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:20210101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220506T100000
DTEND;TZID=Asia/Singapore:20220506T113000
DTSTAMP:20260407T122948
CREATED:20211216T020732Z
LAST-MODIFIED:20220430T132246Z
UID:14719-1651831200-1651836600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Basak Kalkanci
DESCRIPTION:Basak Kalkanci is an associate professor of operations management at the Scheller College of Business at Georgia Tech. Her research focuses on socially and environmentally responsible supply chain management\, and contracting and the role of information in decentralized supply chains. Her research aims to lay the necessary groundwork to enable real-time measurement and management of environmental and social impacts in global supply chains. She earned her Ph.D in Management Science and Engineering from Stanford University and was a postdoctoral associate at the Massachusetts Institute of Technology prior to joining Georgia Tech. Her work appeared in premier journals including Management Science\, Operations Research\, Manufacturing & Service Operations Management\, and Production and Operations Management\, and has been funded by the National Science Foundation. She is the recipient of the Paul Kleindorfer Award in Sustainability (2020)\, Alliance for Research on Corporate Sustainability Emerging Sustainability Scholar Award (2019)\, Georgia Power Professor of Excellence (2015)\, Management Science Meritorious Service Award (2015\, 2017\, 2019\, 2020)\, and M&SOM Meritorious Service Award (2014). She serves as a Senior Editor for the Production and Operations Management Journal and as an Associate Editor for M&SOM. \n\n\n\nName of speaker\nBasak Kalkanci\n\n\nSchedule \n6 May 2022\, 10am – 11.30am\n\n\nLink to register  \n \nhttps://nus-sg.zoom.us/meeting/register/tZcqfuGqqzsrG927xqVoLigsdXKlE82X8kKi\n\n\nTitle of talk\nHow Transparency into Internal and External Responsibility Initiatives Influences Consumer Choice\n\n\nAbstract\nAmid growing calls for transparency and social and environmental responsibility\, companies are employing different strategies to improve consumer perceptions of their brands. Some pursue internal initiatives that reduce their negative social or environmental impacts through responsible operations practices (such as paying a living wage to workers  or engaging in environmentally sustainable manufacturing). Others pursue external responsibility initiatives (such as philanthropy or cause-related marketing). Through two experiments conducted in the field and complementary online experiments\, we compare how transparency into these internal and external initiatives affects customer perceptions and sales. We find that transparency into both internal and external responsibility initiatives tends to dominate generic brand marketing in motivating consumer purchases\, supporting the view that consumers take companies’ responsibility efforts into account in their decision making. Furthermore\, the results provide converging evidence that transparency into a company’s internal responsibility practices can be at least as motivating of consumer sales as transparency into its external responsibility initiatives\, incrementally increasing a consumer’s probability of purchase by 6.40% and 45.85% across our two field experiments\, conducted in social and environmental domains\, respectively. Our results suggest that it may be in the interest of both business and society for managers to prioritize internal responsible operations initiatives to achieve both top- and bottom-line benefits while mitigating social and environmental harms.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-andrew-lim/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/kalkanci_basak_pic.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220530T100000
DTEND;TZID=Asia/Singapore:20220530T113000
DTSTAMP:20260407T122948
CREATED:20220430T132641Z
LAST-MODIFIED:20220812T033515Z
UID:15350-1653904800-1653910200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Hoi-To Wai
DESCRIPTION:Hoi-To Wai received his PhD degree from Arizona State University (ASU) in Electrical Engineering in Fall 2017\, B. Eng. (with First Class Honor) and M. Phil. degrees in Electronic Engineering from The Chinese University of Hong Kong (CUHK) in 2010 and 2012\, respectively. He is an Assistant Professor in the Department of Systems Engineering & Engineering Management at CUHK. He has held research positions at ASU\, UC Davis\, Telecom ParisTech\, Ecole Polytechnique\, LIDS\, MIT. Hoi-To’s research interests are in the broad area of signal processing\, machine learning and distributed optimization with applications to network science. His dissertation has received the 2017’s Dean’s Dissertation Award from the Ira A. Fulton Schools of Engineering of ASU\, and he is a recipient of a Best Student Paper Award at ICASSP 2018. \n\n\n\nName of Speaker\nHoi-To Wai\n\n\nSchedule \n30 May 2022\, 10am – 11.30am\n\n\nVenue (face-to-face)\nI4-01-03 Seminar Room (next to the level 1 café)\n\n\nLink to Register (Online)\nhttps://nus-sg.zoom.us/meeting/register/tZMsf-mpqj0tH9e76YMDTvL9xA1B20JT9uAD\n\n\nTitle of Talk\nStochastic Approximation Schemes with Decision Dependent Data\n\n\nAbstract\nStochastic approximation (SA) is a key method which forms the backbone of many online algorithms relying on streaming data with applications to reinforcement and statistical learning. This talk considers a setting in which the streaming data is not i.i.d.\, but is correlated and decision dependent. First\, we analyze a general SA scheme that indirectly minimizes a smooth but possibly non-convex objective function. We consider an update procedure whose drift term depends on a decision dependent Markov chain and the mean field is not necessarily a gradient map\, leading to asymptotic bias for the one-step updates. We analyze the expected non-asymptotic convergence rate for such general scheme and llustrate this setting with the policy-gradient method for average reward maximization. Second\, we consider extensions of the SA scheme and its analysis. For bi-level optimization via two timescale SA\, we present the non-asymptotic complexity analysis and demonstrate an application to natural actor-critic. For performative prediction with stateful users\, we illustrate that the SGD algorithm in strategical classification can be interpreted as an SA scheme with decision dependent data\, and we present recent results on its expected convergence rate towards a performative stable solution.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-hoi-to-wai/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2022/04/Pic-HTW.jpg
END:VEVENT
END:VCALENDAR