Veil-of-Ignorance Reasoning Favors the Greater Good
The “veil of ignorance” is a moral reasoning device designed to promote impartial decision-making by denying decision-makers access to potentially biasing information about who will benefit most or least from the available options. Veil-of-ignorance reasoning was originally applied by philosophers and economists to foundational questions concerning the overall organization of society. Here, we apply veil-of-ignorance reasoning in a more focused way to specific moral dilemmas, all of which involve a tension between the greater good and competing moral concerns. Across seven experiments (n = 6,261), four preregistered, we find that veil-of-ignorance reasoning favors the greater good. Participants first engaged in veil-of-ignorance reasoning about a specific dilemma, asking themselves what they would want if they did not know who among those affected they would be. Participants then responded to a more conventional version of the same dilemma with a moral judgment, a policy preference, or an economic choice. Participants who first engaged in veil-of-ignorance reasoning subsequently made more utilitarian choices in response to a classic philosophical dilemma, a medical dilemma, a real donation decision between a more vs. less effective charity, and a policy decision concerning the social dilemma of autonomous vehicles. These effects depend on the impartial thinking induced by veil-of-ignorance reasoning and cannot be explained by anchoring, probabilistic reasoning, or generic perspective taking. These studies indicate that veil-of-ignorance reasoning may be a useful tool for decision-makers who wish to make more impartial and/or socially beneficial choices.
Keywords: Policy Making; procedural justice; Ethics; Decision Making; Policy; Fairness;
Citation:
Huang, Karen, Joshua D. Greene, and Max Bazerman. "Veil-of-Ignorance Reasoning Favors the Greater Good." Proceedings of the National Academy of Sciences of the United States of America 116, no. 48 (November 26, 2019). View Details