Home | Abstracts | Accommodation | Call for Papers | Details | Keynote | Organizing Committee | Photos | Presentations | Program | Sponsors | Venue
Archived page — originally published at https://igssworkshop.wordpress.com/call-for-papers/. Archived on 25 July 2026 with permission.
Call for Papers
2021 Call for Papers
Extended Abstracts submission closed. We hope to have you join us for iGSS 2022!
Extended Abstracts submission closed. We hope to have you join us for iGSS 2022!
In January of 2020 the first workshop in IGSS explored the definition and scope of this emerging field of Generative Social Science. IGSS focuses primarily on new ways to discover and develop agent-based models. The agent-based model is recognized as the principal scientific instrument of generative social science, the (necessity) motto of which is, “if you didn’t grow it, you didn’t explain it.” In other words, given an unexplained observed macroscopic social pattern—a wealth distribution, a disease time series, a spatial segregation pattern—we seek a micro-to-macro account. Specifically, we design agents (the micro-scale) intended to generate the macro target, assessing the fit between model-generated and real-world macro structures by means of statistics. This method of agents has been successfully applied in many spheres, from epidemiology to anthropology to economics. Typically, however, agent modelers handcraft the agents, and in particular, the agents’ rules of behavior. Even when a particular model (e.g., the artificial Anasazi model) succeeds in growing the target, it is only one explanatory candidate, leaving open several questions: Is this solution unique? Harking back to the motto of ABM, there may be many ways to grow it!
Can we find a more complete set of rules? Relatedly, how robust is the solution to a small change in the agent rules? If we could discover a “neighborhood of” agent models whose members all generate the target, the result would seem less ad hoc and unstable. These are the core concerns of the nascent field of Inverse Generative Social Science. The essential difference from traditional ABM is this: Rather than craft entire agents, to encode a space of possible agent constituents (rules, parameters) and permissible mathematical and logical concatenations, and search this large space for the fittest agent architectures using Genetic Programming, Decision Trees, Causal State Modeling, Associative Rules and other techniques from Machine Learning and AI. Agents thus become outputs of the model, standing the prevailing “paradigm” on its head. If the vision of Inverse Generative Social Science is achieved, we believe it can be a watershed for agent-based modeling and for the social and biological sciences more generally.
Building on the success of the first IGSS workshop (igss-workshop.org), and on the IGSS Panel at the 2020 European Social Simulation Association, we will be hosting the second iGSS Workshop in the Spring of 2021 (June 8 – 10 2021). This will be a virtual conference, and we invite submissions for consideration for inclusion in this conference. Submissions should consist of an extended abstract of 500 words or fewer that describe the presentation. Similar to the first conference, we are hoping to allow an extended presentation for each presenter to encourage complete discussion in this nascent field. Please submit by March 1st on EasyChair: https://easychair.org/conferences/?conf=igss2021