Uploaded August 2026 | Updated September 2026, 39 minutes ago
Theorists of justice have long imagined a decision-maker capable of acting wisely in every circumstance. Policymakers seldom live up to this ideal. They face well-understood limits, including an inability to anticipate the societal impacts of state intervention along a range of dimensions and values. Policymakers cannot see around corners or address societal problems at their roots. When it comes to regulation and policy-setting, policymakers are often forced, in the memorable words of political economist Charles Lindblom, to “muddle through” as best they can.
Powerful new affordances, from supercomputing to artificial intelligence, have arisen in the decades since Lindblom’s 1959 article that stand to enhance policymaking. Computer-aided modeling holds promise in delivering on the broader goals of forecasting and system analysis developed in the 1970s, arming policymakers with the means to anticipate the impacts of state intervention along several lines—to model, instead of muddle. A few policymakers have already dipped a toe into these waters, others are being told that the water is warm.
The prospect that economic, physical, and even social forces could be modeled by machines confronts policymakers with a paradox. Society may expect policymakers to avail themselves of techniques already usefully deployed in other sectors, especially where statutes or executive orders require the agency to anticipate the impact of new rules on particular values. At the same time, “modeling through” holds novel perils that policymakers may be ill-equipped to address. Concerns include privacy, brittleness, and automation bias of which law and technology scholars are keenly aware. They also include the extension and deepening of the quantifying turn in governance, a process that obscures normative judgments and recognizes only that which the machines can see. The water may be warm but there are sharks in it.
These tensions are not new. And there is danger in hewing to the status quo. (We should still pursue renewable energy even though wind turbines as presently configured waste energy and kill wildlife.) As modeling through gains traction, however, policymakers, constituents, and academic critics must remain vigilant. This being early days, American society is uniquely positioned to shape the transition from muddling to modeling.
papers.ssrn.com/sol3/papers.cfm?abstract_id=3939211
Bio: Ryan Calo is the Virginia and Prentice Bloedel Professor at the University of Washington. He is a founding co-director (with Batya Friedman and Tadayoshi Kohno) of the interdisciplinary UW Tech Policy Lab and a co-founder (with Chris Coward, Emma Spiro, Kate Starbird, and Jevin West) of the UW Center for an Informed Public. Professor Calo holds a joint appointment at the Information School and an adjunct appointment at the Paul G. Allen School of Computer Science and Engineering.
Professor Calo's research on law and emerging technology appears in leading law reviews (California Law Review, Columbia Law Review, Duke Law Journal, UCLA Law Review, and University of Chicago Law Review) and technical publications (MIT Press, Nature, Artificial Intelligence) and is frequently referenced by the national media. His work has been translated into at least four languages. Professor Calo has testified four times before the United States Senate, most recently providing witness testimony on July 11, 2024, before the United States Senate Committee on Commerce, Science and Transportation at a hearing titled “The Need to Protect Americans’ Privacy and the AI Accelerant.” Professor Calo stressed the importance of a comprehensive federal privacy law that both protects Americans’ personal privacy and sets guidelines for businesses developing and implementing AI technology.
Theorists of justice have long imagined a decision-maker capable of acting wisely in every circumstance. Policymakers seldom live up to this ideal. They face well-understood limits, including an inability to anticipate the societal impacts of state intervention along a range of dimensions and values. Policymakers cannot see around corners or address societal problems at their roots. When it comes to regulation and policy-setting, policymakers are often forced, in the memorable words of political economist Charles Lindblom, to “muddle through” as best they can.
Powerful new affordances, from supercomputing to artificial intelligence, have arisen in the decades since Lindblom’s 1959 article that stand to enhance policymaking. Computer-aided modeling holds promise in delivering on the broader goals of forecasting and system analysis developed in the 1970s, arming policymakers with the means to anticipate the impacts of state intervention along several lines—to model, instead of muddle. A few policymakers have already dipped a toe into these waters, others are being told that the water is warm.
The prospect that economic, physical, and even social forces could be modeled by machines confronts policymakers with a paradox. Society may expect policymakers to avail themselves of techniques already usefully deployed in other sectors, especially where statutes or executive orders require the agency to anticipate the impact of new rules on particular values. At the same time, “modeling through” holds novel perils that policymakers may be ill-equipped to address. Concerns include privacy, brittleness, and automation bias of which law and technology scholars are keenly aware. They also include the extension and deepening of the quantifying turn in governance, a process that obscures normative judgments and recognizes only that which the machines can see. The water may be warm but there are sharks in it.
These tensions are not new. And there is danger in hewing to the status quo. (We should still pursue renewable energy even though wind turbines as presently configured waste energy and kill wildlife.) As modeling through gains traction, however, policymakers, constituents, and academic critics must remain vigilant. This being early days, American society is uniquely positioned to shape the transition from muddling to modeling.
papers.ssrn.com/sol3/papers.cfm?abstract_id=3939211
Bio: Ryan Calo is the Virginia and Prentice Bloedel Professor at the University of Washington. He is a founding co-director (with Batya Friedman and Tadayoshi Kohno) of the interdisciplinary UW Tech Policy Lab and a co-founder (with Chris Coward, Emma Spiro, Kate Starbird, and Jevin West) of the UW Center for an Informed Public. Professor Calo holds a joint appointment at the Information School and an adjunct appointment at the Paul G. Allen School of Computer Science and Engineering.
Professor Calo's research on law and emerging technology appears in leading law reviews (California Law Review, Columbia Law Review, Duke Law Journal, UCLA Law Review, and University of Chicago Law Review) and technical publications (MIT Press, Nature, Artificial Intelligence) and is frequently referenced by the national media. His work has been translated into at least four languages. Professor Calo has testified four times before the United States Senate, most recently providing witness testimony on July 11, 2024, before the United States Senate Committee on Commerce, Science and Transportation at a hearing titled “The Need to Protect Americans’ Privacy and the AI Accelerant.” Professor Calo stressed the importance of a comprehensive federal privacy law that both protects Americans’ personal privacy and sets guidelines for businesses developing and implementing AI technology.
