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Evolving Complexity in Cooperative and Competitive Noisy Prediction Games.
1Brandeis University, DEMO Lab. nemtiax@gmail.com.
Artificial Life
|November 8, 2019
Summary
Cooperative and competitive interactions drive the evolution of complex strategies in noisy prediction games. A novel metric effectively measures this complexity, distinguishing it from simple genetic expansion.
Area of Science:
- Evolutionary Game Theory
- Computational Biology
- Artificial Intelligence
Background:
- Previous research explored strategy evolution in prediction games.
- The role of cooperation and competition in driving complexity remains an active area of investigation.
- Existing complexity metrics may not adequately differentiate true complexity from genetic bloat.
Purpose of the Study:
- To investigate the impact of cooperative and competitive interactions on the evolution of complex strategies.
- To extend existing models to noisy game environments.
- To introduce and validate a novel metric for measuring evolutionary complexity.
Main Methods:
- Development of a new organism and mutation model for noisy prediction games.
- Introduction of a novel complexity metric.
- Comparative analysis of the novel metric against simpler metrics like raw strategy size.
Main Results:
- A combination of cooperation and competition most effectively promotes the growth of strategy complexity.
- The novel complexity metric successfully distinguishes genuine complexity from genetic bloat.
- Prior findings on the importance of mixed interactions are confirmed in noisy game settings.
Conclusions:
- Mixed cooperative and competitive dynamics are crucial for evolving complex strategies.
- The developed complexity metric offers a more accurate assessment of evolutionary complexity.
- This work provides a refined framework for studying strategy evolution in complex environments.
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