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Denić (2018, 2019, To appear) observes that the availability of distributive inferences—for sentences with disjunction embedded in the scope of a universal quantifier—depends on the size of the domain quantified over as it relates to the number of disjuncts. Based on her observations, she argues that probabilistic considerations play a role in the computation of implicatures. In this paper we explore a different possibility. We argue for a modification of Denić's generalization, and provide an explanation that is based on intricate logical computations but is blind to probabilities. The explanation is based on the observation that when the domain size is no larger than the number of disjuncts, universal and existential alternatives are equivalent if distributive inferences are obtained. We argue that under such conditions a general ban on 'fatal competition' (Magri 2009a,b, Spector 2014) is activated, thereby predicting distributive inferences to be unavailable. [ABSTRACT FROM AUTHOR]

The mental model theory postulates that the meanings of conditionals are based on possibilities. Indicative conditionals—such as "If he is injured tomorrow, then he will take some leave"—have a factual interpretation that can be paraphrased as It is possible, and remains so, that he is injured tomorrow, and in that case certain that he takes some leave. Subjunctive conditionals, such as, "If he were injured tomorrow, then he would take some leave," have a prefactual interpretation that has the same paraphrase. But when context makes clear that his injury will not occur, the subjunctive has a counterfactual paraphrase, with the first clause: It was once possible, but does not remain so, that he will be injured tomorrow. Three experiments corroborated these predictions for participants' selections of paraphrases in their native Spanish, for epistemic and deontic conditionals, for those referring to past and to future events, and for those with then clauses referring to what may or must happen. These results are contrary to normal modal logics. They are also contrary to theories based on probabilities, which are inapplicable to deontic conditionals, such as, "If you have a ticket, then you must enter the show." [ABSTRACT FROM AUTHOR]

The past 12 years have seen significant steps forward in the science and practice of coastal flood analysis. This paper aims to recount and critically assess these advances, while helping identify next steps for the field. This paper then focuses on a key problem, connecting the probabilistic characterization of flood hazards to their physical mechanisms. Our investigation into the effects of natural structure on the probabilities of storm surges shows that several different types of spatial-, temporal-, and process-related organizations affect key assumptions made in many of the methods used to estimate these probabilities. Following a brief introduction to general historical methods, we analyze the two joint probability methods used in most tropical cyclone hazard and risk studies today: the surface response function and Bayesian quadrature. A major difference between these two methods is that the response function creates continuous surfaces, which can be interpolated or extrapolated on a fine scale if necessary, and the Bayesian quadrature optimizes a set of probability masses, which cannot be directly interpolated or extrapolated. Several examples are given here showing significant impacts related to natural structure that should not be neglected in hazard and risk assessment for tropical cyclones including: (1) differences between omnidirectional sampling and directional-dependent sampling of storms in near coastal areas; (2) the impact of surge probability discontinuities on the treatment of epistemic uncertainty; (3) the ability to reduce aleatory uncertainty when sampling over larger spatial domains; and (4) the need to quantify trade-offs between aleatory and epistemic uncertainties in long-term stochastic sampling. [ABSTRACT FROM AUTHOR]

We studied the effects of probabilistic cues, i.e., of information of limited certainty, in the context of an action task (GL: grip-lift) and of a perceptual task (WP: weight perception). Normal subjects (n = 22) saw four different probabilistic visual cues, each of which announced the likely weight of an object. In the GL task, the object was grasped and lifted with a pinch grip, and the peak force rates indicated that the grip and load forces were scaled predictively according to the probabilistic information. The WP task provided the expected heaviness related to each probabilistic cue; the participants gradually adjusted the object's weight until its heaviness matched the expected weight for a given cue. Subjects were randomly assigned to two groups: one started with the GL task and the other one with the WP task. The four different probabilistic cues influenced weight adjustments in the WP task and peak force rates in the GL task in a similar manner. The interpretation and utilization of the probabilistic information was critically influenced by the initial task. Participants who started with the WP task classified the four probabilistic cues into four distinct categories and applied these categories to the subsequent GL task. On the other side, participants who started with the GL task applied three distinct categories to the four cues and retained this classification in the following WP task. The initial strategy, once established, determined the way how the probabilistic information was interpreted and implemented. [ABSTRACT FROM AUTHOR]

Bayes Rule specifies how probabilities over parameters should be updated given any kind of information. But in some cases, the kind of information provided by both simulation and physical experiments is information on how certain output parameters may change when other input parameters are changed. There are three different approaches to this problem, one of which leads to the Garbage-In/Garbage-Out Paradox, the second of which (Bayesian synthesis) violates the Borel Paradox, and the third of which (Bayesian melding) is a supra-Bayesian heuristic. This paper shows how to derive a fully Bayesian formula which avoids the Garbage-In/Garbage-Out and Borel Paradoxes. We also compare a Laplacian approximation of this formula with Bayesian synthesis and Bayesian melding and find that the Bayesian formula sometimes coincides with the Bayesian melding solution. [ABSTRACT FROM AUTHOR]

According to infinitism, beliefs can be justified by an infinite chain of reasons. So far, infinitism has rarely been taken seriously and often even dismissed as inconsistent. However, Peijnenburg and Atkinson have recently argued that beliefs can indeed be justified by an infinite chain of reasons, if justification is understood probabilistically. In the following, I will discuss the formal result that has led to this conclusion. I will then introduce three probabilistic explications of justification and examine to which extent they support Peijnenburg's and Atkinson's claim. [ABSTRACT FROM AUTHOR]

It has been argued that dual process theories are not consistent with Oaksford and Chater's probabilistic approach to human reasoning (Oaksford and Chater in Psychol Rev 101:608-631, , ; Oaksford et al. ), which has been characterised as a 'single-level probabilistic treatment[s]' (Evans ). In this paper, it is argued that this characterisation conflates levels of computational explanation. The probabilistic approach is a computational level theory which is consistent with theories of general cognitive architecture that invoke a WM system and an LTM system. That is, it is a single function dual process theory which is consistent with dual process theories like Evans' () that use probability logic (Adams ) as an account of analytic processes. This approach contrasts with dual process theories which propose an analytic system that respects standard binary truth functional logic (Heit and Rotello in J Exp Psychol Learn 36:805-812, ; Klauer et al. in J Exp Psychol Learn 36:298-323, ; Rips in Psychol Sci 12:29-134, , ; Stanovich in Behav Brain Sci 23:645-726, , ). The problems noted for this latter approach by both Evans Psychol Bull 128:978-996, (, ) and Oaksford and Chater (Mind Lang 6:1-38, , , ) due to the defeasibility of everyday reasoning are rehearsed. Oaksford and Chater's () dual systems implementation of their probabilistic approach is then outlined and its implications discussed. In particular, the nature of cognitive decoupling operations are discussed and a Panglossian probabilistic position developed that can explain both modal and non-modal responses and correlations with IQ in reasoning tasks. It is concluded that a single function probabilistic approach is as compatible with the evidence supporting a dual systems theory. [ABSTRACT FROM AUTHOR]

In recent years, the psychology of reasoning has been undergoing a paradigm shift, with general Bayesian, probabilistic approaches replacing the older, much more restricted binary logic paradigm. At the same time, dual processing theories have been gaining influence. We argue that these developments should be integrated and moreover that such integration is already underway. The new reasoning paradigm should be grounded in dual processing for its algorithmic level of analysis just as it uses Bayesian theory for its computational level of analysis. Moreover, we propose that, within the new paradigm, these levels of analysis reflect on each other. Bayesianism suggests a specific theoretical understanding of dual processing. Just as importantly, the duality in processing carries over to duality in function; although both types of processes compute degrees of belief, they generate different functions. [ABSTRACT FROM AUTHOR]

We present a fuzzy multi-person decision making model with fuzzy probabilistic information. For doing so, we present the fuzzy probabilistic ordered weighted averaging (FPOWA) operator. It is an aggregation operator that unifies the fuzzy probabilistic aggregation and the fuzzy OWA (FOWA) operator in the same formulation considering the degree of importance that each concept has in the analysis. We study its applicability and we see that it is very broad because all the previous studies that use the probability or the OWA operator can be revised and extended with this new approach. We focus on a multi-person decision making problem that unifies risk and uncertain environments in the same formulation. We implement this approach in a political management problem regarding the selection of fiscal policies. [ABSTRACT FROM AUTHOR]

The response mode bias, in which subjects exhibit different risk attitudes when assessing certainty equivalents versus indifference probabilities, is a well-known phenomenon in the assessment of utility functions. In this empirical study, we develop and apply a cardinal measure of risk attitudes to analyze not only the existence, but also the strength of this phenomenon. Since probability levels involved in decision problems are already known to have a strong impact on behavior, we use this approach to study the impact of probabilities on the extent of the response mode bias. We find that the direction in which probabilities influence measured risk aversion is the opposite in the certainty equivalence (CE) method versus in the probability equivalence (PE) method. Utilizing the CE elicitation approach leads to an increase of risk seeking for gambles involving high probabilities. For the PE method, subjects tend to behave risk averse with gambles of high probabilities. This behavior is reversed in the gain domain. This “tailwhip” effect is consistently replicated in several experiments, involving both loss and gain domains of lotteries. [ABSTRACT FROM AUTHOR]

Starting from Max Weber’s definitions of power we discuss the meaning of his concept Chance and its relationships to the probabilistic notions that play a central role in definitions of indices of measuring voting power. Using Martin Hollis’s distinction between two models of man—plastic and autonomous—we argue that the common measures of voting power when interpreted in terms of probabilities seem to be in better conformity with the model of plastic man than with the model of autonomous man. The paper elaborates on the probability interpretation with applications to the modelling of power measures with a priori unions and to the concept of “real voting power” based on relative frequencies of historical events. Power as potential—which in our view is what Weber very likely meant—remains an elusive concept, but one that should be amenable to game-theoretic analysis. [ABSTRACT FROM AUTHOR]

The ontology of decision theory has been subject to considerable debate in the past, and discussion of just how we ought to view decision problems has revealed more than one interesting problem, as well as suggested some novel modifications of classical decision theory. In this paper it will be argued that Bayesian, or evidential, decision-theoretic characterizations of decision situations fail to adequately account for knowledge concerning the causal connections between acts, states, and outcomes in decision situations, and so they are incomplete. Second, it will be argues that when we attempt to incorporate the knowledge of such causal connections into Bayesian decision theory, a substantial technical problem arises for which there is no currently available solution that does not suffer from some damning objection or other. From a broader perspective, this then throws into question the use of decision theory as a model of human or machine planning. [ABSTRACT FROM AUTHOR]

We show how the tensor product for a 2×2 game with Knightian uncertainty can be calculated. We discuss also a specific example in which we derive the Nash equilibria. The discussion is an application of game theory in terms of tensor products of capacities, introduced in the paper by Kozhan and Zarichnyi (Econ Theory, 2007, Forthcoming). [ABSTRACT FROM AUTHOR]

Attempts to teach statistical thinking using corrective feedback or a ``rule-training'' approach have been only moderately successful. A new training approach is proposed which relies on the assumption that the human mind is naturally equipped to solve many statistical tasks in which the relevant information is presented in terms of absolute frequencies instead of probabilities. In an investigation of this approach, people were trained to solve tasks involving conjunctive and conditional probabilities using a frequency grid to represent probability information. It is suggested that learning by doing, whose importance was largely neglected in prior training studies, has played a major role in the current training. Study 1 showed that training that combines external pictorial representations and learning by doing has a large and lasting effect on how well people can solve conjunctive probability tasks. A ceiling effect prevented comparison of the frequency grid and a conventional pictorial representation (Venn diagrams) with respect to effectiveness. However, the grid representation was found to be more effective in Study 2, which dealt with the more difficult topic of conditional probabilities. These results suggest methods to optimize the teaching of statistical thinking and the presentation of statistical information in the media. [ABSTRACT FROM AUTHOR]