I discussed the pitfalls of over- and underestimating the applicability of modelling results to understanding real world epistemic communities, and how to have an appropriate epistemic attitude towards them.
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I use category theory, the mathematical theory of structure, to illuminate the ways in which mathematical formalisms are used to represent real-world systems.
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This paper is about how an agent should rationally update her probabilistic beliefs when her conceptual space (modelled as an algebra of propositions) grows. This is not like typical cases of learning, which are cases in which an agent comes to revise her beliefs for propositions about which she was already aware. We investigate whether the learning rules for the typical cases of learning can be extended to the case of conceptual growth.
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Here HMI CI Katie Steele argues that on a certain way of modelling an agent's preferences and understanding her "time preferences", exponential time discounting is uniquely rational. However, if "time preferences" are understood differently, then exponential time discounting is not uniquely rational. This helps in understanding why the prescription of exponential time discounting has many defenders but also many detractors.
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Some realistic models of neural spiking take into account spike timing, yet the practical relevance of spike timing is often unclear. I show that polychronous networks reflect a distinct organisational principle from notions of pluripotency, redundancy, or re-use, and argue that properly understanding this phenomenon requires a shift to a time-sensitive, process-based view of computation.
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This paper examines the epistemic value of using topological methods to study the "shape" of data sets. It is argued that the category theoretic notion of "functoriality" aids in translating visual intuitions about structure in data into precise, computable descriptions of real-world systems.
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This paper identifies a new role for mathematics in scientific practice. Atoosa calls this the "bridging'' role of mathematics, according to which mathematics acts as a connecting scheme in our explanatory reasoning about why and how two different descriptions of an empirical phenomenon relate to each other.
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In this talk, Atoosa offered a multi-faceted unifying theory for the varieties of explanations as to why a prediction-based algorithmic decision is obtained. This framework lays the groundwork for establishing the relevant connection between technical, moral, and legal aspects of artificially-intelligent decision-making.
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Colin Klein (ANU), Andrew Barron (Macquarie) and Marta Halina (Cambridge) have been awarded a grant to study "The major transitions in the evolution of cognition" from the Templeton World Charity Foundation. This $1M USD grant will fund research into the major shifts in computational organisation that allowed evolving brains to process information in new ways. Researchers at the ANU, led by CI Klein, will explore the philosophical foundations of computational neuroscience.
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Mind Design III will update Haugeland's classic reader on philosophy of artificial intelligence for the modern era. It will contain a mix of classic and contemporary readings, along with a new introduction to contextualise the topic for students. Expected publication date Q1 2021.
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This talk was given at a conference on Holly Smith’s book, Making Morality Work, held at Rutgers on October 18, 2019. I argued that Making Morality Work poses the problem that moral theories must be 'usable', but then offers a solution that only partly solves it. I offered a way to extend the solution, but argued that even that only partly solves the problem, and that we can’t stop there.
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In this talk, I argue for the practical problems of a counterfactual theory of mathematical explanations in sciences.
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