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Mixed competitive and cooperative interactions drive higher complexity in evolving systems. This study introduces a complexity metric for finite state machines in language prediction games, finding mixed systems outperform purely competitive or cooperative ones.

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Coevolutioncompetitioncomplexitycooperationecosystems

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Area of Science:

  • Evolutionary dynamics
  • Computational intelligence
  • Formal language theory

Background:

  • Previous research focused on purely competitive or cooperative coevolutionary models.
  • Neither purely competitive nor purely cooperative systems continuously drive evolutionary arms races.
  • Understanding drivers of sustained complexity growth is crucial.

Purpose of the Study:

  • To investigate open-ended coevolution using a novel complexity metric.
  • To analyze population dynamics under mixed competitive and cooperative interactions.
  • To identify factors influencing complexity growth rates in evolving systems.

Main Methods:

  • Defined a complexity metric for interacting finite state machines.
  • Simulated populations playing formal language prediction games.
  • Analyzed evolutionary dynamics under different interaction types (competitive, cooperative, mixed).

Main Results:

  • Both purely competitive and cooperative coevolution increased complexity beyond genetic drift.
  • Mixed systems, incorporating both competitive and cooperative interactions, demonstrated significantly higher evolved complexity.
  • Quantified complexity growth rates across different interaction scenarios.

Conclusions:

  • Mixed competitive and cooperative interactions are more effective at driving sustained complexity and evolutionary arms races.
  • The developed complexity metric provides a quantitative measure for studying open-ended coevolution.
  • Findings have implications for artificial life, evolutionary computation, and understanding complex adaptive systems.