Quantitative and empirical demonstration of the Matthew effect in a study of career longevity

The Matthew effect refers to the adage written some two-thousand years ago in the Gospel of St. Matthew: "For to all those who have, more will be given." Even two millennia later, this idiom is used by sociologists to qualitatively describe the dynamics of individual progress and the interplay between status and reward. Quantitative studies of professional careers are traditionally limited by the difficulty in measuring progress and the lack of data on individual careers. However, in some professions, there are well-defined metrics that quantify career longevity, success, and prowess, which together contribute to the overall success rating for an individual employee. Here we demonstrate testable evidence of the age-old Matthew "rich get richer" effect, wherein the longevity and past success of an individual lead to a cumulative advantage in further developing his/her career. We develop an exactly solvable stochastic career progress model that quantitatively incorporates the Matthew effect, and validate our model predictions for several competitive professions. We test our model on the careers of 400,000 scientists using data from six high-impact journals, and further confirm our findings by testing the model on the careers of more than 20,000 athletes in four sports leagues. Our model highlights the importance of early career development, showing that many careers are stunted by the relative disadvantage associated with inexperience.Comment: 13 pages, 7 figures, 4 Tables; Revisions in response to critique and suggestions of referee

Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model

Multi-agent geographical models integrate very large numbers of spatial interactions. In order to validate those models large amount of computing is necessary for their simulation and calibration. Here a new data processing chain including an automated calibration procedure is experimented on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimise three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. The model is thus a useful tool for further multiple applications on empirical historical situations

Identification of DNA Motifs Implicated in Maintenance of Bacterial Core Genomes by Predictive Modeling

Bacterial biodiversity at the species level, in terms of gene acquisition or loss, is so immense that it raises the question of how essential chromosomal regions are spared from uncontrolled rearrangements. Protection of the genome likely depends on specific DNA motifs that impose limits on the regions that undergo recombination. Although most such motifs remain unidentified, they are theoretically predictable based on their genomic distribution properties. We examined the distribution of the “crossover hotspot instigator,” or Chi, in Escherichia coli, and found that its exceptional distribution is restricted to the core genome common to three strains. We then formulated a set of criteria that were incorporated in a statistical model to search core genomes for motifs potentially involved in genome stability in other species. Our strategy led us to identify and biologically validate two distinct heptamers that possess Chi properties, one in Staphylococcus aureus, and the other in several streptococci. This strategy paves the way for wide-scale discovery of other important functional noncoding motifs that distinguish core genomes from the strain-variable regions

Interplanetary Trajectory Optimization with Automated Fly-By Sequences

Critical aspects of spacecraft missions, such as component organization, control algorithms, and trajectories, can be optimized using a variety of algorithms or solvers. Each solver has intrinsic strengths and weaknesses when applied to a given optimization problem. One way to mitigate limitations is to combine different solvers in an island model that allows these algorithms to share solutions. The program Spacecraft Trajectory Optimization Suite (STOpS) is an island model suite of heterogeneous and homogeneous Evolutionary Algorithms (EA) that analyze interplanetary trajectories for multiple gravity assist (MGA) missions. One limitation of STOpS and other spacecraft trajectory optimization programs (GMAT and Pygmo/Pagmo) is that they require a defined encounter body sequence to produce a constant length set of design variables. Early phase trajectory design would benefit from the ability to consider problems with an undefined encounter sequence as it would provide a set of diverse trajectories -- some of which might not have been considered during mission planning. The Hybrid Optimal Control Problem (HOCP) and the concept of hidden genes are explored with the most common EA, the Genetic Algorithm (GA), to compare how the methods perform with a Variable Size Design Space (VSDS). Test problems are altered so that the input to the cost function (the object being optimized) contains a set of continuous variables whose length depends on a corresponding set of discrete variables (e.g. the number of planet encounters determines the number of transfer time variables). Initial testing with a scalable problem (Branin\u27s function) indicates that even though the HOCP consistently converges on an optimal solution, the expensive run time (due to algorithm collaboration) would only escalate in an island model system. The hidden gene mechanism only changes how the GA decodes variables, thus it does not impact run time and operates effectively in the island model. A Hidden Gene Genetic Algorithm ( HGGA) is tested with a simplified Mariner 10 (EVM) problem to determine the best parameter settings to use in an island model with the GTOP Cassini 1 (EVVEJS) problem. For an island model with all GAs there is improved performance when the different base algorithm settings are used. Similar to previous work, the model benefits from migration of solutions and using multiple algorithms or islands. For spacecraft trajectory optimization programs that have an unconstrained fly-by sequence, the design variable limits have the largest impact on the results. When the number of potential fly-by sequences is too large it prevents the solver from converging on an optimal solution. This work demonstrates HGGA is effective in the STOpS environment as well as with GTOP problems. Thus the hidden gene mechanism can be extended to other EAs with members containing design variables that function similarly. It is shown that the tuning of the HGGA is dependent on the specific constraints of the spacecraft trajectory problem at hand, thus there is no need to further explore the general capabilities of the algorithm

Innovation and Corporate Dynamics: A Theoretical Framework

We provide a detailed analysis of a generalized proportional growth model (GPGM) of innovation and corporate dynamics that encompasses the Gibrat’s Law of Proportionate Effect and the Simon growth process as particular instances. The predictions of the model are derived in terms of (i) firm size distribution, (ii) the distribution of firm growth rates, and (iii-iv) the relationships between firm size and the mean and variance of firm growth rates. We test the model against data from the worldwide pharmaceutical industry and find its predictions to be in good agreement with empirical evidence on all four dimensions.Business firm size; firm growth distribution; Gibrat’s Law; Pareto distribution; lognormal distribution, size-variance relationship.

Innovation and corporate dynamics: a theoretical framework

We provide a detailed analysis of a generalized proportional growth model (GPGM) of innovation and corporate dynamics that encompasses the Gibrat’s Law of Proportionate Effect and the Simon growth process as particular instances. The predictions of the model are derived in terms of (i) firm size distribution, (ii) the distribution of firm growth rates, and (iii-iv) the relationships between firm size and the mean and variance of firm growth rates. We test the model against data from the worldwide pharmaceutical industry and find its predictions to be in good agreement with empirical evidence on all four dimensions

Runaway GC evolution in gerbil genomes

Recombination increases the local GC-content in genomic regions through GC-biased gene conversion (gBGC). The recent discovery of a large genomic region with extreme GC-content in the fat sand rat Psammomys obesus provides a model to study the effects of gBGC on chromosome evolution. Here, we compare the GC-content and GC-to-AT substitution patterns across protein-coding genes of four gerbil species and two murine rodents (mouse and rat). We find that the known high-GC region is present in all the gerbils, and is characterised by high substitution rates for all mutational categories (AT-to-GC, GC-to-AT and GC-conservative) both at synonymous and nonsynonymous sites. A higher AT-to-GC than GC-to-AT rate is consistent with the high GC-content. Additionally, we find more than 300 genes outside the known region with outlying values of AT-to-GC synonymous substitution rates in gerbils. Of these, over 30% are organised into at least 17 large clusters observable at the megabase-scale. The unusual GC-skewed substitution pattern suggests the evolution of genomic regions with very high recombination rates in the gerbil lineage, which can lead to a runaway increase in GC-content. Our results imply that rapid evolution of GC-content is possible in mammals, with gerbil species providing a powerful model to study the mechanisms of gBGC