Illuminating the money trail
IDSS affiliate In Song Kim shines a bright light on the dark art of political lobbying.
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Calls For Greater Supply Chain Transparency Get Louder: Can Technology Help?
In a new book, IDSS affiliate Yossi Sheffi argues that we need to better understand the supply chains on which our businesses and society depend, and our conception of supply chains needs to be broadened.
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Study: Shutting down nuclear power could increase air pollution
If reactors are retired, polluting energy sources that fill the gap could cause more than 5,000 premature deaths, researchers including IDSS core faculty Noelle Selin estimate.
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From skeptic to evangelist: MIT Sloan economist runs the numbers on ESG
Conventional wisdom posited that investing with a purpose meant sacrificing return. A framework developed by IDSS core faculty Andrew W. Lo debunks that belief
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An interdisciplinary approach to fighting climate change through clean energy solutions
Principal Research Scientist Audun Botterud tackles a range of cross-cutting problems — from energy market interactions to designing batteries — to get closer to a decarbonized power grid.
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New podcast explores whether data can solve big problems
The new “Data Nation” podcast from IDSS explores what happens when we apply data to the opioid crisis, sports betting, policing, and more.
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Study: Voters are open to messages that go against their party doctrine
New research from team including IDSS affiliates Adam Berinsky and David Rand challenges the view that party loyalty distorts how Americans process evidence and arguments.
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Aligning decision-making processes with democratic values
SES student Manon Revel, who works at the intersection of computational social choice and political theory, hopes to uncover ways to improve governance in AI systems, democracies, and corporate environments.
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Caroline Uhler named SIAM Fellow for 2023
IDSS core faculty is being honored for her “fundamental contributions at the interface of statistics, machine learning, and biology."
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A method for designing neural networks optimally suited for certain tasks
With the right building blocks, MIT researchers including IDSS core faculty Caroline Uhler show that machine-learning models can more accurately perform tasks like fraud detection or spam filtering
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