Good Decisions, Good Causes: Optimality as a Constraint on Attribution of Causal Responsibility

Good Decisions, Good Causes: Optimality as a Constraint on Attribution of Causal Responsibility Samuel G. B. Johnson (samuel.johnson@yale.edu) Department of Psychology, Yale University 2 Hillhouse Ave., New Haven, CT 06520 USA Lance J. Rips (rips@northwestern.edu) Department of Psychology, Northwestern University 2029 Sheridan Road, Evanston, IL 60208 USA Abstract Angie has a shrub, and wants the shrub’s flowers to turn red. She is considering two brands of fertilizer to apply: If she applies Formula PTY, there is a 50% chance that the flowers will turn red. If she applies Formula NRW, there is a 10% chance that the flowers will turn red. Angie chooses Formula PTY, and the flowers turn red. Thus, Angie’s actual choice of Formula PTY led to the outcome with probability P ACT = 50%, and her alternative choice of Formula NRW led to the outcome with P ALT = 10%. Because P ACT > P ALT , Angie’s choice was optimal. However, if Formula NRW had led to the outcome with P ALT = 90%, then P ALT > P ACT , and Angie’s choice would have been suboptimal. Finally, if P ALT had been 50%, then P ACT = P ALT , and there would have been no uniquely optimal decision. There are at least three possible predictions one could make about how judgments of Angie’s responsibility for the outcome would vary as a function of the counterfactual alternatives that Angie forsook. First, according to the Counterfactual Stability view, counterfactual alternatives are irrelevant to computing causal responsibility in situations like Angie’s. This seems plausible, since P ACT is fixed across all conditions at 50%, and the likelihood of goal completion does not depend on the efficacy of the alternative. Moreover, it is stipulated in the vignette that the agent achieves her goal, eliminating uncertainty about the outcome. This result would be obtained if people are permissive of suboptimal decision-making when computing causal responsibility. In actual decision-making practice, after all, computational limitations prevent us from analyzing every possible course of action, so we often settle for an option that is satisfactory even if not optimal (Simon, 1956). Second, according to the Difference-Making view, judgments of responsibility are a linear function of the difference made by the actual choice, relative to the alternative choice. On this view, responsibility judgments would be proportional to [P ACT − P ALT ], known as ∆P in the causal learning literature. On this view, one is most responsible for an outcome when the quality of the alternative options is very low, because the choice made a How do we assign causal responsibility for others’ decisions? The present experiments examine the possibility that an optimality constraint is used in these attributions, with agents considered less responsible for outcomes when the decisions that led to those outcomes were suboptimal. Our first two experiments investigate scenarios in which agents are choosing among multiple options, varying the efficacy of the forsaken alternatives to examine the role of optimality in attributing responsibility. Experiment 3 tests whether optimality considerations also play a role in attribution of causality more generally. Taken together, these studies indicate that optimality constraints are used in lay decision theory and in causal judgment. Keywords: Causal attribution; decision-making; theory of mind; responsibility; lay decision theory. Introduction Many of the decisions we make on a daily basis are thoroughly mediocre. This conclusion has been the joint product of the philosophical discipline of Decision Theory, which aims to characterize the decisions we ought to make given our knowledge and priorities (e.g., Jeffrey, 1965), and the psychological discipline of Judgment and Decision-Making (JDM), which aims to characterize actual decision-making behavior (e.g., Tversky & Kahneman, 1974). JDM research has shown that normative decision theory largely fails as a descriptive theory of human decision-making, documenting a plethora of ways in which our actual decision-making does not live up to normative standards. Less is known, however, about how people conceptualize and evaluate the decisions of others—a research question one might term lay decision theory. The present research begins to examine this question, investigating how people assign causal responsibility to agents for the outcomes of their decisions. In these experiments, we consider situations in which an agent made a decision that led to an outcome with probability P ACT (always 50%), but could have made an alternative decision that would have led to that outcome with probability P ALT (which was varied between 10% and 90% across conditions). For example: