Rationally Biased Learning

Are human perception and decision biases grounded in a form of rationality? You return to your camp after hunting or gathering. You see the grass moving. You do not know the probability that a snake is in the grass. Should you cross the grass - at the risk of being bitten by a snake - or make a long, hence costly, detour? Based on this storyline, we consider a rational decision maker maximizing expected discounted utility with learning. We show that his optimal behavior displays three biases: status quo, salience, overestimation of small probabilities. Biases can be the product of rational behavior

Prioritizing Invasive Species Threats Under Uncertainty

Prioritizing exotic or invasive pest threats in terms of agricultural, environmental, or human health damages is an important resource allocation issue for programs charged with preventing or responding to the entry of such organisms. Under extreme uncertainty, program managers may decide to research the severity of threats, develop prevention or control actions, and estimate cost-effectiveness in order to provide better information and more options when making decisions to choose strategies for specific pests. We examine decision rules based on the minimax and relative cost criteria in order to express a cautious approach for decisions regarding severe, irreversible consequences, discuss the strengths and weaknesses of these rules, examine the roles of simple rules and sophisticated analyses in decision making, and apply a simple rule to develop a list of priority plant pests.invasive species, decision criteria, uncertainty, Resource /Energy Economics and Policy,

ActiveRemediation: The Search for Lead Pipes in Flint, Michigan

We detail our ongoing work in Flint, Michigan to detect pipes made of lead and other hazardous metals. After elevated levels of lead were detected in residents' drinking water, followed by an increase in blood lead levels in area children, the state and federal governments directed over $125 million to replace water service lines, the pipes connecting each home to the water system. In the absence of accurate records, and with the high cost of determining buried pipe materials, we put forth a number of predictive and procedural tools to aid in the search and removal of lead infrastructure. Alongside these statistical and machine learning approaches, we describe our interactions with government officials in recommending homes for both inspection and replacement, with a focus on the statistical model that adapts to incoming information. Finally, in light of discussions about increased spending on infrastructure development by the federal government, we explore how our approach generalizes beyond Flint to other municipalities nationwide.Comment: 10 pages, 10 figures, To appear in KDD 2018, For associated promotional video, see https://www.youtube.com/watch?v=YbIn_axYu9

A New Decision Support Framework for Managing Foot-and-mouth Disease Epidemics

Animal disease epidemics such as the foot-and-mouth disease (FMD) pose recurrent threat to countries with intensive livestock production. Efficient FMD control is crucial in limiting the damage of FMD epidemics and securing food production. Decision making in FMD control involves a hierarchy of decisions made at strategic, tactical, and operational levels. These decisions are interdependent and have to be made under uncertainty about future development of the epidemic. Addressing this decision problem, this paper presents a new decision-support framework based on multi-level hierarchic Markov processes (MLHMP). The MLHMP model simultaneously optimizes decisions at strategic, tactical, and operational levels, using Bayesian forecasting methods to model uncertainty and learning about the epidemic. As illustrated by the example, the framework is especially useful in contingency planning for future FMD epidemic

Risk and Uncertainty in Environmental Economics: From Theory to Policy

A lack of awareness and understanding of risk and uncertainty can lead to poor decision making and higher costs for policy providers, as not accounting for them may produce policy which is inflexible and with a negative effect on welfare. Further, misunderstanding of and/or failure to account for risk and uncertainty can inhibit research and development for policy to which environmental economics can contribute (for example, in developing effective measures of sustainability). The aim of this project is to develop guidelines for ‘Best Practice’ approaches to risk and uncertainty in environmental economics for guiding policy development and implementation, taking into account key issues such as costs, irreversibility, adaptation and dynamics. These guidelines are developed by examining the frameworks commonly used by environmental economists to account for risk and uncertainty (such as the Precautionary Principle and Cost Benefit Analysis) as well as specifically developed theories (e.g. Quiggin’s Rank Dependent Utility Theory), borrowing from other disciplines (e.g. Prospect Theory) and drawing attention to lesser known ideas (e.g. Shackle’s Model).Environmental Economics and Policy,

The Effects of Framing, Risk, and Uncertainty on Contributions toward a Public Account: Experimental Evidence

This paper uses laboratory evidence from four strategically equivalent voluntary contribution games to evaluate differences in contributions toward a public account due to framing, risk, and uncertainty. I test four hypotheses. (1) Individuals contribute more to a public account when the dilemma is framed as the mitigation of a public loss than the provision of a public good. (2) Individuals contribute more to a public account when the loss is certain than when faced with the risk of a loss. (3) Individuals contribute more to a public account when the loss is certain than when environmental uncertainty is associated with the public loss. (4) Individuals contribute more to a public account when the probability of loss is known than when the probability of loss is unknown. I find that contributions are greatest when the dilemma is framed as the mitigation of a certain public loss. Contributions diminish when environmental risk and uncertainty are introduced, but remain higher than for public good provision. Preliminary laboratory evidence suggests that government intervention may be more necessary in the provision of a public good than in the mitigation of a public bad. Furthermore, much of the debate surrounding optimal allocations of insurance and infrastructure investment seems to be the result of environmental uncertainty as opposed to strategic uncertainty

Integrating Learning from Examples into the Search for Diagnostic Policies

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which is the sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeoff between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO* algorithm to solve this MDP. To make AO* efficient, the paper describes an admissible heuristic that enables AO* to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of overfitting. To reduce overfitting, regularizers are integrated into the search algorithms. Finally, the paper compares the proposed methods on five benchmark diagnostic data sets. The studies show that in most cases the systematic search methods produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on todays desktop computers