Browsing by Author "Chacon, Alvaro"

Now showing 1 - 4 of 4
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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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    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.