From Santa Fe Institute Events Wiki
* Florian Artinger, Adaptive Behavior and Cognition, Max Planck Institute for Human Development (firstname.lastname@example.org) * Joslyn Barnhart, Political Science, UCLA (email@example.com) * Alexander Funcke, Evolutionary Culture, Stockholm University (firstname.lastname@example.org) * Thomas Grund, Sociology, Oxford (email@example.com) * Navid Hassanpour, Political Science, Yale (firstname.lastname@example.org) * Dana Jackman, Natural Resources, Michigan (email@example.com) * Jasmin Kominek, Social Science, U. of Hamburg (firstname.lastname@example.org) * Santiago Olivella, Political Science, Washington University (email@example.com) * Sasha Romanosky, Information Systems and Public Policy, Carnegie Mellon (firstname.lastname@example.org)
- Willemien Kets, Economics, SFI.
- John H. Miller (co- director), Economics, Carnegie Mellon University and SFI.
- Scott E. Page (co-director), Economics, U. of Michigan and SFI.
- Jon Wilkins, Theoretical Biology, SFI.
Each student began a research project during the two-week workshop. Below are brief descriptions of these various projects. These projects will form the basis for dissertation chapters and/or journal articles.
Randy Casstevens, Computational Social Science, George Mason University (email@example.com)
Randy is exploring the process of innovation in software development. He compares patterns of human-problem solving, as measured by a public MATLAB Programming Contest, and those that arise via evolutionary computation. The MATLAB contest explored programs that could solve a non-trivial peg solitaire game, and it had over 3000 submissions from over 100 programmers. The evolutionary computation problem was the Artificial Ant Problem from the genetic programming community. Both systems display punctuated equilibria in terms of overall performance metrics, had new efforts exploiting recently introduced innovations, and have some interesting similarities concerning measures of program complexity. One important parameter linked to the growth of program complexity is the amount of parsimony pressure applied to the evolving programs.