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K. Carrie Armel

Researcher at Stanford University

Publications -  17
Citations -  3225

K. Carrie Armel is an academic researcher from Stanford University. The author has contributed to research in topics: Efficient energy use & Behavioral economics. The author has an hindex of 14, co-authored 17 publications receiving 2780 citations. Previous affiliations of K. Carrie Armel include University of California, San Diego.

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Visual fixations and the computation and comparison of value in simple choice

TL;DR: A computational model of value-based binary choice in which fixations guide the comparison process and it is found that the model can quantitatively explain complex relationships between fixation patterns and choices, as well as several fixation-driven decision biases.
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Projecting sensations to external objects: evidence from skin conductance response

TL;DR: These experiments demonstrate the malleability of body image and the brain's remarkable capacity for detecting statistical correlations in the sensory input.
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Is disaggregation the holy grail of energy efficiency? The case of electricity

TL;DR: In this article, a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or whole-building, energy signal is presented. And the authors explain how appliance-level data affords numerous benefits and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data.
Posted Content

Biasing simple choices by manipulating relative visual attention

TL;DR: In this paper, the authors present the results of three behavioral experiments testing the prediction that it is possible to affect real binary choices by manipulating the relative amount of visual attention that decision-makers pay to the two alternatives.
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Real-time Feedback and Electricity Consumption: A Field Experiment Assessing the Potential for Savings and Persistence

TL;DR: In this paper, the authors conducted a field experiment to obtain an estimate of the impact of a real-time feedback technology on residential energy consumption, and found that access to feedback leads to an average reduction in household electricity consumption of 5.7 percent.