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Related Experiment Video

Updated: Jul 11, 2025

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

When expert predictions fail.

Igor Grossmann¹, Michael E W Varnum², Cendri A Hutcherson³

¹Department of Psychology, University of Waterloo, Waterloo, N2L 3G1, ON, Canada.

Trends in Cognitive Sciences

|November 10, 2023

Summary

This summary is machine-generated.

Social scientists excel at lab predictions but struggle with real-world societal changes due to oversimplified models. Integrating foundational models with time series data can improve social science forecasting accuracy.

Keywords:

causal models expert judgment level of analysis modeling complexity predictive power in social sciences societal change

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Area of Science:

Social Sciences
Prediction Science

Background:

Expert judgment is crucial in social sciences for making predictions.
Current prediction accuracy varies significantly between controlled laboratory settings and complex real-world societal phenomena.

Purpose of the Study:

To scrutinize the opportunities and challenges of expert judgment in social science prediction.
To identify limitations in current social science causal models and propose improvements.

Main Methods:

Analysis of existing causal models in social sciences.
Comparison of prediction accuracy in laboratory vs. real-world settings.
Drawing parallels with forecasting methods in physical sciences and meteorology.

Main Results:

Social scientists demonstrate above-chance accuracy for laboratory-based predictions.
Significant challenges exist in predicting real-world societal changes.
Common causal models are often oversimplified, misaligned with phenomena, or lack consideration of broader factors.

Conclusions:

Oversimplified causal models hinder accurate societal change prediction.
An integrated approach combining foundational models with time series data is recommended.
A call for more precise, ambitious predictions and increased intellectual humility in social sciences.