Critical mass and discontinued use of social media

Using simulation, this study compares a critical mass of adopters with a critical mass of those who discontinue their adoption of social media. A network of reflex agents is simulated where each agent has an unchanging threshold and will adopt social media if the number of their friends who have adopted is greater than it. In the first study, the size of the critical mass that adopts is varied, and in the second, the size of the critical mass that discontinues use is varied. The studies show that a critical mass of leavers can cause a community to fail and that this mass can potentially be as small as that needed to influence a community to succeed; although given a certain critical mass, their leaving is less likely to cause failure than their adoption is success. This influence of the critical mass is facilitated by network structure

Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management

This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services

Technological developments since the Deepwater Horizon oil spill

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Dannreuther, N. M., Halpern, D., Rullkotter, J., & Yoerger, D. Technological developments since the Deepwater Horizon oil spill. Oceanography, 34(1), (2021): 192–211, https://doi.org/10.5670/oceanog.2021.126.The Gulf of Mexico Research Initiative (GoMRI) funded research for 10 years following the Deepwater Horizon incident to address five themes, one of which was technology developments for improved response, mitigation, detection, characterization, and remediation associated with oil spills and gas releases. This paper features a sampling of such developments or advancements, most of which cite studies funded by GoMRI but also include several developments that occurred outside this program. We provide descriptions of technological developments, including new techniques or the novel application or enhancement of existing techniques, related to studies of the subsurface oil plume, the collection of data on ocean currents, and oil spill modeling. Also featured are developments related to interactions of oil with particulate matter and microbial organisms, analysis of biogeochemical processes affecting oil fate, human health risks from inhalation of oil spill chemicals, impacts on marine life, and alternative dispersant technologies to Corexit®. Many of the technological developments featured here have contributed to complementary or subsequent research and have applications beyond oil spill research that can contribute to a wide range of scientific endeavors.This research was made possible by the Gulf of Mexico Research Initiative

Exploring Statistical and Population Aspects of Network Complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples

Algorithms and Software for the Analysis of Large Complex Networks

The work presented intersects three main areas, namely graph algorithmics, network science and applied software engineering. Each computational method discussed relates to one of the main tasks of data analysis: to extract structural features from network data, such as methods for community detection; or to transform network data, such as methods to sparsify a network and reduce its size while keeping essential properties; or to realistically model networks through generative models

Interpersonal Status Systems. An Inquiry into Social Networks and Status Dynamics in Schools, Science, and Hollywood

Status systems—vertical orders among persons according to differences in social recognition—are a ubiquitous feature of human societies. Vast streams of research developed to explore how status structures social life. This thesis proposes a unified framework for studying the interplay between social status and social networks. The framework highlights the importance of contextual characteristics for the emergence of status systems in various settings and complements approaches that focus on how individuals gain and perpetuate status. Theoretical expectations derived from this perspective are tested by applying a combination of exponential random graph models and other network-analytical tools to three different empirical settings. The first application investigates whether the structure of friendships and status ascriptions among more than 23,000 adolescents is sensitive to contextual characteristics such as the size or demographic composition of classrooms and grade levels. The second study examines collaboration networks among more than 7,000 neuroblastoma researchers over 40 years. Here, the investigation focuses on changes in the stratification and segregation of collaboration networks as a scientific field grows and matures. Similarly, the third study investigates the interplay between culture, status, and networks among Hollywood filmmakers from 1930 through 2000 by using information on artistic references and collaborations of more than 13,000 filmmakers retrieved from the Internet movie database (IMDb). The results illustrate that the link between status and networks intensifies under certain contextual conditions. One key finding is that larger contexts exhibit networks marked by status recognition in all empirical settings: larger school classes and grade levels produce leading crowds more often than smaller ones, the scientific field of neuroblastoma research developed an elite of researchers as it grew, and social recognition is distributed increasingly unequal during periods in which Hollywood attracted more filmmakers. The thesis closes by comparing the different settings in greater detail and by discussing directions for future research

Estimating credibility of science claims : analysis of forecasting data from metascience projects : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand

The veracity of scientific claims is not always certain. In fact, sufficient claims have been proven incorrect that many scientists believe that science itself is facing a “replication crisis”. Large scale replication projects provided empirical evidence that only around 50% of published social and behavioral science findings are replicable. Multiple forecasting studies showed that the outcomes of replication projects could be predicted by crowdsourced human evaluators. The research presented in this thesis builds on previous forecasting studies, deriving new findings and exploring new scope and scale. The research is centered around the DARPA SCORE (Systematizing Confidence in Open Research and Evidence) programme, a project aimed at developing measures of credibility for social and behavioral science claims. As part of my contribution to SCORE, myself, along with a international collaboration, elicited forecasts from human experts via surveys and prediction markets to predict the replicability of 3000 claims. I also present research on other forecasting studies. In chapter 2, I pool data from previous studies to analyse the performance of prediction markets and surveys with higher statistical power. I confirm that prediction markets are better at forecasting replication outcomes than surveys. This study also demonstrates the relationship between p-values of original findings and replication outcomes. These findings are used to inform the experimental and statistical design to forecast the replicability of 3000 claims as part of the SCORE programme. A full description of the design including planned statistical analyses is included in chapter 3. Due to COVID-19 restrictions, our generated forecasts could not be validated through direct replication, experiments conducted by other teams within the SCORE collaboration, thereby preventing results being presented in this thesis. The completion of these replications is now scheduled for 2022, and the pre-analysis plan presented in Chapter 3 will provide the basis for the analysis of the resulting data. In chapter 4, an analysis of ‘meta’ forecasts, or forecasts regarding field wide replication rates and year specific replication rates, is presented. We presented and published community expectations that replication rates will differ by field and will increase over time. These forecasts serve as valuable insights into the academic community’s views of the replication crisis, including those research fields for which no large-scale replication studies have been undertaken yet. Once the full results from SCORE are available, there will be additional insights from validations of the community expectations. I also analyse forecaster’s ability to predict replications and effect sizes in Chapters 5 (Creative Destruction in Science) and 6 (A creative destruction approach to replication: Implicit work and sex morality across cultures). In these projects a ‘creative destruction’ approach to replication was used, where a claim is compared not only to the null hypothesis but to alternative contradictory claims. I conclude forecasters can predict the size and direction of effects. Chapter 7 examines the use of forecasting for scientific outcomes beyond replication. In the COVID-19 preprint forecasting project I find that forecasters can predict if a preprint will be published within one year, including the quality of the publishing journal. Forecasters can also predict the number of citations preprints will receive. This thesis demonstrates that information about scientific claims with respect to replicability is dispersed within scientific community. I have helped to develop methodologies and tools to efficiently elicit and aggregate forecasts. Forecasts about scientific outcomes can be used as guides to credibility, to gauge community expectations and to efficiently allocate sparse replication resources