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Updated: Mar 30, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Using prediction markets to forecast research evaluations.

Marcus R Munafo¹, Thomas Pfeiffer², Adam Altmejd³

¹MRC Integrative Epidemiology Unit at the University of Bristol , Bristol, UK ; UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology , University of Bristol , Bristol, UK.

Royal Society Open Science

|November 21, 2015

Summary

This summary is machine-generated.

Prediction markets accurately forecast research quality assessments, offering a simpler alternative to traditional evaluations like the Research Excellence Framework (REF2014). This approach harnesses collective intelligence for efficient research quality prediction.

Keywords:

prediction market research evaluation research excellence framework

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Last Updated: Mar 30, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Chemistry
Higher Education Research

Background:

The 2014 Research Excellence Framework (REF2014) assessed UK higher education research quality over six years.
REF2014 faced criticism for its high cost and bureaucratic nature, with suggestions for simpler metric-based alternatives.

Purpose of the Study:

To investigate if a prediction market could yield similar insights to REF2014 for UK chemistry departments.
To evaluate the efficacy of prediction markets in predicting expert-judged research outcomes.

Main Methods:

Established a prediction market for the REF2014 outcomes of 33 UK chemistry departments.
Leveraged the 'wisdom of crowds' principle where participants trade on specific outcomes.

Main Results:

The prediction market provided information comparable to the official REF2014 assessment.
Demonstrated the potential of prediction markets in forecasting research quality.

Conclusions:

Prediction markets offer a viable, potentially more efficient, method for assessing research quality.
Further research could explore prediction markets in other academic disciplines and assessment cycles.