Executive summary: In this exploratory post, Karthik introduces the “relative range heuristic”—a simple decision-making tool that suggests prioritizing the dimension that varies most widely across options, especially when full quantification is impractical; he explains its rationale, formal structure, and potential limitations when applied to real-world tradeoffs.
Key points:
The heuristic: When comparing two options that trade off across dimensions, prioritize the option that is superior on the dimension with the wider intuitive range of variation.
Illustrative examples: The author applies this heuristic to decisions about animal welfare prioritization, medical research strategy, and survey frequency—favoring the option where the dominant factor varies over a greater range (e.g. number of small vs. large animals).
Formalization: The post includes a simple model where the value of each option is the product of key criteria; the heuristic approximates which product will be larger by comparing intuitive range ratios.
Use cases and limitations: This approach is most useful when you lack detailed data but have strong intuitions; it’s less effective when variation spans multiple dimensions or when intuition is weak or disputed.
Cognitive bias warning: The author cautions that humans struggle to intuitively grasp very low probabilities, which might skew expected value comparisons and cause overemphasis on impact over likelihood in high-stakes interventions.
Implication for EA thinking: Many arguments for low-probability, high-impact causes may implicitly rely on the relative range heuristic—but their validity depends on whether large variation in probabilities truly exists or is just cognitively obscured.
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.
Executive summary: In this exploratory post, Karthik introduces the “relative range heuristic”—a simple decision-making tool that suggests prioritizing the dimension that varies most widely across options, especially when full quantification is impractical; he explains its rationale, formal structure, and potential limitations when applied to real-world tradeoffs.
Key points:
The heuristic: When comparing two options that trade off across dimensions, prioritize the option that is superior on the dimension with the wider intuitive range of variation.
Illustrative examples: The author applies this heuristic to decisions about animal welfare prioritization, medical research strategy, and survey frequency—favoring the option where the dominant factor varies over a greater range (e.g. number of small vs. large animals).
Formalization: The post includes a simple model where the value of each option is the product of key criteria; the heuristic approximates which product will be larger by comparing intuitive range ratios.
Use cases and limitations: This approach is most useful when you lack detailed data but have strong intuitions; it’s less effective when variation spans multiple dimensions or when intuition is weak or disputed.
Cognitive bias warning: The author cautions that humans struggle to intuitively grasp very low probabilities, which might skew expected value comparisons and cause overemphasis on impact over likelihood in high-stakes interventions.
Implication for EA thinking: Many arguments for low-probability, high-impact causes may implicitly rely on the relative range heuristic—but their validity depends on whether large variation in probabilities truly exists or is just cognitively obscured.
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.