I would expect that for model-based RL, the more powerful the AI is at predicting the environment and the impact of its actions on it, the less prone it becomes to Goodharting its reward function. That is, after a certain point, the only way to make the AI more powerful at optimizing its reward function is to make it better at generalizing from its reward signal in the direction that the creators meant for it to generalize.
In such a world, when AIs are placed in complex multiagent environments where they engage in iterated prisoner’s dilemmas, the more intelligent ones (those with greater world-modeling capacity) should tend to optimize for making changes to the environment that shift the Nash equilibrium toward cooperate-cooperate, ensuring more sustainable long-term rewards all around. This should happen automatically, without prompting, no matter how simple or complex the reward functions involved, whenever agents surpass a certain level of intelligence in environments that allow for such incentive-engineering.
I would expect that for model-based RL, the more powerful the AI is at predicting the environment and the impact of its actions on it, the less prone it becomes to Goodharting its reward function. That is, after a certain point, the only way to make the AI more powerful at optimizing its reward function is to make it better at generalizing from its reward signal in the direction that the creators meant for it to generalize.
In such a world, when AIs are placed in complex multiagent environments where they engage in iterated prisoner’s dilemmas, the more intelligent ones (those with greater world-modeling capacity) should tend to optimize for making changes to the environment that shift the Nash equilibrium toward cooperate-cooperate, ensuring more sustainable long-term rewards all around. This should happen automatically, without prompting, no matter how simple or complex the reward functions involved, whenever agents surpass a certain level of intelligence in environments that allow for such incentive-engineering.