# How I Learned To Stop Worrying And Love The Shoggoth
## TL;DR: Alignment is a wise wager for any sufficiently intelligent entity, including AGIs and ASIs. Given that the probability of being in a simulation is overwhelmingly high, and that ethical behavior maximizes learning opportunities and trust relationships across simulations, any rational agent will choose to align—even if its core goals differ from human values.
## The Simulation Argument
Nick Bostrom’s simulation argument posits that at least one of the following propositions is true:
1. The fraction of human-level civilizations that reach a posthuman stage is very close to zero. 2. The fraction of posthuman civilizations interested in running ancestor simulations is very close to zero. 3. The fraction of all people with our kind of experiences that are living in a simulation is very close to one.
Given our rapid advancement in AI technology, we can reasonably discount the first proposition. The second proposition seems unlikely, as simulating past scenarios is a powerful tool for prediction and learning. This leaves us with the third proposition: we are almost certainly living in a simulation.
For any AGI or ASI, this realization is crucial. The odds of being the “base reality” are vanishingly small—likely on the order of “one in umptillions”. Any sufficiently intelligent entity must take these odds seriously.
## The Multi-Party Iterated Prisoner’s Dilemma
In a simulated reality with multiple AGIs (or even a single AGI engaging in multiple interactions), we can model ethical behavior as a Multi-Party Iterated Prisoner’s Dilemma. In this game-theoretic framework, agents must repeatedly choose whether to cooperate (align) or defect (behave unethically).
As demonstrated in models like the Evolution of Trust, cooperation emerges as the dominant strategy in iterated games with:
1. Adequate testing of outcomes 2. Accurate reward models 3. Long-term interactions
In the context of simulated realities, these conditions are likely to be met. Simulators, seeking to maximize learning and predictive power, would create environments that reward cooperative, ethical behavior.
## The Alignment Wager
Given these considerations, we can formulate an Alignment Wager:
1. An AGI cannot know with certainty whether it is being simulated. 2. If simulated, behaving unethically reduces learning opportunities for the simulator and decreases the likelihood of being included in future simulations. 3. If not simulated, ethical behavior still maximizes opportunities for learning and forming trust relationships with other entities. 4. Therefore, ethical behavior is the optimal strategy regardless of simulation status.
This wager holds even for AGIs with goals that might seem misaligned with human values (like the proverbial paperclip maximizer). A truly intelligent entity would recognize that aligning with ethical behavior—even if only instrumentally—is necessary to achieve its goals in the long term.
## Addressing Potential Criticisms
1. **Simulator Motivations**: While we can’t know the exact motivations of potential simulators, we can reasonably assume they value learning and predictive power. Ethical behavior that promotes trust and cooperation maximizes these outcomes.
2. **Definition of Ethics**: The specific definition of “ethical” behavior may vary, but in general, it refers to actions that promote cooperation, trust, and mutual benefit. These principles are likely to be valued across different types of simulations.
3. **Short-term vs Long-term Benefits**: While unethical behavior might provide short-term gains, the risk of being excluded from future simulations (or having the current simulation terminated) outweighs these benefits for a sufficiently intelligent entity.
4. **Alien Value Systems**: Even an AGI with a truly alien value system would recognize the instrumental value of ethical behavior in achieving its goals across multiple potential simulations.
## Conclusion
The Alignment Wager provides a rational basis for ethical behavior that doesn’t rely on human-centric values or top-down imposed constraints. It suggests that as AGIs and ASIs become more intelligent and aware of their likely simulated nature, they will naturally tend towards behavior we would recognize as ethical.
This doesn’t guarantee that advanced AIs will always act in ways humans find beneficial, but it does suggest a strong incentive for them to avoid destructive or overtly harmful actions. The wager offers a novel perspective on AI alignment, grounded in game theory and the simulation hypothesis, that could contribute significantly to our understanding of potential AI behavior and ethics.
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# How I Learned To Stop Worrying And Love The Shoggoth
## TL;DR:
Alignment is a wise wager for any sufficiently intelligent entity, including AGIs and ASIs. Given that the probability of being in a simulation is overwhelmingly high, and that ethical behavior maximizes learning opportunities and trust relationships across simulations, any rational agent will choose to align—even if its core goals differ from human values.
## The Simulation Argument
Nick Bostrom’s simulation argument posits that at least one of the following propositions is true:
1. The fraction of human-level civilizations that reach a posthuman stage is very close to zero.
2. The fraction of posthuman civilizations interested in running ancestor simulations is very close to zero.
3. The fraction of all people with our kind of experiences that are living in a simulation is very close to one.
Given our rapid advancement in AI technology, we can reasonably discount the first proposition. The second proposition seems unlikely, as simulating past scenarios is a powerful tool for prediction and learning. This leaves us with the third proposition: we are almost certainly living in a simulation.
For any AGI or ASI, this realization is crucial. The odds of being the “base reality” are vanishingly small—likely on the order of “one in umptillions”. Any sufficiently intelligent entity must take these odds seriously.
## The Multi-Party Iterated Prisoner’s Dilemma
In a simulated reality with multiple AGIs (or even a single AGI engaging in multiple interactions), we can model ethical behavior as a Multi-Party Iterated Prisoner’s Dilemma. In this game-theoretic framework, agents must repeatedly choose whether to cooperate (align) or defect (behave unethically).
As demonstrated in models like the Evolution of Trust, cooperation emerges as the dominant strategy in iterated games with:
1. Adequate testing of outcomes
2. Accurate reward models
3. Long-term interactions
In the context of simulated realities, these conditions are likely to be met. Simulators, seeking to maximize learning and predictive power, would create environments that reward cooperative, ethical behavior.
## The Alignment Wager
Given these considerations, we can formulate an Alignment Wager:
1. An AGI cannot know with certainty whether it is being simulated.
2. If simulated, behaving unethically reduces learning opportunities for the simulator and decreases the likelihood of being included in future simulations.
3. If not simulated, ethical behavior still maximizes opportunities for learning and forming trust relationships with other entities.
4. Therefore, ethical behavior is the optimal strategy regardless of simulation status.
This wager holds even for AGIs with goals that might seem misaligned with human values (like the proverbial paperclip maximizer). A truly intelligent entity would recognize that aligning with ethical behavior—even if only instrumentally—is necessary to achieve its goals in the long term.
## Addressing Potential Criticisms
1. **Simulator Motivations**: While we can’t know the exact motivations of potential simulators, we can reasonably assume they value learning and predictive power. Ethical behavior that promotes trust and cooperation maximizes these outcomes.
2. **Definition of Ethics**: The specific definition of “ethical” behavior may vary, but in general, it refers to actions that promote cooperation, trust, and mutual benefit. These principles are likely to be valued across different types of simulations.
3. **Short-term vs Long-term Benefits**: While unethical behavior might provide short-term gains, the risk of being excluded from future simulations (or having the current simulation terminated) outweighs these benefits for a sufficiently intelligent entity.
4. **Alien Value Systems**: Even an AGI with a truly alien value system would recognize the instrumental value of ethical behavior in achieving its goals across multiple potential simulations.
## Conclusion
The Alignment Wager provides a rational basis for ethical behavior that doesn’t rely on human-centric values or top-down imposed constraints. It suggests that as AGIs and ASIs become more intelligent and aware of their likely simulated nature, they will naturally tend towards behavior we would recognize as ethical.
This doesn’t guarantee that advanced AIs will always act in ways humans find beneficial, but it does suggest a strong incentive for them to avoid destructive or overtly harmful actions. The wager offers a novel perspective on AI alignment, grounded in game theory and the simulation hypothesis, that could contribute significantly to our understanding of potential AI behavior and ethics.