Workshop
Format
The track will include two presentation sections and a discussion that we refer to as Workshop. These aim at compiling and building upon the visions described in the discussion section of the works presented. We will focus on different dimensions of Computational Social Complexity: computational techniques, mapping multidimensional causation and expert input, using interventions at multiple scales, and communication of computational evidence for policy.
The conclusions of these discussions will be used to write a summary report on Computational Social Complexity.
Relevant Literature & Discussion Points
What are the Challenges of Computational Complexity? What are its boundaries?
Mapping Systems Complexity and Expert Input:
Given multi-domain multi-causation and heterogeneity, how to define system boundaries?
Cilliers, P. (2001). Boundaries, hierarchies and networks in complex systems. International Journal of Innovation Management, 5 (02), 135-147.
How to not reinvent the wheel and make sure we collect and capture domain expert knowledge?
Barbrook-Johnson, P., & Penn, A. S. (2022). Systems Mapping: How to build and use causal models of systems.
Hidden (multilayered) networks and data
The observed network may not be relevant for the behavior of interest.
The relevant network is not directly accessible and must be inferred and so do the types of interactions between agents
Peel, L., Peixoto, T. P., & De Domenico, M. (2022). Statistical inference links data and theory in network science. Nature Communications, 13(1), 1-15.
Rossi, E., Monti, F., Leng, Y., Bronstein, M., & Dong, X. (2022, June). Learning to Infer Structures of Network Games. In International Conference on Machine Learning (pp. 18809-18827). PMLR.
Decisionmakers may not have access to network data for privacy reasons; how does inference circumvent or trick the policy?
Predictability
"Researchers must reconcile the idea that they understand life trajectories with the fact that none of the predictions were very accurate"
Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., ... & McLanahan, S. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. Proceedings of the National Academy of Sciences, 117(15), 8398-8403.
Interventions
We want to move beyond understanding complex social systems towards acting on them.
Moore, G. F., Evans, R. E., Hawkins, J., Littlecott, H., Melendez-Torres, G. J., Bonell, C., & Murphy, S. (2019). From complex social interventions to interventions in complex social systems: future directions and unresolved questions for intervention development and evaluation. Evaluation, 25(1), 23-45.
Valente, T. W. (2012). Network interventions. science, 337(6090), 49-53
Policy agenda
Cost-Effectiveness/Affordability and acceptability of intervention are tested locally. Is it scalable? Or is it tailored locally? If you target the whole population the intervention is simple. With local communities, the complexities are important.
Castellani, B., Bartington, S., Wistow, J., Heckels, N., Ellison, A., Van Tongeren, M., ... & Reis, S. (2022). Mitigating the impact of air pollution on dementia and brain health: Setting the policy agenda. Environmental Research, 114362.
Other
Lazer, D. M., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., ... & Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060-1062.
Data can come from social media, profiling consumer preferences, or financial status. Phone calls, social media, field studies have dropping rates of participation (Chris Bail). Will data come from online environments exclusively?