Computational Social Science I-II

Tutorial on July 31st, 2011
2011 International Joint Conference on Neural Networks
San Jose, California
http://www.ijcnn2011.org/tutorial.php

Tutor:

Péter Érdi
Henry R. Luce Professor
Center for Complex Systems Studies
Kalamzoo College,
Kalamazoo, MI
Kalamazoo, MI 49006
http://people.kzoo.edu/~perdi/

and

Department of Biophysics
KFKI Research Institute for Particle and Nuclear Physics of the Hungarian Academy of Sciences,
Budapest
http://cneuro.rmki.kfki.hu/

COMPUTATIONAL SOCIAL SCIENCE I : SOCIODYNAMICS: FROM CONCEPTS TO DATA AND BACK

1. Towards a computational social science: from data mining via social simulations to predicitions
2. Sociodynamic models
2.1. Lessons learned from Tycho de Brahe, Keple and Newton
2.2. Growth process
2.3. From finite-time singularity to crash
2.4 Models of competition and cooperation
2.5. War dynamics
2.6. Dynamic models of drug propagation and control
2.7. Opinion dynamics
2.8. Economic cycles and chaos
2.9. Biological and social epidemics
2.10. Segregation models
2.11 Budget dynamics

3. Prediction of extreme events: scope and limits

4. Equation-based versus agent based simulations: concepts and tools

COMPUTATIONAL SOCIAL SCIENCE II: SOCIAL SYSTEMS AS COMPLEX INTERACTIVE NETWORKS

1. The place of network models is social dynamics
2. Lessons learned from sociologists
3. Network theory in retrospective: a concise review
4. Dynamic network models:
4.1. Propagation on networks,
4.2. Evolution of networks
5. The direct and inverse problem of evolving networks
6. Knowledge networks, innovation networks
7. Citation networks
8. Clustering of networks
9. Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network