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      Subjectivity, Bayesianism, and Causality

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          Abstract

          Bayesian probability theory is one of the most successful frameworks to model reasoning under uncertainty. Its defining property is the interpretation of probabilities as degrees of belief in propositions about the state of the world relative to an inquiring subject. This essay examines the notion of subjectivity by drawing parallels between Lacanian theory and Bayesian probability theory, and concludes that the latter must be enriched with causal interventions to model agency. The central contribution of this work is an abstract model of the subject that accommodates causal interventions in a measure-theoretic formalisation. This formalisation is obtained through a game-theoretic Ansatz based on modelling the inside and outside of the subject as an extensive-form game with imperfect information between two players. Finally, I illustrate the expressiveness of this model with an example of causal induction.

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          Journal
          2014-07-15
          2015-02-16
          1407.4139

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          Custom metadata
          22 pages, 21 figures. Submitted to Special Issue of Pattern Recognition Letters on "Philosophical aspects of pattern recognition"
          cs.AI stat.ME stat.ML

          Machine learning, Artificial intelligence, Methodology

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