Policy Shift Through Numerically-Driven Inferencing: An EPIC Experiment About When Base Rates Matter

Drawing on research areas such as estimation, innumeracy, attitude, scientific conceptual change, social cognition, and judgment and decision making, we offer results from a paradigm we call Numerically-Driven Inferencing (Ranney, Cheng, Nelson, & Garcia de Osuna, 2001). NDI includes observing the effects of presenting critical, germane, and credible base rates that are relevant to social policies; such data, we found, can catalyze changes in belief systems. Here, 130 college students first estimated quantities relevant to important policy issues (e.g., abortion rates), then stated preferences for these values. They next received the true values as feedback, and were again asked for their preferences. This EPIC (Estimate, Prefer, Incorporate-feedback, & Change-policy) method helps quantify relationships among one's understandings of base rates and policies. As some have noted, we too found that people are often poor at estimating base rates. Going beyond past research, we further found that many are quite surprised by the true base rates, and readily revise their numerical preferences after receiving them. Preference changes seem surprise-mediated and are often actual policy shifts (which go beyond the mere re-scaling of preferences in proportion to the feedback). The shifts suggest that conceptual changes among a network of propositions gave rise to belief revisions. We also found that abortion rates queried in different ways yielded notably different policies and policy changes. EPIC may be used to improve numeracy, so we also discuss an NDI curriculum that engages younger people; it may allow us to further consider how numerical cognition and preference co-develop.