We investigate expectation formation in a controlled experimental environment. Subjects are asked to predict the price in a standard asset pricing model. They do not have knowledge of the underlying market equilibrium equations, but they know all past realized prices and their own predictions. Aggregate demand for the risky asset depends upon the forecasts of the participants. The realized price is then obtained from market equilibrium with feedback from six individual expectations. Realized prices differ significantly from fundamental values and typically exhibit oscillations around, or slow convergence to, this fundamental. In all groups participants coordinate on a common prediction strategy.

We examine the force of three types of behavioural dynamics in quantity-setting triopoly experiments: (1) mimicking the successful firm, (2) rules based on following the exemplary firm, and (3) rules based on belief learning. Theoretically, these three types of rules lead to the competitive, the collusive, and the Cournot—Nash outcome, respectively. In the experiment we employ three information treatments, each of which is hypothesized to be conducive to the force of one of the three dynamic rules. To a large extent, the results are consistent with the hypothesized relationships between treatments, behavioural rules, and outcomes.

We investigate expectation formation in a controlled experimental environment. Subjects are asked to predict the price in a standard asset pricing model. They do not have knowledge of the underlying market equilibrium equations, but they know all past realized prices and their own predictions. Aggregate demand for the risky asset depends upon the forecasts of the participants. The realized price is then obtained from market equilibrium with feedback from six individual expectations. Realized prices differ significantly from fundamental values and typically exhibit oscillations around, or slow convergence to, this fundamental. In all groups participants coordinate on a common prediction strategy.

Proper scoring rules provide convenient and highly efficient tools for incentive-compatible elicitations of subjective beliefs. As traditionally used, however, they are valid only under expected value maximization. This paper shows how they can be generalized to modern (“non-expected utility”) theories of risk and ambiguity, yielding mutual benefits: users of scoring rules can benefit from the empirical realism of non-expected utility, and analysts of ambiguity attitudes can benefit from efficient measurements using proper scoring rules. An experiment demonstrates the feasibility of our generalization.