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Big data and prediction: Four case studies.

Robert Northcott¹

¹Birkbeck College, University of London, Department of Philosophy, Malet Street, London, WC1E 7HX, UK.

Studies in History and Philosophy of Science

|June 23, 2020

Summary

This summary is machine-generated.

Big data

Keywords:

Big data Case studies Elections Explanation Prediction Weather

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Area of Science:

Scientific methodology
Data science
Predictive modeling

Background:

The increasing prevalence of data-intensive science, or 'big data'.
The potential shift in scientific priorities towards prediction over causal understanding.
Concerns about the diminishing roles of theory and human expertise in the era of big data.

Purpose of the Study:

To evaluate the impact of big data methods on predictive accuracy in key domains.
To assess whether big data necessitates a re-prioritization of prediction over causal inference.
To examine the implications for the role of traditional scientific theory and human experts.

Main Methods:

Case study analysis of four domains: political elections, weather forecasting, GDP prediction, and economic experiment interventions.
Comparative assessment of big data methodologies against traditional approaches for predictive tasks.

Main Results:

Big data methods demonstrated limited or no improvement in predictive accuracy in the examined cases.
The effectiveness of big data for prediction in these specific domains appears constrained.
Future prospects for significant predictive gains from big data in these areas are uncertain.

Conclusions:

The transformative impact of big data on predictive capabilities requires cautious evaluation.
Big data does not universally supersede the importance of causal understanding, theory, or expert knowledge.
The utility of big data methods is domain-specific and context-dependent, not a universal solution for enhanced prediction.