Explaining Price Dispersion and Dynamics in Laboratory Bertrand Markets

Abstract

This paper develops a quantal-response adaptive learning model which combines sellers' bounded rationality with adaptive belief learning in order to explain price dispersion and dynamics in laboratory Bertrand markets with perfect information. In the model, sellers hold beliefs about their opponents' strategies and play quantal best responses to these beliefs. After each period, sellers update their beliefs based on the information learned from previous play. Maximum likelihood estimation suggests that when sellers have full past price information, the learning model explains price dispersion within periods and the dynamics across periods.The fit is particularly good if one allows for sellers being risk averse. In contrast, Quantal Response Equilibrium does not organize the data well.JEL Numbers: C73,C91, D83, L13.

The impact of the number of sellers on quantal response equilibrium predictions in Bertrand oligopolies

“…PQRE requires the expected payoffs for all prices to be positive. In empirical applications, this restriction on expected payoffs is usually imposed by experimental design or by adding a small positive constant to the payoffs for all prices (e.g., Bayer, Wu, & Chan, ).…”

“…PQRE requires the expected payoffs for all prices to be positive. In empirical applications, this restriction on expected payoffs is usually imposed by experimental design or by adding a small positive constant to the payoffs for all prices (e.g., Bayer, Wu, & Chan, 2014). Baye and Morgan (2004) show that for λ N < 1 ( − 1) ∕ there exists a closed-form representation for a symmetric PQRE choice function.…”

How to measure competition? The role of price dispersion in B2B supply markets

“…First, it is indicated that consumers need information on the offerings available in the market (Barron et al, 2004;Liu et al, 2012). To gather this information, customers must engage in search activities, which produce costs (Baye et al, 2004b;Bayer et al, 2013;Chandra and Tappata, 2011). Some customers are not willing or able to bear the incorporated search costs (Chandra and Tappata, 2011;Sorensen, 2000), ultimately leading to a market which includes well informed and less informed customers (Chen and Zhang, 2011;Morgan et al, 2006).…”

Experiments on Learning, Methods and Voting

“…Experimental and empirical studies robustly find deviations from equilibrium, with prices showing significant dispersion. In the second paper of this issue, 'Explaining price dispersion and dynamics in laboratory Bertrand markets', Bayer et al (2014) introduce a new class of learning models with the following features. Players form beliefs about an opponent's action choice using a weighted fictitious play process.…”

“…An interesting aspect of this model is that it is a generalization of the quantal response equilibrium (McKelvey and Palfrey, 1995). Bayer et al (2014) show that their generalization captures the pricing dynamics well and, therefore, is an appropriate model when there is complete information.…”