Approval-directed agency and the decision theory of Newcomb-like problems

The quest for artificial intelligence poses questions relating to decision theory: How can we implement any given decision theory in an AI? Which decision theory (if any) describes the behavior of any existing AI design? This paper examines which decision theory (in particular, evidential or causal) is implemented by an approval-directed agent, i.e., an agent whose goal it is to maximize the score it receives from an overseer.

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Education Matters for Altruism

Learning is an extremely important activity for altruists. Learning can seem ineffective in the short run, but used properly, it can pay off more than most financial or single-domain-focused investments. It's important for young activists not to neglect learning in order to just "do more to help now."

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Differential Intellectual Progress as a Positive-Sum Project

Fast technological development carries a risk of creating extremely powerful tools, especially AI, before society has a chance to figure out how best to use those tools in positive ways for many value systems. Suffering reducers may want to help mitigate the arms race for AI so that AI developers take fewer risks and have […]

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