Overcoming the Failure of Neoclassical Economics to Capture Excessive Demand: A Learning-to-Neuroforecast Experimental Approach
John Haracz, Indiana University, United States
Posters 3 Poster
Pacific Ballroom H-O
Sat, 27 Aug, 19:30 - 21:30 Pacific Time (UTC -7)
Dynamic stochastic general equilibrium models have been widely criticized for failing to forecast the Global Financial Crisis of 2008-2009 (Guzman & Stiglitz, 2020; Vines & Wills, 2020; Yellen, 2010). This and other flaws of neoclassical economics are presently proposed to arise from the failure of equilibrium-based models to capture excessive demand, which exceeds the balanced excess demand in general equilibrium theory. The present theoretical study seeks potential neuroeconomic biomarkers of excessive demands. A learning-to-neuroforecast (LtN) experimental approach is proposed for elucidating computational mechanisms that underlie excessive demands. Learning to forecast (LtF) experiments have revealed that subjects coordinate on a price trend-following rule in lab asset markets with large price bubbles (Anufriev & Hommes, 2012; Hommes, 2013). Therefore, neuroimaging applied in the LtF setting (i.e., LtN experiments) may yield biomarkers of excessive demands that destabilize financial or commodity markets. A high biomarker prevalence in real markets could indicate that financial- or commodity-market demands have exceeded boundary conditions, beyond which equilibrium-oriented models are less applicable than alternatives (e.g., novel disequilibrium [Guzman & Stiglitz, 2020] or multiple equilibrium models [Vines & Wills, 2020]).