The National Socio-Environmental Synthesis Center (SESYNC), located in Annapolis, MD, invites applications for three summer short courses in Bayesian modeling, spatial agent-based modeling, and social network analysis.
Bayesian Modeling for Socio-Environmental Data
Application Deadline: March 15, 2018
The National Socio-Environmental Synthesis Center (SESYNC) will host a nine-day short course May 29 - June 8, 2018 covering basic principles of using Bayesian models to gain insight from data. The goals of the course are to:
- Provide a principles-based understanding of Bayesian methods needed to train students, evaluate papers and proposals, and solve research problems.
- Communicate the statistical concepts and vocabulary needed to foster collaboration between ecologists, social scientists, and statisticians.
- Provide the conceptual foundations and quantitative confidence needed for self-teaching modern analytic. methods.
Short Course Details:
- The course is aimed at postdoc, researcher, and faculty participants. Grad students may also be considered.
- There is no fee to attend, but participants are responsible for most of their own travel and accommodations.
- Applications are due no later than March 15, 2018, at 5 p.m. Eastern Time (ET).
- Selected participants will be notified by March 31, 2018.
- Visit sesync.us/bayes2018 for more information and to apply.
Social Network Analysis
Application Deadline: March 30, 2018
This 5-day short course, which will take place from July 16 - 20, 2018, will serve as an introduction to the theory and practice of social network analysis (SNA). Where standard statistical analysis assumes that observations on different entities (people, organizations, animals, etc.) are independent, SNA looks to the relationships among these observations to try to explain why this configuration of relationships might exist, or how this network structure explains other attributes of the network. While network science has a long tradition, this field has recently exploded with new data resources in social media and new computational methods, particularly in the application to socio-environmental systems.
- What is network data? What are the problems in collecting it? What kinds of questions can we use it to answer? How is it different from other data?
- structural & locational properties of actors/locations/resources (centrality, prestige, & prominence to determine popular resources, organizations, etc.), structural cohesion (subgroups & cliques), equivalence of actors (structural equivalence & block models to determine niche differentiation or social isomorphism), local analyses (dyadic & triadic analysis, brokerage to determine structural hierarchies and key resources or actors)
- matrix permutation tests, conditional uniform random graphs, network autocorrelation models, introduction to statistical global analyses (p1, p*, ERGMs, & their relatives), temporal models
Visit sesync.us/sna2018 to learn more and apply.
Introduction to Spatial Agent-Based Modeling
Application Deadline: April 2, 2018
This 5-day short course, June 11 - 15, 2018, will serve as an introduction to the theory and practice of spatially-explicit agent-based modeling (ABM). You will learn the essential theoretical background and technical expertise needed to conceptualize, build, and analyze your first ABM. This course will guide you through the basic phases of the ABM research process: formulating a research question, specifying a model, creating a simulation and interpreting the output. The course combines lectures with hands-on model-building sessions where you will build a model using NetLogo to acquire basic and intermediate programming skills. More advanced students are welcome to build a model in a programming language of their choice.
- Essential background: What are S-E systems, and why are ABMs one of the best tools for understanding them?
- Building blocks of spatial processes: forces of attraction and segregation, individual mobile entities, and processes of spread.
- Building blocks of computational social science: objective functions, decision-making theories and models, social networks, and agent learning.
- Designing and building an ABM: model development objectives and best practices, including using the standardized Overview, Design concepts, and Details (ODD) protocol for model documentation.
- Code version control with git repositories
- Interfacing models with spatial databases, harmonizing spatial and non-spatial data for model parameterization.
- Concepts and methods for model evaluation: explanation versus prediction, multifinality and equifinality, outcome and structural accuracy, pattern-oriented modeling.
Learn more about the short course and apply at sesync.us/sabm2018.