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Register a new userStay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations
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Illegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife protection are constrained by the limited resources of law enforcement agencies. To help combat poaching, the Protection Assistant for Wildlife Security (PAWS) is a machine learning pipeline that has been developed as a data-driven approach to identify areas at high risk of poaching throughout protected areas and compute optimal patrol routes. In this paper, we take an end-to-end approach to the data-to-deployment pipeline for anti-poaching. In doing so, we address challenges including extreme class imbalance (up to 1:200), bias, and uncertainty in wildlife poaching data to enhance PAWS, and we apply our methodology to three national parks with diverse characteristics. (i) We use Gaussian processes to quantify predictive uncertainty, which we exploit to improve robustness of our prescribed patrols and increase detection of snares by an average of 30%. We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia. (ii) We present the results of large-scale field tests conducted in Murchison Falls and Srepok Wildlife Sanctuary which confirm that the predictive power of PAWS extends promisingly to multiple parks. This paper is part of an effort to expand PAWS to 800 parks around the world through integration with SMART conservation software.
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In early clinical test evaluations the potential benefits of the introduction of a new technology into the healthcare system are assessed in the challenging situation of limited available empirical data. The aim of these evaluations is to provide additional evidence for the decision maker, who is typically a funder or the company developing the test, to evaluate which technologies should progress to the next stage of evaluation. In this paper we consider the evaluation of a diagnostic test for patients suffering from Chronic Obstructive Pulmonary Disease (COPD). We describe the use of graphical models, prior elicitation and uncertainty analysis to provide the required evidence to allow the test to progress to the next stage of evaluation. We specifically discuss inferring an influence diagram from a care pathway and conducting an elicitation exercise to allow specification of prior distributions over all model parameters. We describe the uncertainty analysis, via Monte Carlo simulation, which allowed us to demonstrate that the potential value of the test was robust to uncertainties. This paper provides a case study illustrating how a careful Bayesian analysis can be used to enhance early clinical test evaluations.
We consider the problem of designing policies for partially observable Markov decision processes (POMDPs) with dynamic coherent risk objectives. Synthesizing risk-averse optimal policies for POMDPs requires infinite memory and thus undecidable. To overcome this difficulty, we propose a method based on bounded policy iteration for designing stochastic but finite state (memory) controllers, which takes advantage of standard convex optimization methods. Given a memory budget and optimality criterion, the proposed method modifies the stochastic finite state controller leading to sub-optimal solutions with lower coherent risk.
Planning under model uncertainty is a fundamental problem across many applications of decision making and learning. In this paper, we propose the Robust Adaptive Monte Carlo Planning (RAMCP) algorithm, which allows computation of risk-sensitive Bayes-adaptive policies that optimally trade off exploration, exploitation, and robustness. RAMCP formulates the risk-sensitive planning problem as a two-player zero-sum game, in which an adversary perturbs the agents belief over the models. We introduce t
Background: Predicted probabilities from a risk prediction model are inevitably uncertain. This uncertainty has mostly been studied from a statistical perspective. We apply Value of Information methodology to evaluate the decision-theoretic implications of prediction uncertainty. Methods: Adopting a Bayesian perspective, we extend the definition of the Expected Value of Perfect Information (EVPI) from decision analysis to net benefit calculations in risk prediction. EVPI is the expected gain in net benefit by using the correct predictions as opposed to predictions from a proposed model. We suggest bootstrap methods for sampling from the posterior distribution of predictions for EVPI calculation using Monte Carlo simulations. In a case study, we used subsets of data of various sizes from a clinical trial for predicting mortality after myocardial infarction to show how EVPI can be interpreted and how it changes with sample size. Results: With a sample size of 1,000, EVPI was 0 at threshold values larger than 0.6, indicating there is no point in procuring more development data for such thresholds. At thresholds of 0.4-0.6, the proposed model was not net beneficial, but EVPI was positive, indicating that obtaining more development data might be justified. Across all thresholds, the gain in net benefit by using the correct model was 24% higher than the gain by using the proposed model. EVPI declined with larger samples and was generally low with sample sizes of 4,000 or greater. We summarize an algorithm for incorporating EVPI calculations into the commonly used bootstrap method for optimism correction. Conclusion: Value of Information methods can be applied to explore decision-theoretic consequences of uncertainty in risk prediction, and can complement inferential methods when developing or validating risk prediction models.
Stroke is a major cause of mortality and long--term disability in the world. Predictive outcome models in stroke are valuable for personalized treatment, rehabilitation planning and in controlled clinical trials. In this paper we design a new model to predict outcome in the short-term, the putative therapeutic window for several treatments. Our regression-based model has a parametric form that is designed to address many challenges common in medical datasets like highly correlated variables and class imbalance. Empirically our model outperforms the best--known previous models in predicting short--term outcomes and in inferring the most effective treatments that improve outcome.
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