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1. Relationship between Delay Discounting and Risk Preference in Chimpanzees (Pan troglodytes) and Humans

   Keupp, Stefanie; Grueneisen, Sebastian; Warneken, Felix; Ludvig, Elliot Andrew; Melis, Alicia P. (July 2021, Proceedings of the Annual Meeting of the Cognitive Science Society)

   Adaptive decisions require that decision makers factor in the subjective values of different possible outcomes, and the probability of these outcomes occurring. Subjective values depend, among other things, on how far an outcome is away in time. This can be captured by assessing an individual’s delay discounting of different options. An individual’s risk preference also affects how attractive particular choice options appear to them. In humans, probability discounting and delay discounting are often related. People who show more risky behaviors also tend to be more impulsive and less patient. Based on such findings, single-process models of delay discounting and probability discounting have been suggested. In the current study, we tested if this relationship is equally present in chimpanzees, one of human’s closest extant evolutionary relatives. We presented 23 chimpanzees with a patience task and a risky-choice task. The patience task was designed to explicitly distinguish between delay preference and self-control (i.e., the ability to wait a given delay). Still, we found no strong correlations between risk and delay preferences. As this task has not been used with humans before, we implemented a computerized version and tested it in a sample of twenty adult participants. Initial results indicate that the task is well suited to capture patience, and it makes a promising candidate to be used in behavioral delay discounting experiments in humans. 

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2. Group Incentives for Environmental Protection and Natural Resource Management

   https://doi.org/10.1146/annurev-resource-111920-020235  

   Segerson, Kathleen (October 2022, Annual Review of Resource Economics)

   Group incentives can and have been used to address a range of environmental and resource problems. These schemes base individual penalties and/or rewards on the performance of a group of individuals or firms who contribute to the environmental or resource problem. The economics literature on team incentives and public goods, as well as the literature specifically on environmental and natural resource management, provides insights into the design of group incentives. This article reviews the literature on group incentives in the context of environmental protection and natural resource policy. This literature suggests that group incentives can be effective and even efficient as environmental policy tools. However, the outcomes under group incentives will likely depend on a combination of the policy design and the nature of the internal group interactions. Within-group interactions are likely to be particularly important when policies involve thresholds so that coordination is needed to reach a cooperative equilibrium. Expected final online publication date for the Annual Review of Resource Economics, Volume 14 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

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3. Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection

   Gao, Daiqi; Liu, Yufeng; Zeng, Donglin (August 2025, Journal of machine learning)

   zhu, Ji (Ed.)

   Dynamic treatment regimes or policies are a sequence of decision functions over multiple stages that are tailored to individual features. One important class of treatment policies in practice, namely multi-stage stationary treatment policies, prescribes treatment assignment probabilities using the same decision function across stages, where the decision is based on the same set of features consisting of time-evolving variables (e.g., routinely collected disease biomarkers). Although there has been extensive literature on constructing valid inference for the value function associated with dynamic treatment policies, little work has focused on the policies themselves, especially in the presence of high-dimensional feature variables. We aim to fill the gap in this work. Specifically, we first obtain the multi-stage stationary treatment policy by minimizing the negative augmented inverse probability weighted estimator of the value function to increase asymptotic efficiency. A penalty is applied on the policy parameters to select important feature variables. We then construct one-step improvements of the policy parameter estimators for valid inference. Theoretically, we show that the improved estimators are asymptotically normal, even if nuisance parameters are estimated at a slow convergence rate and the dimension of the feature variables increases with the sample size. Our numerical studies demonstrate that the proposed method estimates a sparse policy with a near-optimal value function and conducts valid inference for the policy parameters. 

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4. A Novel Class of Unfolding Models for Binary Preference Data

   https://doi.org/10.1017/pan.2024.11  

   Lei, Rayleigh; Rodríguez, Abel (January 2025, Political Analysis)

   Abstract We develop a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions, and are more flexible than alternative “unfolding” models previously introduced in the literature. We then use these models to estimate revealed preferences for legislators in the U.S. House of Representatives and justices on the U.S. Supreme Court. The results from these applications indicate that the new models provide superior complexity-adjusted performance to various alternatives and that the additional flexibility leads to preferences’ estimates that more closely match the perceived ideological positions of legislators and justices. 

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5. Policy Synthesis and Reinforcement Learning for Discounted LTL

   Alur, R; Bastani, O; Jothimurugan, K; Perez, M; Somenzi, F; Trivedi, A (July 2023, CAV 2023, LNCS 13964)

   Enea, C; Lal, A (Ed.)

   The difficulty of manually specifying reward functions has led to an interest in using linear temporal logic (LTL) to express objec- tives for reinforcement learning (RL). However, LTL has the downside that it is sensitive to small perturbations in the transition probabilities, which prevents probably approximately correct (PAC) learning without additional assumptions. Time discounting provides a way of removing this sensitivity, while retaining the high expressivity of the logic. We study the use of discounted LTL for policy synthesis in Markov decision processes with unknown transition probabilities, and show how to reduce discounted LTL to discounted-sum reward via a reward machine when all discount factors are identical. 

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