one s posterior probability for a proposition, α, is limited to cases where the value of a corresponding

More information

Recap Social Choice Functions Fun Game Mechanism Design. Mechanism Design. Lecture 13. Mechanism Design Lecture 13, Slide 1

Recap Social Choice Functions Fun Game Mechanism Design. Mechanism Design. Lecture 13. Mechanism Design Lecture 13, Slide 1 Mechanism Design Lecture 13 Mechanism Design Lecture 13, Slide 1 Lecture Overview 1 Recap 2 Social Choice Functions 3 Fun Game 4 Mechanism Design Mechanism Design Lecture 13, Slide 2 Notation N is the

More information

The Axiomatic Method in Social Choice Theory:

The Axiomatic Method in Social Choice Theory: The Axiomatic Method in Social Choice Theory: Preference Aggregation, Judgment Aggregation, Graph Aggregation Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss

More information

Definitions and Proofs

Definitions and Proofs Giving Advice vs. Making Decisions: Transparency, Information, and Delegation Online Appendix A Definitions and Proofs A. The Informational Environment The set of states of nature is denoted by = [, ],

More information

Ambiguous Language and Differences in Beliefs

Ambiguous Language and Differences in Beliefs Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning Ambiguous Language and Differences in Beliefs Joseph Y. Halpern Computer Science Dept. Cornell

More information

Confirmation Theory. Pittsburgh Summer Program 1. Center for the Philosophy of Science, University of Pittsburgh July 7, 2017

Confirmation Theory. Pittsburgh Summer Program 1. Center for the Philosophy of Science, University of Pittsburgh July 7, 2017 Confirmation Theory Pittsburgh Summer Program 1 Center for the Philosophy of Science, University of Pittsburgh July 7, 2017 1 Confirmation Disconfirmation 1. Sometimes, a piece of evidence, E, gives reason

More information

AN EXTENSION OF THE PROBABILITY LOGIC LP P 2. Tatjana Stojanović 1, Ana Kaplarević-Mališić 1 and Zoran Ognjanović 2

AN EXTENSION OF THE PROBABILITY LOGIC LP P 2. Tatjana Stojanović 1, Ana Kaplarević-Mališić 1 and Zoran Ognjanović 2 45 Kragujevac J. Math. 33 (2010) 45 62. AN EXTENSION OF THE PROBABILITY LOGIC LP P 2 Tatjana Stojanović 1, Ana Kaplarević-Mališić 1 and Zoran Ognjanović 2 1 University of Kragujevac, Faculty of Science,

More information

Meaning, Evolution and the Structure of Society

Meaning, Evolution and the Structure of Society Meaning, Evolution and the Structure of Society Roland Mühlenbernd November 7, 2014 OVERVIEW Game Theory and Linguistics Pragm. Reasoning Language Evolution GT in Lang. Use Signaling Games Replicator Dyn.

More information

Models of Strategic Reasoning Lecture 2

Models of Strategic Reasoning Lecture 2 Models of Strategic Reasoning Lecture 2 Eric Pacuit University of Maryland, College Park ai.stanford.edu/~epacuit August 7, 2012 Eric Pacuit: Models of Strategic Reasoning 1/30 Lecture 1: Introduction,

More information

Reductio, Coherence, and the Myth of Epistemic Circularity. Tomoji Shogenji

Reductio, Coherence, and the Myth of Epistemic Circularity. Tomoji Shogenji Page 1 In Frank Zenker (ed.), Bayesian Argumentation (Springer 2012), pp. 165-184. Reductio, Coherence, and the Myth of Epistemic Circularity Tomoji Shogenji ABSTRACT In the argument by reductio ad absurdum

More information

Negotiation: Strategic Approach

Negotiation: Strategic Approach Negotiation: Strategic pproach (September 3, 007) How to divide a pie / find a compromise among several possible allocations? Wage negotiations Price negotiation between a seller and a buyer Bargaining

More information

Nonlinear Dynamics between Micromotives and Macrobehavior

Nonlinear Dynamics between Micromotives and Macrobehavior Nonlinear Dynamics between Micromotives and Macrobehavior Saori Iwanaga & kira Namatame Dept. of Computer Science, National Defense cademy, Yokosuka, 239-8686, JPN, E-mail: {g38042, nama}@nda.ac.jp Tel:

More information

Toward multiple-agent extensions of possibilistic logic Didier Dubois, Henri Prade

Toward multiple-agent extensions of possibilistic logic Didier Dubois, Henri Prade Toward multiple-agent extensions of possibilistic logic Didier Dubois, Henri Prade Abstract Possibilistic logic is essentially a formalism for handling qualitative uncertainty with an inference machinery

More information

Do Imprecise Credences Make Sense?

Do Imprecise Credences Make Sense? THE BASIC IDEAS OF BAYESIAN EPISTEMOLOGY Do Imprecise Credences Make Sense? Jim Joyce Department of Philosophy The University of Michigan jjoyce@umich.edu Many people including Issac Levi, Peter Walley,

More information

Bayes Correlated Equilibrium and Comparing Information Structures

Bayes Correlated Equilibrium and Comparing Information Structures Bayes Correlated Equilibrium and Comparing Information Structures Dirk Bergemann and Stephen Morris Spring 2013: 521 B Introduction game theoretic predictions are very sensitive to "information structure"

More information

Normal Forms for Priority Graphs

Normal Forms for Priority Graphs Johan van Benthem and Davide rossi Normal Forms for Priority raphs Normal Forms for Priority raphs Johan van Benthem and Davide rossi Institute for Logic, Language and Computation d.grossi@uva.nl Abstract

More information

Towards a General Theory of Non-Cooperative Computation

Towards a General Theory of Non-Cooperative Computation Towards a General Theory of Non-Cooperative Computation (Extended Abstract) Robert McGrew, Ryan Porter, and Yoav Shoham Stanford University {bmcgrew,rwporter,shoham}@cs.stanford.edu Abstract We generalize

More information

Lecture 16 : Definitions, theorems, proofs. MTH299 Transition to Formal Mathematics Michigan State University 1 / 8

Lecture 16 : Definitions, theorems, proofs. MTH299 Transition to Formal Mathematics Michigan State University 1 / 8 Lecture 16 : Definitions, theorems, proofs MTH299 Transition to Formal Mathematics Michigan State University 1 / 8 Meanings Definition : an explanation of the mathematical meaning of a word. Theorem :

More information

UNCERTAINTY. In which we see what an agent should do when not all is crystal-clear.

UNCERTAINTY. In which we see what an agent should do when not all is crystal-clear. UNCERTAINTY In which we see what an agent should do when not all is crystal-clear. Outline Uncertainty Probabilistic Theory Axioms of Probability Probabilistic Reasoning Independency Bayes Rule Summary

More information

Philosophy 148 Announcements & Such. Independence, Correlation, and Anti-Correlation 1

Philosophy 148 Announcements & Such. Independence, Correlation, and Anti-Correlation 1 Branden Fitelson Philosophy 148 Lecture 1 Branden Fitelson Philosophy 148 Lecture 2 Philosophy 148 Announcements & Such Independence, Correlation, and Anti-Correlation 1 Administrative Stuff Raul s office

More information

THE LOGIC OF COMPOUND STATEMENTS

THE LOGIC OF COMPOUND STATEMENTS CHAPTER 2 THE LOGIC OF COMPOUND STATEMENTS Copyright Cengage Learning. All rights reserved. SECTION 2.1 Logical Form and Logical Equivalence Copyright Cengage Learning. All rights reserved. Logical Form

More information

In this chapter, we specify a deductive apparatus for PL.

In this chapter, we specify a deductive apparatus for PL. Handout 5 PL Derivations In this chapter, we specify a deductive apparatus for PL Definition deductive apparatus A deductive apparatus for PL is a set of rules of inference (or derivation rules) that determine

More information

The paradox of knowability, the knower, and the believer

The paradox of knowability, the knower, and the believer The paradox of knowability, the knower, and the believer Last time, when discussing the surprise exam paradox, we discussed the possibility that some claims could be true, but not knowable by certain individuals

More information

Guilt in Games. P. Battigalli and M. Dufwenberg (AER, 2007) Presented by Luca Ferocino. March 21 st,2014

Guilt in Games. P. Battigalli and M. Dufwenberg (AER, 2007) Presented by Luca Ferocino. March 21 st,2014 Guilt in Games P. Battigalli and M. Dufwenberg (AER, 2007) Presented by Luca Ferocino March 21 st,2014 P. Battigalli and M. Dufwenberg (AER, 2007) Guilt in Games 1 / 29 ADefinitionofGuilt Guilt is a cognitive

More information

SEMANTICAL CONSIDERATIONS ON NONMONOTONIC LOGIC. Robert C. Moore Artificial Intelligence Center SRI International, Menlo Park, CA 94025

SEMANTICAL CONSIDERATIONS ON NONMONOTONIC LOGIC. Robert C. Moore Artificial Intelligence Center SRI International, Menlo Park, CA 94025 SEMANTICAL CONSIDERATIONS ON NONMONOTONIC LOGIC Robert C. Moore Artificial Intelligence Center SRI International, Menlo Park, CA 94025 ABSTRACT Commonsense reasoning is "nonmonotonic" in the sense that

More information

Accuracy Arguments. Chapter 10

Accuracy Arguments. Chapter 10 Chapter 10 Accuracy Arguments The previous two chapters considered Representation Theorem and Dutch Book arguments for probabilism. We criticized both types of argument on the grounds that they begin with

More information

Computer Science CPSC 322. Lecture 18 Marginalization, Conditioning

Computer Science CPSC 322. Lecture 18 Marginalization, Conditioning Computer Science CPSC 322 Lecture 18 Marginalization, Conditioning Lecture Overview Recap Lecture 17 Joint Probability Distribution, Marginalization Conditioning Inference by Enumeration Bayes Rule, Chain

More information

An AI-ish view of Probability, Conditional Probability & Bayes Theorem

An AI-ish view of Probability, Conditional Probability & Bayes Theorem An AI-ish view of Probability, Conditional Probability & Bayes Theorem Review: Uncertainty and Truth Values: a mismatch Let action A t = leave for airport t minutes before flight. Will A 15 get me there

More information

10/18/2017. An AI-ish view of Probability, Conditional Probability & Bayes Theorem. Making decisions under uncertainty.

10/18/2017. An AI-ish view of Probability, Conditional Probability & Bayes Theorem. Making decisions under uncertainty. An AI-ish view of Probability, Conditional Probability & Bayes Theorem Review: Uncertainty and Truth Values: a mismatch Let action A t = leave for airport t minutes before flight. Will A 15 get me there

More information

Philosophy 148 Announcements & Such. The Probability Calculus: An Algebraic Approach XI. The Probability Calculus: An Algebraic Approach XII

Philosophy 148 Announcements & Such. The Probability Calculus: An Algebraic Approach XI. The Probability Calculus: An Algebraic Approach XII Branden Fitelson Philosophy 148 Lecture 1 Branden Fitelson Philosophy 148 Lecture 2 Philosophy 148 Announcements & Such Administrative Stuff Branden s office hours today will be 3 4. We have a permanent

More information

Negociating with bounded rational agents in environments with incomplete information using an automated agent

Negociating with bounded rational agents in environments with incomplete information using an automated agent Negociating with bounded rational agents in environments with incomplete information using an automated agent Raz Lin, Sarit Kraus, Jonathan Wilkenfeld, James Barry November 2008 Example Example 2 agents:

More information

Epistemic Game Theory

Epistemic Game Theory Epistemic Game Theory Lecture 3 ESSLLI 12, Opole Eric Pacuit Olivier Roy TiLPS, Tilburg University MCMP, LMU Munich ai.stanford.edu/~epacuit http://olivier.amonbofis.net August 8, 2012 Eric Pacuit and

More information

"Arrow s Theorem and the Gibbard-Satterthwaite Theorem: A Unified Approach", by Phillip Reny. Economic Letters (70) (2001),

Arrow s Theorem and the Gibbard-Satterthwaite Theorem: A Unified Approach, by Phillip Reny. Economic Letters (70) (2001), February 25, 2015 "Arrow s Theorem and the Gibbard-Satterthwaite Theorem: A Unified Approach", by Phillip Reny. Economic Letters (70) (2001), 99-105. Also recommended: M. A. Satterthwaite, "Strategy-Proof

More information

Recursive Ambiguity and Machina s Examples

Recursive Ambiguity and Machina s Examples Recursive Ambiguity and Machina s Examples David Dillenberger Uzi Segal May 0, 0 Abstract Machina (009, 0) lists a number of situations where standard models of ambiguity aversion are unable to capture

More information

Definitions: A binary relation R on a set X is (a) reflexive if x X : xrx; (f) asymmetric if x, x X : [x Rx xr c x ]

Definitions: A binary relation R on a set X is (a) reflexive if x X : xrx; (f) asymmetric if x, x X : [x Rx xr c x ] Binary Relations Definition: A binary relation between two sets X and Y (or between the elements of X and Y ) is a subset of X Y i.e., is a set of ordered pairs (x, y) X Y. If R is a relation between X

More information