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Alicja Kowalewska
Carnegie Mellon University
  1.  34
    Measuring coherence with Bayesian networks.Alicja Kowalewska & Rafal Urbaniak - 2023 - Artificial Intelligence and Law 31 (2):369-395.
    When we talk about the coherence of a story, we seem to think of how well its individual pieces fit together—how to explicate this notion formally, though? We develop a Bayesian network based coherence measure with implementation in _R_, which performs better than its purely probabilistic predecessors. The novelty is that by paying attention to the network structure, we avoid simply taking mean confirmation scores between all possible pairs of subsets of a narration. Moreover, we assign special importance to the (...)
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  2.  57
    Reasoning Without the Conjunction Closure.Alicja Kowalewska - forthcoming - Episteme:1-14.
    Some theories of rational belief assume that beliefs should be closed under conjunction. I motivate the rejection of the conjunction closure, and point out that the consequences of this rejection are not as severe as it is usually thought. An often raised objection is that without the conjunction closure people are unable to reason. I outline an approach in which we can – in usual cases – reason using conjunctions without accepting the closure in its whole generality. This solution is (...)
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  3.  35
    Decision-theoretic and risk-based approaches to naked statistical evidence: some consequences and challenges.Rafal Urbaniak, Alicja Kowalewska, Pavel Janda & Patryk Dziurosz-Serafinowicz - 2020 - Law, Probability and Risk 19 (1):67-83.
    In the debate about the legal value of naked statistical evidence, Di Bello argues that (1) the likelihood ratio of such evidence is unknown, (2) the decision-theoretic considerations indicate that a conviction based on such evidence is unacceptable when expected utility maximization is combined with fairness constraints, and (3) the risk of mistaken conviction based on such evidence cannot be evaluated and is potentially too high. We argue that Di Bello’s argument for (1) works in a rather narrow context, and (...)
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