Citations of:
A probabilistic analysis of argument cogency
Synthese 195 (4):17151740 (2018)
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Throughout this paper, we are trying to show how and why our Mathematical framework seems inappropriate to solve problems in Theory of Computation. More exactly, the concept of turning back in time in paradoxes causes inconsistency in modeling of the concept of Time in some semantic situations. As we see in the first chapter, by introducing a version of “Unexpected Hanging Paradox”,first we attempt to open a new explanation for some paradoxes. In the second step, by applying this paradox, it (...) 

Some arguments are good; others are not. How can we tell the difference? This article advances three proposals as a partial answer to this question. The proposals are keyed to arguments conditioned by different degrees of uncertainty: mild, where the argument’s premises are hedged with pointvalued probabilities; moderate, where the premises are hedged with interval probabilities; and severe, where the premises are hedged with nonnumeric plausibilities such as ‘very likely’ or ‘unconfirmed’. For mild uncertainty, the article proposes to apply a (...) 

This paper offers a new model of belief by embedding the Peircean account of belief into a formal dialogue system that uses argumentation schemes for practical reasoning and abductive reasoning. A belief is characterised as a stable proposition that is derived abductively by one agent in a dialogue from the commitment set (including commitments derived from actions and goals) of another agent. On the model (to give a rough summary), a belief is defined as a proposition held by an agent (...) 

According to the Bayesian paradigm in the psychology of reasoning, the norms by which everyday human cognition is best evaluated are probabilistic rather than logical in character. Recently, the Bayesian paradigm has been applied to the domain of argumentation, where the fundamental norms are traditionally assumed to be logical. Here, we present a major generalisation of extant Bayesian approaches to argumentation that utilizes a new class of Bayesian learning methods that are better suited to modelling dynamic and conditional inferences than (...) 