Operations has traditionally considered decision-making (including queuing, scheduling, pricing, routing) from the perspective of a single firm. However, in several recent socio-economic developments – for example, modern online platforms, decentralized energy generation, and decentralized ledgers – the requisite decisions are instead distributed among many independent people. While these crowd-based operations disrupt centrally controlled practices, they empower individuals and enable new applications. Whether intermediated by a centralized platform or wholly decentralized, the operations of these systems are complicated by the underlying complex network structures, the challenges of large-scale alignment of incentives, and of learning from data. Moreover, the decentralized and people-centric nature of processes on these platforms entails working with “indirect” operational levers such as recommendations, signaling, information design, and user interface design. They further pose additional constraints pertaining to fairness and ethics.
This workshop will bring together researchers from across operations
management, computer science, and economics to explore new challenges and research directions of such crowd-based systems.
Potential areas include:
- Operations with crowds and volunteers
- Distributed ledgers and payment systems
- Decentralized energy markets and cloud energy storage
- Crowdfunding and micro-investing
- Decentralized decision-making and crowdsourced democracy
- Operational challenges in sharing economy and online labor platforms
- Achieving operational goals through information design, recommendations, and other new levers
Please see the call for submission information.
| Submission |
May 21, 2021, Anywhere on Earth |
| Notification |
June 28, 2021 |
| Workshop |
July 23, 2021 |
Keynote Speakers
Dr. Karen Smilowitz is the James N. and Margie M. Krebs Professor in Industrial Engineering and Management Science at Northwestern University, with a joint appointment in the Operations group at the Kellogg School of Management. Dr. Smilowitz is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and non-profit applications, working with transportation providers, logistics specialists and a range of non-profit organizations. She has been instrumental in promoting the use of operations research within the humanitarian and nonprofit sectors through the Woodrow Wilson International Center for Scholars, the American Association for the Advancement of Science, and the National Academy of Engineering, as well as various media outlets. Dr. Smilowitz is Editor-in-chief of the INFORMS journal, Transportation Science. Dr. Smilowitz received the Award for the Advancement of Women in OR/MS from INFORMS and led the winning team in the INFORMS Innovative Applications of Analytics Award.
Sean Meyn was raised by the beach in Southern California. Following his BA in mathematics at UCLA, he moved on to pursue a PhD with Peter Caines at McGill University. After about 20 years as a professor of ECE at the University of Illinois, in 2012 he moved to beautiful Gainesville. He is now Professor and Robert C. Pittman Eminent Scholar Chair in the Department of Electrical and Computer Engineering at the University of Florida, and director of the Laboratory for Cognition and Control. He also holds an Inria International Chair to support research with colleagues in France. His interests span many aspects of stochastic control, stochastic processes, information theory, and optimization. For the past decade, his applied research has focused on engineering, markets, and policy in energy systems.
Panelists
Ashish Goel is a Professor of Management Science and Engineering and (by courtesy) Computer Science at Stanford University, and a member of Stanford's Institute for Computational and Mathematical Engineering. He received his PhD in Computer Science from Stanford in 1999, and was an Assistant Professor of Computer Science at the University of Southern California from 1999 to 2002. His research interests lie in the design, analysis, and applications of algorithms; current application areas of interest include social networks, participatory democracy, Internet commerce, and large scale data processing. Professor Goel is a recipient of an Alfred P. Sloan faculty fellowship (2004-06), a Terman faculty fellowship from Stanford, an NSF Career Award (2002-07), and a Rajeev Motwani mentorship award (2010). He was a co-author on the paper that won the best paper award at WWW 2009, an Edelman Laureate in 2014, and a co-winner of the SigEcom Test of Time Award in 2018.
Y. Karen Zheng is a Sloan School Career Development Professor and an Associate Professor of Operations Management at the MIT Sloan School of Management. Karen’s research studies operations and supply chain management problems with a behavior-centric, data-driven, field-based approach. Her research addresses four general topics: (I) the design and impact of digital platforms to enable efficient physical supply chains in resource constrained environments, (II) the role of information transparency in driving positive behaviors, especially for environmental and social responsibility, (III) the impact of a supply chain’s structural properties on the behaviors of various stakeholders, and (IV) the impact of consumer behaviors on retail operations. In addressing these questions, Karen collaborates with both public and private partners on the ground to ensure that her research leads to positive impacts to society and practice. Karen’s research is recognized by various awards, including the U.S. National Science Foundation CAREER Award, the Management Science Best Paper Award in Operations Management, the MSOM Responsible Research Award, and the INFORMS Doing Good with Good OR Award. Karen received her PhD degree from Stanford University.
Dominic Coey is a research scientist in Core Data Science at Facebook, where he leads the Economics, Algorithms and Optimization team. His research interests are in large-scale causal inference in experimental and observational settings, and using insights from economic theory to inform product strategy and decision-making. His past work has touched on machine learning for variance reduction in experimentation, shrinkage estimators, experimentation in markets, and auction design. He holds a PhD in Economics from Stanford University.
