Aggregate Quantities Research Articles

Morals in Multi-Unit Markets

Abstract We examine how the erosion of morals, norms, and norm compliance in markets depends on the market power of individual traders. Previously studied markets allow traders to exchange at most one unit and provide market power to individual traders by de-activating two forces: (i) the replacement logic, whereby immoral trading is justified by the belief that others would trade otherwise and (ii) market selection, by which the least moral trader determines aggregate quantities. In an experiment, we compare single-unit to (more common) multi-unit markets, which may activate these forces. Multi-unit markets, in contrast to single-unit markets, lead to a complete erosion of morals. This is associated primarily with a deterioration in norm compliance: the observed level of immoral trade is in contrast with the prevailing social norm. The replacement logic is the main mechanism driving this finding.

Difficulties in testing for capital overaccumulation

This paper reconsiders the question of testing for the presence of Pareto suboptimal capital overaccumulation in overlapping generations economies. The paper allows generation‐specific technology shocks to evolve over time according to a stationary Markov chain, and assumes that an econometrician observes a finite sample of aggregate quantities. In this setting, any statistical test of the null hypothesis of capital overaccumulation with size less than one also has zero power against the alternative hypothesis of a dynamically efficient steady state. This result means that the standard assessments of capital overaccumulation based on US aggregate quantity data should be viewed as inconclusive.

Brains in focus: Uncovering protein aggregate changes across Alzheimer’s disease with single‐molecule imaging

AbstractBackgroundOligomers are increasingly implicated in Alzheimer’s disease (AD)1. The characteristics of these protein aggregates remain largely unknown due to their low biological concentration, solubility, small size and structural heterogeneity. This study aims to better classify the diverse protein aggregates found in human brains across AD onset and progression.MethodsPost‐mortem flash‐frozen brain samples (n = 28) spanning the full range of AD stages (Braak 0‐VI) were obtained from London Neurodegenerative Diseases Brain Bank: two brain regions, middle temporal gyrus (MTG) and primary somatosensory cortex (SOM), were obtained from 14 donors. The post‐mortem tissues were homogenised to extract proteins into liquid fractions for single‐molecule studies. Antibodies, including 6E10, HT7, AT8 and SC‐12767, were selected to bind aggregates of amyloid‐β, tau, phosphorylated tau and α‐synuclein respectively. Ultra‐sensitive single‐molecule array (Simoa®) assays measured protein aggregate quantities in the homogenates. Single‐molecule pull‐down (SiMPull) assays, imaged by super‐resolution fluorescence microscopy, measured aggregate size and shape within the homogenates. BCA assays quantified the total protein content of the homogenates for normalisation.ResultsMorphological differences in tau aggregates were observed between disease stages with super‐resolution microscopy. Quantities of phosphorylated and total tau aggregates tend to increase significantly in the MTG and SOM from mid‐stage AD onwards according to Simoa® assays. In comparison to tau, aggregate quantities of amyloid‐β and α‐synuclein displayed less variation between non‐diseased controls and different AD stages.ConclusionsSingle‐molecule techniques can sensitively detect a broad range of protein aggregates from human brains. Novel differences in the morphologies and quantities of aggregates have been identified across AD stages. There is strong potential for uncovering the specific protein aggregates responsible for AD with these methods. This could facilitate earlier disease detection and more effective treatment targeting. 1 Hardy, J. & Selkoe, D. J. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 297, 353‐356 (2002).

Global Flight to Safety, Business Cycles, and the Dollar

We develop a two-country macroeconomic model that we fit to a set of aggregate prices and quantities for the U.S. and the rest of the world. In addition to a standard array of shocks, the model includes time variation in agents’ preference for safe bonds. We allow for a component of this time variation to be common across countries and biased toward dollar-denominated safe assets, and refer to this component as global flight to safety (GFS). We find that GFS shocks are the most important shocks driving world business cycles, and are also important drivers of activity in the U.S. and especially abroad. An adverse GFS shock lowers global GDP and inflation, widens global corporate credit spreads, and appreciates the dollar. These effects are very close to those obtained from a structural VAR which uses the excess bond premium (Gilchrist and Zakrajšek, 2012) as proxy for global flight to safety.

Confidence, Self-Selection, and Bias in the Aggregate

The influence of behavioral biases on aggregate outcomes depends in part on self-selection: whether rational people opt more strongly into aggregate interactions than biased individuals. In betting market, auction and committee experiments, we document that some errors are strongly reduced through self-selection, while others are not affected at all or even amplified. A large part of this variation is explained by differences in the relationship between confidence and performance. In some tasks, they are positively correlated, such that self-selection attenuates errors. In other tasks, rational and biased people are equally confident, such that self-selection has no effects on aggregate quantities. (JEL C91, D44, D91)

Government Spending, Debt Management, and Wealth and Income Inequality in a Growing Monetary Economy*

AbstractThis paper compares the impact of government investment and government consumption on macroeconomic aggregates and inequality when the government deficit is money‐financed while maintaining a fixed debt‐money ratio. Real aggregate quantities are independent of the debt‐money ratio, as is wealth inequality, but income inequality is impacted. We also investigate the impact of these two forms of government expenditure on the macroeconomic aggregates and distributions, illustrating their sharply contrasting effects on the tradeoffs they entail. While government investment is more effective in increasing the growth rate and moderating inflation, it has a more adverse effect on long‐run income inequality.

Kinetic Models for Epidemic Dynamics in the Presence of Opinion Polarization

Understanding the impact of collective social phenomena in epidemic dynamics is a crucial task to effectively contain the disease spread. In this work, we build a mathematical description for assessing the interplay between opinion polarization and the evolution of a disease. The proposed kinetic approach describes the evolution of aggregate quantities characterizing the agents belonging to epidemiologically relevant states and will show that the spread of the disease is closely related to consensus dynamics distribution in which opinion polarization may emerge. In the present modelling framework, microscopic consensus formation dynamics can be linked to macroscopic epidemic trends to trigger the collective adherence to protective measures. We conduct numerical investigations which confirm the ability of the model to describe different phenomena related to the spread of an epidemic.

Spatial aggregation with respect to a population distribution: Impact on inference

Spatial aggregation with respect to a population distribution involves estimating aggregate population quantities based on observations from individuals. In this context, a geostatistical workflow must account for three major sources of aggregation error: aggregation weights, fine scale variation, and finite population variation. However, these sources of aggregation error are commonly ignored, and the population instead treated as a fixed population density surface. We improve common practice by introducing a sampling frame model allowing aggregation models to account for aggregation error simply and transparently. This preserves aggregate point estimates while increasing their uncertainties.We compare the proposed and the traditional approach using two simulation studies mimicking neonatal mortality rate (NMR) data from the 2014 Kenya Demographic and Health Survey. In the traditional approach, undercoverage/overcoverage of interval estimates depends arbitrarily on the aggregation grid resolution, while the new approach is resolution robust. Differences between the aggregation approaches increase as an area’s population decreases, and are particularly large at the second administrative level and finer, but also at the first administrative level for some population quantities. These findings are consistent with those of an application to the true NMR data. We demonstrate in a sensitivity analysis that burden estimates and their uncertainties are not robust to changes in population density and census information, while prevalence estimates and uncertainties seem stable.

Identifying the temporal dynamics of densification and sparsification in human contact networks

Temporal social networks of human interactions are preponderant in understanding the fundamental patterns of human behavior. In these networks, interactions occur locally between individuals (i.e., nodes) who connect with each other at different times, culminating into a complex system-wide web that has a dynamic composition. Dynamic behavior in networks occurs not only locally but also at the global level, as systems expand or shrink due either to: changes in the size of node population or variations in the chance of a connection between two nodes. Here, we propose a numerical maximum-likelihood method to estimate population size and the probability of two nodes connecting at any given point in time. An advantage of the method is that it relies only on aggregate quantities, which are easy to access and free from privacy issues. Our approach enables us to identify the simultaneous (rather than the asynchronous) contribution of each mechanism in the densification and sparsification of human contacts, providing a better understanding of how humans collectively construct and deconstruct social networks.

Generalized likelihood ratio control charts for high‐purity (high‐quality) processes

AbstractGeneralized likelihood ratio (GLR) control charts are useful for tailor‐made monitoring strategies, but they are less developed for discrete processes. In this paper, the GLR control chart framework applied to aggregate cumulative quantities data is extended. Inspired by the technical note on GLR control charts from Lee and Woodall (2018), unnecessary artificial bounds in the GLR chart for geometric data proposed in literature are removed and parameter restriction errors, common in GLR designs, are corrected. Finally, the Gamma GLR chart for continuous‐time time‐between‐event data that can be modeled by a Poisson process is proposed and its performance are evaluated and compared to its traditional competitors.

Tracing transport of protein aggregates in microgravity versus unit gravity crystallization

Microgravity conditions have been used to improve protein crystallization from the early 1980s using advanced crystallization apparatuses and methods. Early microgravity crystallization experiments confirmed that minimal convection and a sedimentation-free environment is beneficial for growth of crystals with higher internal order and in some cases, larger volume. It was however realized that crystal growth in microgravity requires additional time due to slower growth rates. The progress in space research via the International Space Station (ISS) provides a laboratory-like environment to perform convection-free crystallization experiments for an extended time. To obtain detailed insights in macromolecular transport phenomena under microgravity and the assumed reduction of unfavorable impurity incorporation in growing crystals, microgravity and unit gravity control experiments for three different proteins were designed. To determine the quantity of impurity incorporated into crystals, fluorescence-tagged aggregates of the proteins (acting as impurities) were prepared. The recorded fluorescence intensities of the respective crystals reveal reduction in the incorporation of aggregates under microgravity for different aggregate quantities. The experiments and data obtained, provide insights about macromolecular transport in relation to molecular weight of the target proteins, as well as information about associated diffusion behavior and crystal lattice formation. Results suggest one explanation why microgravity-grown protein crystals often exhibit higher quality. Furthermore, results from these experiments can be used to predict which proteins may benefit more from microgravity crystallization.

Performance assessment of pervious concrete road on strength and permeability by using silica fume

Pervious concrete is recognized as an environmentally friendly material to meet the growing demands for pavement infrastructure. In this study, the influence of fine aggregate and coarse aggregate quantities on the properties of pervious concrete and performance characteristics of pervious concrete in terms of permeability, compressive strength, flexural strength, split tensile strength were carried out. Materials used are OPC Type- I of Grade 43, fine aggregate confirming gradation (Zone II) and coarse aggregate mix of 4.75–10 mm, 10–12.5 mm, 12.5–16 mm, 16–20 mm of 15%, 40%, 30% and 15% of the total weight of coarse aggregate respectively. Mixes were prepared with the water-cement ratio of 0.34 and maintaining aggregate-cement ratio as 3.5:1. Here, a total of five numbers mixes were prepared, out of which one for control mixture having M35 grade and remaining four mixtures were prepared by partial replacement of fine aggregated with coarse aggregate in the range of 70–100% by weight. It was observed from the study that with the increase of fine aggregates, various mechanical properties such that compressive strength, flexural strength, split tensile strength increase, and coefficient of permeability decrease. Also, the relationship between the strength, permeability, and total void present in graded aggregate based on angularity number was developed. As the concrete has high porosity, it has low strength as compared to impermeable concrete. To achieve better strength and permeability, a high-performance pervious concrete was developed using cement 43 grade, cementitious material like silica fume which replace the cement by 0%, 4%, 8%, 12%, and 16%, with sand (5%, 7%, and 9%), mentioned the size of coarse aggregate, a small quantity of fine aggregates, superplasticizer (Rheoplast), and a fixed water-cement ratio 0.34. This study represents a detailed overview of performance properties such as compressive strength, split tensile strength, flexural strength, and Cantabro abrasion resistance test at 7, 14, 28 days. The outcome of the experimental test showed that partial replacement of silica fume (4–12%) with cement exhibited better results in comparison with the control mixture.

Probabilistic Forecasts of Arctic Sea Ice Thickness

In recent decades, warming temperatures have caused sharp reductions in the volume of sea ice in the Arctic Ocean. Predicting changes in Arctic sea ice thickness is vital in a changing Arctic for making decisions about shipping and resource management in the region. We propose a statistical spatio-temporal two-stage model for sea ice thickness and use it to generate probabilistic forecasts up to three months into the future. Our approach combines a contour model to predict the ice-covered region with a Gaussian random field to model ice thickness conditional on the ice-covered region. Using the most complete estimates of sea ice thickness currently available, we apply our method to forecast Arctic sea ice thickness. Point predictions and prediction intervals from our model offer comparable accuracy and improved calibration compared with existing forecasts. We show that existing forecasts produced by ensembles of deterministic dynamic models can have large errors and poor calibration. We also show that our statistical model can generate good forecasts of aggregate quantities such as overall and regional sea ice volume. Supplementary materials accompanying this paper appear on-line.

Inequality, disaster risk, and the great recession

What drove large declines in aggregate quantities during the Great Recession? I study this question by building a dynamic stochastic overlapping generations economy in which households hold both low-return liquid and high-return illiquid assets. In this economy, I explore how aggregate quantities and the distribution of households respond to a recession driven by an increased risk of a further economic downturn.I find that an increase in disaster risk, and an empirically consistent fall in TFP, explain a significant fraction of the declines in aggregate consumption and investment observed during the Great Recession. Inequality is essential in driving these aggregate results. Comparing my model to an economy without illiquid assets, I show that household differences in both liquid and illiquid assets play a crucial role in amplifying the effects of an increase in disaster risk.

Model-based evaluation of cooling-off policies

This paper studies the ex-ante prediction and ex-post evaluation of the effects of cooling-off policies when consumers may exhibit a projection bias. We set up a theoretical model in which a firm optimally reacts to consumers' preferences and the regulatory framework and show that neither the adoption of a mandatory cooling-off period nor a return policy is generically superior or consumer welfare improving. We then analyze how market-level data can help to evaluate the policies ex post using baseline statistics. This exploits the firm's optimal response to a policy. With a return policy, data on quantities, return frequencies, and market size are sufficient to always assess the directional change in consumer welfare, while aggregate quantities alone are sufficient with a mandatory cooling off period. We discuss robustness of the model predictions and ex-post assessment to a variety of modifications, and discuss the benefits of the approach for policy design.

Government Spending, Budget Policy, and Wealth and Income Inequality in a Growing Monetary Economy

This paper compares the impact of government investment and government consumption expenditures on macroeconomic aggregates and inequality in an economy in which the government deficit is money-financed, while maintaining a fixed ratio of debt-money. Most real aggregate quantities are independent of the debt-money ratio, which has only weak short-run impacts on wealth and income inequality. We also investigate numerically the impact of these two forms of government expenditure on the macroeconomic aggregates and distributions. Though they have sharp contrasting effects in terms of the tradeoffs they entail, their impact is moderate. Our simulations suggests that there is scope for substantially more intensive government intervention in the recovery of the post-pandemic economy, without serious adverse consequences or encountering issues of sustainability.

Summaries of Articles

Summaries of Articles

Uncertainty damping in kinetic traffic models by driver-assist controls

<p style='text-indent:20px;'>In this paper, we propose a kinetic model of traffic flow with uncertain binary interactions, which explains the scattering of the fundamental diagram in terms of the macroscopic variability of aggregate quantities, such as the mean speed and the flux of the vehicles, produced by the microscopic uncertainty. Moreover, we design control strategies at the level of the microscopic interactions among the vehicles, by which we prove that it is possible to dampen the propagation of such an uncertainty across the scales. Our analytical and numerical results suggest that the aggregate traffic flow may be made more ordered, hence predictable, by implementing such control protocols in driver-assist vehicles. Remarkably, they also provide a precise relationship between a measure of the macroscopic damping of the uncertainty and the penetration rate of the driver-assist technology in the traffic stream.</p>

Double-sided queues with marked Markovian arrival processes and abandonment

In this paper, we study a double-sided queueing model with marked Markovian arrival processes and finite discrete abandonment times. We apply the theory of multi-layer Markov modulated fluid flow (MMFF) processes to analyze the queueing model. First, we define three age processes for the queueing system and convert them into a multi-layer MMFF process. Then we analyze the multi-layer MMFF process to find queueing performance measures related to the age processes, matching rates/probabilities, waiting times, and queue lengths for both sides of the queueing system. We obtain a number of aggregate quantities as well as quantities for individual types of inputs, which can be useful for the analysis and design of, for examples, passenger-taxi service systems and organ transplantation systems.

Recommender systems with selfish users

Recommender systems are a fundamental component of contemporary social-media platforms and require feedback submitted from users in order to fulfill their goal. On the other hand, the raise of advocacy about user-controlled data repositories supports the selective submission of data by user through intelligent software agents residing at the user end. These agents are endowed with the task of protecting user privacy by applying a “soft filter” on personal data provided to the system. In this work, we pose the question: “how should the software agent control the user feedback submitted to a recommender system in a way that is most privacy preserving, while the user still enjoys most of the benefits of the recommender system?”. We consider a set of such agents, each of which aims to protect the privacy of its serving user by submitting to the recommender system server a version of her real rating profile. The fact that issued recommendations to a user depend on the collective rating profiles by all agents gives rise to a novel game-theoretic setup that unveils the trade-off between privacy preservation of each user and the quality of recommendation they receive. Privacy is quantified through a distance metric between declared and an “initial” random rating profile; the latter is assumed to provide a “neutral” starting point for the disclosure of the real profile. We allow different users to have different perception of their privacy through a user-dependent utility function of this distance. The quality of recommendations for each user depends on submitted ratings of all users, including the ratings of the user to whom the recommendation is provided. We prove the existence of a Nash equilibrium point (NEP), and we derive conditions for that. We show that user strategies converge to the NEP after an iterative best-response strategy update sequence that involves circulation of aggregate quantities in the system and no revelation of real ratings. We also present various modes of user cooperation in rating declaration, by which users mutually benefit in terms of privacy. We evaluate and compare cooperative and selfish strategies in their performance in terms of privacy preservation and recommendation quality through real movie datasets.