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How does an economy’s capital respond to aggregate productivity shocks when firms make lumpy investments? We show that capital’s transitional dynamics are structurally linked to two steady-state moments: the dispersion of capital to productivity ratios—an indicator of capital misallocation—and the covariance of capital to productivity ratios with the time elapsed since their last adjustment—an indicator of asymmetric costs of upsizing and downsizing the capital stock. We compute these two sufficient statistics using data on the size and frequency of investment of Chilean plants. The empirical values indicate significant effects of aggregate productivity shocks and favor investment models with a strong downsizing rigidity and random opportunities for free adjustments.

Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state. Here we tested this idea by using a bidirectional BMI to investigate the gain in performance arising from using a state-dependent decoding algorithm. This BMI, implemented in anesthetized rats, controlled the movement of a dynamical system using neural activity decoded from motor cortex and fed back to the brain the dynamical system's position by electrically microstimulating somatosensory cortex. We found that using state-dependent algorithms that tracked the dynamics of ongoing activity led to an increase in the amount of information extracted form neural activity by 22%, with a consequently increase in all of the indices measuring the BMI's performance in controlling the dynamical system. This suggests that state-dependent decoding algorithms may be used to enhance BMIs at moderate computational cost.

In temperate countries, influenza outbreaks are well correlated to seasonal changes in temperature and absolute humidity. However, tropical countries have much weaker annual climate cycles, and outbreaks show less seasonality and are more difficult to explain with environmental correlations. Here, we use convergent cross mapping, a robust test for causality that does not require correlation, to test alternative hypotheses about the global environmental drivers of influenza outbreaks from country-level epidemic time series. By moving beyond correlation, we show that despite the apparent differences in outbreak patterns between temperate and tropical countries, absolute humidity and, to a lesser extent, temperature drive influenza outbreaks globally. We also find a hypothesized U-shaped relationship between absolute humidity and influenza that is predicted by theory and experiment, but hitherto has not been documented at the population level. The balance between positive and negative effects of absolute humidity appears to be mediated by temperature, and the analysis reveals a key threshold around 75 °F. The results indicate a unified explanation for environmental drivers of influenza that applies globally.

Defensive coloration that reduces the risk of predation is considered to be widespread in animals. Many closely related species adopt differing coloration strategies during the life cycle, including crypsis, conspicuousness, and ontogenic change between the two coloration types. Here, we use a dynamic state‐dependent approach to use ecological and intrinsic factors to predict the proportion of the developmental period of immature animals that should be spent as cryptic or conspicuous, and when conspicuous coloration should be reliably associated with investment in defenses. The model predicts that animals should change color more than once during development only in specific circumstances. In contrast, change from crypsis to conspicuous can occur over a range of conditions related to the frequency of detection by predators, but may also depend on the opportunity costs of crypsis and the effect of size on the deterrent effect of conspicuous coloration. We also report the results of a survey of coloration strategies in lepidopteron larvae, and note a qualitative agreement with the predictions of our model in the relationship between body size and coloration strategy. Our results provide explanations for several widespread antipredator coloration phenomena in prey animals, and provide a comprehensive predictive framework for the types of coloration strategies that are employed in nature.

Recent empirical work shows large consumption responses to house price movements. This is at odds with a prominent theoretical view which, using the logic of the permanent income hypothesis, argues that consumption responses should be small. We show that, in contrast to this view, workhorse models of consumption with incomplete markets calibrated to rich cross-sectional micro facts actually predict large consumption responses, in line with the data. To explain this result, we show that consumption responses to permanent house price shocks can be approximated by a simple and robust rule-of-thumb formula: the marginal propensity to consume out of temporary income times the value of housing. In our model, consumption responses depend on a number of factors such as the level and distribution of debt, the size and history of house price shocks, and the level of credit supply. Each of these effects is naturally explained with our simple formula.

The goal of this contribution is to formally present an incremental damage model conceived to predict failure of ductile materials in forming and crash applications. Denoted henceforth by the acronym Generalized Incremental Stress State Dependent Damage Model (GISSMO), the present model’s framework is based on an incremental damage accumulation which is dependent on a failure curve which, in turn, is a function of the current stress state. The damage variable is of scalar nature and inherently takes into account the effects of non-proportional loadings. Furthermore, GISSMO includes the evolution of an instability measure based on a critical strain. When this variable reaches unity, the coupling between the stress tensor and the damage variable is considered. This allows capturing the effects in post-critical regime macroscopically, from strain localization to final element erosion and crack formation. Since spurious mesh dependence is a concern when simulating material behavior up to fracture, a regularization strategy is proposed to compensate for the effects of mesh dependence in a global fashion. The aforementioned aspects of GISSMO are presented and discussed in detail in the present contribution as well as the calibration of the model based on experimental data of a dual-phase steel. It is shown that GISSMO is able to reproduce the fracture behavior of the calibrated material for several load paths.

Technological advances in recent years have seen an explosion of tracking and stable isotope studies of seabirds, often involving repeated measures from the same individuals. This wealth of new information has allowed the examination of the extensive variation among and within individuals in foraging and migration strategies (movements, habitat use, feeding behaviour, trophic status, etc.) in unprecedented detail. Variation is underpinned by key life-history or state variables such as sex, age, breeding stage and residual differences among individuals (termed 'individual specialization'). This variation has major implications for our understanding of seabird ecology, because it affects the use of resources, level of intra-specific competition and niche partitioning. In addition, it determines the responses of individuals and populations to the environment and the susceptibility to major anthropogenic threats. Here we review the effects of season (breeding vs. nonbreeding periods), breeding stage, breeding status, age, sex and individual specialization on foraging and migration strategies, as well as the consequences for population dynamics and conservation.

Several personality traits are mainly expressed in a social context, and others, which are not restricted to a social context, can be affected by the social interactions with conspecifics. In this paper, we focus on the recently proposed hypothesis that social niche specialization (i.e. individuals in a population occupy different social roles) can explain the maintenance of individual differences in personality. We first present ecological and social niche specialization hypotheses. In particular, we show how niche specialization can be quantified and highlight the link between personality differences and social niche specialization. We then review some ecological factors (e.g. competition and environmental heterogeneity) and the social mechanisms (e.g. frequency-dependent, state-dependent and social awareness) that may be associated with the evolution of social niche specialization and personality differences. Finally, we present a conceptual model and methods to quantify the contribution of ecological factors and social mechanisms to the dynamics between personality and social roles. In doing so, we suggest a series of research objectives to help empirical advances in this research area. Throughout this paper, we highlight empirical studies of social niche specialization in mammals, where available.

Neural responses to the same stimulus show significant variability over trials, with this variability typically reduced (quenched) after a stimulus is presented. This trial-to-trial variability (TTV) has been much studied, however how this neural variability quenching is influenced by the ongoing dynamics of the prestimulus period is unknown. Utilizing a human intracranial stereo-electroencephalography (sEEG) data set, we investigate how prestimulus dynamics, as operationalized by standard deviation (SD), shapes poststimulus activity through trial-to-trial variability (TTV). We first observed greater poststimulus variability quenching in those real trials exhibiting high prestimulus variability as observed in all frequency bands. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. Lastly, we replicate our findings in a separate EEG dataset and extend them by finding that trials with high prestimulus variability in the theta and alpha bands had faster reaction times. Together, our results demonstrate that stimulus-related activity, including its variability, is a blend of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period - the state at stimulus onset - with the second dwarfing the influence of the first.

Natural systems are often complex and dynamic (i.e. nonlinear), making them difficult to understand using linear statistical approaches. Linear approaches are fundamentally based on correlation. Thus, they are ill-posed for dynamical systems, where correlation can occur without causation, and causation may also occur in the absence of correlation. “Mirage correlation” (i.e., the sign and magnitude of the correlation change with time) is a hallmark of nonlinear systems that results from state dependency. State dependency means that the relationships among interacting variables change with different states of the system. In recent decades, nonlinear methods that acknowledge state dependence have been developed. These nonlinear statistical methods are rooted in state space reconstruction, i.e. lagged coordinate embedding of time series data. These methods do not assume any set of equations governing the system but recover the dynamics from time series data, thus called empirical dynamic modeling (EDM). EDM bears a variety of utilities to investigating dynamical systems. Here, we provide a step-by-step tutorial for EDM applications with rEDM, a free software package written in the R language. Using model examples, we aim to guide users through several basic applications of EDM, including (1) determining the complexity (dimensionality) of a system, (2) distinguishing nonlinear dynamical systems from linear stochastic systems, and quantifying the nonlinearity (i.e. state dependence), (3) determining causal variables, (4) forecasting, (5) tracking the strength and sign of interaction, and (6) exploring the scenario of external perturbation. These methods and applications can be used to provide a mechanistic understanding of dynamical systems.