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ICDS panel discussion—AI for Scientific Discovery—features REI research associate Sarah Rajtmajer

Rock Ethics Institute research associate Sarah Rajtmajer will be presenting on a panel hosted by Penn State's Institute for Computational and Data Sciences tomorrow, 10/13, at noon.
by David Price Oct 12, 2020

Sarah RajtmajerRock Ethics Institute research associate Sarah Rajtmajer will be presenting on a panel hosted by Penn State’s Institute for Computational and Data Sciences (ICDS) tomorrow, 10/13/20, at noon. The discussion will center around methods and applications of artificial intelligence (AI) for scientific discovery.

For more information about the discussion, and to register, please go here.

Rajtmajer’s panel abstract:

Emergent collective behavior has been a major focus for researchers across the social and behavioral sciences for more than a century. Sociologists, psychologists and economists have proposed theories to explain group behaviors that cannot be understood as a sum of constituent parts. Rather, complex interactions amongst individuals give rise to novel and unexpected system-level organization and norms.

Further challenging our understanding of emergence, macro-social properties feedback to individual behaviors, reinforcing effects (immergence). Increased digital connectedness has accelerated and highlighted these phenomena, as we have witnessed striking examples of self-organization and crowd behavior powered by social media, from the emergence of altruistic norms in crises to radicalization and extremism.

In parallel, this same digital connectivity has furnished researchers with comprehensive behavioral traces of and detailed social ties at massive scale. This data, paired with recent innovations in AI, represents a disruptive opportunity to advance the science of collective behavior. Advances in AI will support critical cross-domain exploration and synthesis of the exceptionally broad and diverse literatures relevant to the enigmatic questions of collective behavior, while model-based machine learning can serve to meaningfully integrate longstanding theories of collective behavior into data-driven predictions.