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Research & Resources
momacs gives researchers, industry leaders, policymakers, and nonprofits a way to collaborate and solve our nation’s biggest challenges.
momacs gives researchers,
industry leaders, policymakers, and
nonprofits a way to collaborate
and solve our nation’s
biggest challenges.
Tools
Momacs is a unifying force, providing researchers from varying areas of expertise with a platform and the common, unifying language of systems science around which to collaborate. In doing so, we invite new voices into the process, allowing science to ask better questions, build better models, and arrive at better solutions to the world’s most complex challenges.
-University of Pittsburgh
OCCAM is a pilot project that takes the first steps to develop an open-access repository that permits sharing artifacts and experimental results among a broad group of stakeholders in computer architecture.
-University of Pittsburgh
FRED (a Framework for Reconstructing Epidemic Dynamics) is a freely available open-source agent-based modeling system based closely on models used in previously published studies of pandemic influenza.
-Harvard University
INDRA (the Integrated Network and Dynamical Reasoning Assembler) assembles information about causal mechanisms into a common format that can be used to build several different kinds of predictive and explanatory models.
-University of Arizona
The Computational Language Understanding (CLU)
Lab at University of Arizona is a team of faculty, students, and research programmers who work together to build systems that extract meaning from natural language texts and other computational linguistics problems.
Process & Work
-Paul R. Cohen
Although agent-based modeling (ABM) is promising and widely used, agent-based curation is surprisingly primitive: With the notable exception of population synthesis methods, there are no algorithms to create large-scale ABMs semi-automatically, and ABM development frameworks make no contact with modern curation technologies such as ontologies, machine reading and machine learning. Worse, the semantics of ABMs are murky, in part because there are no curation tools to enforce semantics.
-Yu-Ru Lin and Rebecca Hwa
Dr. Yu-Ru Lin and Dr. Rebecca Hwa, both Associate Professors from the School of Computing and Information, in collaboration with Dr. Wen-Ting Chung from the School of Education, have recently been awarded a research grants from the DARPA Understanding Group Biases (UGB) program for a project titled, “TRIBAL: A Tripartite Model for Group Bias Analytics.” The base year funding amount is $149,978 with a total award value of $912,072.