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Deciding what to replicate: A decision model for replication study selection under resource and knowledge

Peder Mortvedt Isager1, Robbie C M van Aert2, Štěpán Bahník3

  • 1Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology.

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Summary
This summary is machine-generated.

Replication research requires efficient study selection. This study introduces "replication value" to maximize utility by assessing claim certainty and evidential uncertainty, guiding researchers in choosing the most impactful studies to replicate.

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Area of Science:

  • Scientific methodology
  • Research integrity
  • Decision theory

Background:

  • Robust scientific knowledge relies on the replication of original findings.
  • Researchers face resource constraints, necessitating strategic selection of studies for replication.
  • Existing methods for selecting replication candidates lack a clear framework for maximizing utility.

Purpose of the Study:

  • To propose a theoretical framework for selecting replication studies that maximizes the utility gained.
  • To introduce the concept of "replication value" as a metric for evaluating potential replication candidates.
  • To provide foundational principles for developing concrete guidelines in replication study selection.

Main Methods:

  • The study defines "replication value" as the maximum expected utility from replicating a claim.
  • A causal decision model, incorporating decision theory and causal graph modeling, is used for formalization.
  • The framework considers the value of certainty and the degree of uncertainty based on current evidence.

Main Results:

  • "Replication value" is proposed as a function of the value of certainty and current evidential uncertainty.
  • The framework offers a systematic approach to quantify the potential utility gain from replication efforts.
  • Discussion covers the validity of "replication value" and methods for its quantitative estimation.

Conclusions:

  • The proposed "replication value" offers a theoretically grounded method for optimizing replication study selection.
  • This framework aims to enhance the efficiency and impact of scientific replication.
  • Further research can build upon this foundation to develop practical guidelines for researchers.