Our universe is of astonishing simplicity: almost all physical observations can in principle be described by a few theories that have short mathematical descriptions. But there is a field of computer science which quantifies simplicity namely algorithmic information theory (AIT). In this workshop we will discuss emerging connections between AIT and physics some of which have recently shown up in fields like quantum information theory and thermodynamics. In particular AIT and physics share one goal: namely to predict future observations given previous data. In fact there exists a gold standard of prediction in AIT called Solomonoff induction which is also applied in artificial intelligence. This motivates us to look at a broader question: what is the role of induction in physics? For example can quantum states be understood as Bayesian states of belief? Can physics be understood as a computation in some sense? What is the role of the observer i.e. the agent that is supposed to perform the predictions? These and related topics will be discussed by a diverse group of researchers from different disciplines.
Format results


Quantum speedup in testing causal hypotheses
Giulio Chiribella The University of Hong Kong (HKU)

The Logic of Physical Law
Stefan Wolf Universit della Svizzera italiana


On the concepts of universality in physics and computer science
Gemma De Las Cuevas Universität Innsbruck

A nogo theorem for observerindependent facts
Časlav Brukner Vienna Center for Quantum Science and Technology


Algorithmic information theory: a critical perspective
Tom Sterkenburg LudwigMaximiliansUniversitiät München (LMU)

Normative probability in quantum mechanics
Ruediger Schack University of London

Introduction to Algorithmic Information Theory and Tutorial
Marcus Hutter Australian National University

Can quantum states be understood as Bayesian states of belief?
Wayne Myrvold Western University

Observer Localization in Multiverse Theories
Marcus Hutter Australian National University