CSSSA Webinar Series
- 09 November 2022: Bruce G. Miller presents “Improving Innovation Adoption Projections by Understanding Outgroup Aversion Effects in Network Environments.”
- 28 September 2022: Dr. N. Gizem Bacaksizlar Turbic presents “Group Threat, Political Extremity, and Collective Dynamics in Online Discussions.”
- 13 July 2022: Dr. Santiago Núñez-Corrales presents “A realistic ABM model for COVID-19 contagion in Urbana-Champaign, Fall 2020.”
- 11 May 2022: Dr. Aaron Bramson presents “Neighborhood Discovery via Augmented Network Community Structure.”
- 2 March 2022: Dr. Sascha Hokamp presents “In Memoriam of Kim Michael Bloomquist (1954-2021) – Agent-based Computational Simulations of Tax Evasion and Non-compliance.”
- 6 October 2021: CSSSA presents A roundtable discussion about the status and future of Agent-Based Modeling
- August 2021: Dr. Rebecca Law presents “State Antifragility: an Agent-Based Modeling Approach to Understanding State Behavior.”
- June 2021: Dr. D. Cale Reeves presents “Generating Policy Insight: A Computational Social Science Approach to Policy Analysis.”
Computational Social Science (CSS) is the science that investigates social and behavioral dynamics through social simulation, social network analysis, and social media analysis. The Computational Social Science Society of the Americas (CSSSA) is a professional society that aims to advance the field of computational social science in all its areas, including basic and applied orientations, by holding conferences and workshops, promoting standards of scientific excellence in research, teaching, and publishing research findings and results.
CSSSA has the goals of:
1) Improve the scientific credibility of computational social science.
2) Maintain an institutionally neutral society characterized by scientific integrity.
3) Promote the advancement of computational social science through scientific exchange, transparency, and open discussion.
Scientific progress requires scientists to build upon previous research and developments of their peers and give explicit credit to these peers — to foster trust and integrity. Publishing your research, providing transparency into your algorithms and computer models is strongly encouraged via the Open Agent-Based Modeling Consortium. In addition to providing an open repository for models and simulations OpenABM also provides a valuable forum for collaboration, learning, and networking.