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Jonathan Beauchamp: Genoeconomics arise!

30.10.2026 · 60 Min.

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Today Razib talks to Jonathan P. Beauchamp, an Associate Professor of Economics at George Mason University, where his research focuses on applied microeconomics, behavioral economics, and the emerging field of "genoeconomics". He holds a Ph.D. in Economics from Harvard University and earned his undergraduate and master's degrees from Queen's University. In addition to his faculty position, he serves as a Senior Research Fellow at the Mercatus Center and an Associate Investigator at The Centre for Applied Genomics. Before fully transitioning into academia, Beauchamp gained professional experience as an economist at the International Monetary Fund (IMF) and as an associate at the management consulting firm McKinsey & Company. He is also a core researcher with the Social Science Genetic Association Consortium (SSGAC), contributing to large-scale genome-wide association studies on the genetic basis of economic behaviors, risk tolerance, and educational attainment.

Razib and Beauchamp explore genoeconomics, the use of genetics to investigate education, income, behavior, and inequality. Beauchamp traces the field's evolution from unreliable claims about individual genes to large-scale studies showing that educational attainment, risk tolerance, and patience reflect thousands of tiny genetic effects. They discuss the promise and limits of polygenic prediction, how sibling comparisons help separate genetic effects from family background, and why larger, more diverse datasets and whole-genome sequencing could advance the field. The conversation turns to Beauchamp's research on how increasingly accurate genetic predictions could destabilize insurance markets when consumers know risks that insurers cannot use in pricing. They also examine his work linking the Western Church's marriage restrictions to weaker kinship networks and distinctive European social institutions, before discussing the professional pressures surrounding controversial research and new sibling-based estimates of heritability.

Alle Folgen von Razib Khan’s Unsupervised Learning

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