Bounded Rationality and Learning in Complex Markets

This chapter reviews some work on bounded rationality, expectation formation and learning in complex markets, using the familiar demand-supply cobweb model. We emphasize two stories of bounded rationality, one story of adaptive learning and another story of evolutionary selection. According to the adaptive learning story agents are identical, and can be represented by an ``average agent'', who adapts his behavior trying to learn an optimal rule within a class of simple (e.g. linear) rules. The second story is concerned with heterogeneous, interacting agents and evolutionary selection of different forecasting rules. Agents can choose between costly sophisticated forecasting strategies, such as rational expectations, and freely available simple strategies, such as naive expectations, based upon their past performance. We also confront both stories to laboratory experiments on expectation formation. At the end of the chapter, we integrate both stories and consider an economy with evolutionary selection between a costly sophisticated adaptive learning rule and a cheap simple forecasting rule such as naive expectations.

Call Market Experiments: Efficiency and Price Discovery through Multiple Calls and Emergent Newton Adjustments

We study multiple-unit, laboratory experimental call markets in which orders are cleared by a single price at a scheduled “call”. The markets are independent trading “days” with two calls each day preceded by continuous and public order flow. Markets approach the competitive equilibrium over time. The price formation dynamics operate through the flow of bids and asks configured as the “jaws” of the order book with contract execution structured by an underlying mathematical principle, the Newton method for solving systems of equations. Thus, both excess demand and its slope play a systematic role in call market price discovery

The impact of active and passive investment on market efficiency: a simulation study

We create a simulated financial market and examine the effect of different levels of active and passive investment on fundamental market efficiency. In our simulated market, active, passive, and random investors interact with each other through issuing orders. Active and passive investors select their portfolio weights by optimizing Markowitz-based utility functions. We find that higher fractions of active investment within a market lead to an increased fundamental market efficiency. The marginal increase in fundamental market efficiency per additional active investor is lower in markets with higher levels of active investment. Furthermore, we find that a large fraction of passive investors within a market may facilitate technical price bubbles, resulting in market failure. By examining the effect of specific parameters on market outcomes, we find that that lower transaction costs, lower individual forecasting errors of active investors, and less restrictive portfolio constraints tend to increase fundamental market efficiency in the market

Critical Overview of Agent-Based Models for Economics

We present an overview of some representative Agent-Based Models in Economics. We discuss why and how agent-based models represent an important step in order to explain the dynamics and the statistical properties of financial markets beyond the Classical Theory of Economics. We perform a schematic analysis of several models with respect to some specific key categories such as agents' strategies, price evolution, number of agents, etc. In the conclusive part of this review we address some open questions and future perspectives and highlight the conceptual importance of some usually neglected topics, such as non-stationarity and the self-organization of financial markets.Comment: 51 pages, 9 figures, Proceedings of the School of Physics "E. Fermi", course CLXXVI, 2010, Varenn