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multicampus complexity videoconference.

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  • Sander Greenland, Department of Epidemiology and Department of Statistics, University of

California, Los Angeles Epidemiology, UCLA School of Public Health

  • Title: Causal modeling of bias: A graphical overview of concepts for methods Abstract: In controversial topics and nonexperimental settings, bias (not random error) is the core obstacle to causal inference. Causal diagrams (graphical causal models, especially directed acyclic graphs or DAGs) provide the easiest way to see bias sources, and provide insights into how to deal with those sources. Because of this property, these diagrams have become part of core epidemiologic methods teaching in several programs, including Harvard and UCLA. This talk will provide an overview to illustrate the power and limitations of causal diagrams in this role.
  • Rina Dechter, Professor of Information and Computer Science, Donald Bren School of Information and Computer Science, UCI, Friday, 1:30-3:15, April, 29 2010 Principles for Reasoning with Probabilistic and Deterministic Graphical models
  • John Bragin Friday, 1:30-3:15, March 4, 2011 Foundations of Agent-Based Modeling: With Some Objections and Their Refutation
Abstract: Agent-based simulation modeling is the core methodology of complex systems science. In an agent-based simulation model of a complex adaptive system (in biology or society) each agent is explicitly represented. This includes the perceptual, cognitive and behavioral capacities of each type of agent and the capacities for agents to change in their interactions with other agents and the environment. Agents are autonomous: They act on their own behalf, without direction by a global design or central controller, although they are certainly influenced by other agents and the constraints and opportunities imposed and provided by the global patterns resulting from agent interactions. Although an agent-based simulation model can be written using mathematics, it is more usual today to use a high level programming language consisting of algorithms. Nevertheless, such programs (unlike even the most precise prose) are as rigorous as mathematical proofs, because they must “be complete, consistent and unambiguous if they are to be capable of being executed on a computer. On the other hand, unlike mathematical models, agent-based models can include agents that are heterogeneous in their features and abilities, can model situations that are far from equilibrium, and can deal directly with the consequences of interactions between agents.” (Gilbert, 2008
  • Causality project team Friday, 1:30-3:15, Feb 18, 2011 Evolutionary Causality: Discussions, Demonstrations, Possibilities

Abstract: Bring your laptop to explore the intersci wiki pages on Evolutionary Causality for this free-ranging discussion on three campuses by research project and audience members. The project discussion explains how causal analyses for cross-cultural and cross-national studies is brought under the general rubric of Galton’s problem and Evolutionary causal graphs (title of the NSF proposal). We want to make accessible sharable databases, open access software, and experience on how to develop causal analysis research projects. There will be two speakers in spring term on causality and machine learning.

  • Dwight Read, Anthropology, UCLA, Friday, 1:30-3:15, Oct 22, 2010 "Mathematical Modeling of the Logical Structure of Kinship Terminologies"
  • George Barnett Communications, UCDavis. Friday, 1:30-3:15, Oct 22, 2010 Effects of communication networks on conflict and cooperation (rescheduled)
  • Yen-Sheng Chiang, Sociology, UCI, Friday, 1:30-3:15, Oct 8, 2010 Cooperation Dynamics in Networks When Cued by the Structural Attributes of Nodes
  • Douglas R. White Friday, 1:30-3:15, Sept 24, 2010 Causal analysis networks and early ethnographically well-studied populations (Complex Networks and Dynamics)

Future possibilities

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