We briefly review some work on expectations and learning in complex markets, using the familiar demand-supply
cobweb model. We discuss and combine two different approaches on learning. According to the adaptive
learning approach, agents behave as econometricians using time series observations to form expectations, and
update the parameters as more observations become available. This approach has become popular in macro.
The second approach has an evolutionary flavor and is sometimes referred to as reinforcement learning. Agents
employ different forecasting strategies and evaluate these strategies based upon a fitness measure, e.g. past
realized profits. In this framework, boundedly rational agents switch between different, but fixed behavioral
rules. This approach has become popular in finance. We combine evolutionary and adaptive learning to model
complex markets and discuss whether this theory can match empirical facts and forecasting behavior in laboratory
experiments with human subjects.
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