Charles Delahunt researches Machine Learning methods in the Applied Math department at the University of Washington. He also applies Machine Learning to health care challenges of low- and middle-income countries, at Global Health Labs in Bellevue, Washington.
Charles B. Delahunt
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In this paper we analyze the Gilens dataset using the complementary tools of Random Forest classifiers (RFs), from Machine Learning.
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“Anyone with a pulse knows that in the US today the system is rigged in favour of the wealthy and powerful. One particularly illuminating paper published this month by the Institute for New Economic Thinking quantifies the problem. Building on a persuasive 2014 data set, it shows that when opinion shifts among the wealthiest top 10 per cent of the US population, changes in policy become far more likely. Using AI and machine learning, INET academics Shawn McGuire and Charles Delahunt delved deep into the data. They found that considering the opinions of anyone outside that top 10 per cent was a far less accurate predictor of what happened to government policy. The numbers showed that: “not only do ordinary citizens not have uniquely substantial power over policy decisions; they have little or no independent influence on policy at all”.” — Rana Foroohar, The Financial Times
“Their new working paper, just published by the Institute for New Economic Thinking in New York, gives a rigorously technical analysis of what these tools reveal, and the Institute’s research director, Thomas Ferguson, has helpfully fashioned an introduction to — and a historical context for — the McGuire-Delahunt analysis that lay readers will find easily accessible. Ferguson, himself a pioneer in social science research on political decision making, points out that “the idea that public opinion powers at least the broad direction of public policy in formally democratic countries like the United States has been an article of faith in both political science and public economics for generations.” —Sam Pizzigati