# CAREER: Structure and Incentives for Eliciting and Aggregating Information

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · University of Colorado at Boulder (CO) · $743,315

## Abstract

People, organizations, and automated systems all need to base their decisions on information. But good information is often broadly distributed, expensive to obtain, and difficult to piece together. This project lays out the theoretical foundations for this problem and how to solve it. First, it will study how to evaluate quality, correctness, and economic value of information. This part also studies the design of loss functions in machine learning and includes writing an educational text. Second, the project will study how different pieces of information interact, both economically and algorithmically. Third, it will design systems that incentivize people to provide good information and aggregate it into useful outputs. These systems will be based on the above principles: valuing information economically and also processing it algorithmically. The high-level goal of the project is for these system designs to be useful for private and public organizations. Potential applications include data marketplaces, gathering public health information, and forecasting.

The first part of the project studies the design of proper scoring rules and proper loss functions in decision making contexts. It includes writing a monograph on the field of information elicitation, given a loss function, what types of predictions and information can be provided by minimizing it, and vice versa. In a decision-making context, scoring rules and loss functions each define a value of information. The first part also investigates the relationship between value of information, good decision making, and accurate predictions, focusing on properties such as strong convexity of the utility and/or loss. The second part investigates the complexity of sequentially communicating and aggregating multiple pieces of information for decision making. This problem is related to classical communication complexity but explored in a fully Bayesian context with success measured by expected utility. Here, the proje

## Key facts

- **NSF award ID:** 2542666
- **Awardee organization:** University of Colorado at Boulder (CO)
- **SAM.gov UEI:** SPVKK1RC2MZ3
- **PI:** Bo Waggoner
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), CAREER-Faculty Erly Career Dev, ALGORITHMS, COMPUT GAME THEORY & ECON
- **Estimated total:** $743,315
- **Funds obligated:** $432,921
- **Transaction type:** Continuing Grant
- **Period:** 10/01/2026 → 09/30/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2542666

## Citation

> US National Science Foundation, Award 2542666, CAREER: Structure and Incentives for Eliciting and Aggregating Information. Retrieved via AI Analytics 2026-07-20 from https://api.ai-analytics.org/grant/nsf/2542666. Licensed CC0.

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