MA-BED: Coordinating Multiple Actors in Bayesian Experimental Design

To date, Bayesian experimental design (BED) has largely studied how a single adaptive policy should choose experiments. Yet many real-world data-acquisition systems distribute experimental capacity across multiple actors, such as laboratories, field teams, or decentralised robots. We introduce multi-actor Bayesian experimental design (MA-BED) as a general paradigm for BED in which experimental decisions are distributed across multiple actors, and within this paradigm focus on a fully cooperative setting with decentralised execution. In this regime, actors share an inferential objective but choose experiments using only their local histories, creating a coordination problem: individually informative policies may collectively acquire redundant information. We formalise this failure through an information-theoretic coordination gap and show that, under conditionally independent acquisition, it is exactly characterised by the total correlation among actor histories. To address this problem, we adapt centralised training with decentralised execution (CTDE) to BED, jointly optimising locally executable policies against the information contained in their pooled experimental histories. We instantiate this framework with DAD-based algorithms trained using a contrastive information objective. Empirically, joint training reduces coordination failure, induces greater actor specialisation under heterogeneous experimental costs, and outperforms independently trained policies without requiring communication at execution. These results establish coordination as an important challenge in Bayesian experimental design when data acquisition is distributed across multiple actors.