Do there exist macro (policy) models that adopt learning-to-optimize decision mechanics instead of rational optimisation? I guess hand to mouth consumers are an example. There are many New Keynesian macro models incorporating learning models based on learning-to-forecast experiments (LtFE) results into their design to study policy implications of boundedly rational expectations, like Hommes and Zhu (2014), Bullard and Mitra (2002), Hommes et al. (2018), but they all still optimize based on rational choice, to the best of my knowledge and understanding. But what about models that comine boundedly rational expectations with simple quantity decision heuristics?
Learning-to-optimize (LtOE) refers to a design where subjects are asked to submit their economic quantity decisions regarding consumption, trading or production, without direct elicitation of their forecasts of market aggregate outcomes like prices, output, or inflation. (Assanza et al., 2014 a or b?). These market outcomes are calculated for the subjects by the experimenters. All individual quantity decisions by the subjects are fed into the model macro economy adopted in the experiment, in order to obtain the aggregate outcome. In learning-to-forecast experiments, subjects are asked to directly forecast the aggregate variable of interest, like for example the output gap, inflation, or asset prices. These individual forecasts are fed into the equations for the macro model economy adopted in the experiment, and the resulting value for aggregate variable of interest is then calculated and made available to each subject. All other agents’ decisions such as consumption, production, investment etc., leading to this outcome, are calculated by the experimenters, usually based on optimal, rational assumptions (Hommes, 2018), making use of utility and profit optimization techniques [add ref]. As such, subject’s trading actions are considered rational, conditional on the submitted forecast (Anufriev et al., 2019). Subjects are thus forecasting in a dynamic self-referential system, with market realisations depending endogeneously on subject’s average forecasts. Realisations which in turn influence subjects’ forecasts. Separating the quantity choice from direct forecast elicitation in a LtOE versus a LtFE respectively, is a way of decomposing the problem faced by agents in complex macroeconomic settings so that it does not involve a joint test of rationality in both optimization and expectation formation (Duffy, 2016). These macro experiments provide data at the micro level as well as at the macro level, which can be used to formulate and test both theories of learning and assess the time series properties of aggregate economic variables like output and inflation.
Hommes, C., Lustenhouwer, J., & Mavromatis, K. (2018). Fiscal consolidations and heterogeneous expectations. Journal of Economic Dynamics and Control, 87, 173–205. https://doi.org/10.1016/j.jedc.2017.12.002