How can humanity best reduce suffering?

Our mission is to identify cooperative and effective strategies to reduce involuntary suffering. We believe that in a complex world where the long-run consequences of our actions are highly uncertain, such an undertaking requires foundational research. Currently, our research focuses on reducing risks from emerging technologies. Together with others in the effective altruism community, we want careful ethical reflection to guide the future of our civilization to the greatest extent possible.

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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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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.

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Cause prioritization for downside-focused value systems

This post discusses cause prioritization from the perspective of downside-focused value systems, i.e. views whose primary concern is the reduction of bads such as suffering. According to such value systems, interventions which reduce risks of astronomical suffering are likely more promising than interventions which primarily reduce extinction risks.

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From our blog

22 February 2019

Risk factors for s-risks

Traditional disaster risk prevention has a concept of risk factors. These factors are not risks in and of themselves, but they increase either the probability or the magnitude of a risk. For instance, inadequate governance structures do not cause a specific disaster, but if a disaster strikes it may impede an effective response, thus increasing the damage. Rather than considering individual scenarios of how s-risks could occur, which tends to be highly speculative, this post instead looks at risk factors – i.e. factors that would make s-risks more likely or more severe.

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New research

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.

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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.

Download Read online

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