Resource Allocation

Opening — Why this matters now Large Language Models, edge computing platforms, and cloud inference systems all share a quiet but inconvenient truth: resources are scarce, and not everyone is equal. Some tasks pay more. Some users matter more. Some workloads jump the queue. Yet much of the bandit literature still assumes a polite world—where arms dispense rewards independently, capacity is either infinite or fixed, and every pull is treated equally. That abstraction collapses the moment you introduce priorities, stochastic capacity, and multiple simultaneous plays. ...

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. ...

Twin It to Win It: How BedreFlyt Reimagines Hospital Resource Planning Hospitals often operate under intense pressure, juggling patient needs, staff availability, and limited resources. Now imagine an AI-powered assistant that anticipates those needs, simulates complex patient flows, and delivers optimized resource plans—without burning out the staff. That’s the promise of BedreFlyt, a modular, simulation-driven Digital Twin (DT) designed for hospital wards. Developed at the University of Oslo, BedreFlyt isn’t just another simulation tool. It uniquely integrates: ...