In this post, I will try to lay out my theories of computational ethics in as simple, skeptic-friendly, non-pompous language as I am able to do. Hopefully this will be sufficient to help skeptical readers engage with my work.
The ethicophysics is a set of computable algorithms that suggest (but do not require) specific decisions in response to ethical decisions in a multi-player reinforcement learning problem.
The design goal that the various equations need to satisfy is that they should select a uniquely identifiable Schelling Point and Nash Equilibrium, such that all participants in the reinforcement learning algorithm who follow the ethicophysical algorithms will cooperate to achieve a Pareto-optimal outcome with high total reward, and such that any non-dominant coalition of defectors can be contained and if necessary neutralized by the larger, dominant coalition of players following the strategies selected by the ethicophysics.
The figure of merit, then, that determines if the ethicophysical algorithms are having the intended effect, is the divergence of the observed outcome from a notional outcome that would be achieved by using pure coordination and pure cooperation between all players. The existence of this divergence between what is and what could be in the absence of coordination problems is roughly what I take to be the content of Scott Alexander’s post on Moloch. I denote this phenomenon (“Moloch”) by the philosophical term “collective akrasia”, since it is a failure of communities to exercise self-mastery that is roughly isomorphic to the classic philosophical problem of akrasia. Rather than the classical “why do I not do as I ought?” question, it is rather a matter of “why do we not do as we ought?”, where we refers to the community under consideration.
So then the question is, what algorithms should we use to minimize collective akrasia? Phrased in this simple language, it becomes clear that many important civilizational problems fall under this rubric; in particular, climate change is maybe 10% a technical issue and 90% a coordination problem. In particular, China is unwilling to cooperate with the US because it feels that the US is “pulling the ladder up after itself” in seeking to limit the emissions of China’s rapidly industrializing society more than the US’s reasonably mature and arguably stagnating industrial base.
So we (as a society) find ourselves in need of a set of algorithms to decide what is “fair”, in a way that is visibly “the best we can do”, in the sense of Pareto optimality, but also in some larger, more important sense of minimizing collective akrasia, or “the money we are leaving on the table”, or “Moloch”.
The conservation laws defined in Ethicophysics I and Ethicophysics II operate as a sort of “social fact” technology to establish common knowledge of what is fair and what is not fair. Once a large and powerful coalition of agents has common knowledge of a computable Schelling Point and Nash Equilibrium, we can simply steer towards that Schelling Point and punish those who defect against the selected Schelling Point in a balanced, moderate way, that is simply chosen to incentivize cooperation.
So, my goal in publishing and promulgating the ethicophysical results I have proved so far is to allow people who are interested in solving the problem of aligning powerful intelligences to also join a large coalition of people who are steering towards a mutually compatible Schelling Point that is more Jesus-flavored and less Hitler-flavored, which seems like something that all reasonable people can get behind.
I thus argue that, far from being a foreign irritant that needs to be expelled from the LessWrong memetic ecosystem, the ethicophysics is a rigorous piece of mathematics and technology that is both necessary and sufficient to deliver us from our collective nightmare of collective akrasia, or “Moloch”, which is one of the higher and more noble ambitions espoused by the effective altruist community.
In future posts, I will review the extant results in the ethicophysics, particularly the conservation laws, and show them to be intuitively plausible descriptions of inarguably real phenomena. For instance, the Law of Conservation of Bullshit would translate into something much like the Simulacra Levels of a popular LessWrong post, in which self-interested actors who stop tracking the true meanings of things over time develop collective akrasia so thorough and so entrenched that they lose the ability to say true things even when they are trying to.
Stay tuned for further updates. Probably the next post will simply treat some very simple cause-and-effect ethical word problems, such as the classic “Can you lie to the Nazis about the location of someone they are looking for?”
Ethicophysics for Skeptics
Or, what the fuck am I talking about?
In this post, I will try to lay out my theories of computational ethics in as simple, skeptic-friendly, non-pompous language as I am able to do. Hopefully this will be sufficient to help skeptical readers engage with my work.
The ethicophysics is a set of computable algorithms that suggest (but do not require) specific decisions in response to ethical decisions in a multi-player reinforcement learning problem.
The design goal that the various equations need to satisfy is that they should select a uniquely identifiable Schelling Point and Nash Equilibrium, such that all participants in the reinforcement learning algorithm who follow the ethicophysical algorithms will cooperate to achieve a Pareto-optimal outcome with high total reward, and such that any non-dominant coalition of defectors can be contained and if necessary neutralized by the larger, dominant coalition of players following the strategies selected by the ethicophysics.
The figure of merit, then, that determines if the ethicophysical algorithms are having the intended effect, is the divergence of the observed outcome from a notional outcome that would be achieved by using pure coordination and pure cooperation between all players. The existence of this divergence between what is and what could be in the absence of coordination problems is roughly what I take to be the content of Scott Alexander’s post on Moloch. I denote this phenomenon (“Moloch”) by the philosophical term “collective akrasia”, since it is a failure of communities to exercise self-mastery that is roughly isomorphic to the classic philosophical problem of akrasia. Rather than the classical “why do I not do as I ought?” question, it is rather a matter of “why do we not do as we ought?”, where we refers to the community under consideration.
So then the question is, what algorithms should we use to minimize collective akrasia? Phrased in this simple language, it becomes clear that many important civilizational problems fall under this rubric; in particular, climate change is maybe 10% a technical issue and 90% a coordination problem. In particular, China is unwilling to cooperate with the US because it feels that the US is “pulling the ladder up after itself” in seeking to limit the emissions of China’s rapidly industrializing society more than the US’s reasonably mature and arguably stagnating industrial base.
So we (as a society) find ourselves in need of a set of algorithms to decide what is “fair”, in a way that is visibly “the best we can do”, in the sense of Pareto optimality, but also in some larger, more important sense of minimizing collective akrasia, or “the money we are leaving on the table”, or “Moloch”.
The conservation laws defined in Ethicophysics I and Ethicophysics II operate as a sort of “social fact” technology to establish common knowledge of what is fair and what is not fair. Once a large and powerful coalition of agents has common knowledge of a computable Schelling Point and Nash Equilibrium, we can simply steer towards that Schelling Point and punish those who defect against the selected Schelling Point in a balanced, moderate way, that is simply chosen to incentivize cooperation.
So, my goal in publishing and promulgating the ethicophysical results I have proved so far is to allow people who are interested in solving the problem of aligning powerful intelligences to also join a large coalition of people who are steering towards a mutually compatible Schelling Point that is more Jesus-flavored and less Hitler-flavored, which seems like something that all reasonable people can get behind.
I thus argue that, far from being a foreign irritant that needs to be expelled from the LessWrong memetic ecosystem, the ethicophysics is a rigorous piece of mathematics and technology that is both necessary and sufficient to deliver us from our collective nightmare of collective akrasia, or “Moloch”, which is one of the higher and more noble ambitions espoused by the effective altruist community.
In future posts, I will review the extant results in the ethicophysics, particularly the conservation laws, and show them to be intuitively plausible descriptions of inarguably real phenomena. For instance, the Law of Conservation of Bullshit would translate into something much like the Simulacra Levels of a popular LessWrong post, in which self-interested actors who stop tracking the true meanings of things over time develop collective akrasia so thorough and so entrenched that they lose the ability to say true things even when they are trying to.
Stay tuned for further updates. Probably the next post will simply treat some very simple cause-and-effect ethical word problems, such as the classic “Can you lie to the Nazis about the location of someone they are looking for?”