Dynamic Optimization Modeling

Dynamic Optimization Modeling ZOO 6308

This workshop class targets graduate students who have interests in strategy comparison and the identification of best strategies. It includes instruction in dynamic state variable modeling as well as basic programming in R. No experience with this approach or programming is necessary. Students in this course regularly publish models developed in the class.

Publications Stemming from the Class

Rittschof, CC, SA Hilber, MS Tudor, & CM St. Mary. 2012. Modeling male reproductive strategies and optimal mate number in an orb-web spider. Behavioral Ecology 23(1):1-10. doi: 10.1093/beheco/arr142.

Reitzel, AM & BR Chockley. 2005. Influences of habitat distribution and maternal investment on settlement of lecithotrophic larvae: modeling an ecological transition. Evolutionary Ecology Research 2005(7):183-201.

Isvaran, K & CM St. Mary. 2003.When will males lek? Insights from a dynamic optimization model. Behavioral Ecology. 14(6):876-886.

McCauley, SJ, SS Bouchard, BJ Farina, K Isvaran, S Quader, DW Wood & CM St. Mary. 2000. Energetic dynamics and anuran breeding phenology: insights from a dynamic model. Behavioral Ecology 11(4):429-436.

Recent Syllabus

Dynamic optimization modeling (DOM; also known as dynamic state variable modeling) is a powerful and simple technique for formalizing behavioral and evolutionary hypotheses. This type of modeling is appropriate to address questions in the areas of life history evolution, ecology, behavioral ecology, and any other area in which the relative fitness of alternative choices, or strategies is compared. DOM requires only basic mathematical and programming skills, and the model structure easily allows field quantification of parameters and examination of predictions. Thus, this simulation modeling approach is useful and readily accessible to empiricists and theorists alike.

Format: This course will open with an introduction to dynamic optimization modeling, including the basics of the approach and the aspects of probability theory on which it depends. I will then highlight the application of DOM to questions in behavioral and evolutionary ecology, drawing from the literature. I will then introduce programming in the programming language R, one of various programming tools that can be used to implement these models. In the main portion of the course, we will explore various complexities that can be built into the models and again draw on the literature to see these implemented. This exploration will involve programming some published models and constructing a model of our own devising. Students will then formulate models on their own, based on their research interests, and present them to the class. Finally, the class will develop and analyze one of these models and write a manuscript appropriate for journal submission.

Assignments: Students will formulate dynamic state variable models designed to address a question in their own research area. Those formulations will include summaries of the problem, the model structure, parameters, and equations, and a brief description of how the model would be analyzed once built.

Required Text: CW Clark and M Mangel, 2000. Dynamic state variable models in ecology: Methods and applications. Oxford University Press.