2019
DOI: 10.1093/bjps/axy027
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Inference to the Best Explanation in Uncertain Evidential Situations

Abstract: It has recently been argued that a non-Bayesian probabilistic version of inference to the best explanation (IBE*) has a number of advantages over Bayesian conditionalization (Douven [2013]; Douven and Wenmackers [2017]). We investigate how IBE* could be generalized to uncertain evidential situations and formulate a novel updating rule IBE**. We then inspect how it performs in comparison to its Bayesian counterpart, Jeffrey conditionalization (JC), in a number of simulations where two agents, each updating by I… Show more

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Cited by 12 publications
(14 citation statements)
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“…they are undefined for t = 0 and f = 0). Function α3(t, f ) = t − f (used in Trpin and Pellert, 2019), which measures the difference between truth and falsehood in a set of beliefs, fulfills requirements r1 and r2.…”
Section: α-Valuementioning
confidence: 99%
See 2 more Smart Citations
“…they are undefined for t = 0 and f = 0). Function α3(t, f ) = t − f (used in Trpin and Pellert, 2019), which measures the difference between truth and falsehood in a set of beliefs, fulfills requirements r1 and r2.…”
Section: α-Valuementioning
confidence: 99%
“…This form of measurement is feasible, for example, in Computational Epistemology, where epistemologists have full access to the truth-values of their agents' beliefs. Examples of investigations in this field are Douven (2013), Trpin andPellert (2019), andOlsson (2011). In a typical investigation, computational epistemologists design a computer simulation, where artificial agents (designed to exhibit the epistemic strategy to be evaluated) update their beliefs interacting with an environment (maybe, including other agents) that is randomly generated from fixed parameters.…”
Section: α and Evaluationsmentioning
confidence: 99%
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“…One potential solution in cases like this is that instead of updating by JC we update our credences by following a probabilistic rule that gives additional weight to evidence instead of accounting just for the subjective aspects of the learning experience. One such rule that was proposed in the literature is, for instance, a probabilistic form of inference to the best explanation of uncertain evidence that may outperform JC in some cases (see, e.g., Trpin and Pellert, 2018). Following such an alternative rule presents a potential resolution of these problems, but it comes with a price: by not updating by JC we expose ourselves to potential Dutch books (Skyrms, 1987) and incoherence (Climenhaga, 2017).…”
Section: Subjective and Objective Aspects Of Evidencementioning
confidence: 99%
“…Rather, the conditional probability of E n given each hypothesis remains fixed throughout the process. If the reader finds such a conditional independence assumption unrealistic, then it should be noted that the example may be rephrased into one with a series of biased coins or dice throws instead of microbiological samples of different strains (for similar examples see, e.g.,Douven, 2013;Trpin and Pellert, 2018). This conditional independence is important as Pr(E n |H i ) is one of the key parameters in conditionalization.…”
mentioning
confidence: 99%