Since 2002 Perimeter Institute has been recording seminars, conference talks, public outreach events such as talks from top scientists using video cameras installed in our lecture theatres. Perimeter now has 7 formal presentation spaces for its many scientific conferences, seminars, workshops and educational outreach activities, all with advanced audio-visual technical capabilities.
Recordings of events in these areas are all available and On-Demand from this Video Library and on Perimeter Institute Recorded Seminar Archive (PIRSA). PIRSA is a permanent, free, searchable, and citable archive of recorded seminars from relevant bodies in physics. This resource has been partially modelled after Cornell University's arXiv.org.
Accessibly by anyone with internet, Perimeter aims to share the power and wonder of science with this free library.
In this talk, I show how information theoretic concepts can be used to extend the scope of traditional Bayesianism. I will focus on the learning of indicative conditionals (“If A, then B”) and a Bayesian account of argumentation. We will see that there are also interesting connections to research done in the psychology of reasoning. The talk is partly based on the paper “Bayesian Argumentation and the Value of Logical Validity” (with Ben Eva, forthcoming in Psychological Review, http://philsci-archive.pitt.edu/14491/).
When applied to a physical system, the two main, established notions of information, Shannon Information and Algorithmic Information, explicitly neglect the mechanistic structure of the system under evaluation. Shannon information treats the system as a channel and quantifies correlations between the system’s inputs and outputs, or between its past and future states. Algorithmic information quantifies the length of the shortest program capable of reproducing the system’s outputs or dynamics.
The causal Markov condition relates statistical dependences to causality. Its relevance is meanwhile widely appreciated in machine learning, statistics, and physics. I describe the *algorithmic* causal Markov condition relating algorithmic dependences to causality, which can be used for inferring causal relations among single objects without referring to statistics. The underlying postulate "no algorithmic dependence without causal relation" extends Reichenbach's Principle to a probability-free setting.
Remark to last week's participants: This will be a condensed version of last week's talks. I will drop many details (in particular on the relation to quantum theory) and also drop the introductory slides to algorithmic probability (for this, see Marcus Hutter's introductory talk on Tuesday afternoon, April 10).
The progression of theories suggested for our world, from ego- to geo- to helio-centric models to universe and multiverse theories and beyond, shows one tendency: The size of the described worlds increases, with humans being expelled from their center to ever more remote and random locations. If pushed too far, a potential theory of everything (TOE) is actually more a theories of nothing (TON). Indeed such theories have already been developed. I show that including observer localization into such theories is necessary and sufficient to avoid this problem.
Check back for details on the next lecture in Perimeter's Public Lectures Series