We present the design and analysis of a multi-level game-theoretic model of hierarchical policy-making, inspired by policy responses to the COVID-19 pandemic. Our model captures the potentially mismatched priorities among a hierarchy of policy-makers (e.g., federal, state, and local governments) with respect to two main cost components that have opposite dependence on the policy strength, such as post-intervention infection rates and the cost of policy implementation. Our model further includes a crucial third fac- tor in decisions: a cost of non-compliance with the policy-maker immediately above in the hierarchy, such as non-compliance of state with federal policies. Our first contribution is a closed-form approximation of a recently published agent-based model to com- pute the number of infections for any implemented policy. Second, we present a novel equilibrium selection criterion that addresses common issues with equilibrium multiplicity in our setting. Third, we propose a hierarchical algorithm based on best response dynamics for computing an approximate equilibrium of the hierarchical policy-making game consistent with our solution concept. Finally, we present an empirical investigation of equilibrium policy strategies in this game as a function of game parameters, such as the degree of centralization and disagreements about policy priorities among the agents, the extent of free riding as well as fairness in the distribution of costs.
Active screening is a common approach in controlling the spread of recurring infectious diseases such as tuberculosis and influenza. In this approach, health workers periodically select a subset of population for screening. However, given the limited number of health workers, only a small subset of the population can be visited in any given time period. Given the recurrent nature of the disease and rapid spreading, the goal is to minimize the number of infections over a long time horizon. Active screening can be formalized as a sequential combinatorial optimization over the network of people and their connections. The main computational challenges in this formalization arise from i) the combinatorial nature of the problem, ii) the need of sequential planning and iii) the uncertainties in the infectiousness states of the population.
We present a new algorithm and theoretical results for solving Multi-action Multi-armed Restless Bandits, an important but insufficiently studied generalization of traditional Multi-armed Restless Bandits (MARBs). Multi-action MARBs are capable of handling critical problem complexities often present in AI4SG domains like anti-poaching and healthcare, but that traditional MARBs fail to capture. Limited previous work on Multi-action MARBs has only been specialized to sub-problems. Here we derive BLam, an algorithm for general Multi-action MARBs using Lagrangian relaxation techniques and convexity to quickly converge to good policies via bound optimization. We also provide experimental results comparing BLam to baselines on a simulated distributions motivated by a real-world community health intervention task, achieving up to five-times speedups over more general methods without sacrificing performance.
We propose and study Collapsing Bandits, a new restless multi-armed bandit (RMAB) setting in which each arm follows a binary-state Markovian process with a special structure: when an arm is played, the state is fully observed, thus “collapsing” any uncertainty, but when an arm is passive, no observation is made, thus allowing uncertainty to evolve. The goal is to keep as many arms in the “good” state as possible by planning a limited budget of actions per round. Such Collapsing Bandits are natural models for many healthcare domains in which health workers must simultaneously monitor patients and deliver interventions in a way that maximizes the health of their patient cohort. Our main contributions are as follows: (i) Building on the Whittle index technique for RMABs, we derive conditions under which the Collapsing Bandits problem is indexable. Our derivation hinges on novel conditions that characterize when the optimal policies may take the form of either “forward” or “reverse” threshold policies. (ii) We exploit the optimality of threshold policies to build fast algorithms for computing the Whittle index, including a closed form. (iii) We evaluate our algorithm on several data distributions including data from a real-world healthcare task in which a worker must monitor and deliver interventions to maximize their patients’ adherence to tuberculosis medication. Our algorithm achieves a 3-order-of-magnitude speedup compared to state-of-the-art RMAB techniques, while achieving similar performance.
Detection of burn marks due to wildfires in inaccessible rain forests is important for various disaster management and ecological studies. The fragmented nature of arable landscapes and diverse
cropping patterns often thwart the precise mapping
of burn scars. Recent advances in remote-sensing
and availability of multimodal data offer a viable
solution to this mapping problem. However, the
task to segment burn marks is difficult because of
its indistinguishably with similar looking land patterns, severe fragmented nature of burn marks and
partially labelled noisy datasets.
In this work we present AmazonNET – a convolutional based network that allows extracting of burn
patters from multimodal remote sensing images.
The network consists of UNet- a well-known encoder decoder type of architecture with skip connections. The proposed framework utilises stacked
RGB-NIR channels to segment burn scars from the
pastures by training on a new weakly labelled noisy
dataset from Amazonia.
Our model illustrates superior performance by correctly identifying partially labelled burn scars and
rejecting incorrectly labelled samples, demonstrating our approach as one of the first to effectively
utilise deep learning based segmentation models in
multimodal burn scar identification.