Hello, The Minerva Center for Movement Ecology will hold a course on the NOVA platform in the Hebrew University, Jerusalem, Israel from Feb 2nd until Feb 6th 2014. Teachers are Wayne Getz (Berkeley), Richard Salter (Ohio) and Nick Sippl-Swezey (UCSF). Nova is a new Java-based modeling platform that naturally supports the creation of models in the system dynamics, event-oriented, and agent-based modeling paradigms. Nova uses a visual language to express model design, and provides automatic conversion for such models to script form for execution. Nova’s architecture promotes hierarchical design, code reuse, and extensibility through the use of plug-ins. The Nova Website has been created to foster a vibrant user community by providing support for model and plug-in construction, and user services such as online repositories for user-contributed content. The course syllabus can be found in the following address: http://shnaton.huji.ac.il/index.php/NewSyl/90615/2/2014/ The list of expected learning outcomes (see below) covers highly relevant research topics for many students and post-docs, including probability distributions, sampling, random walks, movement in homogeneous and heterogeneous landscapes, foraging efficiency, decision making and fitness consequences. Please contact me for more details and registration.
Best wishes, Ron Learning outcomes: 1. Gain a comparative knowledge of probability distributions relevant to simulating movement behavior and analyzing movement data, including compact distributions (e.g. uniform and bet), distributions with infinite tails (e.g. the normal, lognormal and Weibull, including the translated Weibull), fat-tailed distribution (e.g. Pareto and Lévy), and circular distributions (e.g. von Mises and wrapped Cauchy). 2. Learn how to sample data from distributions using quantile functions and visualize these data using various graphical methods. 3. Learn how to build simulation models of various kinds of random walks (simple, correlated, biased, Lévy), using the NOVA software platform. 4. Build NOVA simulation models of individuals moving over homogeneous and heterogeneous planes, cellular arrays, and networked-structured patches on landscapes. 5. Build NOVA simulation models of consumer growth, as well as resource extraction and depletion, processes and learn how to explore the impact of various movement decision rules on the ability of individuals to forage efficiently. 6. Learn how to employ artificially neural networks as perceptual modules and how to combine these modules to allow individuals to weigh up different kinds of information (resource exploitation, efficient movement, predation risk minimization) when making decisions on where and how to move. 7. Build, if there is enough time, NOVA simulation models that explore the relative life-time fitness of different movement decision modules using genetic algorithm methodologies.
