Come On Down: Investigating an Informational Strategy to Debias the Anchoring Heuristic

When individuals estimate the price of goods or services, irrelevant factors may affect the estimates. For example, irrelevant numbers in individuals’ environments can cause participants to “anchor” to them as starting point price estimates, such that estimates tend toward the anchor (Tversky & Kahneman, 1974; Chapman & Johnson, 1994). In fact, anchored individuals may pay up to three times as much for a product and buy 32% more products (Ariely, Loewenstein, & Prelec, 2003; Wansink, Kent, & Hoch, 1998). Because anchoring affects purchases large and small, this study investigates how to debias, or reduce the negative effects of, the anchoring heuristic. Debiasing strategies are not easily implemented outside the lab where anchoring has the largest real world effects (Strack & Mussweiler, 1997; Chapman & Johnson, 1994; George, Duffy, & Ahuja, 2000). We therefore investigated an easily implemented informational debiasing strategy offering little disruption to an individual’s daily routine. The debiasing had no effect on anchoring, but further investigation with a larger sample size and higher external validity is necessary before discounting the strategy completely

Come On Down: Investigating an Informational Strategy to Debias the Anchoring Heuristic

When individuals estimate the price of goods or services, irrelevant factors may affect the estimates. For example, irrelevant numbers in individuals’ environments can cause participants to “anchor” to them as a starting point price estimates, such that estimates tend toward the anchor (Tversky & Kahneman, 1974; Chapman & Johnson, 1994). In fact, anchored individuals may pay up to three times as much for a product and buy 32% more products (Ariely, Loewenstein, & Prelec, 2003; Wansink, Kent, & Hoch, 1998). Because anchoring affects purchases large and small, this study investigates how to debias, or reduce the negative effects of, the anchoring heuristic. Debiasing strategies are not easily implemented outside the lab where anchoring has the largest real world effects (Strack & Mussweiler, 1997; Chapman & Johnson, 1994; George, Duffy, & Ahuja, 2000).We investigated an easily implemented informational debiasing strategy that would offer little disruption to an individual’s daily routine and investigated whether it could reduce the negative effects of the anchoring heuristic. The strategy had no effect on anchoring, but further investigation with a larger sample size and higher external validity is necessary before discounting the strategy completely

Can I Work with and Help Others in This Field? How Communal Goals Influence Interest and Participation in STEM Fields

Although science, technology, engineering, and mathematics (STEM) disciplines as a whole have made advances in gender parity and greater inclusion for women, these increases have been smaller or nonexistent in computing and engineering compared to other fields. In this focused review, we discuss how stereotypic perceptions of computing and engineering influence who enters, stays, and excels in these fields. We focus on communal goal incongruity–the idea that some STEM disciplines like engineering and computing are perceived as less aligned with people's communal goals of collaboration and helping others. In Part 1, we review the empirical literature that demonstrates how perceptions that these disciplines are incongruent with communal goals can especially deter women and girls, who highly endorse communal goals. In Part 2, we extend this perspective by reviewing accumulating evidence that perceived communal goal incongruity can deter any individual who values communal goals. Communal opportunities within computing and engineering have the potential to benefit first generation college students, underrepresented minority students, and communally-oriented men (as well as communally-oriented women). We describe the implications of this body of literature: describing how opting out of STEM in order to pursue fields perceived to encourage the pursuit of communal goals leave the stereotypic (mis)perceptions of computing and engineering unchanged and exacerbate female underrepresentation. In Part 3, we close with recommendations for how communal opportunities in computing and engineering can be highlighted to increase interest and motivation. By better integrating and publically acknowledging communal opportunities, the stereotypic perceptions of these fields could gradually change, making computing and engineering more inclusive and welcoming to all