Interacting agents in finance, entry written for the New Palgrave Dictionary of Economics, Second Edition, edited by L. Blume and S. Durlauf, Palgrave Macmillan, forthcoming 2006.

Interacting agents in finance represent a behavioral, agent-based approach in which financial markets are viewed as complex adaptive systems consisting of many boundedly rational agents interacting through simple heterogeneous investment strategies, constantly adapting their behavior in response to new information, strategy performance and through social interactions. An interacting agent system acts as a noise filter, transforming and amplifying purely random news about economic fundamentals into an aggregate market outcome exhibiting important stylized facts such as unpredictable asset prices and returns, excess volatility, temporary bubbles and sudden crashes, large and persistent trading volume, clustered volatility and long memory.

Soros’s Reflexivity Concept in a Complex World: Cauchy Distributions, Rational Expectations, and Rational Addiction

George Soros makes an important analytical contribution to understanding the concept of reflexivity in social science by explaining reflexivity in terms of how his cognitive and manipulative causal functions are connected to one another by a pair of feedback loops (Soros, 2013). Fallibility, reflexivity and the human uncertainty principle. Here I put aside the issue of how the natural sciences and social sciences are related, an issue he discusses, and focus on how his thinking applies in economics. I argue that standard economics assumes a ‘classical’ view of the world in which knowledge and action are independent, but that we live in a complex reflexive world in which knowledge and action are interdependent. I argue that Soros\u27s view provides a reflexivity critique of the efficient market hypothesis seen as depending on untenable claims about the nature of random phenomena and the nature of economic agents. Regarding the former, I develop this critique in terms of Cauchy distributions; regarding the latter I develop it in terms of rational expectations and rational addiction reasoning

Evolutionary Selection of Expectations in Positive and Negative Feedback Markets

An economic environment is a feedback system, where dynamics of aggregate variables depend on individual expectations and also shape them. The type of feedback mechanism is crucial for the aggregate outcome. Experiments with human subjects (Heemeijer et al, 2009) have shown that price converges to the fundamental level in the negative feedback environment but fails to do so under positive feedback. We present an explanation of these experimental results by means of a model of evolutionary switching between heuristics. Active heuristics are chosen endogenously, on the basis of their past performance. Under negative feedback an adaptive heuristic dominates explaining fast price convergence, whereas under positive feedback a trend-following heuristic dominates resulting in persistent price deviation and oscillations.

Asset Price Dynamics with Local Interactions under Heterogeneous Beliefs

This paper investigates the effect of network structure on the asset price dynamics. We propose a simple present value discounted asset pricing model with heterogeneous agents. Every period the agents choose a predictor of the future price on the basis of past performance of their own and alternative strategies and form their demands for a risky asset. The information about the performance of an alternative strategy is available only locally from the directly connected agents. Using the rewiring procedure we produce four types of commonly considered networks: a fully connected network, a regular lattice, a small world, and a random network. The results show that the network structure influences asset price dynamics in terms of the region of stability and volatility. This is mostly due to the different speed of information transmission in the different networks.

Complex evolutionary systems in behavioral finance

Traditional finance is built on the rationality paradigm. This chapter discusses simple models from an alternative approach in which financial markets are viewed as complex evolutionary systems. Agents are boundedly rational and base their investment decisions upon market forecasting heuristics. Prices and beliefs about future prices co-evolve over time with mutual feedback. Strategy choice is driven by evolutionary selection, so that agents tend to adopt strategies that were successful in the past. Calibration of "simple complexity models" with heterogeneous expectations to real financial market data and laboratory experiments with human subjects are also discussed.

The virtues and vices of equilibrium and the future of financial economics

The use of equilibrium models in economics springs from the desire for parsimonious models of economic phenomena that take human reasoning into account. This approach has been the cornerstone of modern economic theory. We explain why this is so, extolling the virtues of equilibrium theory; then we present a critique and describe why this approach is inherently limited, and why economics needs to move in new directions if it is to continue to make progress. We stress that this shouldn't be a question of dogma, but should be resolved empirically. There are situations where equilibrium models provide useful predictions and there are situations where they can never provide useful predictions. There are also many situations where the jury is still out, i.e., where so far they fail to provide a good description of the world, but where proper extensions might change this. Our goal is to convince the skeptics that equilibrium models can be useful, but also to make traditional economists more aware of the limitations of equilibrium models. We sketch some alternative approaches and discuss why they should play an important role in future research in economics.Comment: 68 pages, one figur