Good Enough: The Case for Aspiration-Based AI

Aspiration-based reinforcement learning, a specialized approach to AI that prioritizes satisficing over the traditional goal of maximization. Rather than ruthlessly seeking the highest possible reward, these agents operate based on internal benchmarks and only change their behavior when results fall below their expectations. This model mirrors human psychology by allowing aspiration levels to adapt based on success or failure, creating more stable and predictable habits. When placed in social simulations, these "good enough" agents are more likely to engage in cooperation rather than betrayal, especially when influenced by a nudging agent that sets a positive example. Ultimately, this suggests that designing AI to seek satisfaction instead of pure efficiency may foster more harmonious digital and social environments.

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