{"id":57261,"date":"2026-06-12T11:10:44","date_gmt":"2026-06-12T15:10:44","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine\/?p=57261"},"modified":"2026-06-12T12:17:47","modified_gmt":"2026-06-12T16:17:47","slug":"impulse-control-for-better-decisions","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine\/impact\/impulse-control-for-better-decisions\/","title":{"rendered":"Impulse Control for Better Decisions"},"content":{"rendered":"\n

A mathematical model\u00a0developed by Haoyang\u00a0Cao<\/a> could help businesses,\u00a0financial planners, and\u00a0engineers make smarter\u00a0high-stakes decisions.\u00a0<\/p>\n\n\n\n

Cao, an assistant professor\u00a0of applied mathematics\u00a0and statistics<\/a> and a\u00a0member of the Data\u00a0Science and AI Institute<\/a>,\u00a0and her team focused on\u00a0\u201cimpulse control\u201d decision-making,\u00a0a framework that\u00a0reflects how organizations\u00a0actually operate.\u00a0<\/p>\n\n\n\n

Rather than making tiny, continual changes, companies typically wait until conditions reach a tipping point and then act decisively. <\/p>\n\n\n\n

\u201cOur model is much closer to this reality,\u201d Cao says. \u201cPeople observe a situation over time, and only when the tipping point is reached, they make a move,\u201d she adds, citing examples such as an Amazon warehouse suddenly placing a large restocking order or an investor moving funds in one major trade. \u201cIt\u2019s sudden, not gradual. Real decision-making is messy,\u201d she adds, \u201cand algorithms must embrace that mess.\u201d <\/p>\n\n\n\n

That realism matters. Poorly timed decisions can lead to empty shelves, wasted inventory, unnecessary transaction fees, or lost profits. Yet translating those reactionary choices into mathematics has long challenged researchers. Current methods assume smooth, continuous behavior\u2014but in the real world, decisions often arrive as abrupt shocks. <\/p>\n\n\n\n

\n

“People observe a situation over time, and only when the tipping point is reached, they make a move.” \u2014 Haoyang Cao<\/p>\n<\/blockquote>\n\n\n\n

To bridge that gap, the team used reinforcement learning, where an algorithm learns through exploration and feedback, and built an operator-based framework that tracks how a system \u201crenews\u201d after each jump\u2014for example, how inventory levels reset after a major order. <\/p>\n\n\n\n

Rather than rely on simple rules and idealized equations, their flexible approach learns from experience to discover better timing and order sizes, identifying what \u201ctoo low\u201d means amid shifting demand, prices, and shipping costs. <\/p>\n\n\n\n

The researchers are now expanding the work beyond a single decision-maker to situations where many agents interact, such as competing firms ordering from the same supplier or investors reacting to one another\u2019s trades. <\/p>\n\n\n\n

\u2014 SALENA FITZGERALD<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Haoyang Cao has developed a new mathematical framework helping to determine not just what action to take, but precisely when to take it.<\/p>\n","protected":false},"author":4,"featured_media":57921,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[34],"tags":[],"issue":[105],"class_list":["post-57261","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-impact","issue-spring-2026"],"acf":[],"yoast_head":"\nImpulse Control for Better Decisions - Johns Hopkins Engineering Magazine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/magazine\/impact\/impulse-control-for-better-decisions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Impulse Control for Better Decisions - 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