Probabilities For Two Properties

Let R(X, B) denote the class of probability functions that are defined on algebra X and that represent rationally permissible degrees of certainty for a person whose total relevant background evidence is B. This paper is concerned with characterizing R(X, B) for the case in whichX is an algebra of propositions involving two properties and B is empty. It proposes necessary conditions for a probability function to be in R(X, B), some of which involve the notion of statistical dependence. The class of probability functions that satisfy these conditions, here denoted P_I, includes a class that Carnap once proposed for the same situation. Probability functions in P_I violate Carnap's axiom of analogy but, it is argued, that axiom should be rejected. A derivation of Carnap's model by Hesse has limitations that are not present in the derivation of P_I given here. Various alternative probability models are considered and rejected.     

The Probabilities of Conditionals Revisited

According to what is now commonly referred to as “the Equation” in the literature on indicative conditionals, the probability of any indicative conditional equals the probability of its consequent of the conditional given the antecedent of the conditional. Philosophers widely agree in their assessment that the triviality arguments of Lewis and others have conclusively shown the Equation to be tenable only at the expense of the view that indicative conditionals express propositions. This study challenges the correctness of that assessment by presenting data that cast doubt on an assumption underlying all triviality arguments.     

Why Induction Is No Cure For Baldness

The paper aims at establishing that the premises of both the inductive and the multi‐premised versions of the sorites argument are not apparently acceptable and that, therefore, sorites‐type arguments do not constitute logical or conceptual paradoxes. Rather, it is suggested that such arguments are most properly and fruitfully described as skeptical challenges. A secondary goal of the paper is to focus attention to the unduly neglected inductive version of the argument.     

Probabilities for new theories

Conclusion Contrary to what has been widely supposed, Bayesian theory deals successfully with the introduction of new theories that have never previously been entertained. The theory enables us to say what sorts of method should be used to assign probabilities to these new theories, and it allows that the probabilities of existing theories may be modified as a result.     

Dynamic Update with Probabilities

Current dynamic-epistemic logics model different types of information change in multi-agent scenarios. We generalize these logics to a probabilistic setting, obtaining a calculus for multi-agent update with three natural slots: prior probability on states, occurrence probabilities in the relevant process taking place, and observation probabilities of events. To match this update mechanism, we present a complete dynamic logic of information change with a probabilistic character. The completeness proof follows a compositional methodology that applies to a much larger class of dynamic-probabilistic logics as well. Finally, we discuss how our basic update rule can be parameterized for different update policies, or learning methods.     

Probabilities in implicit learning

The visual system possesses a remarkable ability in learning regularities from the environment. In the case of contextual cuing, predictive visual contexts such as spatial configurations are implicitly learned, retained, and used to facilitate visual search-all without one's subjective awareness and conscious effort. Here we investigated whether implicit learning and its facilitatory effects are sensitive to the statistical property of such implicit knowledge. In other words, are highly probable events learned better than less probable ones even when such learning is implicit? We systematically varied the frequencies of context repetition to alter the degrees of learning. Our results showed that search efficiency increased consistently as contextual probabilities increased. Thus, the visual contexts, along with their probability of occurrences, were both picked up by the visual system. Furthermore, even when the total number of exposures was held constant between each probability, the highest probability still enjoyed a greater cuing effect, suggesting that the temporal aspect of implicit learning is also an important factor to consider in addition to the effect of mere frequency. Together, these findings suggest that implicit learning, although bypassing observers' conscious encoding and retrieval effort, behaves much like explicit learning in the sense that its facilitatory effect also varies as a function of its associative strengths.     

Still No Evidence For Temporally Extended Shock-frequency Reduction As A Reinforcer.

There is no consensus and very little overlap in the criticisms of my target article. Because the primary consequences of avoidance behavior are by definition alterations in the distribution of shocks in time, any theory about the reinforcement of such behavior necessarily must begin with that dimension. However, the safety‐signal version of two‐process theory calls on positively and negatively correlated stimuli, including the responses themselves serving as stimuli, to transmit the effects of those alterations to the relevant behavior. Meanwhile, the Herrnstein—Hineline single‐process theory hypothesizes an additional source of reinforcement: a direct effect of reduction in the density of shock over some extended period of time. I can find no data that selectively support that hypothesis.     

Revising Probabilities and Full Beliefs

Journal of Philosophical Logic - A new formal model of belief dynamics is proposed, in which the epistemic agent has both probabilistic beliefs and full beliefs. The agent has full belief in a...     

Taurek, Numbers and Probabilities

In his paper, “Should the Numbers Count?" John Taurek imagines that we are in a position such that we can either save a group of five people, or we can save one individual, David. We cannot save David and the five. This is because they each require a life-saving drug. However, David needs all of the drug if he is to survive, while the other five need only a fifth each. Typically, people have argued as if there was a choice to be made: either numbers matter, in which case we should save the greater number, or numbers don't matter, but rather there is moral value in giving each person an equal chance of survival, and therefore we should toss a coin. My claim is that we do not have to make a choice in this way. Rather, numbers do matter, but it doesn't follow that we should always save the greater number. And likewise, there is moral value in giving each person an equal chance of survival, but it doesn't follow that we should always toss a coin. In addition, I argue that a similar approach can be applied to situations in which we can save one person or another, but the chances of success are different.     

All Work And No Play Makes Jack A Dull Boy: The Case For Play At All Educational Levels

In this paper, the author explores the importance of play at various stages of literacy development and contends that play is an essential element of learning. The author begins by defining play and literacy, using examples to theoretically explore the play and literacy interface throughout the life span. Finally, she moves into a policy discussion of play and literacy development in today's political climate, including the influence of standardized testing and the Common Core Standards.     

Powers,Probabilities, and Tendencies

Philosophia - In this article, I aim at showing how powers may ground different types of probability in the universe. In Section 1 I single out several dimensions along which the probability of...     

Probabilities of counterfactuals and counterfactual probabilities

Probabilities figure centrally in much of the literature on the semantics of conditionals. I find this surprising: it accords a special status to conditionals that other parts of language apparently do not share. I critically discuss two notable ‘probabilities first’ accounts of counterfactuals, due to Edgington and Leitgeb. According to Edgington, counterfactuals lack truth values but have probabilities. I argue that this combination gives rise to a number of problems. According to Leitgeb, counterfactuals have truth conditions-roughly, a counterfactual is true when the corresponding conditional chance is sufficiently high. I argue that problems arise from the disparity between truth and high chance, between approximate truth and high chance, and from counterfactuals for which the corresponding conditional chances are undefined. However, Edgington, Leitgeb and I can unite in opposition to Stalnaker and Lewis-style ‘similarity’ accounts of counterfactuals.     

Resolving Peer Disagreements Through Imprecise Probabilities

Two compelling principles, the Reasonable Range Principle and the Preservation of Irrelevant Evidence Principle, are necessary conditions that any response to peer disagreements ought to abide by. The Reasonable Range Principle maintains that a resolution to a peer disagreement should not fall outside the range of views expressed by the peers in their dispute, whereas the Preservation of Irrelevant Evidence (PIE) Principle maintains that a resolution strategy should be able to preserve unanimous judgments of evidential irrelevance among the peers. No standard Bayesian resolution strategy satisfies the PIE Principle, however, and we give a loss aversion argument in support of PIE and against Bayes. The theory of imprecise probability allows one to satisfy both principles, and we introduce the notion of a set‐based credal judgment to frame and address a range of subtle issues that arise in peer disagreements.     

Kaufmann on the Probabilities of Conditionals

Kaufmann has recently argued that the thesis according to which the probability of an indicative conditional equals the conditional probability of the consequent given the antecedent under certain specifiable circumstances deviates from intuition. He presents a method for calculating the probability of a conditional that does seem to give the intuitively correct result under those circumstances. However, the present paper shows that Kaufmann’s method is inconsistent in that it may lead one to assign different probabilities to a single conditional at the same time.     

Epistemic Judgments are Insensitive to Probabilities

Multiple epistemological programs make use of intuitive judgments pertaining to an individual’s ability to gain knowledge from exclusively probabilistic/statistical information. This paper argues that these judgments likely form without deference to such information, instead being a function of the degree to which having knowledge is representative of an agent. Thus, these judgments fit the pattern of formation via a representativeness heuristic, like that famously described by Kahneman and Tversky to explain similar probabilistic judgments. Given this broad insensitivity to probabilistic/statistical information, it directly follows that these epistemic judgments are insensitive to a given agent’s epistemic status. From this, the paper concludes that, breaking with common epistemological practice, we cannot assume that such judgments are reliable.     

The Effect of Inference Order and Experience-Related Knowledge on Diagnostic Conjunction Probabilities

Ideally, a decision maker′s diagnostic probability judgments should not be affected by making predictive judgments before making diagnostic inferences. The purpose of this study is to investigate how experience-related knowledge and the inference presentation order affect a decision maker′s diagnostic conjunction probability judgments. Specifically, when decision makers are asked to make diagnoses in different judgment domains with which they have different levels of experience, we examine how making predictions first affects their subsequent diagnostic judgments in a standard conjunction paradigm. Professional auditors with experience in the auditing domain and MBA students with little or no auditing experience participated in the experiment. The results indicate that when the task involves a domain with which people have experience, making predictions prior to diagnoses has a significant influence on their subsequent diagnostic conjunction probabilities. When auditors made diagnoses in a familiar audit task situation, they were strongly influenced by whether or not they were asked to make predictions in advance. However, there was no influence of inference order on auditors′ diagnoses in a medical task, with which they do not have experience-related knowledge. Similarly, MBA students, having no experience-related knowledge in either audit or medical domains, were not affected by the inference order in making diagnoses. In the discussion of these exploratory results, we suggest that this inference order effect may be due to subjects′ anchoring on the predictive probability and insufficiently adjusting it to yield the diagnostic probability judgment.