The Role of Creativity and Randomizers in Human Cognition and Problem Solving

  • Werner Leinfellner
Conference paper
Part of the Theory and Decision Library A: book series (TDLA, volume 46)


Genetic algorithms demonstrate that a higher organism in its environment or society can modify its behavior (humans their societal decisions) by a selective and adaptive learning process which is regimented by ad-hoc game-theoretical and statistical societal default rules. These rules may change even genetically fixed rules; their use can generate new ones which our brain evaluates (Holland’s “credit assignments”; Holland 1995, p 53); the organism must store all of them in its memory system. In short, animals learn (mostly) unconsciously by using default rules (Holland 1995, p 45), humans consciously by using default rules stored in the higher linguistic and the cultural memory3 system. Thus, evolutionary processing by learning, rule generation, and rules of innovations (Holland 1995, p 61) can totally describe the evolutionary and evolutive dynamic. It is characteristic for mental evolutive processing after randomizations to progress gradually by using default rules, step by step, beyond the established knowledge. The use of default rules by humans can lead, as we will show, to mental innovations and the creation of entirely new solutions of conflicts between different mentifacts, sociofacts, artifacts, and technifacts. The advances of scientific research in democratic societies are produced by inventions, teaching, transmitting, and by storing past and new solutions of societal conflicts, as well as by the ultimate successful realizations. They rest upon serial default rules stored by the gigantic, cultural, scientific, evolutive process in our cultural memory3. The process is rule-bound; this is one salient property of TSO (Götschl) theories.


Wave Pattern Democratic Society Lottery Ticket Default Rule Creative Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



I am indebted to J. Götschl for dozens of conversations at the Ludwig Boltzmann Institute for Science and Research, University of Graz. I profited from a graduate seminar on evolution at the University of Rome. I am also indebted to E. Basar and A. Carsetti. I benefited from discussions with M. Allais, J. Harsanyi, E. McClennen, M. Machina, B. Munier, A. Rapoport, R. Selten, J. Nida-Rümelin, B. Skyrms, and M. Wuketits. Last and most important: thanks to my wife Elisabeth Leinfellner for clarifying my thinking and improving the text.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Werner Leinfellner
    • 1
  1. 1.University of NebraskaWienAustria

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