The Evidentialist's Wager

Suppose that an altruistic and morally motivated agent who is uncertain between evidential decision theory (EDT) and causal decision theory (CDT) finds herself in a situation in which the two theories give conflicting verdicts. We argue that even if she has significantly higher credence in CDT, she should nevertheless act in accordance with EDT.

Read more

Cooperation, Conflict, and Transformative Artificial Intelligence: A Research Agenda

Author: Jesse Clifton The Center on Long-Term Risk's research agenda on Cooperation, Conflict, and Transformative Artificial Intelligence outlines what we think are the most promising avenues for developing technical and governance interventions aimed at avoiding conflict between transformative AI systems. We draw on international relations, game theory, behavioral economics, machine learning, decision theory, and formal epistemology. […]

Read more

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.

Read more

Robust program equilibrium

One approach to achieving cooperation in the one-shot prisoner’s dilemma is Tennenholtz’s program equilibrium, in which the players of a game submit programs instead of strategies. These programs are then allowed to read each other’s source code to decide which action to take. Unfortunately, existing cooperative equilibria are either fragile or computationally challenging and therefore unlikely to be realized in practice. This paper proposes a new, simple, more efficient program to achieve more robust cooperative program equilibria.

Read more

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."

Read more

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 […]

Read more

Get involved