Understanding and Shifting Preferences for Battery Electric Vehicles

• URL: http://arxiv.org/abs/2202.08963v1
• Date: Wed, 9 Feb 2022 00:37:40 GMT
• Title: Understanding and Shifting Preferences for Battery Electric Vehicles
• Authors: Nikos Arechiga, Francine Chen, Rumen Iliev, Emily Sumner, Scott Carter, Alex Filipowicz, Nayeli Bravo, Monica Van, Kate Glazko, Kalani Murakami, Laurent Denoue, Candice Hogan, Katharine Sieck, Charlene Wu, Kent Lyons
• Abstract summary: We focus on methods for personalizing interventions based on an individual's demographics to shift the preferences of consumers to be more positive towards Battery Electric Vehicles (BEVs) To address this, we propose to identify personalized factors influencing BEV adoption, such as barriers and motivators. We present a method for predicting these factors and show that the performance is better than always predicting the most frequent factors.
• Score: 4.569915979977171
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying personalized interventions for an individual is an important task. Recent work has shown that interventions that do not consider the demographic background of individual consumers can, in fact, produce the reverse effect, strengthening opposition to electric vehicles. In this work, we focus on methods for personalizing interventions based on an individual's demographics to shift the preferences of consumers to be more positive towards Battery Electric Vehicles (BEVs). One of the constraints in building models to suggest interventions for shifting preferences is that each intervention can influence the effectiveness of later interventions. This, in turn, requires many subjects to evaluate effectiveness of each possible intervention. To address this, we propose to identify personalized factors influencing BEV adoption, such as barriers and motivators. We present a method for predicting these factors and show that the performance is better than always predicting the most frequent factors. We then present a Reinforcement Learning (RL) model that learns the most effective interventions, and compare the number of subjects required for each approach.

Related papers

• NextQuill: Causal Preference Modeling for Enhancing LLM Personalization [82.15961484963256]
  We introduce NextQuill, a novel personalization framework grounded in causal preference modeling.<n>Building on this insight, NextQuill introduces two complementary alignment strategies.<n> Experiments across multiple personalization benchmarks demonstrate that NextQuill significantly improves personalization quality.
  arXiv  Detail & Related papers  (2025-06-03T02:08:55Z)
• The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects [1.6114012813668932]
  We propose a conceptual framework based on an individual's latent proclivity to be influenced by an intervention. We then formalize conditions under which predictive scores serve as effective proxies for amenability. We show that, under plausible assumptions, predictive models can outperform causal effect estimators in ranking individuals by intervention effects.
  arXiv  Detail & Related papers  (2025-04-03T10:20:48Z)
• Leveraging Language Models and Bandit Algorithms to Drive Adoption of Battery-Electric Vehicles [38.38808309924463]
  We leverage prior work on understanding values motivating the adoption of battery electric vehicles. We develop conversational interventions that are personalized to the values of each study participant. We use a contextual bandit algorithm to learn to target values based on the demographics of each participant.
  arXiv  Detail & Related papers  (2024-10-30T18:15:43Z)
• Causal Post-Processing of Predictive Models [1.3124513975412255]
  We propose causal post-processing, a family of techniques for refining predictive scores to better align with causal effects.<n>We introduce three main CPP approaches -- monotonic post-processing, correction post-processing, and model-based post-processing.<n>Our findings highlight the advantages of integrating non-causal predictive models with experimental data, rather than treating them as competing alternatives.
  arXiv  Detail & Related papers  (2024-06-13T20:18:16Z)
• Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems [142.3424649008479]
  We show the uneven distribution of historical interactions and the biased training of recommender models. The key to debiased training lies in eliminating the effect of confounders that influence both the user's historical behaviors and the next behavior. We propose a causal multi-teacher distillation framework (CausalD) to address unobserved confounders.
  arXiv  Detail & Related papers  (2024-05-31T05:31:00Z)
• Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
  In practice, causal researchers do not have a single outcome in mind a priori. In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
  arXiv  Detail & Related papers  (2024-04-29T08:16:30Z)
• Evaluating Interventional Reasoning Capabilities of Large Language Models [58.52919374786108]
  Large language models (LLMs) are used to automate decision-making tasks. In this paper, we evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts.
  arXiv  Detail & Related papers  (2024-04-08T14:15:56Z)
• Using LLMs to Model the Beliefs and Preferences of Targeted Populations [4.0849074543032105]
  We consider the problem of aligning a large language model (LLM) to model the preferences of a human population. Modeling the beliefs, preferences, and behaviors of a specific population can be useful for a variety of different applications.
  arXiv  Detail & Related papers  (2024-03-29T15:58:46Z)
• DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding [50.81809690183755]
  Traffic accidents frequently lead to fatal injuries, contributing to over 50 million deaths until 2023. Previous research primarily assessed the importance of individual participants, treating them as independent entities. We introduce Driving scene Relationship self-Understanding transformer (DRUformer) to enhance the important object detection task.
  arXiv  Detail & Related papers  (2023-11-11T07:26:47Z)
• Zero-shot causal learning [64.9368337542558]
  CaML is a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task. We show that CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training.
  arXiv  Detail & Related papers  (2023-01-28T20:14:11Z)
• Inferring Effect Ordering Without Causal Effect Estimation [1.6114012813668932]
  Predictive models are often employed to guide interventions across various domains, such as advertising, customer retention, and personalized medicine. Our paper addresses the question of when and how these predictive models can be interpreted causally. We formalize two assumptions, full latent mediation and latent monotonicity, that are jointly sufficient for inferring effect ordering without direct causal effect estimation.
  arXiv  Detail & Related papers  (2022-06-25T02:15:22Z)
• Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities [80.37857025201036]
  Key challenge for robotic systems is to figure out the behavior of another agent. Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally. We propose equipping robots with the necessary tools to conduct observational studies on people.
  arXiv  Detail & Related papers  (2022-01-27T22:15:56Z)
• Variational Auto-Encoder Architectures that Excel at Causal Inference [26.731576721694648]
  Estimating causal effects from observational data is critical for making many types of decisions. One approach to address this task is to learn decomposed representations of the underlying factors of data. In this paper, we take a generative approach that builds on the recent advances in Variational Auto-Encoders.
  arXiv  Detail & Related papers  (2021-11-11T22:37:43Z)
• Power Constrained Bandits [46.44025793243983]
  We develop general meta-algorithms to modify existing algorithms. Our meta-algorithms are robust to various model mis-specifications possibly appearing in statistical studies.
  arXiv  Detail & Related papers  (2020-04-13T23:08:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.