self optimization

["Ali Kerem Saysel"]

In stock-flow language, do we have a generic structure describing self optimizing behavior. For example, think about a farmer monitoring only the two factors: pesticide application rate and yields. His strategy is to add more pesticides next year if his income is increasing by increased pesticides and to drop pesticide consumption if income is declining. Can we create a stock - flow formulation for this process. Thank you all. --------------------------------------- Ali Kerem Saysel, Ph.D. Res. ass. in Environmental Sciences Inst. Bogazici University, 80815 Bebek, Istanbul, Turkey. saysel@boun.edu.tr tel: +90 212 2631540 - 2145 fax: +90 212 2575033

["geoff coyle"]

Thats easy. Just create two levels for average income, smoothed over, say, 6 and 12 months (or whatever periods you choose, though be careful about DT). Have an auxiliary for the difference between them and, depending on whether the auxiliary is positive and negative, Bobs your uncle, as we say. In practice, you might want to change the pesticide use if the auxiliary is more than, say 10% of the smaller of the averages, so that you dont chase noise. Changing use next year is getting close to using a discrete formulation and many people object to that. I dont because I want the model to do the same things as the real system and for the same reasons, within the simplification one has used. Hope that helps. Give my regards to Yaman. Regards, Geoff Coyle From: "Geoff Coyle" <Geoff.Coyle@btinternet.com>

[Nelson Repenning]

I have a PhD student, John Foster, working on this problem. He is trying to develop a fully behavioral model of how somebody might solve such an optimization problem. It turns out to be quite a hard problem and we could not find a formulation in the existing literature that really captured it. Many of the economics models have a hill climbing formulation where the decision maker assesses as the gradient of the production function and then heads in the most desirable direction. However, all of these formulations assume that the decision maker knows the functional form of the production technology (perhaps with a significant delay). this does not seem like a realistic assumption. we are working on a formulation in which the decision maker calculates various covariations and uses that information in a hill climbing formulation. We be very interested in hearing from others who have tackled similar issues. nelson -------------------------------------------------------------- Nelson P. Repenning Robert N. Noyce Career Development Assistant Professor Operations Management/System Dynamics Group Alfred P. Sloan School of Management, MIT 30 Wadsworth St, E53-339 Cambridge, MA 02139 phone: 617258-6889 fax:617258-7579 e-mail:nelsonr@mit.edu http://web.mit.edu/nelsonr/www

["John K. Blood"]

Hello! This may be way off base -- Im no mathematician, but some time ago I bought a small book entitled, "Human Motivation and the Dynamic Calculus" authored by Raymond B. Cattell, published by Praeger in 1985. Hardcover. ISBN: 0-03-072009-5 Perhaps you know of this book and the authors work? I had never seen anything like it when I found it. The authors first article upon the Dynamic Calculus appeared in 1935. This book "puts in a nutshell the whole development of dynamic theory and the dynamic calculus," as written in the authors bio. It contains many equations reflecting human behaviors and attitudes, as well as explains how to put the equations to work. In 1975, he co-authored with Dennis Child, "Motivation and Dynamic Structure." I picked this text up on a whim with a feeling that someday my non-mathematical self may find use for it connected with SD. Your question piqued my curiosity and I remembered the lonely volume on my human behavior shelf. Im wondering if this reference may be of assistance? I am also wondering the value of what I have laying before me? Would any of this be useful (even if for ideas) to the study of SD? I hope this helps. Fascinating problem. John Blood Quest Dynamics, Inc. jblood@quest-dynamics.com ** http://www.quest-dynamics.com 1-888-216-2916

[Tom Fiddaman]

Christian Kampmans dissertation, "Feedback Complexity and Market Adjustment: An Experimental Approach" (MIT, 1992), contains some examples of hill-climbing behavior rules (setting price and quantity for profit maximization). Its a bit thick, but I consider it a must-read for advanced SD practice. Tom **************************************************** Thomas Fiddaman, Ph.D. Ventana Systems http://www.vensim.com 8105 SE Nelson Road Tel (253) 851-0124 Olalla, WA 98359 Fax (253) 851-0125 Tom@Vensim.com http://home.earthlink.net/~tomfid ****************************************************