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DTSTART:20250101T000000
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DTSTART;TZID=Asia/Singapore:20260821T100000
DTEND;TZID=Asia/Singapore:20260821T113000
DTSTAMP:20260820T202555
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SUMMARY:DAO-ISEM-IORA Seminar Series: Xiao Lei
DESCRIPTION:Name of Speaker \n\n\nXiao Lei  \n\n\n\n\nSchedule  \n\n\n21 Aug 2026\, 10am – 11.30am \n (60 min talk + 30 min Q&A)\n\n\n\nVenue  \n\n\nHSS-405 \n\n\n\nLink to register \n(via Zoom)\n\nhttps://nus-sg.zoom.us/meeting/register/P408Y8QvSXaLCSZdXPSrtw \n\n\n\n\nTitle \n\n\nAdaptive Design for In-App Advertising Games: A Data-Driven Methodology Validated with Field Experiments \n\n\n\n\nAbstract  \n\n\nRewarded ads\, in which players voluntarily watch videos in exchange for in-game benefits\, have become an important monetization format in both mobile gaming and digital advertising. Yet they create an operational tension: designs that increase immediate ad usage may also frustrate players and reduce future retention. We study this intertemporal trade-off in level-based puzzle games and develop a data-driven framework to maximize players’ expected lifetime rewarded-ad views through dynamic difficulty control. The problem is challenging because firms observe only limited early behavior\, while player response exhibits heterogeneity in skill\, ad tolerance\, and churn propensity\, as well as path dependence driven by difficulty reference and prior ad exposure. We formulate the firm’s decision problem as a partially observable Markov decision process with latent player types and instantiate it with a parsimonious behavioral model of win probability\, ad watching\, and churn. For estimation\, we exploit the model structure to develop a multi-step procedure based on recursive logistic regressions. For deployment\, we compute policies offline using partially observable Monte Carlo planning and compile them into compact lookup tables that satisfy sub-second latency requirements in live game systems. In collaboration with Tencent\, China’s largest digital advertising network\, we implement the framework in commercial games and evaluate it through two large-scale randomized field experiments. Relative to the industry baseline\, the deployed policy increases long-run rewarded-ad views by 19.23% and 13.58%\, while also improving engagement and retention. These results show how dynamic\, data-driven experience design can serve as an implementable revenue-management lever in ad-supported digital platforms. \n\n\n\n\nAbout the Speaker \n\nXiao Lei is an Assistant Professor at HKU Business School. He earned his Ph.D. in Operations Research from Columbia University. His research focuses on digital platform design\, exploring how platforms can both engage and protect their users. His work has been recognized with several prestigious awards\, including the Revenue Management and Pricing (RMP) Section Prize\, an honorable mention for the George Dantzig Dissertation Award\, and Best Paper Awards from the INFORMS Service Science and Behavioral Operations Management societies. Beyond academia\, his research drives real-world impact in collaboration with industry leaders such as Tencent and Activision.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-xiao-lei/
CATEGORIES:IORA Seminar Series
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DTSTART;TZID=Asia/Singapore:20260826T100000
DTEND;TZID=Asia/Singapore:20260826T113000
DTSTAMP:20260820T202555
CREATED:20260820T091925Z
LAST-MODIFIED:20260820T091925Z
UID:28137-1787738400-1787743800@iora.nus.edu.sg
SUMMARY:DAO-ISEM-IORA Seminar Series: Maxim Afanasyev
DESCRIPTION:Name of Speaker\n\n\nMaxim Afanasyev \n\n\n\n\nSchedule \n\n\n28 Aug 2026\, 10am – 11.30am \n (60 min talk + 30 min Q&A)\n\n\n\nVenue \n\n\nHSS-405\n\n\n\nLink to register \n(via Zoom)\n\nhttps://nus-sg.zoom.us/meeting/register/gNT75qF8RbeM66kRmp-lgA\n\n\n\n\nTitle\n\n\nThe Consumer Strikes Back: How Agentic AI Rewrites Market Competition\n\n\n\n\nAbstract \n\n\n\nTraditional economics often models firms as strategic actors and consumers as relatively passive responders. Agentic AI may reverse this asymmetry. Unlike many earlier enterprise technologies\, agentic AI has been adopted rapidly by consumers\, while firms remain constrained by organizational inertia\, legacy systems\, compliance requirements\, and slow process redesign. This creates a setting in which consumers may become strategically more sophisticated than the enterprises serving them. \n\n\nWe develop a model of this consumer first mover advantage and study how agentic AI increases consumers’ ability to self serve\, search\, negotiate\, substitute\, and strategically bypass firm controlled processes. The model shows how this shift can reduce demand for some services and alter consumer behavior in ways that differ sharply from the pre agentic AI environment. These effects are particularly relevant in industries such as legal services\, wealth advisory\, education technology\, e commerce\, banking\, and insurance. \n\n\nThe framework also helps explain several counterintuitive industry observations: AI may shift demand from services toward physical goods\, create new financial stability risks\, and reduce rather than increase consumers’ available leisure time. The results suggest that firms should move beyond viewing AI primarily as a productivity tool and instead treat agentic AI as a source of market structure change\, demand disruption\, and strategic consumer empowerment. \n\n\n\n\n\nAbout the Speaker\n\n\nSenior Executive in AI\, Solutions and Product Management\, with 20+ years of experience and track record in academic research\, management consulting\, technology and financial services. Advisor to C-levels of major companies and prominent entrepreneurs on Applied AI\, Industry trends and emerging technologies. \nJoined DAO Team as Adj Assoc Prof in June\, in addition to current role at Google Cloud\, where provide partners\, sales and engineering teams with insights on industry needs to ensure Google Cloud brings novel AI capabilities which are relevant to its clients\, and monitor adoption and benefits from these capabilities in Asia Pacific and Japan. Before Google\, have been leading large global AI engineering and transformation teams (200+ people and budgets of US$100M+) to build and deploy innovative AI solutions\, deliver AI adoption strategies and unlock business value with new technologies across industries\, both as an employee and as a consultant. Managed multibillion public and private equity technology investments portfolios before joining McKinsey to establish its AI Transformation capabilities. Stanford University PhD in Operations\, Information and Technology. Started career as AI Researcher and worked at leadership roles for major companies in Asia\, Europe and North America. \nAt NUS\, Maxim’s research focuses on AI economics\, Human-AI and AI Transformations. Maxim is teaching Applied AI use case factory (BSM5213) for Master and MBA students and various Executive Education courses on Applied AI Transformation. Maxim is co-author of “Designing the AI Ready Organization” book which is contracted to be published in 1H 2027. Maxim in his NUS capacity is one of two key contributors named at SAFR\, Agentic Governance framework\, released by Monetary Authority of Singapore in July 2026.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-maxim-afanasyev/
CATEGORIES:IORA Seminar Series
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