A laboratory experiment on the heuristic switching model

Abstract

We present results from the first laboratory experiment on the seminal heuristic switching model introduced by Brock and Hommes (1997, 1998). Subjects choose between two alternatives, a sophisticated and stabilizing, but costly, heuristic, and a destabilizing, but cheap, heuristic, and are paid according to the performance of the chosen heuristic. Aggregate choices determine the evolution of a state variable and, consequently, the performance of both heuristics. Theoretically, an increase in the costs for the stabilizing heuristic generates instability and leads to endogenous fluctuations in both the state variable and the fraction of agents using that heuristic. We vary the costs of the stabilizing heuristic in the experiment and find that the predictions of the heuristic switching model are partially confirmed. For low costs the dynamics are stable. For high costs, the dynamics initially are unstable and exhibit the type of bubbles and crashes emblematic for the heuristic switching model. However, over time the pattern of bubbles and crashes disappears and the dynamics become more stable. We estimate a standard discrete choice model on aggregate choice data and observe that * We thank the participants of the Society for the

Speculative asset price dynamics and wealth taxes

“…From a technical perspective, however, our paper is more related to the following papers. In particular, Anufriev et al (2018) experimentally test the asset-pricing model by Brock and Hommes (1998) and report that a reduction in the cost of stabilizing expectation rules tends to produce more stable asset price dynamics, lending the main channel that drives our analytical insights at least some indirect empirical credit. Moreover, Schmitt and Westerhoff (2015) explore how profit taxes may shape the dynamics of the cobweb model by Brock and Hommes (1997) in which farmers switch between rational and naïve expectation rules, depending on the rules' past profitability.…”

Speculative housing markets and rent control: insights from nonlinear economic dynamics

“…In line with Hommes (1997, 1998), the extrapolative expectation rule is free, but the more sophisticated regressive expectation rule, requiring the computation of F , incurs positive information costs . 5 We may also regard parameter as a behavioral bias in favor of the simpler extrapolative expectation rule, as discussed in Anufriev et al (2016Anufriev et al ( , 2018.…”

Nonlinear asset-price dynamics and stabilization policies

“…Day and Huang [25] develop one of the first nonlinear asset-pricing models. 2 In their seminal work, interactions between chartists (following a linear rule) and fundamentalists (following a nonlinear trading rule) may create complex (chaotic) bull and bear market dynamics. Chiarella [19] studies the equally reasonable opposite scenario, i.e., chartists adhere to a nonlinear trading rule, while the trading behavior of fundamentalists is linear.…”

“…See LeBaron [53] for a survey. 2 Some predecessors include Zeeman [90], Beja and Goldman [9] and Frankel and Froot [37]. on models in which a market maker adjusts prices with respect to speculators' order flow.…”

“…While technical trading rules add a destabilizing positive feedback effect to the dynamics, fundamental trading rules entail a stabilizing negative feedback. Since there is widespread empirical evidence that speculators switch between technical and fundamental trading rules (see, e.g., [2,4,12,36], most models in this field possess at least one nonlinearity, giving rise to the following stylized boom-bust generating mechanism. Near the model's fundamental value, technical trading rules tend to dominate the market, and their use may initiate a bubble process.…”