4/17/25 | 4:15pm | E51-376
Ali Aouad
Assistant Professor of Operations Management
MIT
Abstract: The design of a subsidy poses a classic demand‑management problem: choosing an offer set (bundle, assortment, voucher) that respects individual preferences while aiming to satisfy welfare goals. In the food security context, in-kind subsidies are major safety nets in the Global South to curb malnutrition, involving large-scale supply chains—the Public Distribution System in India serves hundreds of millions of beneficiaries. We introduce a high-frequency, transaction-level tracking method that reveals how households respond to alternative in-kind subsidies in under-served communities in India. Our experiments expose trade-offs between take-up (demand) and efficiency (nutritional or environmental goals). Consequently, we develop a probabilistic choice model to capture subsidy take-up behavior and optimize its design under various objectives and constraints. For simple variants of our model, we provide provably efficient approximation schemes. If time permits, I will outline extensions of this research agenda and illustrate how a demand management lens can inform other public sector operations.
Joint work with Kamalini Ramdas, Alp Sungu, and Zhicong Hu
Bio: Ali Aouad is an Assistant Professor of Operations Management at MIT Sloan. His research studies the design of algorithms under uncertainty and decision processes to broadly match demand and supply in areas covering matching and market design, revenue management, and public sector operations. His work was recognized by several awards, including the 2024 POMS Analytics Research Challenge Prize, the 2022 INFORMS Second Prize of the Junior Faculty Interest Group, and a selection for funding in the European Research Council’s 2022 HORIZON Starting Grant. Before MIT, he was an Associate Professor at London Business School (UK) and an Applied Scientist at Uber Technologies (2018-2019), where we contributed to new matching and pricing technology and product innovations. Ali earned his PhD in Operations Research from MIT ORC in 2017.