Date: Tuesday, September 25th, 2018
Time: 12:30-2:00 PM
Location: Kresge 3438
Title: (Epistemic) Probabilities are Degrees of Support, not Degrees of (Rational) Belief
Abstract:
I argue that when we use ‘probability’ language in epistemic contexts – e.g., when we ask how probable some hypothesis is, given the evidence available to us – we are talking about degrees of support, rather than degrees of belief. The probability of A given B is the (mind-independent) degree to which B supports A, not the degree to which someone with B as their evidence believes A, or the degree to which someone would or should believe A if they had B as their evidence. My central argument is that degrees of support can correctly model good reasoning in cases of old evidence where degrees of belief cannot. I consider ways to revise orthodox degree-of-belief Bayesianism in order to account for this kind of case, and argue that such revisions end up unable to account for reasoning about probabilities conditional on evidence that it is impossible not to possess.
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Your argument about using ‘probability’ language to reflect degrees of support rather than degrees of belief is both insightful and thought-provoking. The distinction you make highlights an important shift from subjective perspectives to objective reasoning, especially in cases of old evidence.
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Really interesting discussion! Nevin Climenhaga’s approach to epistemology brings fresh insights into how we form justified beliefs — it’s fascinating to see how these foundational questions are still so relevant today.
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This is a fascinating argument about probability as objective support rather than subjective belief. It really highlights how careful reasoning matters, especially when dealing with unavoidable evidence. If you’re interested in sharpening your reasoning and critical thinking even more, check out Pro Gamey — lots of great resources there!
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Your argument for interpreting epistemic probabilities as degrees of support rather than degrees of belief is thought-provoking and tackles a key challenge in Bayesian epistemology. The focus on old evidence cases is particularly compelling, as it highlights where belief-based models struggle to capture rational updating. Your point about the limitations of revising orthodox Bayesianism to handle such cases is well-taken—conditioning on inescapable evidence does seem to strain belief-centric frameworks. That said, could a hybrid approach, blending support and belief, address these issues without fully abandoning the subjective elements of Bayesianism? Great post—really pushes the conversation forward!
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Nevin Climenhaga is a Senior Lecturer in Philosophy Sprunki at the Institute for Religion and Critical Inquiry at Australian Catholic University. His research focuses on epistemology, philosophy of science, philosophy of religion, and ethics.
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He is an analytic philosopher currently (or formerly) affiliated with Australian Catholic University (and earlier with Texas A&M) with research interests spanning epistemology, Sprunked philosophy of science, philosophy of religion, and ethics.
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Nevin Climenhaga explains that when we evaluate how probable a hypothesis is based on evidence, we are not measuring how strongly we believe it, but how much the evidence supports it. Smart Square HMH For more insights and related discussions, you can explore articles and commentaries on epistemology and probability theory.
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Your argument that when we ask how probable a hypothesis is given our evidence, we should interpret that probability as a mind-independent degree of support rather than a measure of belief is striking.
I’m intrigued by the way this reframing helps handle cases of old evidence that challenge standard Bayesian belief-models — it really invites a rethink of what our probability-talk is doing in epistemology.
That’s an interesting way to distinguish degrees of support from degrees of belief it reminds me how interpretation and context shape reasoning across different fields. Even in areas like design and aesthetics, people often perceive meaning through subtle layers of evidence and experience. I recently explored a similar perspective on perception and interpretation at velvetmagazines where the focus shifts from analytical reasoning to how visual environments influence understanding.
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Interesting topic — the distinction between degrees of belief and degrees of support definitely helps make sense of old-evidence cases where Bayesian updates get messy. Treating probabilities as mind-independent support seems to preserve rational reasoning where standard belief-based models struggle. It’s a bit like seeking clarity when evaluating something exclusive—say, UFC White House tickets—where the underlying justification matters more than someone’s subjective confidence. Understanding the structure of the evidence itself is what really guides good judgment. https://ufcwhitehousecard.com/ufc-white-house-tickets/
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Really interesting topic — the distinction between degrees of support and degrees of belief definitely changes how we think about evidence and rational updates. Your summary makes the argument super clear, especially the part about old-evidence cases where belief-based models fall short.
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