The Network Picture of Labor Flow

We construct a data-driven model of flows in graphs that captures the essential elements of the movement of workers between jobs in the companies (firms) of entire economic systems such as countries. The model is based on the observation that certain job transitions between firms are often repeated over time, showing persistent behavior, and suggesting the construction of static graphs to act as the scaffolding for job mobility. Individuals in the job market (the workforce) are modelled by a discrete-time random walk on graphs, where each individual at a node can possess two states: employed or unemployed, and the rates of becoming unemployed and of finding a new job are node dependent parameters. We calculate the steady state solution of the model and compare it to extensive micro-datasets for Mexico and Finland, comprised of hundreds of thousands of firms and individuals. We find that our model possesses the correct behavior for the numbers of employed and unemployed individuals in these countries down to the level of individual firms. Our framework opens the door to a new approach to the analysis of labor mobility at high resolution, with the tantalizing potential for the development of full forecasting methods in the future.Comment: 27 pages, 6 figure

Frictional Unemployment on Labor Flow Networks

We develop an alternative theory to the aggregate matching function in which workers search for jobs through a network of firms: the labor flow network. The lack of an edge between two companies indicates the impossibility of labor flows between them due to high frictions. In equilibrium, firms' hiring behavior correlates through the network, generating highly disaggregated local unemployment. Hence, aggregation depends on the topology of the network in non-trivial ways. This theory provides new micro-foundations for the Beveridge curve, wage dispersion, and the employer-size premium. We apply our model to employer-employee matched records and find that network topologies with Pareto-distributed connections cause disproportionately large changes on aggregate unemployment under high labor supply elasticity

The cause of universality in growth fluctuations

Phenomena as diverse as breeding bird populations, the size of U.S. firms, money invested in mutual funds, the GDP of individual countries and the scientific output of universities all show unusual but remarkably similar growth fluctuations. The fluctuations display characteristic features, including double exponential scaling in the body of the distribution and power law scaling of the standard deviation as a function of size. To explain this we propose a remarkably simple additive replication model: At each step each individual is replaced by a new number of individuals drawn from the same replication distribution. If the replication distribution is sufficiently heavy tailed then the growth fluctuations are Levy distributed. We analyze the data from bird populations, firms, and mutual funds and show that our predictions match the data well, in several respects: Our theory results in a much better collapse of the individual distributions onto a single curve and also correctly predicts the scaling of the standard deviation with size. To illustrate how this can emerge from a collective microscopic dynamics we propose a model based on stochastic influence dynamics over a scale-free contact network and show that it produces results similar to those observed. We also extend the model to deal with correlations between individual elements. Our main conclusion is that the universality of growth fluctuations is driven by the additivity of growth processes and the action of the generalized central limit theorem.Comment: 18 pages, 4 figures, Supporting information provided with the source files

Changing How We Discount to Make Public Policy More Responsive To Citizens'Time Preferences

Laws and regulations have both benefits and costs, comparisons of which provide useful information. When such benefits and costs occur over time it is normal practice to discount them, with costs in the distant future considered less important than those occurring in the present, and distant benefits less valuable than present benefits. Conventionally, this discounting is done at a constant rate each period'so-called exponential discounting' with both cost and benefit streams being treated as if they were financial assets. However, humans are not exponential discounters. Rather, people tend to discount more than exponentially in the short run but less than exponentially in the long run. Such 'hyperbolic' discounting is empirically ubiquitous but neglected administratively. The recent "data quality act" requires that information utilized by Federal agencies "adhere to a basic standard of quality, including objectivity, utility and integrity." Here we argue that exponential discounting of many benefit streams, particularly non-pecuniary ones, fails the "data quality" test and should be abandoned in favor of empirically-observed hyperbolic discounting.

The role of B cells in primary progressive multiple sclerosis

The success of ocrelizumab in reducing confirmed disability accumulation in primary progressive multiple sclerosis (PPMS) via CD20-targeted depletion implicates B cells as causal agents in the pathogenesis of PPMS. This review explores the possible mechanisms by which B cells contribute to disease progression in PPMS, specifically exploring cytokine production, antigen presentation, and antibody synthesis. B cells may contribute to disease progression in PPMS through cytokine production, specifically GM-CSF and IL-6, which can drive naïve T-cell differentiation into pro-inflammatory Th1/Th17 cells. B cell production of the cytokine LT-α may induce follicular dendritic cell production of CXCL13 and lead indirectly to T and B cell infiltration into the CNS. In contrast, production of IL-10 by B cells likely induces an anti-inflammatory effect that may play a role in reducing neuroinflammation in PPMS. Therefore, reduced production of IL-10 may contribute to disease worsening. B cells are also capable of potent antigen presentation and may induce pro-inflammatory T-cell differentiation via cognate interactions. B cells may also contribute to disease activity via antibody synthesis, although it\u27s unlikely the benefit of ocrelizumab in PPMS occurs via antibody decrement. Finally, various B cell subsets likely promulgate pro- or anti-inflammatory effects in MS

The Network Composition of Aggregate Unemployment

We develop a theory of unemployment in which workers search for jobs through a network of firms, the labor flow network (LFN). The lack of an edge between two companies indicates the impossibility of labor flows between them due to high frictions. In equilibrium, firms' hiring behavior correlates through the network, modulating labor flows and generating aggregate unemployment. This theory provides new micro-foundations for the aggregate matching function, the Beveridge curve, wage dispersion, and the employer-size premium. Using employer-employee matched records, we study the effect of the LFN topology through a new concept: `firm-specific unemployment'

The Network Composition of Aggregate Unemployment

We develop a theory of unemployment in which workers search for jobs through a network of firms, the labor flow network (LFN). The lack of an edge between two companies indicates the impossibility of labor flows between them due to high frictions. In equilibrium, firms' hiring behavior correlates through the network, modulating labor flows and generating aggregate unemployment. This theory provides new micro-foundations for the aggregate matching function, the Beveridge curve, wage dispersion, and the employer-size premium. Using employer-employee matched records, we study the effect of the LFN topology through a new concept: `firm-specific unemployment'

Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices

Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley

A s the only social science that has access to data of sufficient duration to reveal long-term changes in patterned human behavior, archaeology traditionally has been concerned with describing and explaining how societies adapt and evolve in response to changing conditions. A major impediment to rigorous investigation in archaeology-the inability to conduct reproducible experiments-is one shared with certain other sciences, such as astronomy, geophysics, and paleontology. Computational modeling is providing a way around these difficulties. k Within anthropology and archaeology there has been a rapidly growing interest in so-called agent-based computational model