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# CEFISES Seminar: Tamaz Tokhadze, “The Problem of Context-Sensitivity for the Formal Theories of Belief-Credence Interaction”

## March 8@14:00-16:00 CET

**Livestream ** https://youtu.be/mvyA7Dcc7uk

**Series**: OLOFOS

**Speaker**: Tamaz Tokhadze (Ilia State University)

**Title**: “The Problem of Context-Sensitivity for the Formal Theories of Belief–Credence Interaction”

**Abstract**

In the past decade or so, new work in formal epistemology has provided novel and precise coherence principles between categorical beliefs and numerical credences (e.g., Lin and Kelly 2012, 2021; Leitgeb 2013, 2014, 2017). The characteristic of this work is to combine logical norms on belief and probabilistic norms on credence into a plausible theory of how belief and credence should hang together. Focusing on Leitgeb’s theory, this paper discusses a well-recognized problem of context sensitivity for such formal approaches. On these theories, you may rationally believe X if you are concerned with this proposition only; but if you want to consider X together withsome other proposition(s), then believing X may no longer be rational. As Titelbaum (2020, 11) has put it: “… when an agent’s evidence remains constant, Leitgeb allows her beliefs to crumble in the face of partitional change.” This paper aims to go beyond the simple context-sensitivity of such formal theoriesand provide a richer setting that allows us to articulate a more context-invariant and stable conception of belief. This setting is developed within the framework of Bayesian networks.

My proposal is motivated by one of the central functions of rational categorial belief: its role in simplifying and supporting reliable reasoning. Following Foley (2009), Lin and Kelly (2012), and Staffel (2019), I take it that rational beliefconsiderably simplifies reasoning compared to probabilistic reasoning. But this simplification comes with a price. As pointed out by Foley (2009), when we reason with a large set of propositions that are not strongly theoretically intertwined, such reasoning is often unreliable: the joint probability of a relatively large set of premises – where each premise is required for an inference – may not be high and can be very low. These ideas will be used to motivate the thesis that contexts relevant to whether an agent believes X are the contexts that represent the causal or evidential structure of the agent’s evidence concerning X. I will precisify and defend the thesis by using the tools from Bayesian network theory (Bovens and Hartmann 2003; Fenton and Neil 2019). In conclusion, I’ll discuss the implications of the defended view for the lottery and preface paradoxes, and suggesta unified solution.

References:

[1] Fenton, N., & Neil, M. (2018). Risk assessment and decision analysis with Bayesian networks. Crc Press.

[2] Foley, R. (2009). Beliefs, Degrees of Belief, and the Lockean Thesis. In Degrees of Delief (pp. 37-47). Dordrecht: Springer.

[3] Leitgeb, H. (2013). Reducing Belief Simpliciter to Degrees of Belief. Annals of Pure and Applied Logic, 164(12)., 1338-1389.

[4] Leitgeb, H. (2014). The Stability Theory of Belief. Philosophical Review 123.2, 131-71.

[5] Leitgeb, H. (2017). The Stability of Belief. How Rational Belief Coheres with Probability. Oxford: Oxford University Press.

[6] Lin, H., & Kelly, K. T. (2012). Propositional Reasoning that Tracks Probabilistic Reasoning. Journal of philosophical logic 41.6, 957-981.

[7] Lin, H., & Kelly, K. T. (2021). Beliefs, Probabilities, and their Coherent Correspondence. In I Douven (Ed.), Lotteries, knowledge and Rational Belief: Essays on the Lottery Paradox (pp. 185-222). Cambridge University Press.

[8] Staffel, J. (2018). How do Beliefs Simplify Reasoning? Noûs. 937-962.

[9] Titelbaum, M. G. (2020). The Stability of Belief: How Rational Belief Coheres with Probability, by Hannes Leitgeb. Mind.