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Implement Schwoba sen gnitz strategy with specific behaviors based on opponent's actions.
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Hello Axelrod Team,
I would like to submit "Schwoba sen gnitz", a highly robust, adaptive strategy to the repository. It is based on a successful empirical design I developed with TurboPascal during the German c't tournament era back in the late 80's, now fully ported to clean Python.
Author: Georg 'HackyHackberger' Schmidt
Behavioral Record:
In my private offline simulations against common adaptive baselines, this strategy successfully contains and outperforms standard pattern-recognizing models, neural networks, and stochastic gamblers:
Core Mechanics:
Organic Self-Play:
It starts 100% cooperatively. If it meets a clone of itself, both stay peaceful forever (Action.C), maximizing tournament efficiency without relying on fragile, noise-prone "secret handshakes".
The Reactive Phase Shift:
The moment the opponent defects once, the internal tracking mode wakes up permanently. From this point on, it introduces a low unprovoked defection rate of 10.2% to systematically test boundaries and exploit softer strategies.
De-escalation:
To prevent destructive, infinite echo-loops with other reciprocal strategies (like standard TFT), it forgives and dampens hostility with a 89.7% probability.
Quarantine Lock:
If an opponent continuously defects for 10 rounds, it identifies it as an unredeemable defector (Grim or AllD) and locks down into permanent defection to minimize further damage.
I look forward to seeing "Schwoba sen gnitz" in the official tournament framework!