Executive summary: The author argues against precise Bayesianism, advocating instead for indeterminate beliefs in cases where the available information does not warrant a determinate probability estimate. This perspective, rooted in imprecise probabilities, challenges the assumption that rationality requires always having a “best guess” and has significant implications for decision-making under uncertainty.
Key points:
Indeterminate beliefs challenge precise Bayesianism – Rational agents should sometimes suspend judgment when conflicting considerations make a precise probability estimate arbitrary.
Imprecise probabilities provide a structured alternative – Instead of committing to a single probability distribution, beliefs can be represented by a set of distributions that capture epistemic uncertainty.
Decision-making under indeterminacy differs from classical expected value maximization – The “maximality” rule suggests preferring an action only if it has higher expected utility under every distribution in the representor.
Precise forecasts are not always preferable – While precise predictions can outperform chance in some domains (e.g., geopolitical forecasting), their reliability does not generalize to all decision contexts, especially those involving the long-term future.
Rejecting determinate beliefs does not imply inaction or randomness – Instead, decision-making can be guided by other normative considerations, such as moral pluralism or minimizing regret.
The argument for indeterminate priors extends to ideal agents – Even a logically omniscient agent may lack a determinate prior over fundamental aspects of reality, suggesting that epistemic indeterminacy is not merely a human limitation but a feature of rational belief.
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Executive summary: The author argues against precise Bayesianism, advocating instead for indeterminate beliefs in cases where the available information does not warrant a determinate probability estimate. This perspective, rooted in imprecise probabilities, challenges the assumption that rationality requires always having a “best guess” and has significant implications for decision-making under uncertainty.
Key points:
Indeterminate beliefs challenge precise Bayesianism – Rational agents should sometimes suspend judgment when conflicting considerations make a precise probability estimate arbitrary.
Imprecise probabilities provide a structured alternative – Instead of committing to a single probability distribution, beliefs can be represented by a set of distributions that capture epistemic uncertainty.
Decision-making under indeterminacy differs from classical expected value maximization – The “maximality” rule suggests preferring an action only if it has higher expected utility under every distribution in the representor.
Precise forecasts are not always preferable – While precise predictions can outperform chance in some domains (e.g., geopolitical forecasting), their reliability does not generalize to all decision contexts, especially those involving the long-term future.
Rejecting determinate beliefs does not imply inaction or randomness – Instead, decision-making can be guided by other normative considerations, such as moral pluralism or minimizing regret.
The argument for indeterminate priors extends to ideal agents – Even a logically omniscient agent may lack a determinate prior over fundamental aspects of reality, suggesting that epistemic indeterminacy is not merely a human limitation but a feature of rational belief.
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.