# Pollock s Theory of Defeasible Reasoning

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1 s Theory of Defeasible Reasoning Jonathan University of Toronto Northern Institute of Philosophy June 18, 2010

2 Outline 1 2 Inference 3 s 4 Success Stories: The of Acceptance 5 6 Topics

3

4 1 Problematic Bayesian Idealizations 1 Computational Demands 2 Storage Demands 3 Logical Omniscience 2 Representing Sequential Reasoning 3 of Acceptance 1 The Lottery Paradox 2 The Lottery Paradox Paradox 3 The Preface Paradox 4 The Epistemological Role of Non-Doxastic States 5 Dissatisfaction with Standard Non-Monotonic Logics

5 Problematic Bayesian Idealizations Computational Demands: a Bayesian agent updates all her probabilities with each new piece of evidence. Computationally demanding, often wasteful. At odds with our actual reasoning. Storage Demands: a Bayesian agent stores a real number for each conditional belief, a combinatorial nightmare ( 2008). Suppose an agent has 300 beliefs. The number of conditional probabilities of the form p(a B 1...B n ) that must be stored is about > the number of particles in the universe. Logical Omniscience: a Bayesian agent assigns probability 1 to all logical truths, but we surely can t and don t. (2008) advertises his framework as avoiding the first two problems. I m advertising it as avoiding the last.

6 Representing Sequential Reasoning A lot of our reasoning appears to be sequential, in two ways: Collecting reasons. Deploying reasons. Bayesianism, DST, ranking theory, etc. all ignore this reality. As a result, they may fail to acknowledge beliefs that are justified despite not taking account of all the evidence. If other cognitive demands (pragmatic or epistemic) rationally interrupt a train of reasoning, you may be justified in believing the conclusions drawn so far.

7 of Acceptance s treatment of the paradoxes of acceptance respects the following desiderata. Preface: you are justified in believing the claims in your book. Lottery: you are not justified in believing your ticket will win. Conjunction: if you are justified in believing A and B, you are justified in believing A&B. This package is very hard to come by.

8 The Epistemological Role of Non-Doxastic States On many epistemological views, non-doxastic states play a role in justifying beliefs: Perceptual states Memories Module outputs On some views, non-doxastic states alone justify:, Pryor On others, they do so in conjunction with background beliefs: Vogel, White? But formal epistemologies almost never address the justificatory role of non-doxastic states.

9 Dissatisfaction with Standard Non-Monotonic Logics s reasons for dissatifaction with other non-monotonic formalisms vary from case to case: Too limited Implausible results Off-topic For a survey, see ( 1995: 104-9).

10 Inference

11 Epistemic States In s system, an agent s epistemic state is represented by an inference graph. Nodes: reasons and the propositions they bear on. Directed edges: relations of support and defeat. Example: F B P

12 Defeat: Rebutting vs. Undercutting acknowledges two kinds of defeaters: 1 Rebutters: R is a rebutting defeater of P if it is a reason for P. 2 Undercutters: U is an undercutting defeater of P as a reason for Q if it is a reason for (P wouldn t be true unless Q were true). The negated conditional is symbolized P Q. So the previous example is properly represented: F B F B P

13 Example: Rebutting Defeat Example: Pam says that Robert will be at the party, whereas Qbert says he won t be: R R P Q

14 Inference Rules Where do the arrows come from? That is, when is one thing a reason for another? proposes a number of inference rules in various writings, but does not pretend to have a complete list. The methodology: propose rules that seem plausible and test them on numerous examples. Finding a list of complete rules that yield sensible results is a major burden of the theory. Compare the Bayesian s task of specifying rationality constraints on priors: Reflection, PP, Indifference, etc.

15 Inference Rules: Some Examples 1 Perceptual Justification x s appearing R is a defeasible reason for believing that x is R. ( 1971, 1974) Temporal Projection Believing is a defeasible reason for believing + t), the strength of the reason being a monotonic decreasing function of t (for appropriate P). ( 2008) Discontinuity Defeat 1 is an undercutting defeater for the inference by Temporal Projection from 0 to 2. (ibid) Statistical Syllogism If r > 1/2 then Fc & p(g F) r is a prima facie reason for Gc, the strength of the reason being a monotonic increasing function of r. ( 1990,1995) Subproperty Defeat Hc & p(g F&H) p(g F) is an undercutting defeater for the Statistical Syllogism. 1 NB: these are simplified glosses, omitting important qualifiers and details.

16 Initial Nodes: Perception Inference rules tell us how to introduce new nodes into the graph given initial nodes, but where do initial nodes come from? In other words, what can be used as a reason without appeal to a supporting reason? is surprisingly brief on this point. Formally, we just help ourselves to a set of premises: input. He does say, Epistemic reasoning starts with premises that are input to the reasoner. In human beings, these are provided by perception. ( 1995: 39) Perception provides the premises in input from which epistemic cognition reasons forward [... ] (ibid: 47)

17 Initial Nodes: Further Candidates Should other things be included in input too? Existing (justified) beliefs Memory states Outputs of non-perceptual modules Fortunately, we can explore the formalism and many of its applications without answering this question. But it does raise important, tricky questions about what an inference graph is supposed to represent. An agent s epistemic state at a time: the reasons and inferences she is currently aware of? A record of her reasoning over time: all the reasons and inferences she has taken account of in her lifetime? The framework s appeal may depend heavily on our choice here.

18 s

19 Defeat Statuses We want to be able to figure out what beliefs are justified given the reasons and inferences taken into account so far. We want an algorithm for assigning the statuses defeated and undefeated to nodes in a given graph. Using to symbolize defeated and + to symbolize undefeated, we want results like: F B F + R R + B P + + P Q +

20 : A First Attempt Definition: D-initial Node A node is D-initial iff neither it nor any of its ancestors are termini of a defeat link. Then here s a plausible, first attempt: (1) D-initial nodes are undefeated. (2) If the immediate ancestors of node A are undefeated, and all nodes defeating it are defeated, then A is undefeated. (3) If A has a defeated immediate ancestor, or there is an undefeated node that defeats A, then A is defeated. This proposal gets the right results for Tweety and other simple examples.

21 A Problem: Collective Defeat But it does poorly in cases of collective defeat, like our example of conflicting testimony. The only assignments consistent with (1) (3) are: + R R R + R + P Q + + P Q + Both are counterintuitive and unjustifiably anti-symmetric.

22 Another Problem: Self-Defeat If we assign to Q, we violate (2): R P Q Q + P

23 Another Problem: Self-Defeat If we assign + to Q, we violate (3): + + R P Q + Q + P

24 Partial & Maximal Assignments These complications (and others) motivate a more sophisticated approach: Definition: Partial Status Assignments A partial status assignment assigns + and to at least some nodes and satisfies: (P1) All D-initial nodes are undefeated. (P2) A is undefeated iff the immediate ancestors of A are undefeated, and all nodes defeating A are defeated. (P3) A is defeated iff A has a defeated immediate ancestor, or there is an undefeated node that defeats A. Definition: Maximal Status Assignment A status assignment is maximal iff it is partial and is not contained in any larger partial assignment.

25 The Final Proposal Proposal: Supervaluation A node is undefeated iff every maximal status assignment gives it a +; otherwise it is defeated. We can quickly verify that this solves our earlier problems: Collective Defeat: there are two maximal assignments, and R and R each get in one of them. So both are defeated. Self-Defeat: there is only one maximal assignment, which merely assigns + to P. So everything else comes out defeated.

26 The of Acceptance

27 The Lottery Paradox A fair lottery of 100 tickets, with exactly one winner. Let D = The description of the lottery. T i = Ticket #i will win. Then the paradoxical inference graph is: D T 1 T 2. & i ( T i ) i (T i ) T 100

28 Solving the Lottery Paradox The solution lies in noticing that there is a rebutting defeater for each T i. For example, the rebutting defeater for T 1 is the argument for T 1 based on i (T i ) and T 2,..., T 100. T 1 T 1 D T 2 i (T i ). T 100

29 Solving the Lottery Paradox The solution lies in noticing that there is a rebutting defeater for each T i. Similarly, the rebutting defeater for T 2 is the argument for T 2 based on i (T i ) and T 1, T 3,..., T 100. T 1 D T 2 T 2 i (T i ). T 100

30 Solving the Lottery Paradox Every T i gets a on at least one maximal status assignment: For every T k there is a status assignment that assigns + to all the other T i s and to i (T i ). On that status assignment, T k gets a +. So T k gets a. So, in the final reckoning, each T i comes out defeated. So you are not justified in believing of any ticket that it will lose.

31 The Lottery Paradox Paradox Suppose you read about the lottery in the newspaper (R). We then have a different paradoxical challenge: T 1 R D T 2. & i ( T i ) T 100 D The argument has a self-defeating structure! So aren t we unjustified in believing the lottery will happen as described?

32 Solving the Lottery Paradox Paradox This paradox is avoided because the argument for D will always depend on a defeated premise. On every assignment, one of the T i gets a. So the argument for & i (T i ) has a defeated premise on every assignment. So D gets on every assignment. (2008) advertises this result as a superiority of his system over McCarthy s (1980) circumscription semantics for non-monotonic logic (and various sophistications of it).

33 The Preface Paradox The preface paradox appears to have the same structure as the lottery, and so threatens to get the same, skeptical result. Let B = your background knowledge. C i = Claim #i in the book is true. C 1 C 1 B C 2 C 2 i ( C i ). C 100 C 100

34 Solving the Preface Paradox s solution is to undermine the argument for each C i. Each C i is supported by a deductive argument from the remaining C i and i ( C i ). For example, C 100 is supported by a deductive argument from C 1,...,C 99 and i ( C i ) But given C 1,...,C 99, the argument supporting i ( C i ) is defeated! Why? Because if the first 99 claims are true, we no longer have reason to believe that the book contains a falsehood. Our reason to believe the book contains a falsehood is statistical; books of this length typically contain falsehoods. But books of this length where the first 99 claims are true do not typically contain falsehoods!

35 Solving the Preface Paradox The statistical inference from B to to i ( C i ) suffers subproperty defeat on every assignment. Let F: p(falsehood Length) 1. S: p(falsehood Length & C 2 C 100 are true) 1. C 1 S B C 2 F i ( C i ) i ( C i ). C 100 F

36 The Lottery vs. The Preface s treatment of the lottery and the preface trades on a crucial difference: In the lottery, the T i are negatively relevant to one another. In the preface, the C i are not negatively relevant to one another; they are either independent or positively relevant.

37

38 The Generalized Lottery Paradox A threat: any proposition can be viewed as a lottery proposition. (Korb 1992; Douven & Williamson 2006) Every proposition is a member of an inconsistent set of equally, statistically supported propositions. Thus every proposition is subject to collective defeat. Take any proposition P and a fair, 100-ticket lottery: Consider the set of propositions {P, (P & T 1 ),..., (P & T 100 )} Each member is highly probable. The set is inconsistent. So the members suffer collective defeat; none is justified.

39 A Reply explicitly qualifies the Statistical Syllogism with a projectability constraint: To infer that Gc from the fact that p(g F) > r, G must be projectable with respect to F. This restriction is designed to prevent projection based on gruesome statistics. Arguably, one s statistical evidence for a proposition like (P & T i ) (if we even have such evidence) is gruesome. So might reply that these propositions can t even be introduced into the inference graph by appeal to SS. 2 2 Cf. footnote 5 of (Douven & Williamson 2006).

40 Bootstrapping s treatment of the preface threatens to lead to bootstrapping. is deeply committed to the Conjunction Principle. So you re not only justified in believing each claim in your book, you re justified in believing their conjunction! Such immodesty has a way of fuelling itself: Struck by your accomplishment, you increase your estimation of your reliability as a researcher. Heartened, you sit down to write another book, which again turns out to be error-free! Lather, rinse, repeat. You conclude that you are infallible.

41 A Shameless Plug This problem for supports a general view I like. The received view: bootstrapping is a problem for basic knowledge theories like reliabilism and dogmatism. (Vogel 2000, 2008; Cohen 2002; van Cleve 2003) My view: bootstrapping is not a symptom of basic knowledge, it is a problem for everyone. Bootstrapping puzzles show that justified beliefs/knowledge cannot always be used as premises in further reasoning. (, forthcoming) Another example: Williamson s E = K thesis. Suppose Starla reads the first sentence in today s paper, P, coming to know that P and that the newspaper says P. She conditionalizes her evidential probabilities on this new knowledge, increases the probability that the newspaper is reliable.

42 Mixed Lotteries Lasonen-Aarnio (2010) objects that s theory must treat mixed lotteries like the preface paradox: A mixed lottery: take one ticket from the Ontario lottery, one from the Quebec lottery, one from the Texas lottery, one from the UK lottery, etc. The probability of each ticket losing is very high. The probability of at least one winning is very high. But the T i are not negatively relevant; they are probabilistically independent. So the sub-property defeat that yielded the non-skeptical result in the preface paradox should happen here too. In short: mixed lotteries have the probabilistic structure of a preface case, so they should get the same, non-skeptical result.

43 My Response It s not clear to me that is committed to treating the mixed lottery the same as a preface. In a mixed lottery, each ticket is still a member of a regular lottery. So each T i still suffers collective defeat. In terms of defeat statuses: it is still the case that for each T i, there is a status assignment that gives it a. Adding to a standard lottery graph the extra structure that comes with a mixed lottery does not rule out the status assignment that assigned to T i.

44 Topics

45 Variable Degrees of Justification A natural next step is to ask how to compute defeat statuses when the degrees of justification of various arrows varies. See ( 2001) for the details, or the expanded version online (have your LISP compiler handy). Some notable features of s views here: The Weakest Link Principle: the degree of support for a conclusion of an argument is the lowest degree of support in its ancestry. Non-Accrual of Reasons: having more than one reason for a conclusion does not increase its degree of justification.

46 Interest-Driven Reasoning One of the most striking features of s implementation of his system for defeasible reasoning (OSCAR) is the fact that it is interest-driven. OSCAR doesn t just churn out theorems in some random or lexicographic order. It searches for answers relevant to the questions or practical problems at hand. The architecture for this behaviour is laid out in Chapter 4 of Cognitive Carpentry.

47 Decisions & Planning (1995: ) rejects standard decision theory. Standard decision theory overlooks the importance of planning. The Button Problem: if you press buttons A, B, C, and D, you get 10; if you press button E you get 5. argues that, on standard decision theory, pushing button A does not maximize expected utility. (1995: ch. 5) opts for a two-tier theory of practical reasoning: Agents fist construct plans aimed at goals. They then choose plans based on expected utility maximization.

48 I [1] Stewart Cohen. Basic knowledge and the problem of easy knowledge. Philosophy and Phenomenological Research, 65(2): , [2] Maria Lasonen-Aarnio. Is there a viable account of well-founded belief. Erkenntnis, 72(2): , [3] John L.. Perceptual knowledge. The Philosophical Review, 80(2): , [4] John L.. Knowledge and Justification. Princeton University Press, [5] John L.. The paradox of the preface. Philosophy of Science, 53(2): , [6] John L.. Cognitive Carpentry: A Blueprint for How to Build a Person. MIT Press, [7] John L.. Defeasible reasoning with variable degrees of justification. Artificial Intelligence, 133(2): , [8] John L.. Defeasible reasoning. In Jonathan E. Adler and Lance J. Rips, editors, Reasoning: Studies of Human Inference and its Foundations. Cambridge University Press, 2008.

49 II [9] James Van Cleve. Is knowledge easy or impossible? externalism as the only alternative to skepticism. In The Skeptics: Contemporary Essays. Aldershot, UK: Ashgate Publishing, [10] Jonathan Vogel. Reliabilism leveled. Journal of Philosophy, XCVII(11): , [11] Jonathan Vogel. Epistemic bootstrapping. Journal of Philosophy, CV(9): , [12] Jonathan. Bootstrapping in general. Philosophy and Phenomenological Research, forthcoming.

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