### Convexity and Information Elicitation

How can we score a weather forecaster so she gives the most accurate predictions?Glenn Brier's answer to this question in 1950 founded the field of elicitation: incentivizing strategic agents to reveal information.

The answers have deep connections to convexity, a fundamental feature that comes up all the time in machine learning and algorithms.

This series is based on my tutorial at EC '16 with Rafael Frongillo. You can

**see the slides from that tutorial at the above link**.

- 2016-09-20: Convexity
- 2016-09-22: Proper Scoring Rules
- 2017-04-12: Eliciting Properties
- 2017-04-22: Eliciting Finite Properties
- 2017-10-03: Prediction Markets
- 2018-03-16: Eliciting Continuous Scalars

### Value of Information and Decisionmaking

How to formalize the value of a piece of information for purposes of decision or prediction?- 2016-09-24: Generalized Entropies and the Value of Information
- 2016-10-02: Risk Aversion and Max Entropy
- 2016-10-07: Divergences and Value of Information
- 2017-09-28: Risk Aversion and Decisionmaking

### Math

Math is cool, fun, and important.This series covers useful or interesting posts that are purely about mathematics.

- 2016-09-20: Convexity
- 2016-10-20: Convex Duality
- 2017-10-06: Useful Bounds via Taylor's Theorem

### Probability

Probability is so much fun that it gets its own section.- 2017-01-06: k-way Collisions of Balls in Bins
- 2017-10-07: Intro to Measure Concentration
- 2017-12-18: Subgaussian Variables and Concentration
- 2018-03-17: Tight Bounds for Gaussian Tails and Hazard Rates