Deterministic probability: neither chance nor credence

Some have argued that chance and determinism are compatible in order to account for the objectivity of probabilities in theories that are compatible with determinism, like Classical Statistical Mechanics (CSM) and Evolutionary Theory (ET). Contrarily, some have argued that chance and determinism are incompatible, and so such probabilities are subjective. In this paper, I argue that both of these positions are unsatisfactory. I argue that the probabilities of theories like CSM and ET are not chances, but also that they are not subjective probabilities either. Rather, they are a third type of probability, which I call counterfactual probability. The main distinguishing feature of counterfactual-probability is the role it plays in conveying important counterfactual information in explanations. This distinguishes counterfactual probability from chance as a second concept of objective probability.     

First- and third-person approaches in implicit learning research

How do we find out whether someone is conscious of some information or not? A simple answer is “We just ask them”! However, things are not so simple. Here, we review recent developments in the use of subjective and objective methods in implicit learning research and discuss the highly complex methodological problems that their use raises in the domain.     

We are All Bayesian,Everyone is Not a Bayesian

Medical research makes intensive use of statistics in order to support its claims. In this paper we make explicit an epistemological tension between the conduct of clinical trials and their interpretation: statistical evidence is sometimes discarded on the basis of an (often) underlined Bayesian reasoning. We suggest that acknowledging the potentiality of Bayesian statistics might contribute to clarify and improve comprehension of medical research. Nevertheless, despite Bayesianism may provide a better account for scientific inference with respect to the standard frequentist approach, Bayesian statistics is rarely adopted in clinical research. The main reason lies in the supposed subjective elements characterizing this perspective. Hence, we discuss this objection presenting the so-called Reference analysis, a formal method which has been developed in the context of objective Bayesian statistics in order to define priors which have a minimal or null impact on posterior probabilities. Furthermore, according to this method only available data are relevant sources of information, so that it resists the most common criticisms against Bayesianism.

     

A Logic For Inductive Probabilistic Reasoning

Inductive probabilistic reasoning is understood as the application of inference patterns that use statistical background information to assign (subjective) probabilities to single events. The simplest such inference pattern is direct inference: from “70% of As are Bs” and “a is an A” infer that a is a B with probability 0.7. Direct inference is generalized by Jeffrey’s rule and the principle of cross-entropy minimization. To adequately formalize inductive probabilistic reasoning is an interesting topic for artificial intelligence, as an autonomous system acting in a complex environment may have to base its actions on a probabilistic model of its environment, and the probabilities needed to form this model can often be obtained by combining statistical background information with particular observations made, i.e., by inductive probabilistic reasoning. In this paper a formal framework for inductive probabilistic reasoning is developed: syntactically it consists of an extension of the language of first-order predicate logic that allows to express statements about both statistical and subjective probabilities. Semantics for this representation language are developed that give rise to two distinct entailment relations: a relation ⊨ that models strict, probabilistically valid, inferences, and a relation that models inductive probabilistic inferences. The inductive entailment relation is obtained by implementing cross-entropy minimization in a preferred model semantics. A main objective of our approach is to ensure that for both entailment relations complete proof systems exist. This is achieved by allowing probability distributions in our semantic models that use non-standard probability values. A number of results are presented that show that in several important aspects the resulting logic behaves just like a logic based on real-valued probabilities alone.     

Subjective probabilities and values of socio-economic events at different degrees of uncertainty

Abstract.— The relationship between values (negative] positive) and subjective probabilities was studied. Stimuli consisted of unique socio-economic events represented in the political discussion in Finland. Uncertainty was measured as hesitation in estimating probabilities, The results indicated different relationships between values and probability estimates at different degrees of uncertainty. At certainty the relation was u-shaped, at some uncertainty the variables were nearly uncorrelated, and at uncertainty an linear relationship between values and subjective probabilities was found.     

Likelihood judgment based on previously observed outcomes: the alternative-outcomes effect in a learning paradigm

Previous research has demonstrated that intuitive perceptions of certainty regarding a focal outcome are sensitive to variations in how evidence supporting nonfocal alternatives is distributed, even when such variations have no bearing on objective probability. We investigated this alternative-outcomes effect in a learning paradigm in which participants made likelihood judgments on the basis of their memory for past observations of relevant outcomes. In Experiment 1, a manipulation of evidence (observed frequencies) across alternative outcomes influenced not only intuitive certainty estimates about a focal outcome but also numeric subjective probabilities. Experiment 2 ruled out the possibility that these effects were attributable to the influence of information loss on frequency estimations. The findings were consistent with the heuristic comparison account, which suggests that the judged likelihood of a focal outcome will be disproportionately influenced by the strength (frequency) of the strongest alternative outcome.     

Deceptive updating and minimal information methods

The technique of minimizing information (infomin) has been commonly employed as a general method for both choosing and updating a subjective probability function. We argue that, in a wide class of cases, the use of infomin methods fails to cohere with our standard conception of rational degrees of belief. We introduce the notion of a deceptive updating method and argue that non-deceptiveness is a necessary condition for rational coherence. Infomin has been criticized on the grounds that there are no higher order probabilities that ??support?? it, but the appeal to higher order probabilities is a substantial assumption that some might reject. Our elementary arguments from deceptiveness do not rely on this assumption. While deceptiveness implies lack of higher order support, the converse does not, in general, hold, which indicates that deceptiveness is a more objectionable property. We offer a new proof of the claim that infomin updating of any strictly-positive prior with respect to conditional-probability constraints is deceptive. In the case of expected-value constraints, infomin updating of the uniform prior is deceptive for some random variables but not for others. We establish both a necessary condition and a sufficient condition (which extends the scope of the phenomenon beyond cases previously considered) for deceptiveness in this setting. Along the way, we clarify the relation which obtains between the strong notion of higher order support, in which the higher order probability is defined over the full space of first order probabilities, and the apparently weaker notion, in which it is defined over some smaller parameter space. We show that under certain natural assumptions, the two are equivalent. Finally, we offer an interpretation of Jaynes, according to which his own appeal to infomin methods avoids the incoherencies discussed in this paper.     

A Conceptual Overview of the Self‐Presentational Concerns and Response Tendencies of Focus Group Participants

Focus group respondents are often requested to perform tasks that require them to convey information about themselves. However, despite the potential for respondents to have self‐presentational concerns, research on focus group productivity has virtually ignored extant scholarship on impression management. This shortcoming is addressed by presenting a conceptual overview of the effects of self‐presentational concerns on focus group participation. A product of this overview is a conceptual model that posits that the amount and nature of information that people convey about themselves to others is a function of their eagerness to make desired impressions and their subjective probabilities of doing so. According to the model, when focus group participants are highly motivated to make desired impressions, they may be reluctant to present unbiased images of themselves. However, they are not likely to deceive unless they are confident in their abilities to ascertain and enact desired images. Those who are motivated to make desired impressions but are doubtful of doing so are likely to protect themselves by concealing self‐relevant information or avoiding self‐relevant issues. Implications of this model for research and practice are discussed.     

主观概率判断的支持理论

支持理论是一个关于主观概率判断的非外延性理论,它有以下几个主要观点：1.主观概率判断受到描述的影响,具有描述的依赖性;2.主观概率判断的结果是判断者对中心假设的相对支持的反映;3.主观概率在二元判断中表现出二元互补性,在多元判断中表现出次可加性;4.主观概率判断存在分解效应和促进效应     

Client as expert: A Delphi poll of clients’ subjective experience of therapeutic alliance formation variables

This study extends prior research that identified client-derived variables believed to contribute to therapeutic alliance development. Forty-two clients participated in a three-round-modified Delphi poll in which the subjective importance of 74 client-derived, common alliance formation variables was rated using a six-point scale. Participants consensually identified 23 variables as highly important, five as moderately important and three as low in importance to the formation of a strong alliance. Variables related to validation and asking about parts of the client’s life other than the presenting concern were considered as most important by at least one-third of the sample. Limitations of the study are presented and implications for theory, measurement, practice and future research are briefly discussed.     

Teenagers' perceived and actual probabilities of pregnancy

This study sought to determine how teenagers' actual and perceived probabilities of pregnancy are related. Data were collected from interviews with 425 women aged nineteen or younger who attended the Door, a multiservice center for youth in New York City. Subjects were asked to estimate their likelihood of becoming pregnant the last time they had intercourse, and to indicate the dates of both last intercourse and last menstrual period in order to determine their objective and subjective assessments of risk of pregnancy. It was found that the perceived probability of pregnancy was not highly correlated with actual risk: the distributions of perceived probability of pregnancy were nearly identical for both those who were and were not at actual risk. Moreover, respondents' level of knowledge about the timing of ovulation was not significantly related to the accuracy of their risk assessment. The findings also indicate that teenagers' use of contraception is guided more by their perceived risk of pregnancy than by the actual risk. Policy implications are discussed.     

The Mental Status Examination: Its Use by Professional Counselors

Although not a formal psychometric instrument, the mental status examination (MSE) has been used predominantly in psychiatry, clinical psychology, and social work for several decades, but is being increasingly used by counselors in work settings requiring assessment, diagnosis, and treatment of mental disorders. The MSE is used to obtain information about the client's level of functioning and self‐presentation. Generally conducted (formally or informally) during the initial or intake interview, the MSE can also provide counselors with a helpful format for organizing objective (observations of clients) and subjective (data provided by clients) information to use in diagnosis and treatment planning.     

Causal Probability

Examples growing out of the Newcomb problem have convinced many people that decision theory should proceed in terms of some kind of causal probability. I endorse this view and define and investigate a variety of causal probability. My definition is related to Skyrms' definition, but proceeds in terms of objective probabilities rather than subjective probabilities and avoids taking causal dependence as a primitive concept.     

Prognosis in medicine: An analysis of its meaning and rôles

The medical concept of prognosis is analysed into its basic constituents: patient data, medical intervention, outcome, utilities and probabilities; and sources of utility and probability values are discussed. Prognosis cannot be divorced from contemplated medical action, nor from action to be taken by the patient in response to prognostication. Regrettably, the usual decision-theoretic approach ignores this latter aspect. Elicitation of utilities, decision contemplation and prognostic counselling interweave, diagnostics playing a subsidiary role in decision-oriented clinical practice. At times the doctor has grounds for withholding information. As this is known to the patient, prognostic counselling becomes a conflict-prone and rationality-thwarting activity. The meaning of standard phrases such as “prognosis of a disease”, “the prognosis of this patient”, “the prognosis is unknown”, is examined.     

Subjective probabilities and moral judgments

The two experiments reported here examined the relationship between subjective probability estimates and moral judgments (credit and blame assignment, trait attributions, and behavior evaluations). Subjects read about situations that varied in outcome valence (moral or immoral); in addition, the nature of situational demands (Experiment 1) or behavior frequency (Experiment 2) was varied. In the first experiment, subjective probabilities were related to judgments of immoral behaviors (but not moral behaviors), whereas the situational demands only had an impact on judgments of moral behaviors. Experiment 2 included a wider range of behavioral situations, and the probability estimates and moral judgments were assessed independently. In contrast to the first experiment, subjective probabilities were related to trait and behavior ratings of both moral and immoral acts. Consistent with the first experiment, however, subjective probabilities predicted blame but not credit. Across both studies, the prior expectancies were more strongly related to evaluations of immoral acts than moral acts. Implications for understanding the determinants of judgments of moral and immoral acts are discussed.     

Unknown probabilities

From a point of view like de Finetti's, what is the judgmental reality underlying the objectivistic claim that a physical magnitude X determines the objective probability that a hypothesis H is true? When you have definite conditional judgmental probabilities for H given the various unknown values of X, a plausible answer is sufficiency, i.e., invariance of those conditional probabilities as your probability distribution over the values of X varies. A different answer, in terms of conditional exchangeability, is offered for use when such definite conditional probabilities are absent.     

Content Effects on Decision Making

How does the domain or subject matter of a decision problem affect the outcome of the decision? Although decision-making research typically dismisses content as merely a cover story, the present research shows that it plays a fundamental role in the decision process by influencing the information processing that underlies it. An experiment is reported in which the same basic decision problem was presented in several content domains (legal traffic tickets, academic course grades, stock investments, and casino gambling). The changes in content led to changes in both strategies and mental representations, which in turn led to changes in decision outcomes, even though measures of the subjective utilities of the options remained unchanged.     

Robust interactive decision-analysis (RID): Behavioral results and implications

We have proposed a novel interactive procedure for performing decision analysis, called Robust Interactive Decision Analysis (RID), which permits a decision maker (DM) to voluntarily and interactively express strong (viz, sure) binary preferences for actions, partial decision functions, and full decision functions, and only imprecise probability and utility function assessments. These serve as INPUTS TO operators to prune the state probability space and decision space until an optimal choice strategy is obtained. The viability of the RID approach depends on a DM's ability to provide such information consistently and meaningfully. On a limited scale we experimentally investigate the behavioral implications of the RID method in order to ascertain its potential operational feasibility and viability. More specifically, we examine whether a DM can (1) express strong preferences between pairs of vectors of unconditional and conditional payoffs or utilities consistently; (2) provide imprecise (ordinal and interval) state probabilities that are individually as well as mutually consistent with the state probabilities imputed from the expressed strong preferences. The results show that a DM can provide strong individually and mutually consistent preference and ordinal probability information. Moreover, most individuals also appear to be able to provide interval probabilities that are individually and mutually consistent with their strong preference inputs. However, the several violations observed, our small sample size, and the limited scope of our investigation suggest that further experimentation is needed to determine whether and/or how such inputs should be elicited. Overall, the results indicate that the RID method is behaviorally viable.     

Group interpersonal skills training in a natural setting: an experimental study.

This study assessed effectiveness of group interpersonal skills training conducted in a natural setting with nonanalogue clients. Subjects (Ss) in a behavioral-training condition received 4 hr of instruction consisting of modeling, behavioral rehearsal, coaching, feedback and reinforcement. Training focused on positive and negative social responses and on initiating interactions, as well as reacting to interactions initiated by others. Subjects in a discussion-control condition engaged in focused discussion of interpersonal concerns but received no experiential practice. Within a pre-test-post-test control group design, subjective and objective measures were used to assess training effects. When compared to Ss involved in group discussion. Ss participating in group behavioral training revealed greater pre- to post-test changes on selfreported probability of engaging in selected interpersonal responses and on objective measures of eye contact, speech duration, positive affective responses, use of no-statements, compliance, refusals and requests for new behavior. Support for generalization of training is presented and methodological issues are discussed.     

A random support model of the calibration of subjective probabilities

A stochastic model of the calibration of subjective probabilities based on support theory (Rottenstreich and Tversky, 1997, Tversky and Koehler, 1994) is presented. This model extends support theory—a general representation of probability judgment—to the domain of calibration, the analysis of the correspondence between subjective and objective probability. The random support model can account for the common finding of overconfidence, and also predicts the form of the relationship between overconfidence and item difficulty (the “hard–easy effect”). The parameters of the model have natural psychological interpretations, such as discriminability between correct and incorrect hypotheses, and extremity of judgment. The random support model can be distinguished from other stochastic models of calibration by: (a) using fewer parameters, (b) eliminating the use of variable cutoffs by mapping underlying support directly into judged probability, (c) allowing validation of model parameters with independent assessments of support, and (d) applying to a wide variety of tasks by framing probability judgment in the integrative context of support theory.