Theories of subjective probability are viewed as formal languages for analyzing evidence and expressing degrees of belief. This article focuses on two probability langauges, the Bayesian language and the language of belief functions (Shafer, 1976). We describe and compare the semantics (i.e., the meaning of the scale) and the syntax (i.e., the formal calculus) of these languages. We also investigate some of the designs for probability judgment afforded by the two languages.

Isaac Levi and I have different views of probability and decision making. Here, without addressing the merits, I will try to answer some questions recently asked by Levi (1985) about what my view is, and how it relates to his.

The Bayesian model is used in psychology as the reference for the study of dynamic probability judgment. The main limit induced by this model is that it confines the study of revision of degrees of belief to the sole situations of revision in which the universe is static (revising situations). However, it may happen that individuals have to revise their degrees of belief when the message they learn specifies a change of direction in the universe, which is considered (...) as changing with time (updating situations). We analyze the main results of the experimental literature with regard to elementary qualitative properties of these two situations of revision. First, the order effect phenomenon is confronted with the commutative property. Second, an apparent new phenomenon is presented: the redundancy effect that is confronted with the idempotence property. Finally, results obtained in this kind of experimental situations are reinterpreted in the light of pragmatic analysis. (shrink)

Bayesians often confuse insistence that probability judgment ought to be indeterminate (which is incompatible with Bayesian ideals) with recognition of the presence of imprecision in the determination or measurement of personal probabilities (which is compatible with these ideals). The confusion is discussed and illustrated by remarks in a recent essay by R. C. Jeffrey.

ABSTRACTProbability judgements entail a conjunction fallacy if a conjunction is estimated to be more probable than one of its conjuncts. In the context of predication of alternative logical hypothesis, Bayesian logic provides a formalisation of pattern probabilities that renders a class of pattern-based CFs rational. BL predicts a complete system of other logical inclusion fallacies. A first test of this prediction is investigated here, using transparent tasks with clear set inclusions, varying in observed frequencies only. Experiment 1 uses data where (...) BL makes dominant predictions; Experiment 2's predictions were less clear, and we additionally investigated judgements about the second-most probable hypotheses. The results corroborated a pattern-probability account and cannot be easily explained by other theories of CFs. IFs were not limited to conjunctions, but rather occurred systematically for several logical connectives. Thus, pattern-based prob... (shrink)

We advance a model of human probability judgment and apply it to the design of an extrapolation algorithm. Such an algorithm examines a person's judgment about the likelihood of various statements and is then able to predict the same person's judgments about new statements. The algorithm is tested against judgments produced by thirty undergraduates asked to assign probabilities to statements about mammals.

Research into the cognition of conditionals has predominantly focused on conditional reasoning, producing a range of theories which explain associated phenomena with considerable success. However, such theories have been less successful in accommodating experimental data concerning how agents assess the probability of indicative conditionals. Since an acceptable account of conditional reasoning should be compatible with evidence regarding how we evaluate conditionals’ likelihoods, this constitutes a failing of such theories. Section 1 introduces the most dominant established approach to conditional reasoning: (...) mental models theory. Surveying a range of experimental results, I show that mental models theory (along with competing theories) is incapable of fully accounting for findings regarding judgements about conditionals’ probabilities. Section 2 introduces an alternative account of deductive reasoning, the erotetic theory, recently proposed by Koralus and Mascarenhas (2013). Section 3 argues that, given a natural extension, this theory is able to explain the otherwise unaccounted for data. (shrink)

Occasionally, people are called upon to estimate probabilities after an event has occurred. In hindsight, was this an outcome we could have expected? Could things easily have turned out differently? One strategy for performing post hoc probability judgements would be to mentally turn the clock back and reconstruct one's expectations before the event. But if asked about the probability of an alternative, counterfactual outcome, a simpler strategy is available, based on this outcome's perceived closeness to what actually happened. (...) The article presents five studies exploring the relationship between counterfactual closeness and counterfactual probability. The first study indicates that post hoc probabilities typically refer to the counterfactual rather than the factual outcome. Studies 2-5 show that physical, temporal, or conceptual proximity play a decisive role for post hoc probability assessments of counterfactual events. When margins are narrow, the probabilities of, for instance, winning a match (when losing), and of losing (when actually winning) may even be rated higher than the corresponding probabilities of what really happened. Closeness is also more often referred to, and rated to be a better reason for believing there is a ''good chance" of the counterfactual rather than of the factual result occurring. Finally, the closeness of the alternative outcome in success and failure stories is shown to be significantly correlated to its rated probability. (shrink)

Gambles which induce the decision-maker to experience ambiguity about the relative likelihood of events often give rise to ambiguity-seeking and ambiguity-avoidance, which imply violation of additivity and Savage's axioms. The inability of the subjective Bayesian theory to account for these empirical regularities has determined a dichotomy between normative and descriptive views of subjective probability. This paper proposes a framework within which the two perspectives can be reconciled. First, a formal definition of ambiguity is given over a continuum ranging from (...) ignorance to risk, and including ambiguous contexts as subsets. Second, it is shown that the systems of inductive logic account for the effects of ambiguity. Then, Carnap's X-system is applied as a psychological model and compared to Einhorn and Hogarth's non-normative psychological model. Finally, the implications of this research to the modeling of subjective probability judgements are discussed. (shrink)

This paper addresses and rejects claims that one can demonstrate experimentally that most untutored subjects are systematically and incurably irrational in their probability judgements and in some deductive reasoning tasks. From within a strongly subjectivist theory of probability, it develops the notions of resiliency —a measure of stability of judgements—and robustness —a measure of expected stability. It then becomes possible to understand subjects' behaviour in the Wason selection task, in examples which have been claimed to involve a 'base-rate (...) fallacy', in appearing to ignore laws of large numbers and to overestimate their own success rate as entirely rational: a matter of combining first-order probability assignments with judgements about the robustness of those assignments. (shrink)

Explanation and prediction play an important role in medical decision making, particularly for diagnostic and treatment decisions. For the most part, explanations as well as predictions are derived from causal knowledge and have to be made under uncertainty. In cognitive psychology, these phenomena have been approached from two directions. On the one hand, there is research on knowledge representation and inferential reasoning (Holland et al. 1986; Anderson 1990). On the other hand, there is research on heuristics and biases in judgments (...) under uncertainty (Kahneman et al. 1982; Rehm & Gadenne 1990). However, no exact models have yet been developed to describe the process that leads from a given knowledge base to judgments of probabilities, i.e., from causal knowledge about the world to subjective expressions of the uncertainty associated with explanations or predictions. (shrink)

We describe a computational model of two central aspects of people's probabilistic reasoning: descriptive probability estimation and inferential probability judgment. This model assumes that people's reasoning follows standard frequentist probability theory, but it is subject to random noise. This random noise has a regressive effect in descriptive probability estimation, moving probability estimates away from normative probabilities and toward the center of the probability scale. This random noise has an anti-regressive effect in inferential judgement, (...) however. These regressive and anti-regressive effects explain various reliable and systematic biases seen in people's descriptive probability estimation and inferential probability judgment. This model predicts that these contrary effects will tend to cancel out in tasks that involve both descriptive estimation and inferential judgement, leading to unbiased responses in those tasks. We test this model by applying it to one such task, described by Gallistel et al. ). Participants' median responses in this task were unbiased, agreeing with normative probability theory over the full range of responses. Our model captures the pattern of unbiased responses in this task, while simultaneously explaining systematic biases away from normatively correct probabilities seen in other tasks. (shrink)

Consider the task of predicting which soccer team will win the next World Cup. The bookmakers may judge Brazil to be the team most likely to win, but also judge it most likely that a European rather than a Latin American team will win. This is an example of a non-aligned hierarchy structure: the most probable event at the subordinate level (Brazil wins) appears to be inconsistent with the most probable event at the superordinate level (a European team wins). In (...) this paper we exploit such structures to investigate how people make predictions based on uncertain hierarchical knowledge. We distinguish between aligned and non-aligned environments, and conjecture that people assume alignment. Participants were exposed to a non-aligned training set in which the most probable superordinate category predicted one outcome, whereas the most probable subordinate category predicted a different outcome. In the test phase participants allowed their initial probability judgments about category membership to shift their final ratings of the probability of the outcome, even though all judgments were made on the basis of the same statistical data. In effect people were primed to focus on the most likely path in an inference tree, and neglect alternative paths. These results highlight the importance of the level at which statistical data are represented, and suggest that when faced with hierarchical inference problems people adopt a simplifying heuristic that assumes alignment. (shrink)

This research examined choice behaviour and probability judgement in a counterintuitive reasoning problem called the Monty Hall problem (MHP). In Experiments 1 and 2 we examined whether learning from a simulated card game similar to the MHP affected how people solved the MHP. Results indicated that the experience with the card game affected participants' choice behaviour, in that participants selected to switch in the MHP. However, it did not affect their understanding of the objective probabilities. This suggests that there (...) is dissociation between implicit knowledge gained from the task and the explicit understanding as to why switching was the best strategy. In Experiment 3, the number of prizes and doors were manipulated to examine how participants construed the problem space of the MHP. Results revealed that participants partition the probability judgement to reflect the number of prizes over the number of unopened doors. (shrink)

Richard Jeffrey is beyond dispute one of the most distinguished and influential philosophers working in the field of decision theory and the theory of knowledge. His work is distinctive in showing the interplay of epistemological concerns with probability and utility theory. Not only has he made use of standard probabilistic and decision theoretic tools to clarify concepts of evidential support and informed choice, he has also proposed significant modifications of the standard Bayesian position in order that it provide a (...) better fit with actual human experience. Probability logic is viewed not as a source of judgment but as a framework for explaining the implications of probabilistic judgments and their mutual compatability. This collection of essays spans a period of some 35 years and includes what have become some of the classic works in the literature. There is also one completely new piece, while in many instances Jeffrey includes afterthoughts on the older essays. (shrink)

Examining the social and political upheavals that characterized the collapse of public judgment in early modern Europe, Liberating Judgment offers a unique account of the achievement of liberal democracy and self-government. The book argues that the work of John Locke instills a civic judgment that avoids the excesses of corrosive skepticism and dogmatic fanaticism, which lead to either political acquiescence or irresolvable conflict. Locke changes the way political power is assessed by replacing deteriorating vocabularies of legitimacy with (...) a new language of justification informed by a conception of probability. For Locke, the coherence and viability of liberal self-government rests not on unassailable principles or institutions, but on the capacity of citizens to embrace probable judgment. The book explores the breakdown of the medieval understanding of knowledge and opinion, and considers how Montaigne's skepticism and Descartes' rationalism--interconnected responses to the crisis--involved a pragmatic submission to absolute rule. Locke endorses this response early on, but moves away from it when he encounters a notion of reasonableness based on probable judgment. In his mature writings, Locke instructs his readers to govern their faculties and intellectual yearnings in accordance with this new standard as well as a vocabulary of justification that might cultivate a self-government of free and equal individuals. The success of Locke's arguments depends upon citizens' willingness to take up the labor of judgment in situations where absolute certainty cannot be achieved. (shrink)

A diagnostic judgment of a teacher can be seen as an inference from manifest observable evidence on a student’s behavior to his or her latent traits. This can be described by a Bayesian model of in-ference: The teacher starts from a set of assumptions on the student (hypotheses), with subjective probabilities for each hypothesis (priors). Subsequently, he or she uses observed evidence (stu-dents’ responses to tasks) and knowledge on conditional probabilities of this evidence (likelihoods) to revise these assumptions. Many (...) systematic deviations from this model (biases, e.g. base-rate neglect, inverse fallacy) are reported in literature on Bayesian reasoning. In a teacher’s situation the information (hypotheses, priors, likelihoods) is usually not explicitly represented numerically (as in most research on Bayesian reasoning), but only by qualitative esti-mations in the mind of the teacher. In our study, we ask to which extent individuals (approximately) apply a rational Bayesian strategy or resort on other biased strategies of processing information for their diagnostic judgments. We explicitly pose this question with respect to non-numerical settings. To investigate this question, we developed a scenario that visually displays all relevant information (hypotheses, priors, likelihoods) in a graphically displayed hypothesis space (called “hypothegon”) – without recurring to numerical representations or mathematical procedures. 42 pre-service teach-ers were asked to judge the plausibility of different misconceptions of six students based on their responses to decimal comparison tasks (e.g. 3.39 > 3.4). Applying a Bayesian classification proce-dure, we identified three updating strategies: A Bayesian update strategy (BUS, processing all probabilities), a combined evidence strategy (CES, ignoring the prior probabilities but including all likelihoods), and a single evidence strategy (SES, only using the likelihood of the most probable hypothesis). In study 1, an instruction on the relevance of using all probabilities (priors and likelihoods) only weakly increased the processing of more information. In study 2, we found strong evidence that a visual explication of the prior-likelihood interaction led to an increase of processing the interaction of all relevant information. These results show that the phenomena found in general research on Bayesian reasoning in numerical settings extend to diagnostic judgments in non-numerical settings. (shrink)

We propose a theory that relates perceived evidence to numerical probability judgment. The most successful prior account of this relation is Support Theory, advanced in Tversky and Koehler. Support Theory, however, implies additive probability estimates for binary partitions. In contrast, superadditivity has been documented in Macchi, Osherson, and Krantz, and both sub- and superadditivity appear in the experiments reported here. Nonadditivity suggests asymmetry in the processing of focal and nonfocal hypotheses, even within binary partitions. We extend Support (...) Theory by revising its basic equation to allow such asymmetry, and compare the two equations’ ability to predict numerical assessments of probability from scaled estimates of evidence for and against a given proposition. Both between- and within-subject experimental designs are employed for this purpose. We find that the revised equation is more accurate than the original Support Theory equation. The implications of asymmetric processing on qualitative assessments of chance are also briefly discussed. (shrink)

Research suggests that moral evaluations change during adulthood. Older adults (75+) tend to judge accidentally harmful acts more severely than younger adults do, and this age‐related difference is in part due to the greater negligence older adults attribute to the accidental harmdoers. Across two studies (N = 254), we find support for this claim and report the novel discovery that older adults’ increased attribution of negligence, in turn, is associated with a higher perceived likelihood that the accident would occur. We (...) propose that, because older adults perceive accidents as more likely than younger adults do, they condemn the agents and their actions more and even infer that the agents’ omission to exercise due care is intentional. These findings refine our understanding of the cognitive processes underpinning moral judgment in older adulthood and highlight the role of subjective probability judgments in negligence attribution. (shrink)

Abductive reasoning assigns special status to the explanatory power of a hypothesis. But how do people make explanatory judgments? Our study clarifies this issue by asking: How does the explanatory power of a hypothesis cohere with other cognitive factors? How does probabilistic information affect explanatory judgments? In order to answer these questions, we conducted an experiment with 671 participants. Their task was to make judgments about a potentially explanatory hypothesis and its cognitive virtues. In the responses, we isolated three constructs: (...) Explanatory Value, Rational Acceptability, and Entailment. Explanatory judgments strongly cohered with judgments of causal relevance and with a sense of understanding. Furthermore, we found that Explanatory Value was sensitive to manipulations of statistical relevance relations between hypothesis and evidence, but not to explicit information about the prior probability of the hypothesis. These results indicate that probabilistic information about statistical relevance is a strong determinant of Explanatory Value. More generally, our study suggests that abductive and probabilistic reasoning are two distinct modes of inference. (shrink)

The conjunction fallacy has been a key topic in debates on the rationality of human reasoning and its limitations. Despite extensive inquiry, however, the attempt to provide a satisfactory account of the phenomenon has proved challenging. Here we elaborate the suggestion (first discussed by Sides, Osherson, Bonini, & Viale, 2002) that in standard conjunction problems the fallacious probability judgements observed experimentally are typically guided by sound assessments of _confirmation_ relations, meant in terms of contemporary Bayesian confirmation theory. Our main (...) formal result is a confirmation-theoretic account of the conjunction fallacy, which is proven _robust_ (i.e., not depending on various alternative ways of measuring degrees of confirmation). The proposed analysis is shown distinct from contentions that the conjunction effect is in fact not a fallacy, and is compared with major competing explanations of the phenomenon, including earlier references to a confirmation-theoretic account. (shrink)

Carnap [1] aims to provide a formal explication of an informal concept (relation) he calls “confirmation”. He clarifies “E confirms H” in various ways, including: (∗) E provides some positive evidential support for H. His formal explication of “E confirms H” (in [1]) is: (1) E confirms H iff Pr(H | E) > r, where Pr is a suitable (“logical”) probability function, and r is a threshold value.

A familiar adage in the philosophy of science is that general hypotheses are better supported by varied evidence than by uniform evidence. Several studies suggest that young children do not respect this principle, and thus suffer from a defect in their inductive methodology. We argue that the diversity principle does not have the normative status that psychologists attribute to it, and should be replaced by a simple rule of probability. We then report experiments designed to detect conformity to the (...) latter rule in children's inductive judgment. The results suggest that young children in both the United States and Taiwan are sensitive to the constraints imposed by the rule on judgments of probability and evidential strength. We conclude with a suggested reinterpretation of the thesis that children's inductive methodology qualifies them as “little scientists.”. (shrink)

PROBEX (PROBabilities from EXemplars), a model of probabilistic inference and probability judgment based on generic knowledge is presented. Its properties are that: (a) it provides an exemplar model satisfying bounded rationality; (b) it is a “lazy” algorithm that presumes no pre‐computed abstractions; (c) it implements a hybrid‐representation, similarity‐graded probability. We investigate the ecological rationality of PROBEX and find that it compares favorably with Take‐The‐Best and multiple regression (Gigerenzer, Todd, & the ABC Research Group, 1999). PROBEX is fitted (...) to the point estimates, decisions, and probability assessments by human participants. The best fit is obtained for a version that weights frequency heavily and retrieves only two exemplars. It is proposed that PROBEX implements speed and frugality in a psychologically plausible way. (shrink)

There is a vast literature that seeks to uncover features underlying moral judgment by eliciting reactions to hypothetical scenarios such as trolley problems. These thought experiments assume that participants accept the outcomes stipulated in the scenarios. Across seven studies, we demonstrate that intuition overrides stipulated outcomes even when participants are explicitly told that an action will result in a particular outcome. Participants instead substitute their own estimates of the probability of outcomes for stipulated outcomes, and these probability (...) estimates in turn influence moral judgments. Our findings demonstrate that intuitive likelihoods are one critical factor in moral judgment, one that is not suspended even in moral dilemmas that explicitly stipulate outcomes. Features thought to underlie moral reasoning, such as intention, may operate, in part, by affecting the intuitive likelihood of outcomes, and, problematically, moral differences between scenarios may be confounded with non-moral intuitive probabilities. (shrink)

The proceedings of a June 1995 conference in Luino, Italy. One poem and 16 papers explore various issues in the philosophy of science with an emphasis on the foundations of probability and statistics and quantum mechanics. The topics include subjective probability, Bayesian statistics, probability kinematics, causal decision making, and probability and realism in quantum mechanics. The problem of collecting new evidence and updating probability judgements are addressed in reference to different applications. No index. Reprinted from (...) Erkenntnis vol. 45, nos. 2-3 (1996). Annotation copyrighted by Book News, Inc., Portland, OR. (shrink)

Probability has become one of the most characteristic con cepts of modern culture, and a 'probabilistic way of thinking' may be said to have penetrated almost every sector of our in tellectual life. However it would be difficult to determine an explicit list of 'positive' features, to be proposed as identifica tion marks of this way of thinking. One would rather say that it is characterized by certain 'negative' features, i. e. by certain at titudes which appear to be (...) the negation of well established tra ditional assumptions, conceptual frameworks, world outlooks and the like. It is because of this opposition to tradition that the probabilistic approach is perceived as expressing a 'modern' in tellectual style. As an example one could mention the widespread diffidence in philosophy with respect to self -contained systems claiming to express apodictic truths, instead of which much weaker pretensions are preferred, that express 'probable' interpretations of reality, of history, of man (the hermeneutic trend). An ana logous example is represented by the interest devoted to the study of different patterns of 'argumentation', dealing wiht reasonings which rely not so much on the truth of the premisses and stringent formal logic links, but on a display of contextual conditions (depending on the audience, and on accepted stan dards, judgements, and values), which render the premisses and the conclusions more 'probable' (the new rhetoric). (shrink)

Descartes’ first book, 1618’s Compendium of Music, focuses on biomechanical reactions in the human body but also claims that the purpose of art is to arouse emotions. By the end of the 1630s, however, he had given up on precisely predicting how that arousal may occur. This article contends, though, that Descartes’ abandonment of that project is a result of using an inappropriate psychological model for such predictions. An appropriate model is developed in his last book, 1649’s The Passions of (...) the Soul. Especially by attending to generosity and desire, the probability of predicting aesthetic judgments is at least increased. In increasing it, Descartes’ abandonment need not be total. (shrink)

We propose a model of uncertain belief. This models coherent beliefs by a filter, ????, on the set of probabilities. That is, it is given by a collection of sets of probabilities which are closed under supersets and finite intersections. This can naturally capture your probabilistic judgements. When you think that it is more likely to be sunny than rainy, we have{????|????(????????????????????)>????(????????????????????)}∈????. When you think that a gamble ???? is desirable, we have {????|Exp????[????]>0}∈????. It naturally extends the model of credal (...) sets; and we will show it captures all the expressive power of the desirable gambles model. It also captures the expressive power of sets of desirable gamble sets (with a mixing axiom, but no Archimadean axiom). (shrink)