Corporate Average Fuel Economy Standards and the Market for New Vehicles

This paper presents an overview of the economics literature on the effect of Corporate Average Fuel Economy (CAFE) standards on the new vehicle market. Since 1978, CAFE has imposed fuel economy standards for cars and light trucks sold in the U.S. market. This paper reviews the history of the standards, followed by a discussion of the major upcoming changes in implementation and stringency. It describes strategies that firms can use to meet the standards and reviews the CAFE literature as it applies to the new vehicle market. The paper concludes by highlighting areas for future research in light of the upcoming changes to CAFE.CAFE, costs, structural estimation

On the instrumental value of hypothetical and counterfactual thought.

People often engage in “offline simulation”, considering what would happen if they performed certain actions in the future, or had performed different actions in the past. Prior research shows that these simulations are biased towards actions a person considers to be good—i.e., likely to pay off. We ask whether, and why, this bias might be adaptive. Through computational experiments we compare five agents who differ only in the way they engage in offline simulation, across a variety of different environment types. Broadly speaking, our experiments reveal that simulating actions one already regards as good does in fact confer an advantage in downstream decision making, although this general pattern interacts with features of the environment in important ways. We contrast this bias with alternatives such as simulating actions whose outcomes are instead uncertain

Private sector responses to public investments and policy reforms: The case of fertilizer and maize market development in Kenya

millions fed, food security, Fertilizer, maize, Liberalization market,

A Revised Exoplanet Yield from the Transiting Exoplanet Survey Satellite (TESS)

The Transiting Exoplanet Survey Satellite (TESS) has a goal of detecting small planets orbiting stars bright enough for mass determination via ground-based radial velocity observations. Here we present estimates of how many exoplanets the TESS mission will detect, physical properties of the detected planets, and the properties of the stars that those planets orbit. This work uses stars drawn from the TESS Input Catalog Candidate Target List and revises yields from prior studies that were based on Galactic models. We modeled the TESS observing strategy to select approximately 200,000 stars at 2-minute cadence, while the remaining stars are observed at 30-min cadence in full-frame image data. We placed zero or more planets in orbit around each star, with physical properties following measured exoplanet occurrence rates, and used the TESS noise model to predict the derived properties of the detected exoplanets. In the TESS 2-minute cadence mode we estimate that TESS will find 1250+/-70 exoplanets (90% confidence), including 250 smaller than 2 Earth-radii. Furthermore, we predict an additional 3100 planets will be found in full-frame image data orbiting bright dwarf stars and more than 10,000 around fainter stars. We predict that TESS will find 500 planets orbiting M-dwarfs, but the majority of planets will orbit stars larger than the Sun. Our simulated sample of planets contains hundreds of small planets amenable to radial velocity follow-up, potentially more than tripling the number of planets smaller than 4 Earth-radii with mass measurements. This sample of simulated planets is available for use in planning follow-up observations and analyses.Comment: Accepted for publication in ApJS. Table 2 is available in machine-readable format from https://doi.org/10.6084/m9.figshare.613767

Extracting low-dimensional psychological representations from convolutional neural networks

Deep neural networks are increasingly being used in cognitive modeling as a means of deriving representations for complex stimuli such as images. While the predictive power of these networks is high, it is often not clear whether they also offer useful explanations of the task at hand. Convolutional neural network representations have been shown to be predictive of human similarity judgments for images after appropriate adaptation. However, these high-dimensional representations are difficult to interpret. Here we present a method for reducing these representations to a low-dimensional space which is still predictive of similarity judgments. We show that these low-dimensional representations also provide insightful explanations of factors underlying human similarity judgments.Comment: Accepted to CogSci 202

Cohort postponement and period measures

We introduce a new class of models in which demographic behavior such as fertility is postponed by differing amounts depending only on cohort membership. We show how this model fits into a general framework of period and cohort postponement that includes the existing models in the literature, notably those of Bongaarts and Feeney and Kohler and Philipov. The cohort-based model shows the effects of cohort shifts on period fertility measures and provides an accompanying tempo-adjusted measure of period total fertility in the absence of observed shifts. Simulation reveals that when postponement is governed by cohorts, the cohort-based indicator outperforms the Bongaarts and Feeney model that is now in widespread use. The cohort-based model is applied to fertility in several modern populations.

How slowing senescence changes life expectancy

Mortality decline has historically been a result of reductions in the level of mortality at all ages. The slope of mortality increase with age has been remarkably stable. A number of leading researchers on aging, however, suggest that the next revolution of longevity increase will be the result of slowing down the rate of aging, lessening the rate at which mortality increases as we get older. In this paper, we show mathematically how varying the pace of senescence influences life expectancy. We provide a formula that holds for any baseline hazard function. Our result is analogous to Keyfitz's "entropy" relationship for changing the level of mortality. Interestingly, the influence of the shape of the baseline schedule on the effect of senescence changes is the complement of that found for level changes. We also provide a generalized formulation that mixes level and slope effects.

A Conjoint Analysis of Public Preferences for Agricultural Land Preservation

Public preferences for the nonmarket services of permanently preserved agricultural land are measured and compared using conjoint analysis. The results from a survey of 199 Delawareans suggest environmental and nonmarket-agricultural services are the most important preserved-land attributes. Results also suggest that open space associated with wetlands on farms is neither an amenity nor a disamenity. On the margin, preserved parcels with agricultural and environmental attributes provide net benefits, which may exceed $1,000,000 for a 1,000-acre parcel. Preserved forestland provides benefits per acre that are statistically equivalent to cropland, though forestland may be less expensive to preserve.Land Economics/Use,

Modeling Human Categorization of Natural Images Using Deep Feature Representations

Over the last few decades, psychologists have developed sophisticated formal models of human categorization using simple artificial stimuli. In this paper, we use modern machine learning methods to extend this work into the realm of naturalistic stimuli, enabling human categorization to be studied over the complex visual domain in which it evolved and developed. We show that representations derived from a convolutional neural network can be used to model behavior over a database of >300,000 human natural image classifications, and find that a group of models based on these representations perform well, near the reliability of human judgments. Interestingly, this group includes both exemplar and prototype models, contrasting with the dominance of exemplar models in previous work. We are able to improve the performance of the remaining models by preprocessing neural network representations to more closely capture human similarity judgments.Comment: 13 pages, 7 figures, 6 tables. Preliminary work presented at CogSci 201