Cited by 95Human-Automation Interaction and SafetyTeam Dynamics and PerformanceDecision-Making and Behavioral Economics

Challenges of human—machine collaboration in risky decision-making

Open access
Xiong, Wei, Fan, Hongmiao, Ma, Liang, Wang, Chen·2022·Frontiers of Engineering Management

Abstract

The purpose of this paper is to delineate the research challenges of human—machine collaboration in risky decision-making. Technological advances in machine intelligence have enabled a growing number of applications in human—machine collaborative decision-making. Therefore, it is desirable to achieve superior performance by fully leveraging human and machine capabilities. In risky decision-making, a human decision-maker is vulnerable to cognitive biases when judging the possible outcomes of a risky event, whereas a machine decision-maker cannot handle new and dynamic contexts with incomplete information well. We first summarize features of risky decision-making and possible biases of human decision-makers therein. Then, we argue the necessity and urgency of advancing human—machine collaboration in risky decision-making. Afterward, we review the literature on human—machine collaboration in a general decision context, from the perspectives of human—machine organization, relationship, and collaboration. Lastly, we propose challenges of enhancing human—machine communication and teamwork in risky decision-making, followed by future research avenues.

Integrity signals

Registry

Crossref · 10.1007/s42524-021-0182-0

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • According to Xiong et al., machines will be helpful in gathering data and conveying key messages, while humans can obligate emotional influences and make an unbiased judgment.

    “According to Xiong et al. [168], machines will be helpful in gathering data and conveying key messages, while humans can obligate emotional influences and make an unbiased judgment.”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Xiong, W., Fan, H., Ma, L., & Wang, C.
Affiliations
Tsinghua University (CN)
Corresponding author
Wang, Chen
Venue
Frontiers of Engineering Management
Published
2022-01-27
Volume
9
Issue
1
Pages
89-103
ISSN
  • 2095-7513 (ISSN-L)
  • 2096-0255
OpenAlex
W4213319945
Cited by
95
References
17
Citations by year
2026: 27 · 2025: 23 · 2024: 16 · 2023: 21 · 2022: 8
FWCI
16.07
Type
journal article
Language
en
Open access
Locations
Frontiers of Engineering Management (publishedVersion, cc-by)
Topics
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Social Sciences › Psychology › Social Psychology › Team Dynamics and Performance
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
Keywords
human-machine collaboration, risky decision making, cognitive biases, human-machine communication, incomplete information, decision making under uncertainty

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