Can Causal Sense-Making Benefit Foresight, Rather than Biasing Hindsight?

Can Causal Sense-Making Benefit Foresight, Rather than Biasing Hindsight? Edward Munnich (emunnich@usfca.edu) Jennifer Milazzo (jlmilazzo@usfca.edu) Jade Stannard (jostannard@usfca.edu) Katherine Rainford (kprainford@usfca.edu) Department of Psychology, 2130 Fulton St. San Francisco, CA 94117 USA Abstract Upon reading headlines like “Traffic Fatalities Increased/Decreased Last Year,” people often overestimate how well they would have anticipated changes. This hindsight bias has been linked to causal sensemaking that minimizes one’s feeling of surprise after learning an outcome. In this paper, we consider whether the sensemaking process, which contributes to bias in hindsight, could be recruited to our benefit in foresight. We found that 1. Foresight participants— who estimated fatality statistics and listed causal factors before learning true statistics—were more surprised than Hindsight participants—who listed causal factors only after learning true statistics. 2. To the extent that Foresight participants were successful in listing causal factors in the opposite of their expected direction, they showed improvement in a second set of estimates they made prior to learning the true statistics; however, this improvement did not correspond to decreased surprise when they learned true statistics. We discuss implications for contrast vs. uncertainty theories of surprise, and for the possibility of useful belief revision triggered by unexpected statistics and consideration of alternative causation. Keywords: causal reasoning, consider the opposite, explanation, foresight, hindsight bias, sensemaking, surprise In 2005, there were 145 traffic fatalities per million Americans. Before reading further, please estimate how many traffic fatalities there were five years later in 2010. Next, think to yourself what factors caused traffic fatalities to increase or decrease from 2005 to 2010. Base rate statistics regarding trends in public safety, the economy, and the environment are readily available in the media or by searching online, and estimating base rates before receiving surprising feedback has been shown to affect personal and public policy preferences (e.g., Munnich, Ranney, Nelson, Garcia de Osuna, & Brazil, 2003; Ranney, Cheng, Nelson, & Garcia de Osuna, 2001). In particular, people find base rate statistics relevant when they have a compelling causal explanation for the causal mechanism behind the numbers (Tversky & Kahneman, 1982). Furthermore, base rates inform our actions to the extent that a causal explanation implicates a particular action—for example, Hagmayer and Sloman (2009) found people to be more likely to plan an action (e.g., recommend that a friend do more chores) when a statistic was presented along with a direct cause (e.g., people who do chores are healthier because they get exercise doing chores) as opposed to a common cause (e.g., people who do chores more are more conscientious, which also leads them to take better care of their health). Now, please recall your estimate of 2010 traffic fatalities. In fact, auto fatalities declined from 145 per million US residents in 2005 to 106 per million in 2010. To the extent that the 2010 statistic is surprising to you, the surprise may prompt you to revise your beliefs about what contributes to traffic fatalities, and what factors can mitigate fatalities. On the other hand, had we presented the actual statistic at the beginning of the paper, many readers would have shown hindsight bias—the tendency to overestimate how well one would have anticipated an outcome before learning it (e,g., Fischhoff, 1975). A major contributing factor to hindsight bias is causal sensemaking (Ash, 2009; Pezzo, 2003; Roese & Olson, 1996; Schkade & Kilbourne, 1991): When we encounter an outcome that we would not have expected, if we are able to find a coherent explanation of the outcome, we often feel that we would have expected it all along. This paper examines our ability to engage in a sensemaking process before learning an unexpected outcome—ideally at a point when our actions could still make a difference. In other words, we ask whether people can turn a process that amounts to a bias in hindsight into a benefit in foresight? To illustrate, if it starts to rain on the walk to work, one might remember a forecast a few days earlier that a storm was moving into the area—making sense of the drops falling on one’s heads—and falsely reason that one expected it to rain (hindsight bias). But how could one have invoked the forecast at a point when one could have brought an umbrella and avoided getting wet? One way to accomplish this is by considering the opposite of an expected outcome in foresight, which has been shown to reduce bias (Slovic & Fischhoff, 1977; Lord, Lepper, & Preston, 1984; cf. Ranney, Rinne, Yarnell, Munnich, Miratrix, & Schank, 2008). In fact, one need not even consider the exact opposite outcome: Hirt and Markman (1995) found that just considering causes for a salient alternative to the expected outcome is sufficient to trigger debiasing. Obviously, sensemaking can only be as good as the knowledge one has, but, in principle, one should have access to all of the causal explanations before learning an outcome, that one would be able to think of immediately after learning an outcome. If one could think of these explanations a bit earlier, they could presumably benefit foresight, rather than biasing hindsight. Were sensemaking solely focused on weighing the