This issue contributes to the discussion of the relation between individual and aggregate behavior. The basic feature of all the models presented is that they allow for direct interaction between the individuals. This, it is shown, leads to a number of interesting phenomena that are difficult to account for in standard models. The basic message is that the inclusion of heterogeneous interacting agents allows us to escape from the pitfalls associated with the reduction to a representative individual and furthermore to show that aggregates will, in general, have behavior that is different from that of the component individuals. Lastly, complex aggregate behavior is not necessarily the result of complicated individual reasoning, it can emerge from very simple rule following by individuals.

Different theories of expectation formation and learning usually yield different outcomes for realized market prices in dynamic models. The purpose of this paper is to investigate expectation formation and learning in a controlled experimental environment. Subjects are asked to predict the next period's aggregate price in a dynamic commodity market model with feedback from individual expectations. Subjects have no information about underlying market equilibrium equations, but can learn by observing past price realizations and predictions. We conduct a stable, an unstable, and a strongly unstable treatment. In the stable treatment, rational expectations (RE) yield a good description of observed aggregate price fluctuations: prices remain close to the RE steady state. In the unstable treatments, prices exhibit large fluctuations around the RE steady state. Although the sample mean of realized prices is close to the RE steady state, the amplitude of the price fluctuations as measured by the variance is significantly larger than the amplitude under RE, implying persistent excess volatility. However, agents' forecasts are boundedly rational in the sense that fluctuations in aggregate prices are unpredictable and exhibit no forecastable structure that could easily be exploited.

Different theories of expectation formation and learning usually yield different outcomes for realized market prices in dynamic models. The purpose of this paper is to investigate expectation formation and learning in a controlled experimental environment. Subjects are asked to predict the next period's aggregate price in a dynamic commodity market model with feedback from individual expectations. Subjects have no information about underlying market equilibrium equations, but can learn by observing past price realizations and predictions. We conduct a stable, an unstable, and a strongly unstable treatment. In the stable treatment, rational expectations (RE) yield a good description of observed aggregate price fluctuations: prices remain close to the RE steady state. In the unstable treatments, prices exhibit large fluctuations around the RE steady state. Although the sample mean of realized prices is close to the RE steady state, the amplitude of the price fluctuations as measured by the variance is significantly larger than the amplitude under RE, implying persistent excess volatility. However, agents' forecasts are boundedly rational in the sense that fluctuations in aggregate prices are unpredictable and exhibit no forecastable structure that could easily be exploited.

We study the competitive equilibrium of large random economies with linear activities using methods of statistical mechanics. We focus on economies with C commodities, N firms, each running a randomly drawn linear technology, and one consumer. We derive, in the limit N, C ∞ with n=N/C fixed, a complete description of the statistical properties of typical equilibria. We find two regimes, which in the limit of efficient technologies are separated by a phase transition, and argue that endogenous technological change drives the economy close to the critical point.

In this paper we present and discuss a simple financial accelerator agent-based model, whose conceptual core is the interaction of heterogeneous firms and the banking system. Its simplicity notwithstanding, the model is able to replicate through simulations a large number of stylized facts concerning the shape and evolution over time of the distribution of firms' sizes, growth rates, profits, and “bad debt.”

In various agent-based models, the stylized facts of financial markets (unit roots, fat tails, and volatility clustering) have been shown to emerge from the interactions of agents. However, the complexity of these models often limits their analytical accessibility. In this paper we show that even a very simple model of a financial market with heterogeneous interacting agents is capable of reproducing these ubiquitous statistical properties. The simplicity of our approach permits us to derive some analytical insights using concepts from statistical mechanics. In our model, traders are divided into two groups, fundamentalists and chartists, and their interactions are based on a variant of the herding mechanism introduced by A. Kirman (Ants, rationality, and recruitment, Quarterly Journal of Economics 108, 137–156, 1993). The statistical analysis of simulated data points toward long-term dependence in the autocorrelations of squared and absolute returns and hyperbolic decay in the tail of the distribution of raw returns, both with estimated decay parameters in the same range as those of empirical data. Theoretical analysis, however, excludes the possibility of “true” scaling behavior because of the Markovian nature of the underlying process and the boundedness of returns. The model, therefore, only mimics power law behavior. Similarly to the phenomenological volatility models analyzed by LeBaron (Stochastic volatility as a simple generator of apparent financial power laws and long memory, Quantitative Finance 1, 621–631, 2001), the usual statistical tests are not able to distinguish between true and pseudo-scaling laws in the dynamics of our artificial market.

We consider a model in which foreign and domestic traders buy the assets of both of two countries. Speculators in both countries use chartist or fundamentalist rules for forecasting the exchange rate. Demand for the assets of each country is determined by these forecasts. Perceptions of the fundamentals in each country are not necessarily the same. Rules are used with a certain probability depending on an agent's previous experience with them. The demands of the fundamentalist and chartist agents in the two countries determine the temporary equilibrium exchange rate at each point in time. This is unique under certain assumptions. With traders of both nationalities there is no need, as in other models, for an exogenous supply of foreign exchange. The model produces realistic features of the equilibrium exchange rate series. Periods in which the exchange rate tracks the fundamentals of one of the countries alternate with others in which bubbles appear.

This paper studies the relationship between rational herding and cross correlations in security returns. It demonstrates analytically and numerically that herding, as a temporary, fragile convergence of investment behavior, can endogenously induce asset dependency. Furthermore, there exists a self-reinforcing process, in which market extreme events amplify the herd effect, which further exacerbates asset dependency. Considering the Taiwan and U.K. equity markets, we find that the simulated markets in the presence of herding have results closer to the real patterns of asset dependency than a static model with isolated, noninteracting individuals. Our findings cast doubts on the current view that transparent financial regulation is always desirable. Moreover, this paper finds statistical evidence of asymmetric correlation patterns in both the top 50 stocks in the U.K. and Taiwan equity markets. This suggests that portfolio diversification as a means of managing portfolio risk is unlikely to be effective in periods of extreme losses in these markets.