This jointly sponsored journal club will bring students from the engineering and business communities together to review seminal literature around decision theory in the context of signal and information processing.
Each session will include three presentations by meeting participants, each lasting 25 minutes with 5 additional minutes of discussion time. The focus of this journal club will be to broaden knowledge base and research scope for PhD students.
Friday Session
27 Jan | 12:00-13:00
Journal Club Topic: Economic information acquisition for data-driven learning and decision making.
Maytal Saar-Tsechansky
Associate Professor of Information, Risk and Operations Management at the McCombs School of Business
Maytal Saar-Tsechansky is an Associate Professor of Information, Risk and Operations Management at the McCombs School of Business, The University of Texas at Austin. She is currently also a Visiting Faculty at the Judge Business School at the University of Cambridge.
Her research interests include machine learning and data mining methods for data-driven business intelligence and decision making. Her research has been published in the Journal of Finance, Management Science, Information Systems Research, Journal of Machine Learning Research, and Machine Learning Journal, among other venues. She serves on the editorial board of the Machine Learning Journal, is an Associate Editor for the Information Systems Research (ISR) journal and the INFOMRS Journal on Computing. She was a guest editor of the Special Issue on Utility-Based Data Mining in the Journal of Data Mining and Knowledge Discovery, and is a frequent Program Committee member in the premier machine learning and data mining conferences. At McCombs, Maytal has developed and teaches courses on business intelligence with data mining in the Executive MBA, full-time MBA, and the undergraduate business programmes. Maytal received her PhD from New York University’s Stern School of Business and obtained a B.S and M.S in Industrial Engineering from Ben Gurion University, Israel.
To better cope with business environments that are information-intensive, complex, and highly dynamic, companies increasingly rely on intelligent, data-driven methods to automatically “learn” from experiences over time to improve future decision-making. However, reliable data-driven induction depends on two critical ingredients: an effective induction (learning) method, and informative, relevant data. Importantly, the capacity of even the most effective technique to extract a reliable model of any real-world phenomenon is bounded by what information is available about prior experiences. In practice, organizations often acquire information only passively, through routine business transactions. However, the opportunity costs of acquiring information only passively can be substantial.
This challenge poses some fundamental and fascinating scientific questions: How does one enable predictive modeling techniques to reason intelligently about opportunities to actively acquire information? How should knowledge (or uncertainty) about the particular decisions that predictive models aim to inform, influence what information is best to acquire? And how can we incorporate prior knowledge into data-driven learning, so as to mitigate the high cost of learning from scratch? I will present an overview of my research on economic data mining in which I address these and related questions. I will also present results of the applications of the methods I developed to a broad range of decision problems, including in the context of market mechanism design, personalized marketing, recommender systems, and fraud detection.
Maytal Saar-Tsechansky and Foster Provost, “Decision-centric Active Learning of Binary-Outcome Models”, Information Systems Research, Vol. 18, No. 1, pp. 1–19, 2007
Melanie Milovac
PhD candidate in Management, University of Cambridge
Melanie Milovac is a PhD candidate in Management at University of Cambridge Judge Business School. She is working in the Organisational Behavior subject group with Dr Jochen Menges and Professor Martin Kilduff. Melanie received a Bachelor of Science in psychology at the University of Heidelberg and an MPhil degree in Innovation, Strategy and Organisation at the University of Cambridge.
Her research interests include emotion expression and suppression in organisations, and the social consequences of self-expression. She also worked as a research assistant with Professor Michael Morris at Columbia Business School on cultural differences in organisations and gender differences in negotiations. Furthermore, she worked as a research assistant with Professor Christiane Schwieren at the University of Heidelberg on ingroup biases in competitive situations.
Emotions as social currency: How do people evaluate the risks of emotion expression?
In every social situation people decide anew whether to express or suppress emotions. Particularly in organisations emotion expression is essential for achieving individual goals such as building trust in relationships or persuading a counterpart of a certain point of view. However, emotion expression can also defeat its purpose and make the expresser vulnerable. To understand how individuals evaluate the risks of emotion expression, a theoretical framework building on prospect theory (Kahneman & Tversky, 1979) is presented. It is proposed that also when it comes to emotion expression people are risk-seeking in loss situations and risk-averse in gain situations. However, the riskiness of emotion expression is expected to vary with the nature of the emotion involved. This framework introduces a new theoretical perspective on emotion expression, and seeks to explain how emotion expression is utilised and can be utilised as a social currency in organisations.
Daniel Kahneman and Amos Tversky, 1979. “Prospect Theory: An Analysis of Decision under Risk”, Econometrica, Vol. 47, No. 2, pp. 263-292
Professor William Fitzgerald
Professor of Applied Statistics and Signal Processing, Department of Engineering, University of Cambridge
Dr Joan Lasenby
Senior Lecturer in Information Engineering, Department of Engineering, University of Cambridge
