RAMSEY’S TEST, ADAMS’ THESIS, AND LEFT-NESTED CONDITIONALS

2010 ◽  
Vol 3 (3) ◽  
pp. 467-484 ◽  
Author(s):  
RICHARD DIETZ ◽  
IGOR DOUVEN
Keyword(s):  
Conditional Probability ◽  
Van Fraassen ◽  
Indicative Conditional ◽  
Ramsey’S Test

Adams famously suggested that the acceptability of any indicative conditional whose antecedent and consequent are both factive sentences amounts to the subjective conditional probability of the consequent given the antecedent. The received view has it that this thesis offers an adequate partial explication of Ramsey’s test, which characterizes graded acceptability for conditionals in terms of hypothetical updates on the antecedent. Some results in van Fraassen (1976) may raise hope that this explicatory approach to Ramsey’s test is extendible to left-nested conditionals, that is, conditionals whose antecedent is itself conditional in form. We argue that this interpretation of van Fraassen’s results is to be rejected. Specifically, we provide an argument from material inadequacy against a generalization of Adams’ thesis for left-nested conditionals.

scholarly journals Reconditioning the conditional [Recondicionando o condicional]

2016 ◽  
Vol 23 (40) ◽  
pp. 9-27
Author(s):  
David Miller
Keyword(s):  
Conditional Probability ◽  
Probability Function ◽  
Primary Concern ◽  
Conditional Probabilities ◽  
Indicative Conditional ◽  
Everyday Language ◽  
Conditional Statements ◽  
The Absolute ◽  
Truth Value

Many authors have hoped to understand the indicative conditional construction in everyday language by means of what are usually called conditional probabilities. Other authors have hoped to make sense of conditional probabilities in terms of the absolute probabilities of conditional statements. Although all such hopes were disappointed by the triviality theorems of Lewis (1976), there have been copious subsequent attempts both to rescue CCCP (the conditional construal of conditional probability) and to extend and to intensify the arguments against it. In this paper it will be shown that triviality is avoidable if the probability function is replaced by an alternative generalization of the deducibility relation, the measure of deductive dependence of Miller and Popper (1986). It will be suggested further that this alternative way of orchestrating conditionals is nicely in harmony with the test proposed in Ramsey (1931), and also with the idea that it is not the truth value of a conditionalstatement that is of primary concern but its assertability or acceptability.

New Paradigm Psychology of Conditional Reasoning and Its Philosophical Sources

2021 ◽  
pp. 58-75
Author(s):  
David Over
Keyword(s):  
Natural Language ◽  
Conditional Probability ◽  
Conditional Reasoning ◽  
New Paradigm ◽  
Indicative Conditional ◽  
Ramsey Test ◽  
Psychology Of Reasoning ◽  
Psychological Experiments

There is a new Bayesian, or probabilistic, paradigm in the psychology of reasoning, with new psychological accounts of the indicative conditional of natural language and of conditional reasoning. Dorothy Edgington has had a major impact on this new paradigm, through her views on inference from uncertain premises, the relation between the probability of the indicative conditional, P(if p then q), and the conditional probability, P(q|p), and the use of the Ramsey test to evaluate conditionals. Accounts are given in this chapter of the psychological experiments in the new paradigm that confirm empirical hypotheses inspired by her work and other philosophical sources.

Counterfactuals and Probability

2019 ◽  
pp. 182-202
Author(s):  
Robert C. Stalnaker
Keyword(s):  
Conditional Probability ◽  
Truth Conditions ◽  
Indicative Conditional ◽  
Truth Values ◽  
Counterfactual Conditionals ◽  
Retrospective Assessment ◽  
Objective Chance ◽  
The Relationship

This chapter continues the attempt, begun in chapter 10, to reconcile the thesis that conditionals have truth conditions with accounts such as Dorothy Edgington’s that aim to explain conditionals as expressing a distinctive kind of attitude represented by conditional probability. This time the focus is on subjunctive or counterfactual conditionals. It is argued that the propositional analysis helps to explain the cases, emphasized by Edgington, where counterfactual statements seem to be retrospective assessment of what was earlier said with an indicative conditional. It is also argued that the propositional analysis can allow for cases where counterfactuals have probability values but not truth-values, and more generally that it can help to explain the relationship between counterfactuals and objective chance.

Facial Likability and Smiling Enhance Cooperation, but Have No Direct Effect on Moralistic Punishment

2016 ◽  
Vol 63 (5) ◽  
pp. 263-277 ◽  
Author(s):  
Laura Mieth ◽  
Raoul Bell ◽  
Axel Buchner
Keyword(s):  
Conditional Probability ◽  
Direct Effect ◽  
Prisoner's Dilemma ◽  
Prisoner’S Dilemma ◽  
Prisoner’S Dilemma Game ◽  
Costly Punishment ◽  
Prisoner's Dilemma Game ◽  
Moralistic Punishment

Abstract. The present study serves to test how positive and negative appearance-based expectations affect cooperation and punishment. Participants played a prisoner’s dilemma game with partners who either cooperated or defected. Then they were given a costly punishment option: They could spend money to decrease the payoffs of their partners. Aggregated over trials, participants spent more money for punishing the defection of likable-looking and smiling partners compared to punishing the defection of unlikable-looking and nonsmiling partners, but only because participants were more likely to cooperate with likable-looking and smiling partners, which provided the participants with more opportunities for moralistic punishment. When expressed as a conditional probability, moralistic punishment did not differ as a function of the partners’ facial likability. Smiling had no effect on the probability of moralistic punishment, but punishment was milder for smiling in comparison to nonsmiling partners.

Interlocking patterns of conditional probability: The reinforcement matrix.

2002 ◽  
Vol 3 (1) ◽  
pp. 30-40
Author(s):  
Joseph D. Cautilli ◽  
Donald A. Hantula
Keyword(s):  
Conditional Probability

The dependence of the majority voting decision-making probabilities on a multi-expert binary system experts number

2020 ◽  
pp. 7-14
Author(s):  
E. D. Avedyan ◽  
Le Thi Trang Linh
Keyword(s):  
Decision Making ◽  
Binary System ◽  
Conditional Probability ◽  
Majority Voting ◽  
Correct Solution ◽  
Conditional Probabilities ◽  
Analytical Results ◽  
Independent Expert ◽  
Mutually Independent

The article presents the analytical results of the decision-making by the majority voting algorithm (MVA). Particular attention is paid to the case of an even number of experts. The conditional probabilities of the MVA for two hypotheses are given for an even number of experts and their properties are investigated depending on the conditional probability of decision-making by independent experts of equal qualifications and on their number. An approach to calculating the probabilities of the correct solution of the MVA with unequal values of the conditional probabilities of accepting hypotheses of each statistically mutually independent expert is proposed. The findings are illustrated by numerical and graphical calculations.

Probability

2017 ◽  
Author(s):  
Andrew Gelman ◽  
Deborah Nolan
Keyword(s):  
Conditional Probability ◽  
Bayes Rule ◽  
Probability Models ◽  
Classroom Activities ◽  
Basic Probability

This chapter contains many classroom activities and demonstrations to help students understand basic probability calculations, including conditional probability and Bayes rule. Many of the activities alert students to misconceptions about randomness. They create dramatic settings where the instructor discerns real coin flips from fake ones, students modify dice and coins in order to load them, students “accused” of lying based on the outcome of an inaccurate simulated lie detector face their classmates. Additionally, probability models of real outcomes offer good value: first we can do the probability calculations, and then can go back and discuss the potential flaws of the model.

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