# Information Structure Design in Team Decision Problems

## TL;DR

This paper introduces scalable greedy algorithms for designing information structures in team decision problems, aiming to optimize performance and resilience against adversarial agents, despite the lack of supermodularity.

## Contribution

It proposes simple greedy algorithms for information structure design in team problems and demonstrates their practical effectiveness through numerical experiments.

## Key findings

- Greedy algorithms can effectively improve team performance.
- The set function for information links is not supermodular.
- Numerical results show near-optimal performance of the proposed methods.

## Abstract

We consider a problem of information structure design in team decision problems and team games. We propose simple, scalable greedy algorithms for adding a set of extra information links to optimize team performance and resilience to non-cooperative and adversarial agents. We show via a simple counterexample that the set function mapping additional information links to team performance is in general not supermodular. Although this implies that the greedy algorithm is not accompanied by worst-case performance guarantees, we illustrate through numerical experiments that it can produce effective and often optimal or near optimal information structure modifications.