Individual Learning About Consumption

The standard approach to modelling consumption/saving problems is to assume that the decisionmaker is solving a dynamic stochastic optimization problem However under realistic descriptions of utility and uncertainty the optimal consumption/saving decision is so difficult that only recently economists have managed to find solutions using numerical methods that require previously infeasible amounts of computation Yet empirical evidence suggests that household behavior conforms fairly well with the prescriptions of the optimal solution raising the question of how average households can solve problems that economists until recently could not This paper examines whether consumers might be able to find a reasonably good ’rule-of-thumb?approximation to optimal behavior by trial-and-error methods as Friedman (1953) proposed long ago We find that such individual learning methods can reliably identify reasonably good rules of thumb only if the consumer is able to spend absurdly large amounts of time searching for a good rule

Validating Predictions of Unobserved Quantities

The ultimate purpose of most computational models is to make predictions, commonly in support of some decision-making process (e.g., for design or operation of some system). The quantities that need to be predicted (the quantities of interest or QoIs) are generally not experimentally observable before the prediction, since otherwise no prediction would be needed. Assessing the validity of such extrapolative predictions, which is critical to informed decision-making, is challenging. In classical approaches to validation, model outputs for observed quantities are compared to observations to determine if they are consistent. By itself, this consistency only ensures that the model can predict the observed quantities under the conditions of the observations. This limitation dramatically reduces the utility of the validation effort for decision making because it implies nothing about predictions of unobserved QoIs or for scenarios outside of the range of observations. However, there is no agreement in the scientific community today regarding best practices for validation of extrapolative predictions made using computational models. The purpose of this paper is to propose and explore a validation and predictive assessment process that supports extrapolative predictions for models with known sources of error. The process includes stochastic modeling, calibration, validation, and predictive assessment phases where representations of known sources of uncertainty and error are built, informed, and tested. The proposed methodology is applied to an illustrative extrapolation problem involving a misspecified nonlinear oscillator

Context-dependent use of visual cues in the shell selection behaviour of the hermit crab Pagurus bernhardus

Animals avoid predator attack in different ways; some carry defensive structures to reduce predation, with the classic example being hermit crabs and their use of a mollusc shell as a portable refugium. During shell selection, various shell characteristics are investigated by the crab to determine their suitability. Here we consider the role of visual cues. Previous research suggests that some hermit crabs are more likely to initially choose a conspicuous shell but also to move to backgrounds against which they are less conspicuous, suggesting a short-term/long-term trade-off. Across experiments in which we manipulated shell and background colour, we show initially that Pagurus bernhardus prefer black shells over white but this preference was lost in the absence of visual cues. We then show that the strength of preference was dependent on background colour. We repeated this last experiment with red and yellow shells against red or yellow backgrounds to investigate whether this preference extended to chromatic hues. A preference for darker (red) shells was expressed, but preference alteration with background was not observed. P. bernhardus therefore discriminate between shells in terms of shell and background colour, and discrimination may be rooted in a preference for darker shaded shells.PostprintPeer reviewe

Srs2 promotes synthesis-dependent strand annealing by disrupting DNA polymerase δ-extending D-loops.

Synthesis-dependent strand annealing (SDSA) is the preferred mode of homologous recombination in somatic cells leading to an obligatory non-crossover outcome, thus avoiding the potential for chromosomal rearrangements and loss of heterozygosity. Genetic analysis identified the Srs2 helicase as a prime candidate to promote SDSA. Here, we demonstrate that Srs2 disrupts D-loops in an ATP-dependent fashion and with a distinct polarity. Specifically, we partly reconstitute the SDSA pathway using Rad51, Rad54, RPA, RFC, DNA Polymerase δ with different forms of PCNA. Consistent with genetic data showing the requirement for SUMO and PCNA binding for the SDSA role of Srs2, Srs2 displays a slight but significant preference to disrupt extending D-loops over unextended D-loops when SUMOylated PCNA is present, compared to unmodified PCNA or monoubiquitinated PCNA. Our data establish a biochemical mechanism for the role of Srs2 in crossover suppression by promoting SDSA through disruption of extended D-loops

Principles of small-scale aquaponics

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Comparative mitochondrial genomics of snakes: extraordinary substitution rate dynamics and functionality of the duplicate control region

<p>Abstract</p> <p>Background</p> <p>The mitochondrial genomes of snakes are characterized by an overall evolutionary rate that appears to be one of the most accelerated among vertebrates. They also possess other unusual features, including short tRNAs and other genes, and a duplicated control region that has been stably maintained since it originated more than 70 million years ago. Here, we provide a detailed analysis of evolutionary dynamics in snake mitochondrial genomes to better understand the basis of these extreme characteristics, and to explore the relationship between mitochondrial genome molecular evolution, genome architecture, and molecular function. We sequenced complete mitochondrial genomes from Slowinski's corn snake (<it>Pantherophis slowinskii</it>) and two cottonmouths (<it>Agkistrodon piscivorus</it>) to complement previously existing mitochondrial genomes, and to provide an improved comparative view of how genome architecture affects molecular evolution at contrasting levels of divergence.</p> <p>Results</p> <p>We present a Bayesian genetic approach that suggests that the duplicated control region can function as an additional origin of heavy strand replication. The two control regions also appear to have different intra-specific versus inter-specific evolutionary dynamics that may be associated with complex modes of concerted evolution. We find that different genomic regions have experienced substantial accelerated evolution along early branches in snakes, with different genes having experienced dramatic accelerations along specific branches. Some of these accelerations appear to coincide with, or subsequent to, the shortening of various mitochondrial genes and the duplication of the control region and flanking tRNAs.</p> <p>Conclusion</p> <p>Fluctuations in the strength and pattern of selection during snake evolution have had widely varying gene-specific effects on substitution rates, and these rate accelerations may have been functionally related to unusual changes in genomic architecture. The among-lineage and among-gene variation in rate dynamics observed in snakes is the most extreme thus far observed in animal genomes, and provides an important study system for further evaluating the biochemical and physiological basis of evolutionary pressures in vertebrate mitochondria.</p

An Evaluation of Lake Trout Suppression in Yellowstone Lake, Yellowstone National Park

Introduced lake trout (Salvelinus namaycush) threaten to extirpate native Yellowstone cutthroat trout (Oncorhynchus clarkii bouvieri) from Yellowstone Lake, Yellowstone National Park. A National Park Service gill netting program has removed nearly 400,000 lake trout from Yellowstone Lake since 1995. Lake trout population size has not been estimated; therefore, it is difficult to determine the proportion that has been removed. Our objectives were to (1) examine catch as a function of effort to determine if the suppression program has caused lake trout abundance to decline, (2) determine if certain population metrics have changed over time as a function of harvest, and (3) develop age-structured models to determine the level of mortality required to cause population growth rate to decline below 1.0 (replacement). Catch has continued to increase as a function of effort, indicating lake trout abundance is increasing. Population metrics were not clearly indicative of a response to harvest, but were comparable to North American lake trout populations where harvest has occurred. Results from an age-structured matrix model determined the rate of population growth was 1.1 given the current rate of fishing mortality and that population growth rate would be 1.3 in the absence of fishing mortality. The current rate of population growth is positive; however, it is slower than it would be in the absence of lake trout suppression. Fishing mortality needs to increase by at least 10 percent to reduce population growth rate below 1.0 in the future