Browsing by Author "Kausel, Edgar E."

Now showing 1 - 18 of 18
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    A longitudinal approach for understanding algorithm use
    (WILEY, 2022) Chacón Hiriart, Álvaro Marcelo; Kausel, Edgar E.; Reyes Torres Tomas Hernan
    Research suggests that algorithms-based on artificial intelligence or linear regression models-make better predictions than humans in a wide range of domains. Several studies have examined the degree to which people use algorithms. However, these studies have been mostly cross-sectional and thus have failed to address the dynamic nature of algorithm use. In the present paper, we examined algorithm use with a novel longitudinal approach outside the lab. Specifically, we conducted two ecological momentary assessment studies in which 401 participants made financial predictions for 18 days in two tasks. Relying on the judge-advisor system framework, we examined how time interacted with advice source (human vs. algorithm) and advisor accuracy to predict advice taking. Our results showed that when the advice was inaccurate, people tended to use algorithm advice less than human advice across the period studied. Inaccurate algorithms were penalized logarithmically; the effect was initially strong but tended to fade over time. This suggests that first impressions are crucial and produce significant changes in advice taking at the beginning of the interaction, which later tends to stabilize as days go by. Therefore, inaccurate algorithms are more likely to accrue a negative reputation than inaccurate humans, even when having the same level of performance.
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    A statistical analysis of reinforced concrete wall buildings damaged during the 2010, Chile earthquake
    (2015) Junemann, Rosita; Llera Martin, Juan Carlos de la; Hube Ginestar, Matías Andrés; Cifuentes Lira, Luis Abdón; Kausel, Edgar E.; Pontificia Universidad Católica de Chile. National Research Center for Integrated Natural Disaster Management; Pontificia Universidad Católica de Chile. Department of Industrial and Systems Engineering
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    Are engineers more likely to avoid algorithms after they see them err? A longitudinal study
    (2024) Chacon, Alvaro; Reyes, Tomas; Kausel, Edgar E.
    Research suggests the superior predictive capabilities of algorithms compared to humans. However, people's reluctance to use algorithms after witnessing their inaccuracies has hindered their widespread adoption. Studies have explored this reluctance, but little is known about how different people use algorithms. We focused on algorithm utilisation by engineers, conducting two longitudinal ecological momentary assessment studies outside the lab to explore differences in how engineers and non-engineers engage with inaccurate algorithms. These studies involved 427 participants, predicting currency exchange rates or maximum weather temperatures over nine days based on the judge-advisor system framework. Our results showed a significant three-way interaction between the effects of advice source, whether participants were engineers or non-engineers, and time. Specifically, the trend of inaccurate algorithm use significantly decreased over time for engineers, highlighting the importance of considering the end-users when implementing algorithms.
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    Attention-driven reaction to extreme earnings surprises
    (2023) Reyes, Tomas; Batista, Julian A.; Chacon, Alvaro; Martinez, Diego; Kausel, Edgar E.
    We investigate the relationship between investor attention and stock returns in the context of extreme earnings surprises. We propose a novel mechanism that describes this interaction: high attention to very positive and very negative earnings news results in faster incorporation of information into stock prices, an overreaction effect, and a subsequent partial reversal. We test this mechanism using post-announcement abnormal returns and measure investor attention using internet search volume. We confirm that abnormal attention to earnings announcements is positively related to post-announcement abnormal returns when earnings surprises are very positive and negatively related when earnings surprises are very negative. More importantly, we argue that investors exhibit attention-driven overreactions to these extreme earnings surprises since the initial effects of abnormal attention on abnormal returns are subsequently partially reversed.
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    Does enhancing the vividness in connection with the future self increase savings behavior? A field experiment
    (2024) Kausel, Edgar E.; Reyes, Tomas; Larach, Francisco; Chacon, Alvaro; Enei, Gonzalo
    Individuals frequently struggle with the challenge of sufficiently saving for retirement, a problem that can significantly impact the quality of life for retirees. Numerous strategies have been devised to mitigate this issue, ranging from traditional methods such as monetary incentives and tax advantages to more innovative approaches aimed at strengthening the individual's connection with their future self. The latter, though theoretically promising, has not yet been field-tested. The underlying premise is that by amplifying the perceptual vividness of one's future self, individuals might be more inclined to make decisions in line with their long-term interests. This study evaluates this hypothesis through a field experiment involving 415 customers of an investment firm. Participants were randomly assigned into three groups: one without any future self-reference (the control group), a second group presented with a text referencing their future selves, and a third group that was given the same text along with a digitally-aged image of themselves. The results indicate that interventions cultivating a more vivid connection to their future selves increase individuals' intentions to save for retirement. This effect on intentions, however, only translated into a short-term, modest impact on the actual amount of money invested.
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    Does facial structure predict academic performance?
    (2018) Kausel, Edgar E.; Ventura, Santiago; Vargas, Mauricio “Pacha”; Díaz, David; Vicencio, Fabián
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    Does overconfidence pay off when things go well? CEO overconfidence, firm performance, and the business cycle
    (2020) Reyes, Tomás H.; Vassolo, Roberto; Kausel, Edgar E.; Torres, D. P.; Zhang, S.
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    Dynamic overconfidence : a growth curve and cross lagged analysis of accuracy, confidence, overestimation and their relations
    (2020) Kausel, Edgar E.; Carrasco, F.; Reyes, Tomás H.; Hirmas, A.; Rodríguez, A.
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    Outcome bias in subjective ratings of performance : Evidence from the (football) field
    (2019) Kausel, Edgar E.; Ventura, S.; Rodriguez, A.
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    Preventing algorithm aversion: People are willing to use algorithms with a learning label
    (2025) Chacon, Alvaro; Kausel, Edgar E.; Reyes, Tomas; Trautmann, Stefan
    As algorithms often outperform humans in prediction, algorithm aversion is economically harmful. To enhance algorithm utilization, we suggest emphasizing their learning capabilities, i.e., their increasing predictive precision over time, through the explicit addition of a "learning" label. We conducted five incentivized studies in which 1,167 participants may prefer algorithms or take up algorithmic advice in a financial or healthcare related task. Our results suggest that people use algorithms with a learning label to a greater extent than algorithms without such a label. As the accuracy of advice improves beyond a threshold, the use of algorithms with a learning label increases more than algorithms without a label. Thus, we show that a salient learning attribute can positively affect algorithm use in both the financial and health domain.
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    The illusion of validity: how effort inflates the perceived validity of interview questions
    (2023) Zhang, Don C.; Kausel, Edgar E.
    Interviewers are often confident in the validity of their interview questions. What drives this confidence and is it justified? In three studies, we found that question creators judged their own interview questions as more valid than when the same questions are judged by an evaluator. We also found that effort expenditure inflated the perceived validity of interview questions but not question quality. Question creators' perceptions of validity were primarily driven by their self-confidence, and not the question quality. As an intervention, we nudged participants into holding more favourable attitudes towards better questions (i.e., structured questions) by allowing them to choose a subset of them from a pre-written list. Together, we found that while effort expenditure was responsible for the illusion of validity when evaluating unstructured (i.e., low-quality) questions, the same mechanism could also be used to improve interviewers' acceptance of structured questions. Implications for structured interviews and the scientist-practitioner gap are discussed.
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    The Reciprocal Relationships Between Escalation, Anger, and Confidence in Investment Decisions Over Time
    (2018) Jackson, Alexander T.; Howes, Satoris S.; Kausel, Edgar E.; Young, Michael E.; Loftis, Megan E.
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    Using narratives and numbers in performance prediction: Attitudes, confidence, and validity
    (2022) Niessen, A. Susan M.; Kausel, Edgar E.; Neumann, Marvin
    In a preregistered prediction-task experiment, we investigated the effect of narrative versus quantified information on decision-maker perceptions, confidence, predictor weighting, and predictive accuracy when making performance predictions. We also investigated the effect of who quantifies information (the decision maker or someone else). As expected, we found higher perceived informativeness and use intentions for narrative than quantified information. Information presented narratively was also weighted somewhat more heavily than quantified information. Using quantitative information quantified by decision makers themselves yielded higher perceived autonomy and use intentions than quantitative information quantified by someone else. However, no differences in prediction confidence were found and self- and other-produced quantifications received identical weight. Moreover, unexpectedly, differences in weighting did not translate to differences in predictive accuracy.
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    When and Why Narcissists Exhibit Greater Hindsight Bias and Less Perceived Learning
    (2020) Howes, S. S.; Kausel, Edgar E.; Jackson, A. T.; Reb, J.