Case-Based Learning in the Cobweb Model

In this paper, we propose a new approach to model bounded rationality in macroeconomics. This method is based on Case-based Decision Theory (Gilboa and Schmeidler, 1995), which been shown to match human choice behavior in experiments (Pape and Kurtz, 2013; Guilfoos and Pape, 2014). The idea is that case-based learning agents behave like ‘average people’ who do not possess structural knowledge of the economy and respond primarily to variables that are directly relevant to their own wellbeing. These agents accumulate a memory bank that stores past cases that they have encountered, and, when they are confronted with a new choice, they judge how similar the current circumstances to cases in memory, and use that judgement to forecast payoffs of alternative actions. We apply this learning approach to the Cobweb model. We find that market prices converge to the rational expectations prices if agents search for a better outcome persistently. If agents are not sufficiently persistent, then multiple equilibria abound, and the rational expectations equilibrium becomes a special case. On the other hand, if they are too persistent, they can search forever and never achieve convergence.