Aggregating local preferences to guide marginal policy adjustments
We propose a social choice rule for aggregating preferences elicited from surveys into a marginal adjustment of policy from the status quo. The mechanism is: (i) symmetric in its treatment of survey respondents; (ii) ordinal, using only the orientation of respondents' indifference surfaces; (iii) local, using only preferences in the neighborhood of current policy; and (iv) what we call "first-order strategy-proof," making the gains from misreporting preferences second order. The mechanism could be applied to guide policy based on how policy affects responses to subjective well-being surveys.
"We propose a social choice rule for aggregating preferences elicited from surveys into a marginal adjustment of policy from the status quo. The mechanism is: (i) symmetric in its treatment of survey respondents; (ii) ordinal, using only the orientation of respondents' indifference surfaces; (iii) local, using only preferences in the neighborhood of current policy; and (iv) what we call "first-order strategy-proof," making the gains from misreporting preferences second order. The mechanism could be applied to guide policy based on how policy affects responses to subjective well-being surveys."
"We propose a social choice rule for aggregating preferences elicited from surveys into a marginal adjustment of policy from the status quo. The mechanism is: (i) symmetric in its treatment of survey respondents; (ii) ordinal, using only the orientation of respondents' indifference surfaces; (iii) local, using only preferences in the neighborhood of current policy; and (iv) what we call "first-order strategy-proof," making the gains from misreporting preferences second order. The mechanism could be applied to guide policy based on how policy affects responses to subjective well-being surveys."@en
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This is a placeholder reference for a Topic entity, related to a WorldCat Entity. Over time, these references will be replaced with persistent URIs to VIAF, FAST, WorldCat, and other Linked Data resources.