BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IORA - Institute of Operations Research and Analytics - ECPv6.15.11//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:IORA - Institute of Operations Research and Analytics
X-ORIGINAL-URL:https://iora.nus.edu.sg
X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Singapore
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:+08
DTSTART:20200101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211105T100000
DTEND;TZID=Asia/Singapore:20211105T113000
DTSTAMP:20260407T151958
CREATED:20210812T025601Z
LAST-MODIFIED:20211028T063827Z
UID:14198-1636106400-1636111800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Yuqian Xu
DESCRIPTION:Yuqian Xu is an assistant professor of Operations Management at Kenan-Flagler Business School\, University of North Carolina\, Chapel Hill. Her research focuses on understanding worker and consumer behaviors in the banking industry and digital platforms\, in which she investigates both theoretical and empirical problems. Her focus of methodology includes applied probability\, stochastic modeling\, econometrics\, and machine learning. In her research\, she has been collaborating with different companies\, including JD.com\, Tencent\, Bank of China\, etc. She has given talks in different academic\, industry\, and government conferences and organizations\, such as Federal Reserve Bank and China Banking Regulatory Committee. \nHer research has been published in journals including Management Science\, Operations Research\, Production and Operations Management\, etc. She has a B.S. in Mathematics from the Kuang Yaming Honors School of Intensive Instruction in Science and Arts at Nanjing University\, China. She received her Ph.D. degree (Beta Gamma Sigma) in 2017 from NYU Stern School of Business with the Herman E. Krooss Dissertation Award. \n  \n\n\n\nName of Speaker\nDr Xu Yuqian\n\n\nSchedule\n5 November 2021\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0sdu-rqDMiHNTXwyxJUDNqD7WOMTf-u8dp\n\n\nTitle\nOperational Risk Management: Optimal Inspection Policy\n\n\nAbstract\nOperational risk is one of the major risks in the financial industry; major banks around the world lost nearly $210 billion from operational risk events between 2011 and 2016 (Huber and Funaro 2018) and inspection on operational risk is required by the Basel Committee on Banking Supervision. Motivated by the importance of operational risk and its current industry regulation\, we study how a financial firm can optimally design inspection policies to manage operational risk losses. Specifically\, we propose a continuous-time principal-agent model framework to examine a financial firm’s (principal) optimal inspection policy and their employees’ (agent) effort towards lowering the risk event occurrences. We first consider two commonly used inspection policies\, namely\, random and periodic policies\, and characterize the optimal inspection strategy under each policy. We identify conditions for two different modes of inspection (effort inducement and error correction) as well as nuanced interactions among inspection frequency\, penalty charged on errors\, and wage paid to employees. We then compare the performances of random and periodic policies. We find that contrary to conventional wisdom\, the random policy is not always optimal; it is dominated by the periodic policy if the inspection cost is sufficiently low. Furthermore\, we construct a hybrid policy that strictly dominates random policy and weakly dominates periodic policy\, which suggests that a proper reduction of the random element in the inspection policy can always improve its performance. Finally\, calibrating model parameters using operational risk data from a major bank in China\, we numerically show that our key insights about random\, periodic\, and hybrid policies are robust to various model extensions.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-yuqian-xu/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/08/Xu-Yuqian-picture.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211112T100000
DTEND;TZID=Asia/Singapore:20211112T113000
DTSTAMP:20260407T151958
CREATED:20210812T025706Z
LAST-MODIFIED:20211101T084312Z
UID:14199-1636711200-1636716600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Vijay Kamble
DESCRIPTION:Vijay Kamble is an Assistant Professor of Information and Decision Sciences in the College of Business Administration\, and of Computer Science (by courtesy)\, at the University of Illinois at Chicago. He previously obtained his Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2015) and was a postdoc in the Social Algorithms lab at the Management Science and Engineering Dept. of Stanford University (2015-17). \nHis current research interests are in the areas of machine learning\, statistical learning theory\, market design\, and optimization with applications to revenue management\, pricing\, and the design and optimization of online platforms and marketplaces. \n  \n\n\n\nName of Speaker\nDr Vijay Kamble\n\n\nSchedule\n12 November 2021\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZcsf-CqqT8rHt2NWfT9LgIr1u-ghDpdIlPc\n\n\nTitle\nPseudo-competitive games and algorithmic pricing\n\n\nAbstract\nWith recent advances in artificial intelligence methodologies\, algorithmic pricing in the face of unknown or uncertain demand has become ubiquitous in the practice of revenue management. While such algorithmic approaches are known to satisfy attractive revenue guarantees in well-behaved\, non-strategic environments\, the outcomes arising from such approaches in competitive settings remain poorly understood.  Motivated by the goal of studying outcomes of algortihmic price competition in practical environments\, we study a game of price competition amongst firms selling homogeneous goods\, defined by the property that a firm’s revenue is independent of any competing prices that are strictly lower. We call this the pseudo-competitive property and the games of price competition induced by such revenue functions pseudo-competitive games. \nWe show that this property is induced by any customer choice model involving utility-maximizing choice from an adaptively determined consideration set\, encompassing a variety of empirically validated choice models studied in the literature. For these games\, we show a one-to-one correspondence between pure-strategy local Nash equilibria with distinct prices and the prices generated by the firms sequentially setting local best-response prices in different orders. In other words\, despite being simultaneous-move games\, they have a sequential-move equilibrium structure. Although this structure is attractive from a computational standpoint\, we find that it makes these games particularly vulnerable to the existence of strictly-local Nash equilibria\, in which the price of a firm is only a local best-response to competitors’ prices when a globally optimal response with a potentially unboundedly higher payoff is available. We moreover show\, both theoretically and empirically\, that price dynamics resulting from the firms utilizing gradient-based dynamic pricing algorithms to respond to competition may often converge to such an undesirable outcome. To address this concern\, we finally propose an algorithmic approach that incorporates global experimentation under certain regularity assumptions on the revenue curves. \nThis is joint work with Chamsi Hssaine and Sid Banerjee\, both from Cornell ORIE.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-vijay-kamble/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/08/Dr-Kamble-pic.jpg
END:VEVENT
END:VCALENDAR