This paper addresses an ongoing experience in the design of an artificial agent taking decisions and combining them with the decisions taken by human agents. The context is a serious game research project, aimed at computer-based support for participatory management of protected areas (and more specifically national parks) in order to promote biodiversity conservation and social inclusion. Its objective is to help various stakeholders (e.g., environmentalist, tourism operator) to collectively understand...

Given a fixed dependency graph $G$ that describes a Bayesian network of binary variables $X_1,\cdots ,X_n$, our main result is a tight bound on the mutual information $I_c(Y_1,\cdots ,Y_k)={\sum }_{j=1}^{k}H\left(Y_j\right)/c-H(Y_1,\cdots ,Y_k)$ of an observed subset $Y_1,\cdots ,Y_k$ of the variables $X_1,\cdots ,X_n$. Our bound depends on certain quantities that can be computed from the connective structure of the nodes in $G$. Thus it allows to discriminate between different dependency graphs for a probability distribution, as we show from numerical experiments.