Faculty, staff and students...
Computer Lab, seminar listings, contact information...
Events, seminars, and academic deadlines...
Find documents and people...
More detail on the latest CSCS news...

  • Comments?
    email webmaster


  • CSCS Seminars -- Winter, 2006

    The default day and time for CSCS seminars is: Thursdays, 4:00pm
    However, note the place may vary from week to week.



    Thursday, January 12
    4:00 - 5:30pm

    1469 Mason

    "Bouncing, Splashing, Stepping and Climbing: Physics, Biology and Engineering of Complex Interactions"
    (Abstract in pdf)

    Daniel I. Goldman
    Poly-PEDAL Laboratory
    Dept. of Integrative Biology
    UC Berkeley


    Thursday, January 19
    4:00 - 5:30pm

    335 West Hall

    "Statistical physics, computer simulation and probability"
    (Abstract)

    Raissa D'Souza
    Asst. Prof. of Mechanical Engineering
    UC Davis


    Tuesday, January 24 -- NOTE SPECIAL DAY
    4:00 - 5:30pm

    340 West Hall.

    "The Secret Nanoscale Life of Plants, Purple Bacteria and People"
    (Abstract)

    Neil Johnson
    Prof. of Physics, Lincoln College
    University of Oxford


    Thursday, January 26
    4:00 - 5:30pm

    335 West Hall.

    "Two's Company, Three's a Crowd: Traders, Traffic, Teenagers and Terrorists"
    (Abstract)

    Neil Johnson
    Prof. of Physics, Lincoln College
    University of Oxford


    Thursday, Februray 16
    4:00 - 5:30pm

    4088 East Hall. -- Note location!

    "On pattern formation and cell aggregation in biology"
    (Abstract)

    Mark Alber
    University Notre Dame


    Thursday, Februray 23
    4:00 - 5:30pm

    No talk.


    Two Related Talks -- Wednessday 8 March

    The University of Michigan, Department of Epidemiology.
    Auditorium I, SPH I (109 Observatory)
    3:00-4.00pm

    Leslie A. Real, PhD
    Asa G. Candler Professor of Biology
    Center for Disease Ecology
    Emory University

    "Predicting the Spatial Dynamics and Control of Epidemics: Rabies as a Model"
    (Abstract)

    University of Michigan Bioinformatics Seminar
    Wednesday, March 8, 2006
    Palmer Commons Building, Great Lakes South room (4th floor)
    4:00-5:00pm

    Dr. Lauren Ancel Meyers
    Department of Integrative Biology
    University of Texas at Austin

    "Using contact network epidemiology to evaluate flu vaccination strategies"
    (Abstract)


    Thursday, March 23
    4:00 - 5:30pm

    Individual Adaptive Behavior in Models of Complex Systems
    (Abstract)

    Steve Railsback
    Lang, Railsback & Associates and Humboldt State University, Arcata, California


    Thursday, March 30
    4:00 - 5:30pm

    Robust Inference with Computational Science and Long Term Policy Analysis.
    (Abstract)

    Stephen Bankes
    Professor, Pardee RAND Graduate School; CTO, Evolving Logic Inc.


    Thursday, April 6
    4:00 - 5:30pm

    Simulating Automobile Market Place Evolution
    (Abstract)

    John Sullivan
    Ford Research Labs.


    Thursday, April 13
    4:00 - 5:30pm

    Title TBA.

    Jim Murphy


    More talks to be scheduled
    4:00 - 5:30pm






    Abstracts


    Neil Johnson
    "The Secret Nanoscale Life of Plants, Purple Bacteria and People"

    To what extent does a spinach depend on quantum mechanics? Everybody knows that plants (and indeed many bacteria) produce food through photosynthesis -- however nobody understands the detailed dynamics of the light-harvesting process. Current thinking suggests that the remarkable efficiency of photosynthesis owes a lot to physicists' favorite phenomena: many-body excitations, hopping processes, random walks and delocalization. But given that light-harvesting is observed to occur on the picosecond timescale -- and that decoherence is expected to act on similar timescales -- could more exotic quantum phenomena such as entanglement also play a role? And if we develop similar designs for artificial nanostructure systems, could they be made to exhibit novel collective light-matter phenomena, or act as next-generation nanoscale devices?

    This talk will mix fact and fiction: Fact, in the sense that there exists a reasonable yet incomplete experimental picture of photosynthetic time-scales and length-scales. And fiction in the sense that we will let our minds wander, guided by a range of plausible model Hamiltonians, in order to explore what might be possible in such light-matter nanosystems. Our calculations show that quantum entanglement can affect the photosynthetic process in several important ways, and provide an unambiguous experimental signature for detecting such entanglement. In addition, we predict that such systems could be designed to exhibit rich light-matter phase diagrams. At the end of the talk, I turn to consider whether microtubules within neurons in the brain might also exhibit, or be made to exhibit, such collective phenomena.


    Neil Johnson
    "Two's Company, Three's a Crowd: Traders, Traffic, Teenagers and Terrorists"

    Why do people decide to join or leave groups? And what is the effect of the resulting group dynamics on the macroscopic behavior of the system in question? From financial markets through to insurgent warfare, the importance of understanding how the ecology of a given population evolves and acts over time, is of great importance. Depending on the situation, it may pay to seek strength in numbers -- on the other hand, such aggregation may lead to over-use of some limited resource. In this talk I will analyze several real-world situations, comparing theoretical models to empirical data. The goal is to understand when crowds might be more likely to emerge based on the characteristics of the individuals concerned, and whether these crowds then represent a potential threat to the system's stability. In addition to such endogenous crowd formation, I will look at the effect of exogenous 'kicks' from outside influences such as news, rumors, memes or antigens. The results have possible relevance to a wide variety of social, economic and biological phenomena -- in addition, they yield as a by-product an intriguing generalization of conventional many-body theory in Physics.


    Raissa D'Souza
    Statistical physics, computer simulation and probability

    Statistical physics, computer simulation and discrete mathematics are intimately related through the study of shared models. These are primarily lattice models, such as the Ising model, yet can also involve discrete structures such as networks. Several models illustrate the current interplay between these three fields, while also providing cautionary tales of interpolating results obtained in one realm to the other. This talk will survey several such models, focusing on pattern formation in a simple first-order jamming transition. Finally, modeling of network phenomena will be discussed, focusing on an underlying optimization framework which gives rise to network properties observed pervasively in real-world data.


    Mark Alber
    On pattern formation and cell aggregation in biology.

    In this talk I will review recent progress in modeling collective behavior in Myxobacteria using stochastic discrete systems [1,2]. Myxobacteria are social bacteria that swarm and glide on surfaces, and feed cooperatively. When starved, tens of thousands of cells change their movement pattern from outward spreading to inward concentration; they form aggregates that become fruiting bodies. Cells inside fruiting bodies differentiate into round, nonmotile, environmentally resistant spores. Traditionally, cell aggregation has been considered to imply chemotaxis, a long-range cell interaction. However, myxobacteria aggregation is the consequence of direct cell-contact interactions, not chemotaxis. I will also present the foundation of a unified, object-oriented, three-dimensional environment for modeling morphogenesis [3-5], which allows one to integrate multiple submodels at scales from subcellular to those of tissues and organs. Our current implementation combines a modified discrete model from statistical mechanics, the Cellular Potts Model, with a continuum reaction-diffusion model and a state automaton with well-defined conditions for cell differentiation transitions to model genetic regulation.


    Stephen Bankes
    Robust Inference with Computational Science and Long Term Policy Analysis

    The world faces profound social, economic, environmental, and technological transitions. How we choose to meet our challenges -- stemming global terror, halting the spread of AIDS and other infectious diseases, achieving sustainable development, managing new genetic technologies, etc. -- will resonate throughout the 21st century. So, it is important to think about the long term. But even when we value the long-term, it can be hard to translate concerns into action. The inability to devise objective, actionable plans for the long term often leaves goals relating to the future unvoiced because they cannot be connected to credible near-term actions.

    Computer modeling can be very important in dealing with the complexity of important societal problems, and innovations such as Agent Based Modeling provide a basis to capture much of more of what is known in computers. But no model, regardless of its quality, can be expected to predict long term outcomes. In order for computer modeling to be rigorously applied to these and similar problems, methods are needed to derive reliable inference from the knowledge embodied by models, without requiring predictive accuracy. This talk will describe one framework for doing so, and its application to a variety of long term policy problems.

    These methods harness computation not to solve the intractable problem of predicting the long-term future, but instead to enable a fundamentally different, more sensible question: Given what we know today, how should we act to best shape the future to our liking? We can use computers to create and consider myriad plausible futures, likely to include at least one similar to what may actually unfold. We can then discover near-term actions that perform well, compared to the alternatives, over all these futures, often through clever hedging actions and adaptation to updated information. Finally, the computer can be used to seek plausible futures that "break" a chosen strategy. After repeated iterations to shore up revealed weaknesses, the resulting strategy can support a consensus for successful action. In the end, the process yields near-term strategies not merely optimized for some "best guess" scenario but rather robust across a multitude of scenarios.

    The result is a powerful enhancement to the human capacity to reason in the face of enormous uncertainty. This approach combines some of the best features of the qualitative scenario-building and quantitative decisionmaking tools developed and applied for more than five decades. These new tools may help address a paradox of decisionmaking: our greatest potential influence for shaping the future may often be precisely over those time scales where our gaze is most dim. Further, they provide an avenue for escaping the fruitless arguments that routinely arise among stakeholders over which future is the one for which we must prepare.

    Bankes, Steven (1993) Exploratory Modeling for Policy Analysis", Operations Research, vol. 41, No. 3, pp. 435-449.
    Bankes, Steven (2002) Tools and Techniques for Developing Policies for Complex and Uncertain Systems, Proceedings of the National Academy of Sciences, 99, pp. 7263-7266.
    Popper, S.W., Robert J. Lempert, and Steven C. Bankes (2005): "Shaping the Future," Scientific American, vol 292, No. 4, April.
    Lempert, R. J., Popper, S.W., and Bankes, S.C. (2002). "Confronting Surprise." Social Science Computing Review 20(4): 420-440.


    Steve Railsback
    Individual Adaptive Behavior in Models of Complex Systems

    In real natural and economic systems, system properties emerge from adaptive individual behavior---decisions made by individuals in response to changes in themselves and their environment, presumably to improve the individual's future success. When we try to understand a complex system by modeling it, we must decide which adaptive behaviors need to be included in the model and how to model those behaviors. Our experience with individual-based models for management ecology (see: www.humboldt.edu/~ecomodel) indicates that models with few or no adaptive behaviors tend to be uninteresting and poorly able to reproduce basic system behaviors, but models with too many adaptive behaviors can be difficult to build and validate.

    Several approaches to modeling adaptive behavior are widely used. Economists seem particularly fond of using adaptive computation to artificially evolve decision-making traits. Ecologists have often used heuristics or logical rules. We have had success with a third approach: explicit fitness-seeking. An explicit measure of expected future fitness is defined; in ecology this measure could be as simple as growth rate or as complex as a prediction of future reproductive success. (Translated to economics, an explicit measure of individual utility could range from income rate to expected probability of attaining a future goal such as comfortable retirement.) Decisions are then made by selecting the alternative that maximizes the fitness measure. If the fitness measure is well-designed and the model thoroughly analyzed, this approach can give model individuals good (yet realistically bounded) decision-making ability over a wide range of alternatives and conditions, and also provide mechanistic understanding of how individuals make decisions and how decision-making traits affect the system.

    Our work on stream trout illustrates these ideas. We developed and tested models for two key adaptive behaviors: habitat selection (choosing which habitat patch to occupy) and activity selection (deciding whether to feed or hide). First, we determined that conventional theory of behavioral ecology was too simplistic to be useful in a model with realistic variation over time and space and among individuals. We then developed appropriate fitness measures and conducted extensive analyses to show that they provide trout with the ability to make good tradeoffs between growth and mortality risk over very diverse conditions. Currently we are using the model to investigate the relative importance of these two adaptive behaviors to population properties such as abundance, variability, resilience, and resistance to disturbance.

    Readings:

    Tests of Theory for Diel Variation in Salmonid Feeding Activity and Habitat Use. Steven F. Railsback etal. Ecology 86(4): 947-959 (2005).

    Analysis of Habitat-Selection Rules Using an Individual-Based Model. Steven F. Railsback and Bret C. Harvey. Ecology 83(7) 1817-1830 (2002).

    Movement rules for individual-based models of stream fish. Steven F. Railsback etal. Ecological Modelling 123: 73-89 (1999).


    Leslie Real
    Predicting the Spatial Dynamics and Control of Epidemics: Rabies as a Model.

    Rabies, the most important viral zoonotic disease world-wide, has been undergoing epidemic expansion along the eastern seaboard of the United States since the mid-1970s following an accidental introduction of rabid raccoons from a source of endemic infection in the southeastern US. Using data submitted from US States to the Centers for Disease Control and Prevention, we have constructed stochastic simulations of the spatial dynamics of rabies as it has spread into new geographic region. The simulation was constructed as an interaction network with nodes of the network defined by township and county centroids. Interaction strengths along specific connections were sensitive to local geographic conditions and parameterized against reported data on the time and spatial location of detected rabid animals. The parameterized model has proven to be a valuable model in general for strategic planning for disease emergence and to direct the development of spatial control strategies.


    Lauren Ancel Meyers
    Using contact network epidemiology to evaluate flu vaccination strategies

    The threat of avian influenza and the 2004-2005 influenza vaccine supply shortage in the United States has sparked a debate about optimal vaccination strategies to reduce the burden of morbidity and mortality caused by the influenza virus. I will discussa comparative analysis of two classes of suggested vaccination strategies: mortality-based strategies that target high risk populations and morbidity-based that target high prevalence populations. Applying the methods of contact network epidemiology to a model of disease transmission in a large urban population, we have evaluated the efficacy of these strategies across a wide range of viral transmission rates and for two different age-specific mortality distributions.We found that the optimal strategy depends critically on the viral transmission level (reproductive rate) of the virus: morbidity-based strategies outperform mortality-based strategies for moderately transmissible strains, while the reverse is true for highly transmissible strains. These results hold for a range of mortality rates reported for prior influenza epidemics and pandemics. Furthermore, we have shown that vaccination delays and multiple introductions of disease into the community have a more detrimental impact on morbidity-based strategies than mortality-based strategies.


    John Sullivan
    Simulating Automobile Market Place Evolution.

    The prospect of success for new technology offerings in the U. S. automobile market place is fraught with uncertainty. For example, HEVs, a comparatively new technology offering, have captured considerable media attention, but have, nonetheless, tended to appeal to a limited number of buyers (early adopters), who appear to be little influenced by the vehicle's higher cost and lack of performance and functionality improvements. However, because issues like climate change and energy security loam on the horizon, demand for new more energy efficient technologies is expected to increase, especially as fuel prices begin to rise, whether driven by changes in fuel supply, taxation policies, or both. Hence, understanding what combination of factors can lead to shifts in consumer vehicle preferences is of obvious value to vehicle manufacturers. Characterizing the magnitude and timing of such market transitions have important product strategy implications. For this purpose, an Agent Based Simulation (ABS) has been developed to describe such a transition. Though the simulation model is primarily in the verification stage of development, a set of preliminary results obtained from the model will be discussed. Also covered are the fundamental behavioral assumptions employed in the model. A brief discussion of the connection of this work with sustainable development considerations is also presented.