Information Structure Design in Team Decision Problems
For researchers in multi-agent systems and team decision-making, this work provides scalable heuristics for information structure design, though it is incremental due to the absence of theoretical guarantees.
The paper tackles information structure design in team decision problems, proposing greedy algorithms to add information links for optimizing team performance and resilience. Numerical experiments show the algorithms often achieve optimal or near-optimal results despite lacking worst-case guarantees.
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.