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How Moore's Law Shaped Political Science Research
Insights from the Field
Moore's Law
computational methods
statistical methods
databases
descriptive analysis
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JITP
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Political Science Computing: A Review of Trends in Computer Evolution and Political Science Research was authored by Euel W. Elliott, Karl Ho and Jennifer S. Holmes. It was published by Taylor & Francis in JITP in 2009.

📈 What Was Reviewed

A review of the last four decades maps how rising computing power transformed the practice of political science. The study traces the development of computing resources and the gradual removal of computational limits on political methodologies.

🧮 How Trends Were Measured

Descriptive analysis links technological change to methodological change. Specifically, the analysis examines the pattern of computing performance growth—Moore's Law, defined here as a doubling of hardware power roughly every 18 months—and its temporal association with two observable shifts in the discipline:

  • Adoption of more advanced statistical methods
  • Increased availability and use of large political databases

The relationship is reported as a close association rather than a demonstrated causal mechanism.

🔑 Key Findings

  • Computing power has increased markedly over approximately forty years, consistent with Moore's Law (doubling about every 18 months).
  • This sustained increase is closely associated with greater uptake of advanced statistical techniques in political science research.
  • The same period saw a marked expansion in database availability and the routine use of larger, more complex datasets.
  • While these patterns will be familiar to many researchers, the analysis provides concrete, descriptive detail about the timing and scale of the profession's methodological evolution.

🔮 Why It Matters

The documented association between hardware trends and methodological change offers a clearer view of how technological constraints shape scholarly practice and suggests likely directions as computational capacity continues to grow: more complex models, larger datasets, and further shifts in research design and infrastructure. These patterns provide practical hints for anticipating future developments in political science research.

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