Week 7: pricing and market clearing in a predator-prey market

In this penultimate episode of this Bayesian Decision Analytics coursel, we build a simple market clearing mechanism by finding a price which equates aggregate MooseC0 and WolfCo demands against an exogenousl supply. Following John Muth's article "Rational Expectations and the Theory of Price Movements," (Econometrica , Jul., 1961, Vol. 29, No. 3 (Jul., 1961), pp. 315-335 (1961)), we fexplore the dynamic movement of price, demand, and supply, given the information at hand in the market, represented by a subset of all information in the market, filtered through our representation of the market, namely our predator-prey model of customer retention and eventual demand. The price optimization follows James Sterman's Business Dynamics, , approach based on Michael Powell's conjugate gradient optimization algorithm. A mouthful or two! At a first approximation we arrive at a rational expectations equilibrium in this realistic case of cuthroat competition. As we wander though this maze of boxes, arrows, spreadsheet simulations, and graphs, we may do well to recall our purpose in this course, namely to examine the impact of highly interactive market decision makers (overall potential customers, MooseCo, WolfCo, an anonymous supplier) on market states (price, customers, demand, supply), across time. We have one more task, namely, to examine the information content of prices, demand, and supply as strategic to binary decision alternatives in a Bayesian probabilistic context.