Mechanism Design

When you think of AI in public decision-making, you might picture chatbots handling service requests or predictive models flagging infrastructure risks. But what if we let large language models (LLMs) actually allocate resources—acting as digital social planners? That’s exactly what this new study tested, using Participatory Budgeting (PB) both as a practical decision-making task and a dynamic benchmark for LLM reasoning. Why Participatory Budgeting Is the Perfect Testbed PB is more than a budgeting exercise. Citizens propose and vote on projects—parks, public toilets, community centers—and decision-makers choose a subset to fund within a fixed budget. It’s a constrained optimization problem with a human twist: budgets, diverse preferences, and sometimes mutually exclusive projects. ...

The most radical idea in Michael I. Jordan’s latest manifesto isn’t a new model, a benchmark, or even a novel training scheme. It’s a reorientation. He argues that we’ve misdiagnosed the nature of intelligence—and in doing so, we’ve built AI systems that are cognitively brilliant yet socially blind. The cure? Embrace a collectivist, economic lens. This is not techno-utopianism. Jordan—a towering figure in machine learning—offers a pointed critique of both the AGI hype and the narrow symbolic legacy of classical AI. The goal shouldn’t be to build machines that imitate lone geniuses. It should be to construct intelligent collectives—systems that are social, uncertain, decentralized, and deeply intertwined with human incentives. In short: AI needs an economic imagination. ...