DIMACS is pleased to announce the upcoming Special Focus on Mechanisms and Algorithms to Augment Human Decision Making. The special focus aims to improve decision-support systems by leveraging both human and machine intelligence through study of tools to augment decision making in individuals and organizations. Available tools include (1) mechanisms to elicit complex probabilities and preferences from people, rewarding them appropriately (2) algorithms to combine human judgments and data-driven predictions, and (3) algorithms to aggregate potentially conflicting preferences under social-choice objectives.
The Special Focus on Mechanisms and Algorithms to Augment Human Decision Making will be led by incoming DIMACS Director David Pennock. It will launch in late 2019 and run through the end of 2022. Planned activities bring together theoretical computer scientists studying algorithmic game theory, machine learning theory, and NP-hard counting problems; AI computer scientists studying human computation, Bayesian inference, and satisfiability; statisticians studying scoring rules and belief aggregation; economists studying prediction markets, financial markets, and wagering mechanisms; marketing scientists studying surveys and polls; blockchain pioneers implementing decentralized prediction markets and other experimental market constructs; social and behavioral scientists studying human behavior modeling; and human-computer interaction researches designing interfaces to facilitate elicitation.
The special focus builds on recent advances in computational social choice, crowdsourced democracy, and crowdsourced forecasting, including prediction markets and scoring rules. Topics include eliciting probability distributions and statistics of distributions, decentralized elicitation using markets or blockchain, complexity of elicitation, participatory budgeting, fair division, incentivizing exploration, and digital democracy. Within each topic, the SF will seek to characterize what is impossible, intractable, and tractable to compute either exactly or approximately. Planned special focus workshops include:
- Eliciting Complex Information
- Algorithmic Social Choice
- Eliciting Beyond Labels from the Crowd
- Preference Aggregation
- Learning from Partially Reliable Data (or Learning from Real Data)
These workshops will explore questions such as: which statistics of distributions can we compute by minimizing a loss (or maximizing a score) over elicited data with a limited number of responses; how can we characterize or design loss functions to expose desired statistics; and can we develop a more rigorous theory for how to combine potentially conflicting preferences reasonably and fairly. In addition to investigating predictions and preferences, special focus events will look at the decision-making systems that bring it all together.
To receive information about special focus activities, you can join the special focus mailing list.
More than a decade ago the DIMACS Special Focus on Computation and the Socio-Economic Sciences held the first open gathering of researchers in prediction markets and helped establish prediction markets as an important academic and industry discipline. With the Special Focus on Mechanisms and Algorithms to Augment Human Decision Making, we will revisit this topic more broadly through the lens of AI and intelligent systems.
The DIMACS Special Focus on Mechanisms and Algorithms to Augment Human Decision Making is supported by DIMACS and its partners, and by the National Science Foundation under grant number CCF-1941871.
Printable description of the special focus: [PDF]