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axioms for extensive quantities, reprinted in Suppes (1969). I have had the great fortune of being a student of these inspiring teachers. As a student and later as ...
ZOLTAN DOMOTOR

QUALITATIVE PROBABILITIES REVISITED

ABSTRACT. Granted a de Finetti style qualitative comparative probability relation on a Boolean algebra, necessary and sufficient conditions are given for the existence of an agreeing probability measure on the algebra in finite, countable and arbitrary situations. Partially ordered linear spaces and order-preserving linear functionals are used in proving the results and in explaining why the axiomatization of qualitative probability relations is bound to be complex. The inherent technical difficulites can be overcome by relying on nonstandard representations that are also provided. Extensive work done by Suppes in this area is also discussed, in conjunction with the problem of uniqueness and simplicity. The central aim of this work is to provide a more holistic setting for the axiomatization of comparative probability and its associated representational methodology.

1. INTRODUCTION

The principal object of this paper is to provide axioms for a de Finetti style qualitative probability relation, necessary and sufficient for the existence of an agreeing probability measure in arbitrary Boolean algebras. I take this contribution to be a continuation of the works of Scott (1964), Suppes (1969), and Suppes and Zanotti (1976), mainly in bringing development of the theory of comparative belief more nearly abreast with that of representational measurement theory. In the course of expounding the necessary probabilistic apparatus, I comment amply on what I take to be Suppes's pivotal ideas and results in comparison with related literature. In 1964 Dana Scott published his widely known paper on finite measurement models, in which he applies vector space separation theorems to great effect - to obtain several measurement representation results in general, and to characterize finite qualitative probability structures in particular. My starting point is the familiar 'Scott condition' for qualitative probabilities in finite Boolean algebras, which was quickly included also in Suppes (1967) for its remarkable power and simplicity. But first let me state briefly how I became aware of the issues surrounding the 'measurement' of subjective probability. 197 P. Humphreys (ed.), Patrick Suppes: Scientific Philosopher, Vol. 1, © 1994 Kluwer Academic Publishers.

197-238.

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I owe a primary dept to my teacher, Patrick Suppes, who above all brought me to Stanford in the mid 1960s and taught me how to speak and think in 'measurementese'. I recall most vividly his persistent and gentle prodding in the course of my laughable attempts to find what he called "simple and powerful" measurement theoretic axioms that result in "important representation theorems". During my student years, Suppes's dictum had become so important to many of us that my budget sharing house mate - a well known philosopher and frugal cook, who attended Suppes's intellectually contagious seminars around that time - refused to offer any food on days that had passed without my proving a representation theorem. Those around us, at the time, knew well why I ate so little. I recall, too, that it was Patrick Suppes who drew my attention to Scott's work, whose significance he had quickly appreciated. Some years earlier, they wrote a joint paper on the nature of measurement theories, and of course Suppes was interested in measurement theory at least as early as 1951, as evidenced by his lesser known paper on axioms for extensive quantities, reprinted in Suppes (1969). I have had the great fortune of being a student of these inspiring teachers. As a student and later as a frequent visitor at Stanford, I almost always found the door to Suppes's Ventura Hall office open. In spite of the waiting pile of work each day - "today I'm snowed under", he used to say apologetically - Suppes gave me generously of his time. I am tremendously grateful to him. As many of us are aware, Patrick Suppes has contributed very significantly to a number of areas in measurement theory in general, and measuring subjective probability in particular. I find his papers and books always striking, not only for their importance, but also for the elegance and clarity with which they are written. As far as I can make out, in his work in measurement theory he is driven to find the simplest axioms and the right definitions that enable him to prove almost effortlessly various results under very few assumptions. In this essay, I look at a family of those representation theorems for qualitative probabilities that share the Scott condition and can be derived from various convex set separation theorems or linear functional existence theorems. I begin with the simplest, i.e., Suppes's preferredjinite situation, and then gradually work my way to countable and arbitrary (infinite) qualitative probability spaces. At the end of the paper I introduce an ordered linear space framework that in my view explains why

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representation of qualitative probabilities in infinite domains is bound to be complex. As we shall see, one reason why the decisive axioms for general qualitative probability relations tend to be messy is that they provide existence criteria for order-preserving linear functionals (and hence indirectly also for finitely additive measures) in a very roundabout way - the direct way being normed topological axioms. It appears that no satisfying account has yet been given of the representation of comparative probabilities in infinite Boolean algebras. In particular, I take it to remain an unfinished task to provide simple axioms for a qualitative probability relation in an infinite algebra of events, necessary and sufficient for the existence of an agreeing finitely additive probability measure. Since the situation does not seem to be getting any better in this respect, and since there is no point in having a comparative belief that nobody seems to know how to measure simply, I take this opportunity to present a list of necessary and sufficient conditions that I managed to extract so far, and let others judge whether I was able to live up to Suppes's celebrated dictum of simplicity. Also, I will briefly comment on Scott's (old) unpublished results, summarized in Domotor (1969). It is always a pleasure to acknowledge the originality of his ideas. Along the way, I state a couple of simple facts about the uniqueness of probabilistic representation. 2. MEASURING BELIEF: SOME DEFINITIONS AND PRELIMINARY OBSERVATIONS

The measurement-theoretic viewpoint of 'subjective' probability has become standard after the mid fifties. It was pioneered by Savage (1954) and popularized by Suppes (1956) who, anticipating an important trend in the development of the foundations of probability (and utility), was instrumental in bringing measurement and probability theories even closer together. Indeed, measurement of rational belief was looked at by Suppes as a special case of extensive measurement. Piecing together incompatible events was more like adding physical masses, and comparing the probabilities of occurrence of these events looked very much like comparing objects with respect to their masses. This sort of physical analogy is successfully exploited in Suppes (1969, introductory pages), where uniqueness is the principal bonus. For a systematic codification of the flurry of subsequent research activity in this area, see Krantz, Luce, Suppes and Tversky (1971).

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Measurement theory of rational belief can be seen as part of a larger theory of observation. Here we shall confine ourselves to granting that an observer reasons about the objects of his or her chancy world and parts thereof in terms of • possible, mutually exclusive, and collectively exhaustive outcomes of an experiment, intended to be performed on the object of interest; • observable events, associated with the experiment, whose occurrence depends exclusively on the experimental outcomes; • comparative judgment of the probability of occurrence of events. In accord with the standard trick of modern science and relative to the extant stage of our empirical knowledge, we proceed to attach to these carefully distinguished epistemic items suitable mathematical objects. In particular, the totality of possible outcomes is usually parametrized by a nonempty set n, called the sample space, that the observer regularly revises in face of new information. Relative to the idea of occurrence, events are forced to band together so as to form a logic-like algebra. In the case of classical experiments it has proven adequate to parametrize the realm of observable events by a Boolean algebra Ql of subsets of the sample space n, called the event algebra. So certain subsets of n are singled out as parametrizers of particular events, and we make sure that all empirically meaningful combinations of events fit the Boolean laws of subsets of n. Should the nature of complementary events be called into question, as is the case in constructivist situations, then the event algebra Ql will have to be revised into something considerably more abstract and general, e.g., an intuitionistic Heyting algebra (of open sets of n) - the 'logic' of finite observations. Now if the distributivity law fails in an instance of conjunctively combined events, as is the case in quantum experiments, then the event algebra Ql is revised again. For example, Suppes (1966) suggests using Dynkin algebras instead of Boolean algebras. These are nonempty families of subsets of the sample space n, closed under complementation and disjoint unions of events. In the world of classical systems and the one to be considered in this paper, the collection of observable events forms a Boolean algebra. Along traditional lines, the attached parametrizing space-algebra pair (n, Ql) is called a Boolean space. We say that a Boolean space is finite just in case its algebra of events is finite. Likewise, a Boolean space is called countable iff its Boolean algebra is countable. Since

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not every Boolean algebra can carry a strictly positive and finitely additive probability measure, and since some Boolean algebras are more complete than others, these distinctions will be most instrumental in settling the strength of axioms for qualitative probabilities. Our notation for set-theoretic and probabilistic concepts is standard. We use 0 to refer to the impossible event (zero element), and we write to designate the sure event (unit element) in the event algebra 2(. Finally, the event calculus comes with its customary Boolean operations, including complementation A, union A U B, disjoint union A + B, intersection A n B, and the Boolean subevent relation A ~ B for all events A and B in 2(. With regard to the observer, the assumption is that the judgments of probability are representable in terms of a single binary relation ::5 between events in 2l, called the observer's comparative or qualitative probability. So for a pair of events A and Bin 2(, we write

°

A::5B to mean that event A is judged by the observer as at least as probable as the eventB. The list of derived notions we shall need includes 1. equiprobability:

A

rv

B =df A ::5 B

1\

B ::5 A,

intended to mean that events A and B are judged to be equally probable: A is as probable as B. 2. strict probability:

A -< B =df A ::5 B

1\

not A

rv

B,

intended to mean that A is judged to be (strictly) less probable than B. By way of a familiar mathematical representation trick, we have now arrived at a parametrizing qualitative probability structure (0, 2l, ::5) that has a mathematical life of its own. In particular, we can begin to apply the rich resources of representational measurement theory to it, to look for conditions constraining ::5 that guarantee an agreeing Kolmogorovian probabilistic representation, and so forth. Moving in the reverse direction, we can search for new empirical or epistemological applications of the freshly isolated mathematical object (0,2(,

::5).

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Here I wish to proceed in the forward direction and return to the measurement theoretic problem of representation of qualitative probability structures. I begin, as a way of entering the core of our subject, by specifying a minimal list of conditions that every classical comparative probability relation ought to satisfy. The list is well known and is usually attributed to Bruno de Finetti, although there are traces of it also in Bernstein (1917, pp. 10-25). So we state it as a definition, using de Finetti's name as part of our terminology. DEFINITION 1. A mathematical object (n, m,~) is called a de Finetti space if and only if 1. (n, m) is a Boolean space; 2. ~ is a binary comparative probability relation on m, satisfying the following axioms for all A, B, C, and D in m: (i) (ii)

0 -< n; 0 ~ A;

(iii) [A ~ B 1\ B ~ G] ===> A ~ G; (iv) A ~ B V B ~ A; (v) [A ~ BI\G ~ D] ===> A+C ~ B+D,giventhattheevent pairs (A, G) and (B, D) are disjoint; (vi) A -< B ¢::::::} A + C -< B + G, where the pairs (A, C) and (B, G) are disjoint. On the question of terminology, in this paper I will use the term 'de Finetti space' throughout. De Finetti spaces encapsulate the essence of rationality, exhibited in traditional judgments of comparative belief. Leaving the problem of justification of de Finetti's criteria for rational belief aside, first we must ask whether his comparative belief can be measured. That is, we wish to know whether for every de Finetti space (n, m, ~) there exists an agreeing finitely additive probability measure P on (n, m) that 'measures' or realizes the given comparative belief~. Here 'agreeing' refers to the following representation condition: VA, BE m[A ~ B

¢::::::}

P(A) ~ P(B)].

We know from Savage (1954) that infinite de Finetti spaces do not come with guaranteed agreeing measures in general. Furthermore, we know from the work of Kraft, Pratt, and Seidenberg (1959) that finite de Finetti

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spaces are equally deficient. Simply, in general, de Finetti's rationality constraints governing j are not sufficient for guaranteeing the existence of an agreeing finitely additive probability measure on (0, Ql)! This shortcoming of de Finetti spaces preempts all the other questions that we might ask otherwise (e.g., is transitivity of the equiprobability relation '" justified?). Lack of measurement seems to make the very notion of a de Finetti space useless. Nevertheless, we shall continue to use it for convenience of expression: it will serve well as the shorthand for the familiar and longer list of necessary conditions (nontriviality, nonnegativity, transitivity, comparability, weak and strong monotonicity) that we plan to impose on all qualitative probability relations j, studied in this paper. It is now clear that the measurement program for qualitative probability with which we began remains unfulfilled, if we have no guarantees for the measurement of j with an agreeing probability. We must therefore seek additional empirically justifiable constraints, to be imposed on (0, Ql, j), sufficient for the existence of an agreeing measure on (0, Ql). To get much further with the theory of qualitative probability spaces, we must introduce more axioms about the ways in which qualitative probability can be manipulated. Conceptually there are three fundamentally different ways in which this can be carried out and, as we shall soon see, all of them have been used in the literature in one way or another: • Constraints formulated in a higher-order language of Boolean spaces (0, Ql) (including finiteness and sigma-additivity); • Axioms stated in a second-order language of comparative probability j (including Archimedeanness and continuity); • Conditions expressed in a suitably enlarged language of random variables, ordered by a comparative expectation relation that extends j. Having set the stage, w~ now turn to presenting various representation theorems, first in finite, then in countable and finally in arbitrary Boolean spaces. 3. QUALITATIVE PROBABILITY IN FINITE BOOLEAN SPACES

The present section is devoted to a detailed discussion of the nature and basic consequences of the familiar Scott condition:

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SCUIT'S CONDITION. For any pair of finite sequences AI, ... , An and BI, ... , Bn in Ql we have

given that

u

holds for all k with 1 ~ k

~

n.

Notation. For any de Finetti space (0, Ql, :::S) we write

$(0, Ql, :::S) as an abbreviation for the foregoing Scott condition, interpreted in and applied to the de Finetti space (0, Ql, :::S). Thus all sample space and event algebra variables are presumed to take their values in the de Finetti space (0, Ql, :::S). Readers familiar with Scott's paper will quickly note that the condition stated above is different from that in Scott (1964). Dana Scott uses an equational constraint

involving characteristic functions Ai of events Ai. We know, of course, that every event A E Ql comes with its unique two-valued characteristic function A: ---t IR, where A(w) =df 1 if w E and 0 otherwise. Using mathematical induction, one quickly verifies that the condition I used above is indeed equivalent to that of Scott. All one has to realize we have I:1