Decision-Making Under Uncertainty in Research Synthesis: Designing for the Garden of Forking Paths
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Abstract (arXiv)
To make evidence-based recommendations to decision-makers, researchers conducting systematic reviews and meta-analyses must navigate a garden of forking paths: a series of analytical decision-points, each of which has the potential to influence findings. To identify challenges and opportunities related to designing systems to help researchers manage uncertainty around which of multiple analyses is best, we interviewed 11 professional researchers who conduct research synthesis to inform decision-making within three organizations. We conducted a qualitative analysis identifying 480 analytical decisions made by researchers throughout the scientific process. We present descriptions of current practices in applied research synthesis and corresponding design challenges: making it more feasible for researchers to try and compare analyses, shifting researchers' attention from rationales for decisions to impacts on results, and supporting communication techniques that acknowledge decision-makers' aversions to uncertainty. We identify opportunities to design systems which help researchers explore, reason about, and communicate uncertainty in decision-making about possible analyses in research synthesis.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Jan 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 0; 2025: 1; 2026: 2.
5 papers.
- 2025AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Sciencecited by 4
- 2021User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analyticscited by 2
- 2023Designing Data: Proactive Data Collection and Iteration for Machine Learningcited by 2
- 2026Visualizing Uncertainty-to-Action Composition for Human Oversightcited by 0
- 2026ForkSCOPE: Charting the Agentic Garden of Forking Pathscited by 0
- 2026ForkSCOPE: Charting the Agentic Garden of Forking Paths11 Sep 2026
- 2026Visualizing Uncertainty-to-Action Composition for Human Oversight17 Aug 2026
- 2025AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science23 Feb 2025
- 2023Designing Data: Proactive Data Collection and Iteration for Machine Learning24 Jan 2023
- 2021User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics6 Jan 2021