Planting trees at the right places: Recommending suitable sites for growing trees using algorithm fusion

Large-scale planting of trees has been proposed as a low-cost natural solution for carbon mitigation, but is hampered by poor selection of plantation sites, especially in developing countries. To aid in site selection, we develop the ePSA (e-Plantation Site Assistant) recommendation system based on algorithm fusion that combines physics-based/traditional forestry science knowledge with machine learning. ePSA assists forest range officers by identifying blank patches inside forest areas and ranking each such patch based on their tree growth potential. Experiments, user studies, and deployment results characterize the utility of the recommender system in shaping the long-term success of tree plantations as a nature climate solution for carbon mitigation in northern India and beyond.

Beliefs and Expertise in Sequential Decision Making

This work explores a sequential decision making problem with agents having diverse expertise and mismatched beliefs. We consider an $N$-agent sequential binary hypothesis test in which each agent sequentially makes a decision based not only on a private observation, but also on previous agents' decisions. In addition, the agents have their own beliefs instead of the true prior, and have varying expertise in terms of the noise variance in the private signal. We focus on the risk of the last-acting agent, where precedent agents are selfish. Thus, we call this advisor(s)-advisee sequential decision making. We first derive the optimal decision rule by recursive belief update and conclude, counterintuitively, that beliefs deviating from the true prior could be optimal in this setting. The impact of diverse noise levels (which means diverse expertise levels) in the two-agent case is also considered and the analytical properties of the optimal belief curves are given. These curves, for certain cases, resemble probability weighting functions from cumulative prospect theory, and so we also discuss the choice of Prelec weighting functions as an approximation for the optimal beliefs, and the possible psychophysical optimality of human beliefs. Next, we consider an advisor selection problem wherein the advisee of a certain belief chooses an advisor from a set of candidates with varying beliefs. We characterize the decision region for choosing such an advisor and argue that an advisee with beliefs varying from the true prior often ends up selecting a suboptimal advisor, indicating the need for a social planner. We close with a discussion on the implications of the study toward designing artificial intelligence systems for augmenting human intelligence.