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In our lab experiment, participants will observe price sequences predetermined by some stochastic processes, make buying decisions and guess the probability of price increases. Both the price change magnitudes and the set of available information are manipulated across conditions. In this setting, experienced and observed outcomes carry the same information value for a Bayesian agent, but not for a reinforcement learner. The main purpose of this project is to evaluate the relative importance of belief-based versus reinforcement-based decision rules in repeated decision-making with feedback under uncertainty, and to test the sensitivity of investment decisions and beliefs to experience and description. We will also elicit participants' attitudes towards risk and loss, in order to compare the predictive power of models with and without learning.
