CoChat
Sign Up
Cited by 18Forecasting Techniques and ApplicationsHuman-Automation Interaction and SafetyMobile Crowdsensing and Crowdsourcing

Quantifying machine influence over human forecasters

Open access
Abeliuk, Andrés, Benjamin, Daniel M., Morstatter, Fred, Galstyan, Aram·2020·Scientific Reports
DOI 10.1038/s41598-020-72690-4
Open this paper in CoChat — free accountOpen PDF

Abstract

Crowdsourcing human forecasts and machine learning models each show promise in predicting future geopolitical outcomes. Crowdsourcing increases accuracy by pooling knowledge, which mitigates individual errors. On the other hand, advances in machine learning have led to machine models that increase accuracy due to their ability to parameterize and adapt to changing environments. To capitalize on the unique advantages of each method, recent efforts have shown improvements by “hybridizing” forecasts—pairing human forecasters with machine models. This study analyzes the effectiveness of such a hybrid system. In a perfect world, independent reasoning by the forecasters combined with the analytic capabilities of the machine models should complement each other to arrive at an ultimately more accurate forecast. However, well-documented biases describe how humans often mistrust and under-utilize such models in their forecasts. In this work, we present a model that can be used to estimate the trust that humans assign to a machine. We use forecasts made in the absence of machine models as prior beliefs to quantify the weights placed on the models. Our model can be used to uncover other aspects of forecasters’ decision-making processes. We find that forecasters trust the model rarely, in a pattern that suggests they treat machine models similarly to expert advisors, but only the best forecasters trust the models when they can be expected to perform well. We also find that forecasters tend to choose models that conform to their prior beliefs as opposed to anchoring on the model forecast. Our results suggest machine models can improve the judgment of a human pool but highlight the importance of accounting for trust and cognitive biases involved in the human judgment process.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-020-72690-4

Cites

79 of 79 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • These results mirror other recent research revealing human-AI collaborative decision-making does not necessarily lead to more accurate results than either humans or AI alone.

    “Likewise, these results mirror other recent research revealing human–AI collaborative decision-making does not necessarily lead to more accurate results than either humans or AI alone (72–76).”

    From Deepfake detection by human crowds, machines, and machine-informed crowds (2022) · cites this paper as [75]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

1 of the 80 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

  1. Fiedler, K. & Juslin, P.) 327-352 (Cambridge University Press, Cambridge, 2006).

Cited by

Sorted by citations

Related

Authors
Abeliuk, A., Benjamin, D. M., Morstatter, F., & Galstyan, A.
Affiliations
University of Southern California (US)
Corresponding author
Abeliuk, Andrés
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2020-09-29
Volume
10
Issue
1
Pages
15940-15940
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-020-72690-4
OpenAlex
W3089716996
Cited by
18
References
80
Citations by year
2026: 2 · 2025: 4 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 7
FWCI
1.43
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Intelligence Advanced Research Projects Activity (2017-17071900005)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
Keywords
hybrid forecasting, geopolitical forecasting, cognitive biases, anchoring, prior beliefs, forecast accuracy, expert advisors, human judgment
MeSH terms
Machine Learning, Decision Making, Forecasting, Humans, Judgment, Social Behavior, Models, Statistical, Crowdsourcing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review