Preference for human or algorithmic forecasting advice does not predict if and how it is used

Himmelstein, Mark; Budescu, David V.

doi:10.1002/bdm.2285

Cited by 19 publications

(15 citation statements)

References 65 publications

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“…If they believe the advice is beneficial, they use it just as they use human advice. This finding is in line with Himmelstein and Budescu (2023), who found no difference between reliance on algorithmic and human advice when forecasting real geopolitical events.…”

Section: Discussionsupporting

confidence: 90%

Algorithms in selection decisions: Effective, but unappreciated

Rabinovitch,

Budescu,

Meyer

2024

Behavioral Decision Making

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Selection decisions are often affected by irrelevant variables such as gender or race. People can discount this irrelevant information by adjusting their predictions accordingly, yet they fail to do so intuitively. In five online studies (N = 1077), participants were asked to make selection decisions in which the selection test was affected by irrelevant attributes. We examined whether in such decisions people are willing to be advised by algorithms, human advisors or prefer to decide without advice. We found that people fail to adjust for irrelevant information by themselves, and those who received advice from an algorithm or human advisor made better decisions. Interestingly, although most participants stated they prefer advice from human advisors, they tend to rely equally on algorithms in actual selection tasks. The sole exception is when they are forced to choose between an algorithm and a human advisor. In that case, they pick human advisors. We conclude that while algorithms may not be people's preferred source of advice in selection decisions, they are equally useful and can be implemented.

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Section: Discussionsupporting

confidence: 90%

Algorithms in selection decisions: Effective, but unappreciated

Rabinovitch,

Budescu,

Meyer

2024

Behavioral Decision Making

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“…For instance, shifting from 100 to 90 in spite advice of 110 is considered non-weighting in the truncation approach whereas it is considered full weighting in the ratio-of-absolute-differences approach. Therefore, Bonaccio and Dalal (2006) recommend analyzing the data twice, with and without "problematic" WOA values, to guarantee invariant conclusions (e.g., Himmelstein & Budescu, 2022;Hütter & Ache, 2016).…”

Section: A Mixed-effects Regression Model Of Advice Takingmentioning

confidence: 99%

Mixed-Effects Regression Weights for Advice Taking and Related Phenomena of Information Sampling and Utilization

Rebholz¹,

Biella²,

Hütter³

2023

Preprint

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Advice taking and related research are dominated by deterministic weighting indices such as ratio-of-differences-based formulas for investigating informational influence. They are intuitively simple but entail various measurement problems and restrict research to a certain paradigmatic approach. As a solution, we propose process-consistent mixed-effects regression modeling for specifying how strongly peoples’ judgment is influenced by external information. Our formal derivation of the proposed weighting measures is accompanied by a detailed elaboration on their most important technical and statistical subtleties. Essentially, the approach differentiates between components of endogenous (i.e., final judgment) and exogenous (e.g., initial judgment and advice) nature by relying on accordingly specified multilevel models. Corresponding mixed-effects regression coefficients of various exogenous sources of information hence also reflect individual weighting but are based on a conceptually consistent representation of the endogenous judgment process. We use this modeling approach to revisit empirical findings from sequential collaboration and advice taking paradigms. Specifically, whereas we do not obtain evidence for systematic order effects in sequential collaboration, we document recency effects in the weighting of sequentially sampled advice. We argue that process-consistent modeling of information sampling and utilization has the potential to increase the replicability of our science and opens up new avenues for innovative research. Moreover, the proposed method is relevant beyond sequential collaboration and advice taking. Mixed-effects regression weights can also inform research on related cognitive phenomena such as multidimensional belief updating, anchoring effects, hindsight biases, or attitude change.

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“…Expertise in humans represents an important factor because experts are more trusted than laypersons due to lack of experience (Gino & Schweitzer, 2008;Sniezek et al, 2004;Tzioti et al, 2014;Waern & Ramberg, 1996). Other studies did not show a clear picture (e.g., Chacon et al, 2022;Himmelstein & Budescu, 2022;Poston et al, 2009). These results can in part be explained by the different contexts and environments of the studies (Thurman et al, 2019).…”

Section: Advice-takingmentioning

confidence: 79%

Decision-making in the era of AI support—How decision environment and individual decision preferences affect advice-taking in forecasts.

Mayer¹,

Fuchs²,

Lingnau³

2023

Journal of Neuroscience, Psychology, and Economics

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Decisionmakers often receive advice from different forecast sources (e.g., human or machine), which they can integrate into their own assessments. Previous studies led to different results regarding the use of forecasts depending on their source. With the ongoing digitalization, it can be assumed, first, that forecasts will be used more frequently in general and, second, that forecasts will increasingly be created digitally or in an automated manner. To investigate these factors in more detail, a vignette experiment was conducted with over 600 participants. In particular, we focused on the decision preference of the forecast users to take a deeper look at the fit of a forecast source, decision preference, and advice-taking. The results show that both a longer time horizon as well as greater trust in advice increase the acceptance of advice. With regard to the forecast source, we find that in a long-term scenario the advice by artificial intelligence is preferred over a human advice. Concerning the relationship of trust and decision preference on the acceptance of advice, our results show that the impact of the forecast source on trust in advice is moderated by trust in digital technologies and that the moderation by trust in digital technologies is itself moderated by decision preference, which highlights the relevance of a fit between the forecast source and an individual’s decision preference.

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Preference for human or algorithmic forecasting advice does not predict if and how it is used

Cited by 19 publications

References 65 publications

Algorithms in selection decisions: Effective, but unappreciated

Algorithms in selection decisions: Effective, but unappreciated

Mixed-Effects Regression Weights for Advice Taking and Related Phenomena of Information Sampling and Utilization

Decision-making in the era of AI support—How decision environment and individual decision preferences affect advice-taking in forecasts.

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