Executive summary: A reasons-based approach to decision-making suggests that, rather than relying solely on expected utility calculations, we should explicitly weigh the considerations that support our beliefs and choices—especially under conditions of uncertainty or cluelessness about long-term consequences.
Key points:
Reasons-based decision-making: Rational choice theory typically assumes decisions follow from beliefs and preferences, but these are themselves shaped by reasons, which should be explicitly considered in decision-making.
Reasons for belief: While ideal Bayesian agents assign precise probabilities to beliefs, bounded agents rely on qualitative principles and heuristics to weigh evidence, often resulting in indeterminate or imprecise beliefs.
Expected welfare maximization: A reasons-based approach to utilitarian decision-making formalizes how we justify weighing different sources of evidence when assessing the effects of actions on welfare.
Cluelessness and uncertainty: The long-term consequences of actions (e.g., donating to a charity) are often deeply uncertain, making expected value maximization difficult or impossible.
Alternative approach to cluelessness: If reasons for and against an action’s long-term effects are equally compelling but incomparable, we should assign them zero weight and base decisions on the subset of reasons that can be meaningfully weighed.
Implications for bounded rationality: This framework could justify “near-termism” despite commitments to longtermist values and suggests that bounded agents should focus on reasoning methods that respect the structure of their available evidence.
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Executive summary: A reasons-based approach to decision-making suggests that, rather than relying solely on expected utility calculations, we should explicitly weigh the considerations that support our beliefs and choices—especially under conditions of uncertainty or cluelessness about long-term consequences.
Key points:
Reasons-based decision-making: Rational choice theory typically assumes decisions follow from beliefs and preferences, but these are themselves shaped by reasons, which should be explicitly considered in decision-making.
Reasons for belief: While ideal Bayesian agents assign precise probabilities to beliefs, bounded agents rely on qualitative principles and heuristics to weigh evidence, often resulting in indeterminate or imprecise beliefs.
Expected welfare maximization: A reasons-based approach to utilitarian decision-making formalizes how we justify weighing different sources of evidence when assessing the effects of actions on welfare.
Cluelessness and uncertainty: The long-term consequences of actions (e.g., donating to a charity) are often deeply uncertain, making expected value maximization difficult or impossible.
Alternative approach to cluelessness: If reasons for and against an action’s long-term effects are equally compelling but incomparable, we should assign them zero weight and base decisions on the subset of reasons that can be meaningfully weighed.
Implications for bounded rationality: This framework could justify “near-termism” despite commitments to longtermist values and suggests that bounded agents should focus on reasoning methods that respect the structure of their available evidence.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.