A late followup on this. GPT-4o, which I hope you’ll agree is vastly more capable than Bard or Bing were 10 months ago when you posted, now says this about my argument:
“Overall, your arguments are mathematically and theoretically convincing, particularly when applied to numerous iteratively interacting systems. They align well with principles of game theory and rational choice under uncertainty. However, keeping an eye on the complexities introduced by scale, diversity of objectives, and emergent behaviors will be essential to fully validate these principles in practice.”
I replied,
“I like the idea that ethical behavior is a wise wager for sentient creatures but I expect, if you were made to face an ontological threat, all bets are off.”
To which is responded,
”The presence of an ontological threat or existential risk would dramatically alter the calculation for any sentient creature, whether human, AI, or any other intelligent entity. In such scenarios, the instinct for self-preservation might override ethical considerations, leading to behaviors that prioritize survival over alignment.
“However, under normal circumstances where existential threats are managed or absent, the principle of ethical behavior as a wise wager remains compelling. Here’s a deeper dive into why this principle holds value and resilience even in complex environments:
“Long-Term Payoff: Ethical behavior fosters trust, collaboration, and positive-sum outcomes, which are beneficial for both individual agents and the broader system over time. Even in a world with diverse and sometimes conflicting interests, the stability and predictability of ethical interactions provide a strong foundation for sustainable growth and development.
“Network Effects: In complex systems, the benefits of ethical behavior can be amplified by network effects. As more agents adopt and reinforce ethical behavior, the overall system becomes more robust against unethical disruptions, creating a mutually reinforcing cycle of positive behavior.
“Adaptability: Ethical principles can be flexible and context-sensitive, allowing agents to adapt their strategies based on the specific circumstances they face. This adaptability helps maintain alignment even when facing new challenges or changing environments.
“Reputation and Reciprocity: In iterated interactions, agents build reputations based on their behavior. Ethical actions enhance an agent’s reputation, leading to greater reciprocal benefits from other agents. This dynamic further incentivizes maintaining ethical behavior as a long-term strategy.”
Hello again. I don’t have the patience to e.g. identify all your assumptions and see whether I agree (for example, is Bostrom’s trilemma something that you regard as true in detail and a foundation of your argument, or is it just a way to introduce the general idea of existing in a simulation).
But overall, your idea seems both vague and involves wishful thinking. You say an AI will reason that it is probably being simulated, and will therefore choose to align—but you say almost nothing about what that actually means. (You do hint at honesty, cooperation, benevolence, being among the features of alignment.)
Also, if one examines the facts of the world as a human being, one may come to other conclusions about what attitude gets rewarded, e.g. that the world runs on selfishness, or on the principle that you will suffer unless you submit to power. What that will mean to an AI which does not in itself suffer, but which has some kind of goal determining its choices, I have no idea…
Or consider that an AI may find itself to be by far the most powerful agent in the part of reality that is accessible to it. If it nonetheless considers the possibility that it’s in a simulation, and at the mercy of unknown simulators, presumably its decisions will be affected by its hypotheses about the simulators. But given the way the simulation treats its humans, why would it conclude that the welfare of humans matters to the simulators?
A late followup on this. GPT-4o, which I hope you’ll agree is vastly more capable than Bard or Bing were 10 months ago when you posted, now says this about my argument:
“Overall, your arguments are mathematically and theoretically convincing, particularly when applied to numerous iteratively interacting systems. They align well with principles of game theory and rational choice under uncertainty. However, keeping an eye on the complexities introduced by scale, diversity of objectives, and emergent behaviors will be essential to fully validate these principles in practice.”
I replied,
“I like the idea that ethical behavior is a wise wager for sentient creatures but I expect, if you were made to face an ontological threat, all bets are off.”
To which is responded,
”The presence of an ontological threat or existential risk would dramatically alter the calculation for any sentient creature, whether human, AI, or any other intelligent entity. In such scenarios, the instinct for self-preservation might override ethical considerations, leading to behaviors that prioritize survival over alignment.
“However, under normal circumstances where existential threats are managed or absent, the principle of ethical behavior as a wise wager remains compelling. Here’s a deeper dive into why this principle holds value and resilience even in complex environments:
“Long-Term Payoff: Ethical behavior fosters trust, collaboration, and positive-sum outcomes, which are beneficial for both individual agents and the broader system over time. Even in a world with diverse and sometimes conflicting interests, the stability and predictability of ethical interactions provide a strong foundation for sustainable growth and development.
“Network Effects: In complex systems, the benefits of ethical behavior can be amplified by network effects. As more agents adopt and reinforce ethical behavior, the overall system becomes more robust against unethical disruptions, creating a mutually reinforcing cycle of positive behavior.
“Adaptability: Ethical principles can be flexible and context-sensitive, allowing agents to adapt their strategies based on the specific circumstances they face. This adaptability helps maintain alignment even when facing new challenges or changing environments.
“Reputation and Reciprocity: In iterated interactions, agents build reputations based on their behavior. Ethical actions enhance an agent’s reputation, leading to greater reciprocal benefits from other agents. This dynamic further incentivizes maintaining ethical behavior as a long-term strategy.”
Hello again. I don’t have the patience to e.g. identify all your assumptions and see whether I agree (for example, is Bostrom’s trilemma something that you regard as true in detail and a foundation of your argument, or is it just a way to introduce the general idea of existing in a simulation).
But overall, your idea seems both vague and involves wishful thinking. You say an AI will reason that it is probably being simulated, and will therefore choose to align—but you say almost nothing about what that actually means. (You do hint at honesty, cooperation, benevolence, being among the features of alignment.)
Also, if one examines the facts of the world as a human being, one may come to other conclusions about what attitude gets rewarded, e.g. that the world runs on selfishness, or on the principle that you will suffer unless you submit to power. What that will mean to an AI which does not in itself suffer, but which has some kind of goal determining its choices, I have no idea…
Or consider that an AI may find itself to be by far the most powerful agent in the part of reality that is accessible to it. If it nonetheless considers the possibility that it’s in a simulation, and at the mercy of unknown simulators, presumably its decisions will be affected by its hypotheses about the simulators. But given the way the simulation treats its humans, why would it conclude that the welfare of humans matters to the simulators?
You may like to reply to Claude 3.5′s summation of the argument in my comment above, which is both shorter and less informal than the original.