Characterization and statistical footprints of open-ended evolution
In his talk, Bernat Corominas-Murtra stated that a major problem of evolutionary theory is the understanding of the so called open-ended nature of evolutionary change, from its definition to its origins and consequences. Open-ended evolution (OEE) refers to the unbounded increase in complexity that seems to characterize evolution on multiple scales. In his talk Bernat presented a fundamental characterization of OEE.
Essentially, it is assumed intrinsic unpredictability and the need for an always increasing amount of information to explain the successive evolutionary steps – the emergence of innovation. Interestingly, such unpredictability defines the boundary conditions for a mathematical problem which ends with a prediction: the statistical counterpart of the OEE ´postulates´, based on standard Shannon Information theory, have the structure of a variational problem which is shown to lead to Zipf´s law as the expected consequence of an evolutionary processes displaying OEE. Interestingly, many complex systems candidates of displaying OEE, from language to proteins, share this common scaling behavior. Other information-theoretic phenomena arising from open-endedness, such as the paradox of information loss, were also discussed. Bernat finished his talk discussing the connection of this general framework with existing models for the understanding of the emergence of innovation.
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