Showing papers by "W. Brian Arthur published in 1999"
TL;DR: In this article, the authors present results from an experimental computer simulated stock market, where artificial intelligence algorithms take on the role of traders and make predictions about the future, and buy and sell stock as indicated by their expectations of future risk and return.
749 citations
Journal Article•
TL;DR: This paper proposes three means by which complexity tends to grow as systems evolve, which are intermittent and epochal, and reversible, so that collapses in complexity may occur randomly from time to time.
Abstract: It is often taken for granted that as systems evolve over time they tend to become more complex But little is understood about the mechanisms that might cause evolution to favor increases in complexity over time This paper proposes three means by which complexity tends to grow as systems evolve In co-evolutionary systems it may grow by increases in ``species'' diversity: under certain circumstances new species may provide further niches that call forth further new species in a steady upward spiral In single systems it may grow by increases in structural sophistication: the system steadily cumulates increasing numbers of subsystems or sub-functions or sub-parts to break through performance limitations, or enhance its range of operation, or handle exceptional circumstances Or, it may suddenly increase by ``capturing software'': the system captures simpler elements and learns to ``program'' these as ``software'' to be used to its own ends Growth in complexity in all three mechanisms is intermittent and epochal And in the first two is reversible, so that collapses in complexity may occur randomly from time to time Ilustrative examples are drawn from biology, but from economics, adaptive computation, artificial life, and evolutionary game theory
40 citations
TL;DR: The Santa Fe Artificial Stock Market consists of a central computational market and a number of artificially intelligent agents, which make their investment decisions by attempting to forecast the future return on the stock, using genetic algorithms to generate, test, and evolve predictive rules.
Abstract: The Santa Fe Artificial Stock Market consists of a central computational market and a number of artificially intelligent agents. The agents choose between investing in a stock and leaving their money in the bank, which pays a fixed interest rate. The stock pays a stochastic dividend and has a price which fluctuates according to agent demand. The agents make their investment decisions by attempting to forecast the future return on the stock, using genetic algorithms to generate, test, and evolve predictive rules. The artificial market shows two distinct regimes of behavior, depending on parameter settings and initial conditions. One regime corresponds to the theoretically predicted rational expectations behavior, with low overall trading volume, uncorrelated price series, and no possibility of technical trading. The other regime is more complex, and corresponds to realistic market behavior, with high trading volume, high intermittent volatility (including GARCH behavior), bubbles and crashes, and the presence of technical trading. One parameter that can be used to control the regime is the exploration rate, which governs how rapidly the agents explore new hypotheses with their genetic algorithms. At a low exploration rate the market settles into the rational expectations equilibrium. At a high exploration rate it falls into the more realistic complex regime. The transition is fairly sharp, but close to the boundary the outcome depends on the agents’ initial “beliefs”—if they believe in rational expectations they occur and are a local attractor; otherwise the market evolves into the complex regime.
36 citations
01 Jan 1999
TL;DR: The story of the sciences in the 20th century is one of a steady loss of certainty as mentioned in this paper, where much of what was real and machine-like and objective and determinate at the start of the century, by mid-century was a phantom, unpredictable, subjective and indeterminate.
Abstract: The story of the sciences in the 20th Century is one of a steady loss of certainty. Much of what was real and machine-like and objective and determinate at the start of the century, by mid-century was a phantom, unpredictable, subjective and indeterminate. What had defined science at the start of the century—its power to predict, its clear subject/object distinction—no longer defines it at the end. Science after science has lost its innocence. Science after science has grown up.
32 citations
Posted Content•
TL;DR: In this paper, a market of artificially intelligent traders is constructed to buy and sell a risky asset along with a risk free bond, and prices of the risky asset are determined endogenously from the interactions of the strategies which make trades and gather data.
Abstract: A market of artificially intelligent traders is constructed to buy and sell a risky asset along with a risk free bond. Prices of the risky asset are determined endogenously from the interactions of the strategies which make trades and gather data. Each trader tries to learn about the world around it while enhancing its trading strategies. The primary purpose of this paper is to demonstrate that such a market replicates some of the basic empirical features of many asset markets including the persistence of volatility and trading volume, weak trends in prices, and leptokurtosis in returns. Also, for certain parameter values agents converge to a well defined rational expectations equilibrium.
9 citations