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Changes in total surplus and deadweight loss are traditional measures of economic welfare. We propose necessary and sufficient conditions for rationalizing consumer demand data with a quasilinear utility function. Under these conditions, consumer surplus is a valid measure of consumer welfare. For nonmarketed goods, we propose necessary and sufficient conditions on market data for efficient production , i.e. production at minimum cost. Under these conditions we derive a cost function for the nonmarketed good, where producer surplus is the area above the marginal cost curve.

A novel method for quantitative echocardiographic interpretations is introduced based on the calculation of ratio indices in which each raw M-mode measurement is divided by the aortic root dimension (Ao). "Aorta-based" indices were calculated with the animal's measured aortic root dimension (Ao(m)) as the length standard. Conversely, "weight-based" indices employed an idealized estimate of aortic dimension (Ao(w)) with a weighted least squares linear regression against the cube root of body weight (Ao(w) = kW(1/3)). Use of these indices circumvented undesirable statistical characteristics inherent in linear regression of echocardiographic dimensions against body weight and, to a lesser extent, body surface area. Compared with the regressions, ratio indices resulted in substantial refinement of the predictive range for each M-mode measurement in dogs, particularly with decreasing body size. Weight-based indices outperformed aorta-based indices in this regard. To refine the predictive range, neither type of index was clearly advantageous in cats compared with the simple average method typically employed for that species. Several of the raw M-mode measurements, however, were correlated with body weight in cats and horses, indicating the need for an appropriate correction for body size in these species. The ratio index method was suitable for this purpose. Summary statistics derived from normal dogs (n = 53), cats (n = 32), and horses (n = 17) are presented for each index, including novel clinical indices calculated from area ratios. The latter were designed to represent body size-adjusted lett ventricular stroke area (ie, volume overload) and myocardial wall area (ie, hypertrophy).

Optimism-bias is inconsistent with the independence of decision weights and payo¤s found in models of choice under risk, such as expected utility theory and prospect theory. Hence, to explain the evidence suggesting that agents are optimistically biased, we propose an alternative model of risky choice, a¤ective decision-making, where decision weights-which we label a¤ective or perceived risk-are endogenized. A¤ective decision making (ADM) is a strategic model of choice under risk, where we posit two cognitive processes: the "rational" and the "emotional" processes. The two processes interact in a simultaneous-move intrapersonal potential game, and observed choice is the result of a pure strategy Nash equilibrium in this potential game. We show that regular ADM potential games have an odd number of locally unique pure strategy Nash equilibria, and demonstrate this …nding for a¤ective decision making in insurance markets. We prove that ADM potential games are refutable, by axiomatizing the ADM potential maximizers.

Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While location error can often be ignored in coarsely sampled data, fine-scale data require much more care, and tools to do this have been lacking. Current approaches to dealing with location error largely fall into two categories—either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. Unfortunately, both of these approaches have serious flaws. Here, we provide a general framework to account for location error in the analysis of animal tracking data, so that their potential can be unlocked. We apply our error-model-selection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices are used to track a wide range of animal species comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. Then, using empirical data on tracked individuals from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. We furthermore demonstrate how error-informed analyses on calibrated tracking data can be necessary to ensure that estimates are accurate and insensitive to location error, and allow researchers to use all of their data. Because error-induced biases depend on so many factors—sampling schedule, movement characteristics, tracking device, habitat, etc.—differential bias can easily confound biological inference and lead researchers to draw false conclusions.

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