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A series of experiments investigated why people value optimism and whether they are right to do so. In Experiments 1A-B, participants prescribed more optimism for someone implementing decisions than for someone deliberating, indicating that people prescribe optimism selectively, when it can affect performance. Furthermore, participants believed optimism improved outcomes when a person's actions had considerable, rather than little, influence over the outcome (Experiment 2). Experiments 3-4 tested the accuracy of this belief; optimism improved persistence, but it did not improve performance as much as participants expected. Experiments 5A-B found that participants overestimated the relationship between optimism and performance even when their focus was not on optimism exclusively. In sum, people prescribe optimism when they believe it has the opportunity to improve the chance of success-unfortunately, people may be overly optimistic about just how much optimism can do. Word count: 138Keywords: optimism, bias, accuracy, deliberation, implementation, performance 2 OPTIMISTIC ABOUT OPTIMISM Optimistic About Optimism:The Belief That Optimism Improves Performance "Optimism is faith that leads to achievement." --Helen Keller (1903, p. 67) "Wisdom is clearly to believe what one desires, for the belief is one of the indispensable preliminary conditions of the realization of its object." --William James (1882, p. 75) William James, writing in 1882, and Helen Keller, in 1903, believed that optimism leads to achievement. Empirical research has begun to explore directly whether, like them, people generally believe in the benefits of optimism. One study to tackle this question suggests that they do (Armor, Massey, & Sackett, 2008). Their participants recommended optimism over pessimism or realism in a variety of situations. People believed, in the authors' words, that "it is right to be wrong about the future." Optimism, in this view, has so much to recommend it that it is worth sacrificing accuracy for (Schneider, 2001).It would be nice if it was possible to be optimistic and accurate at the same time. But if optimism is the inclination to expect the best possible outcome, that would require the best outcome to be the most likely outcome. If people always prescribe optimism over accuracy, as Armor, Massey, and Sackett (2008) suggest, that would be remarkable because there are many advantages to being realistic. Accurate forecasts can help people decide where best to invest their limited time and money in education, recreation, social relationships, and professional opportunities (Baumeister, Campbell, Krueger, & Vohs, 2003;Forsyth, Lawrence, Burnette, & Baumeister, 2007). There is an undeniable benefit to anticipating potential risks, losses, embarrassments, and disasters. Overly optimistic entrepreneurs lose a great deal of money on businesses that fail (Balasuriya, Muradoglu, & Ayton, 2010;Camerer & Lovallo, 1999).Excessive optimism can undermine the motivation to take protective action against risks 3 OPTIMI...

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Algorithms-scripts for mathematical calculations-are powerful. Even though algorithms often outperform human judgment, people resist allowing a numerical formula to make decisions for them (Dawes, 1979). Nevertheless, people increasingly depend on algorithms to inform their decisions. Eight experiments examined trust in algorithms. Experiments 1A and 1B found that advice influenced participants more when they thought it came from an algorithm than when they thought it came from other people. This effect was robust to presenting the advisor jointly or separately (Experiment 2). Experiment 3 tested a moderator; excessive confidence in one's own knowledge attenuated reliance on algorithms. These tests are important because participants can improve their accuracy by relying more on algorithms (Experiment 4). Experiments 5 and 6 tested a mechanism for reliance: subjectivity of the decision. For objective decisions, participants preferred algorithmic advice and for subjective decisions, participants preferred advice from people. Experiment 6 tested the interaction of subjectivity and the availability of expert advice. Participants preferred an expert to an algorithm, regardless of the domain (Experiment 6). Experiment 7 examined how decision makers' own expertise influenced reliance on algorithms. Experts in national security, who regularly make forecasts, relied less on algorithmic advice than lay people did. These results shed light on the important question of when people rely on algorithmic advice over advice from people and have implications for the use of technological algorithms.