Grant Schoenebeck
University of Michigan, MI·Computer Science
Groups and societies have to reach decisions and learn what is true, even when information is scattered, changes quickly and is distorted by people's own interests. This research uses theoretical computer science and economics to understand how information combines with incentives in two settings: formal group decisions, and learning that happens across social networks and social media.
- Fields
Computer Science
Mathematics & Statistics
Social Science
- Topics
social choice theory
information elicitation
mechanism design
- Grants
- 2 known grants, through Sep 2027
What they work on
- Develop models of informed group decision making that combine agents' subjective and objective preferences, information and answers to questions, drawing on social choice theory and information elicitation.
- Define fairness and efficiency criteria for informed group decisions and design incentive-aware mechanisms and equilibrium concepts for strategic agents, then validate them on the OPRA system.
- Identify the necessary and sufficient conditions for crowds to converge quickly and reliably on the truth, in both sequential and repeated-updating models of social learning, including polarized settings.
- Analyze the computational complexity of social learning and design algorithms that tune platform design parameters to encourage fast, robust learning of the truth.
More about the research
The first project, done in collaboration with other institutions, launches "informed group decision making." It combines social choice theory, the mathematics of voting and combining preferences, with information elicitation, methods for drawing out what people truly know. The work has three parts. The first develops models that combine agents' subjective and objective preferences, information and answers to questions. The second defines fairness and efficiency criteria and designs mechanisms that meet them when agents are truthful and cooperative. The third proposes equilibrium concepts and incentive-aware mechanisms for agents who act strategically. The resulting models and algorithms will be deployed and refined on the open-source Online Preference Reporting and Aggregation (OPRA) system.
Why it matters
Online platforms and groups that must reach decisions together could use these models to design rules that encourage people to share what they truly know, treat members fairly, and help crowds reach the truth instead of polarizing. The OPRA system offers a practical setting in which to test these mechanisms.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Simina Branzei
Purdue University, IN·Computer Science·Early-career
Analyze market dynamics in which computationally limited players with only local information interact over time, testing whether globally efficient allocations emerge and whether markets grow or shrink.
Juba Ziani
Georgia Institute of Technology, GA·Computer Science·Early-career
Model how individual data providers shape the outcomes of machine learning systems through strategic choices about what data to share, combining tools from computer science and economics.
Hadi Hosseini
Pennsylvania State University, PA·Computer Science·Early-career
Design matching and allocation algorithms that remain fair when participants' preferences are uncertain or noisy, rather than assuming preferences are reported exactly.
The school
University of Michigan: 124 labs on PI Pages, $2.1B a year on research, 963 research doctorates a year.
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