Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods

The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order to minimize the expected value of a cost function, i.e., that humans are risk neutral. Yet, in practice, humans are often far from being risk neutral. To fill this gap, the objective of this paper is to devise a framework for risk-sensitive IRL in order to explicitly account for a human's risk sensitivity. To this end, we propose a flexible class of models based on coherent risk measures, which allow us to capture an entire spectrum of risk preferences from risk-neutral to worst-case. We propose efficient non-parametric algorithms based on linear programming and semi-parametric algorithms based on maximum likelihood for inferring a human's underlying risk measure and cost function for a rich class of static and dynamic decision-making settings. The resulting approach is demonstrated on a simulated driving game with ten human participants. Our method is able to infer and mimic a wide range of qualitatively different driving styles from highly risk-averse to risk-neutral in a data-efficient manner. Moreover, comparisons of the Risk-Sensitive (RS) IRL approach with a risk-neutral model show that the RS-IRL framework more accurately captures observed participant behavior both qualitatively and quantitatively, especially in scenarios where catastrophic outcomes such as collisions can occur.Comment: Submitted to International Journal of Robotics Research; Revision 1: (i) Clarified minor technical points; (ii) Revised proof for Theorem 3 to hold under weaker assumptions; (iii) Added additional figures and expanded discussions to improve readabilit

DECISION MAKING UNDER CATASTROPHIC RISK AND LEARNING: THE CASE OF THE POSSIBLE COLLAPSE OF THE WEST ANTARCTIC ICE SHEET

A collapse of the West-Antarctic Ice Sheet (WAIS) would cause a sea level rise of 5-6 metres, perhaps even within one hundred years, with catastrophic consequences. The probability of such a collapse is small but increasing with the rise of the atmospheric concentrations of greenhouse gas and the resulting climate change. This paper investigates how the potential collapse of the WAIS affects the optimal rate of greenhouse gas emission control. We design a decision and learning tree in which decision are made about emission reduction at regular intervals. At the same time, the decision makers receive new information on the probability of a WAIS collapse and the severity of its impacts. The probability of a WAIS collapse is endogenous and contingent on greenhouse gas concentrations. We solve this optimisation problem by backward induction. We find that a potential WAIS collapse substantially bring the date of the optimal emission reduction forward and increases its amount if the probability is high enough, if the impacts are high enough, or if the decision maker is risk averse enough. We also find that, as soon as a WAIS collapse is a foregone fact, emission reduction falls to free up resource to prepare for adapting to the inevitable.Decision making under uncertainty, West-Antarctic ice sheet

Factors Affecting Crop Insurance Purchase Decisions in Northern Illinois

When making crop insurance purchase decisions, farmers must consider multiple factors. This paper examines such factors through the use of a survey conducted in a 42 county region of Northern Illinois during 2005. Participants were asked who most influenced their crop insurance purchase decision and if the availability of a Premium Discount Plan (PDP) affected their decision. Respondents indicated that they generally made crop insurance purchase decisions independently, and that the availability of a PDP influenced about 25% of the decisions made. Questions about the importance of ten specific purchase factors were also asked in two distinct groups of five factors each. In one group of factors, price of the insurance was found to be more important than the probability of receiving a claim payment. The other group of factors revealed that government subsidization of premium and weather concerns were highly important to survey participants. Results have also been summarized according to the risk attitude of respondents. Crop insurance participation, plan and coverage level, and other demographic data were collected as well. Further analysis will be conducted to determine relationships between purchase decision factors and the characteristics of the respondents.Risk and Uncertainty,

Irreversible and Catastrophic

As many treaties and statutes emphasize, some risks are distinctive in the sense that they are potentially irreversible or catastrophic; for such risks, it is sensible to take extra precautions. When a harm is irreversible, and when regulators lack information about its magnitude and likelihood, they should purchase an "option" to prevent the harm at a later date; the Irreversible Harm Precautionary Principle. This principle brings standard option theory to bear on environmental law and risk regulation. And when catastrophic outcomes are possible, it makes sense to take special precautions against the worst-case scenarios; the Catastrophic Harm Precautionary Principle. This principle is based on two foundations: an appreciation of people's failure to appreciate the expected value of truly catastrophic losses; and an understanding of the distinction between risk and uncertainty. The Irreversible Harm Precautionary Principle must, however, be applied with a recognition that irreversible harms are sometimes on all sides of social problems, and that such harms may be caused by regulation itself. The Catastrophic Harm Precautionary Principle must be applied with an understanding that in some cases, eliminating the worst-case scenario causes far more serious problems than it solves. The normative arguments are illustrated throughout with reference to the problem of global warming; other applications include injunctions in environmental cases, genetic modification of food, protection of endangered species, and terrorism.

Employee Health Insurance Decisions In a Flexible Benefits Environment

Empirical investigations of flexible benefits plans, an increasingly popular type of plan that allows employees to choose among multiple benefits options, have been limited. This study investigates hypotheses relating to the determinants of employees\u27 choices among six different health insurance options under a flexible benefits plan. Using employee-specific selection and demographic data provided by a large firm, we estimate a logistic regression model to analyze the effects of employee and plan characteristics on choice of health care plan. Results suggest that health plan decisions are significantly influenced by option premium, deductible and coinsurance amounts, and by employees\u27 age, gender, salary, and marital status. The results are considered within an expected utility maximization model

Ethics of the scientist qua policy advisor: inductive risk, uncertainty, and catastrophe in climate economics

This paper discusses ethical issues surrounding Integrated Assessment Models (IAMs) of the economic effects of climate change, and how climate economists acting as policy advisors ought to represent the uncertain possibility of catastrophe. Some climate economists, especially Martin Weitzman, have argued for a precautionary approach where avoiding catastrophe should structure climate economists’ welfare analysis. This paper details ethical arguments that justify this approach, showing how Weitzman’s “fat tail” probabilities of climate catastrophe pose ethical problems for widely used IAMs. The main claim is that economists who ignore or downplay catastrophic risks in their representations of uncertainty likely fall afoul of ethical constraints on scientists acting as policy advisors. Such scientists have duties to honestly articulate uncertainties and manage (some) inductive risks, or the risks of being wrong in different ways

Earthquake economics

Earthquakes ; Insurance, Disaster

The Case for Limited Auditor Liability - The Effects of Liability Size on Risk Aversion and Ambiguity Aversion

Both the US and the EU consider limiting auditor liability in order to ensure the viability of the audit market, but fear its potentially negative impact on audit quality. Our paper discusses the existing empirical results on this topic in the auditing and behavioral economics literature, and provides new evidence based on a controlled laboratory experiment. Our experiment involves real losses and allows for direct inference of behaviour under limited and unlimited liability in situations of ambiguous liability risk. Our findings imply that limited liability can induce an efficient level of audit effort, while unlimited liability induces an inefficiently high level of audit effort. This paper contributes to the literature on auditor liability, as well behavioral economics research in general, by addressing recent controversial issues on behavior in the presence of ambiguity and real losses.