## What is Bayesian Efficiency?

Bayesian Efficiency is a formula to allocate resources across a population to optimize for a particular parameter, even if not all of the population’s details are unknown. This efficiency approach is a variation of Pareto efficiency that uses Bayes’ theory of probability to fill in the knowledge gaps about a population. The ultimate goal is still the same though: to allocate resources in such a way that one preference criterion is optimized and any further reallocation would make at least one individual in the population worse off.

### What’s the Difference between Bayesian Efficiency and Pareto Efficiency?

While having the same goal, Bayesian efficiency addresses the unsolved problems from Pareto efficiency in three ways:

- Uses probability to account for incomplete information about individual members or a whole population’s parameters.
- Decides when to conduct the efficiency evaluation. The efficiency check can be made before the agent sees the population details (ex-ante stage), at the interim stage after the agent sees population details, or later when the agent has complete information about all variables (ex-post stage).
- Finally, Bayesian efficiency adds an incentive qualifier so that the allocation rule is actually followed.