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dc.contributor.authorValerdi, Dr. Ricardo
dc.date.accessioned2014-01-17T18:56:58Z
dc.date.available2014-01-17T18:56:58Z
dc.date.issued2009-11-18
dc.identifier.urihttp://hdl.handle.net/1721.1/84069
dc.description.abstractBiases continue to be an important aspect of human judgment and decision making because they often occur subconsciously and can frequently lead to unfavorable outcomes. Optimism bias is one type of cognitive illusion that is often overlooked because of its association with good health and positive outcomes. However, the existence of optimism bias in human judgment can be very damaging especially when it distorts a person's view of future events. In order to better understand optimism bias we explore the benefits and downsides of optimism as well as some empirically-based origins of both optimism and pessimism. This provides a backdrop for a methodology for quantifying optimism and pessimism followed by a discussion about certain professions that make well-calibrated decisions. Results are explored from an optimism survey given to a cohort of eighty systems engineers, which ultimately portray the degree to which optimism bias influences decision making in the estimation of cost and schedule of large projects. A calibration exercise is designed to calibrate optimism in systems engineers with the ultimate goal of helping cost estimation realism. Finally, prescriptive advice is provided to help individual decision makers better optimize their optimism. The implications of this work are twofold. First, the mechanism for quantifying optimism in systems engineers provides useful insight into the degree of optimism that exists among this group of decision makers. This can influence a number of decision making processes that may traditionally be seen as immune from biases due to their routine nature. Second, the process for calibrating optimism provides a way to validate the effectiveness of optimism reduction techniques on different types of decision makers. It also helps to distinguish between certain people who are more receptive to bias corrections and are therefore more likely to be better estimators in real life.en_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectsystems engineering (SE)en_US
dc.subjectoptimismen_US
dc.titleOptimizing Optimism in Systems Engineersen_US
dc.typePresentationen_US


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