Apply for the Santa Fe Institute 2017 Graduate Workshop in Computational Social Science Modeling and Complexity – Deadline 14 February 2017

January 16, 2017January 17, 2017 Tom Briggscomplexity science, computational social science, conference, CSS, network science, Santa Fe Institute, social network analysis

In the summer of 2016, I had the good fortune to be selected along with nine other students to attend the 2016 Graduate Workshop in Computational Social Science and Modeling (GWCSS) at the Santa Fe Institute in Santa Fe, NM. Carnegie Mellon Professor John H. Miller and University of Michigan Professor Scott E. Page were our excellent Santa Fe faculty guides for the workshop, which included a combination of lectures on complexity science, complex systems, and computational modeling by Professors Miller and Page, as well as other Santa Fe faculty including Chris Kempes, Simon DeDeo, and Mirta Galesic.

The workshop was a two-week, residential intensive in beautiful Santa Fe. Concurrently, SFI runs a four-week Complex Systems Summer School which brings additional notables in the field of complexity and computational social science, including economist Brian Arthur, Robert Boyd whose 1988 book on cultural evolution coauthored by Peter J. Richerson has been cited more than 7000 times, and Doyne Farmer. As a GWCSS attendee, I was able to attend any of the Summer School lectures I could slip away for.

Lectures and discussions were wide-ranging but all anchored in the science of complexity and computational modeling as tools of scientific investigation. A sampling of our discussions in the GWCSS:

* model diversity (Scott Page)
* evolutionary computation (John Miller)
* information theory and conflict (Simon DeDeo)
* network structure and performance (Mirta Galesic)

I was incredibly lucky to attend the 2016 workshop with a distinguished cohort of fellow graduate students, from whom I learned a great deal during conversation, discussion, and spirited debate over the course of our two weeks together.

During the 2016 GWCSS, I began work on an agent-based model to investigate the role of personality variables in situations in a work setting (view project on ResearchGate). This work continues.

I am grateful to have had the opportunity to attend the 2016 Graduate Workshop in Computational Social Science and recommend it highly.

If you have an interest in complexity science and computational social science, consider applying for either the 2017 GWCSS or the 2017 Complex Systems Summer School at the Santa Fe Institute.

Please note that the application deadline for the 2017 GWCSS is 14 February 2017.

More information about the 2016 workshop I attended, including a reading list, is available from the 2016 website and wiki.

Getting Started with Agent-Based Modeling (ABM)

July 12, 2016July 12, 2016 Tom BriggsABM, agent-based modeling, computational modeling, computational social science, CSS, NetLogo, SNA, social network analysis

A colleague recently asked how to get started with agent-based modeling (ABM).

It’s never been easier to learn ABM, whether you’re a social scientist, physical scientist, engineer, computer scientist, or from any discipline, really.

If you want to start right this minute, the very best thing to do is to head over to Uri Wilensky’s NetLogo website, download NetLogo (available for any OS) free of charge, and then work through the three learning tutorials available under “Learning NetLogo” in the User Manual.

The first tutorial is titled “Models” and, as its title suggests, introduces you to interacting with existing NetLogo models such as the Wolf-Sheep Predation model of an ecosystem.

The second tutorial is titled “Commands” and takes you a bit deeper in issuing commands to the NetLogo interface.

The third tutorial is titled “Procedures” and walks you through building a model from scratch – writing the necessary NetLogo code to implement a basic agent-based model.

After the three tutorials, the NetLogo website encourages reading through the guides available in the NetLogo documentation (Interface, Info Tab, Programming) and making use of the NetLogo Dictionary, a comprehensive index of NetLogo methods, procedures, and keywords.

What’s great about NetLogo is that it is fairly intuitive and “programming” or “coding” in NetLogo is very quickly learned, making a first agent-based model possible in a very short time.

If you prefer using a textbook as a guide, my recommendation is Uri Wilensky and Bill Rand’s Introduction to Agent-Based Modeling (find in a library), which uses NetLogo and includes companion code and models to run through all of the essentials of agent-based modeling.

Please see my review of Wilensky and Rand’s Introduction to Agent-Based Modeling for more detail on the book – which is excellent – and what it covers.

If you want to get started with ABM, download NetLogo today.

A short 2009 video describing NetLogo and some capabilities:

A small pitfall in film actor social network analysis using IMDB data

March 1, 2016March 1, 2016 Tom Briggscomputational modeling, CSS, data science, network science, SNA, social network analysis

This is a short post on a minor but consequential pitfall of social network analyses of film actors.

One thing that has always bothered me about social network analysis of so-called “actor networks” using data from IMDB is the very simple fact that these analyses are based on the assumption that because two actors appear in the same film, they know each other.

This is simply not true.

Modern filmmaking techniques and the high cost of actor set time incentivizes filmmakers not to have expensive actors on set at the same time unless absolutely necessary. Instead, stand-ins are often used in place of star actors–especially in dialogue scenes–and footage is later edited to put the two star actors together in the finished product.

So, in theory, two actors can appear in the same film and even in the same scenes but never actually be on set together. Extrapolating, two actors could appear in the same film and never actually meet.

I’ve been waiting to find a solid example and finally found one.

Robert Rodriguez (@Rodriguez), the writer-director-producer best known for his films Sin City, From Dusk Til Dawn, Once Upon a Time in Mexico, and Spy Kids, was interviewed on the Tim Ferriss Show and described exactly this situation occurring during Sin City. Rodriguez describes Sin City as one of the most rapidly-executed projects he ever worked on, from initial concept and collaboration with Frank Miller to actually shooting the film in a matter of months. In fact, Rodriguez describes shooting scenes for Sin City with actor Mickey Rourke, in which Rodriguez or another crew member would stand in for the villain who at that time hadn’t been cast. Rutger Hauer was later cast and the complementary footage was shot for the scenes. According to Rodriguez, Rourke and Hauer claim they never met, despite appearing together in a Sin City scene in which Rourke’s character appears to have his hands on Hauer’s throat.

The lesson is what every good data scientist and computational modeler should always keep in mind: justify all assumptions and always include or at least consult subject-matter experts who know the system and data being studied!