A moral trade-off system produces intuitive judgments that are rational and coherent and strike a balance between conflicting moral values

妥协 困境 社会心理学 心理学 社会困境 激励 价值(数学) 认识论 经济 计算机科学 微观经济学 哲学 政治学 法学 机器学习
作者
Ricardo Andrés Guzmán,María Teresa Barbato,Daniel Sznycer,Leda Cosmides
出处
期刊:Proceedings of the National Academy of Sciences of the United States of America [Proceedings of the National Academy of Sciences]
卷期号:119 (42) 被引量:17
标识
DOI:10.1073/pnas.2214005119
摘要

How does the mind make moral judgments when the only way to satisfy one moral value is to neglect another? Moral dilemmas posed a recurrent adaptive problem for ancestral hominins, whose cooperative social life created multiple responsibilities to others. For many dilemmas, striking a balance between two conflicting values (a compromise judgment) would have promoted fitness better than neglecting one value to fully satisfy the other (an extreme judgment). We propose that natural selection favored the evolution of a cognitive system designed for making trade-offs between conflicting moral values. Its nonconscious computations respond to dilemmas by constructing “rightness functions”: temporary representations specific to the situation at hand. A rightness function represents, in compact form, an ordering of all the solutions that the mind can conceive of (whether feasible or not) in terms of moral rightness. An optimizing algorithm selects, among the feasible solutions, one with the highest level of rightness. The moral trade-off system hypothesis makes various novel predictions: People make compromise judgments, judgments respond to incentives, judgments respect the axioms of rational choice, and judgments respond coherently to morally relevant variables (such as willingness, fairness, and reciprocity). We successfully tested these predictions using a new trolley-like dilemma. This dilemma has two original features: It admits both extreme and compromise judgments, and it allows incentives—in this case, the human cost of saving lives—to be varied systematically. No other existing model predicts the experimental results, which contradict an influential dual-process model.

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