Olav Benjamin Vassend

• Vassend, Olav Benjamin (2026). A Causal Markov Condition for Value. Proceedings of Machine Learning Research (PMLR). 337, p. 6862–6884. doi: https:/proceedings.mlr.press/v337/. Full text in Research Archive Show summary

  This paper proposes a causal independence principle for *value*—the *value Causal {Markov} Condition* (v-CMC)—and develops the conceptual and mathematical foundations of a "causal value theory" linking causality and utility. After motivating a local formulation of the v-CMC, we introduce a probability–value duality that translates standard causal-inference results into the value setting. In particular, we formulate local, global, and decomposition versions of the v-CMC and prove their equivalence. We also define 𝑣-separation and show that it is sound and complete for conditional value independence. Furthermore, we derive a {Bellman}-type recursion as a special case of the v-CMC, thereby generalizing standard {Bellman} recursion from linear chains to causal DAGs. Finally, we show how the v-CMC supports modular transfer and updating of utility information across causal contexts and develop algorithms for causally structured utility elicitation and canonical influence-diagram construction.

• Vassend, Olav Benjamin (2026). A Divergence-Based Method for Weighting and Averaging Model Predictions. Proceedings of Machine Learning Research (PMLR). 300, p. 1855–1863. Full text in Research Archive Show summary

  This paper uses a minimum divergence framework to introduce a new way of calculating model weights that can be used to average probabilistic predictions from statistical and machine learning models. The method is general and can be applied regardless of whether the models under consideration are fit to data using frequentist, Bayesian, or some other fitting method. The proposed method is motivated in two different ways and is shown empirically to perform better than or on a par with standard model averaging methods, including model stacking and model averaging that relies on Akaike-style negative exponentiated model weighting, especially when the sample size is small. Our theoretical analysis explains why the method has a small-sample advantage.

• Skipper, Mattias & Vassend, Olav Benjamin (2024). The bayesian and the abductivist. Noûs. ISSN 0029-4624. doi: 10.1111/nous.12539. Full text in Research Archive
• Vassend, Olav Benjamin (2023). On the Ecological and Internal Rationality of Bayesian Updating and Other Belief Updating Strategies. British Journal for the Philosophy of Science. ISSN 0007-0882. doi: 10.1086/724447. Full text in Research Archive
• Vassend, Olav Benjamin (2023). What hinge epistemology and Bayesian epistemology can learn from each other. Asian Journal of Philosophy. 2(2). doi: 10.1007/s44204-023-00104-z. Full text in Research Archive Show summary

  Hinge epistemology and Bayesianism are two prominent approaches in contemporary epistemology, but the relationship between these approaches has not been systematically studied. This paper formalizes the central commitments of hinge epistemology in a Bayesian framework and argues for the following two theses: (1) many of the types of claims that are treated as paradigmatic hinges in the hinge epistemology literature, such as the claim that there exists an external world of physical objects, are not capable of enabling rational inquiry, even though this is typically regarded as a central property of hinges; (2) the standard Bayesian story of how rational inquiry proceeds is incorrect or at best incomplete.

• Vassend, Olav Benjamin (2023). Sometimes it is better to do nothing: A new argument for causal decision theory. Ergo - An Open Access Journal of Philosophy. 73(1). doi: 10.3998/ergo.3594. Full text in Research Archive Show summary

  It is often thought that the main significant difference between evidential decision theory and causal decision theory is that they recommend different acts in Newcomb-style examples (broadly construed) where acts and states are correlated in peculiar ways. However, this paper presents a class of non-Newcombian examples that evidential decision theory cannot adequately model whereas causal decision theory can. Briefly, the examples involve situations where it is clearly best to perform an act that will not influence the desired outcome. On evidential decision theory—but not causal decision theory—this situation turns out to be impossible: acts that an agent does not think influence the desired outcome are never optimal. Typically, sophisticated versions of evidential decision theory emulate causal decision theoretic reasoning by (implicitly) conditioning on causal confounders, but in the kind of example considered here, this trick does not work. The upshot is that there is more to causal reasoning than has so far been appreciated.

• Vassend, Olav Benjamin (2022). Justifying the Norms of Inductive Inference. British Journal for the Philosophy of Science. ISSN 0007-0882. 73(2), p. 135–160. doi: 10.1093/bjps/axz041. Full text in Research Archive Show summary

  Bayesian inference is limited in scope because it cannot be applied in idealized contexts where none of the hypotheses under consideration is true and because it is committed to always using the likelihood as a measure of evidential favouring, even when that is inappropriate. The purpose of this article is to study inductive inference in a very general setting where finding the truth is not necessarily the goal and where the measure of evidential favouring is not necessarily the likelihood. I use an accuracy argument to argue for probabilism and I develop a new kind of argument to argue for two general updating rules, both of which are reasonable in different con texts. One of the updating rules has standard Bayesian updating, Bissiri et al.’s ([2016]) general Bayesian updating, Douven’s ([2016]) IBE-based updating, and my (Vassend ([forthcoming]) quasi-Bayesian updating as special cases. The other updating rule is novel.

• Vassend, Olav Benjamin (2021). Why change your beliefs rather than your desires? Two puzzles. Analysis. ISSN 0003-2638. 81(2), p. 275–281. Full text in Research Archive
• Vassend, Olav Benjamin (2020). A Verisimilitude Framework for Inductive Inference, with an Application to Phylogenetics. British Journal for the Philosophy of Science. ISSN 0007-0882. 71(4), p. 1359–1383. Full text in Research Archive
• Vassend, Olav Benjamin (2019). New Semantics for Bayesian Inference: The Interpretive Problem and Its Solutions. Philosophy of Science. ISSN 0031-8248. 86(4), p. 696–718. Full text in Research Archive
• Vassend, Olav Benjamin (2019). Confirmation and the Ordinal Equivalence Thesis. Synthese. ISSN 0039-7857. 196(3), p. 1079–1095. Full text in Research Archive
• Vassend, Olav Benjamin (2018). Comment: The Inferential Information Criterion from a Bayesian Point of View. Sociological methodology. ISSN 0081-1750. 48(1), p. 91–97. Full text in Research Archive
• Vassend, Olav Benjamin (2018). Goals and the Informativeness of Prior Probabilities. Erkenntnis: An International Journal of Scientific Philosophy. ISSN 0165-0106. 83(4), p. 647–670. Full text in Research Archive
• Vassend, Olav Benjamin & Castro, Clinton (2018). Ideal Counterpart Theorizing and the Accuracy Argument for Probabilism. Analysis. ISSN 0003-2638. 78(2), p. 207–216. Full text in Research Archive
• Vassend, Olav Benjamin; Sober, Elliott & Fitelson, Branden (2018). The Philosophical Significance of Stein's Paradox. European Journal for Philosophy of Science. ISSN 1879-4912. 71(3), p. 411–433. Full text in Research Archive
• Vassend, Olav Benjamin (2015). Confirmation Measures and Sensitivity. Philosophy of Science. ISSN 0031-8248. 82(5), p. 892–904. Full text in Research Archive

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• Vassend, Olav Benjamin (2021). Review of 'Bayesian Philosophy of Science' by Sprenger and Hartmann. Erkenntnis: An International Journal of Scientific Philosophy. ISSN 0165-0106. Full text in Research Archive

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