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Working Paper
Learning and excess volatility
We introduce adaptive learning behavior into a general equilibrium lifecycle economy with capital accumulation. Agents form forecasts of the rate of return to capital assets using least squares autoregressions on past data. We show that, in contrast to the perfect foresight dynamics, the dynamical system under learning possesses equilibria characterized by persistent excess volatility in returns to capital. We explore a quantitative case for these learning equilibria. We use an evolutionary search algorithm to calibrate a version of the system under learning and show that this system can ...
Working Paper
Learning in a model of economic growth and development
We study a model of economic growth and development with a threshold externality. The model has one steady state with a low and stagnant level of income per capita and another steady state with a high and growing level of income per capita. Both of these steady states are locally stable under the perfect foresight assumption. We introduce learning into this environment. Learning acts as an equilibrium selection criterion and provides an interesting transition dynamic between steady states. We find that for sufficiently low initial values of human capital-values that would tend to characterize ...
Working Paper
Learning in a large square economy
Learning is introduced into a sequence of large square endowment economies indexed by n, in which agents live n periods. Young agents need to forecast n - 1 periods ahead in these models in order to make consumption decisions, and thus these models constitute multi-step ahead systems. Real time learning is introduced via least squares. The systems studied in this paper are sometimes locally convergent when n = 2,3 but are never locally convergent when . Because the economies studied are analogous, nonconvergence can be attributed solely to the multi-step ahead nature of the forecast problem ...
Working Paper
Bargaining Under Liquidity Constraints: Nash vs. Kalai in the Laboratory
We report on an experiment in which buyers and sellers engage in semi-structured bargaining in two dimensions: how much of a good the seller will produce and how much money the buyer will offer the seller in exchange. Our aim is to evaluate the empirical relevance of two axiomatic bargaining solutions, the generalized Nash bargaining solution and Kalai's proportional bargaining solution. These bargaining solutions predict different outcomes when buyers are constrained in their money holdings. We first use the case when the buyer is not liquidity constrained to estimate the bargaining power ...
Working Paper
A model of learning and emulation with artificial adaptive agents
We study adaptive learning behavior in a sequence of n-period endowment overlapping generations economies with fiat currency, where n refers to the number of periods in agents' lifetimes. Agents initially have heterogeneous beliefs and seek to form multi-step-ahead forecasts of future prices using a forecast rule chosen from a vast set of possible forecast rules. Agents take optimal actions given their forecasts of future prices. They learn in every period by creating new forecast rules and by emulating the forecast rules of other agents. Computational experiments with artificial adaptive ...
Working Paper
Learning and structural change in macroeconomic data
We include learning in a standard equilibrium business cycle model with explicit growth. We use the model to study how the economy's agents could learn in real time about the important trend-changing events of the postwar era in the U.S., such as the productivity slowdown, increased labor force participation by women, and the "new economy" of the 1990s. We find that a large fraction of the observed variance of output relative to trend can be attributed to structural change in our model. However, we also find that the addition of learning and occasional structural breaks to the standard and ...
Working Paper
Using genetic algorithms to model the evolution of heterogeneous beliefs
Genetic algorithms have been used by economists to model the process by which a population of heterogeneous agents learn how to optimize a given objective. However, most general equilibrium models in use today presume that agents already know how to optimize. If agents face any uncertainty, it is typically with regard to their expectations about the future. In this paper, we show how a genetic algorithm can be used to model the process by which a population of agents with heterogeneous beliefs learns how to form rational expectation forecasts. We retain the assumption that agents optimally ...
Working Paper
On learning and the stability of cycles
We study a general equilibrium model where the multiplicity of stationary periodic perfect foresight equilibria is pervasive. We investigate the extent of which agents can learn to coordinate on stationary perfect foresight cycles. The example economy, taken from Grandmont (1985), is an endowment overlapping generations model with fiat money, where consumption in the first and second periods of life are not necessarily gross substitutes. Depending on the value of a preference parameter, the limiting backward (direction of time reversed) perfect foresight dynamics are characterized by steady ...
Journal Article
Monetary theory in the laboratory
Empirical tests of macroeconomic and monetary theories are typically conducted using non-experimental field data provided by government agencies. Modern theories, however, have increasingly imposed restrictions on individual behavior that are not embodied in any available field data. An alternative method for testing such theories is to conduct controlled laboratory experiments with paid human subjects. In this article, John Duffy provides a critical survey of recent papers that have used laboratory methods to test modern monetary-theory predictions. While the survey focuses on the results ...