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 {{Randomness, Structure and Causality}}   {{Randomness, Structure and Causality}} 
   
−   +  [[Media:Agenda.pdfAgenda PDF]] 
−  == Abstracts ==
 
−   
−  <br>
 
−  
 
−  '''To a Mathematical Theory of Evolution and Biological Creativity'''
 
−  <br>
 
−  <br>
 
−  Chaitin, Gregory (gjchaitin@gmail.com)
 
−  <br>
 
−  IBM Watson Research Center
 
−  <br>
 
−  <br>
 
−  We present an informationtheoretic analysis of Darwin’s theory of evolution, modeled as a hillclimbing algorithm on a fitness landscape. Our space of possible organisms consists of computer programs, which are subjected to random mutations. We study the random walk of increasing fitness made by a single mutating organism. In two different models we are able to show that evolution will occur and to characterize the rate of evolutionary progress, i.e., the rate of biological creativity.
 
−  <br>
 
−  <br>
 
−  Links: [[File:Darwin.pdf]]
 
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−  
 
−  '''The Vocabulary of GrammarBased Codes and the Logical Consistency of Texts'''
 
−  <br>
 
−   
−  Debowski, Lukasz (ldebowsk@ipipan.waw.pl)<br>
 
−  Polish Academy of Sciences<br>
 
−  <br>
 
−  <p>
 
−  We will present a new explanation for the distribution of words in
 
−  natural language which is grounded in information theory and inspired
 
−  by recent research in excess entropy. Namely, we will demonstrate a
 
−  theorem with the following informal statement: If a text of length <math>n</math>
 
−  describes <math>n^\beta</math> independent facts in a repetitive way then the
 
−  text contains at least <math>n^\beta/\log n</math> different words. In the
 
−  formal statement, two modeling postulates are adopted. Firstly, the
 
−  words are understood as nonterminal symbols of the shortest
 
−  grammarbased encoding of the text. Secondly, the text is assumed to
 
−  be emitted by a finiteenergy strongly nonergodic source whereas the
 
−  facts are binary IID variables predictable in a shiftinvariant
 
−  way. Besides the theorem, we will exhibit a few stochastic processes
 
−  to which this and similar statements can be related.
 
−  <br>
 
−  <br>
 
−   
−  Links: [[http://arxiv.org/abs/0810.3125]] and [[http://arxiv.org/abs/0911.5318]]
 
−   
−  
 
−  '''Automatic Identification of InformationProcessing Structures in Cellular Automata'''
 
−  <br>
 
−   
−  Mitchell, Melanie (mm@cs.pdx.edu)
 
−  <br>
 
−  SFI & Portland State University
 
−  <br>
 
−  <br>
 
−  Cellular automata have been widely used as idealized models of natural spatiallyextended dynamical systems. An open question is how to best understand such systems in terms of their informationprocessing capabilities. In this talk we address this question by describing several approaches to automatically identifying the structures underlying information processing in cellular automata. In particular, we review the computational mechanics methods of Crutchfield et al., the local sensitivity and local statistical complexity filters proposed by Shalizi et al., and the information theoretic filters proposed by Lizier et al. We illustrate these methods by applying them to several one and twodimensional cellular automata that have been designed to perform the socalled density (or majority) classification task.
 
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−  
 