Science 2 April 1999:
Vol. 284 no. 5411 pp. 107-109
reading notes:
"The result—complexity economics—is not an adjunct to standard economic theory but theory at a more general, out-of-equilibrium level."
"increasing returns problems in economics are best seen as dynamic processes with random events and natural positive feedbacks—as nonlinear stochastic processes"
"The common finding that economic structures can crystallize around small events and lock in is beginning to change policy in all of these areas toward an awareness that governments should avoid both extremes of coercing a desired outcome and keeping strict hands off, and instead seek to push the system gently toward favored structures that can grow and emerge naturally. Not a heavy hand, not an invisible hand, but a nudging hand."
"Within this computerized market, we found two phases or regimes. If parameters are set so that our artificial agents update their hypotheses slowly, the diversity of expectations collapses quickly into homogeneous rational ones. The reason is that if a majority of investors believes something close to the rational expectations forecast, then resulting prices will validate it, and deviant or mutant predictions that arise in the population of expectational models will be rendered inaccurate. Standard finance theory, under these special circumstances, is upheld. But if the rate of updating of hypotheses is increased, the market undergoes a phase transition into a complex regime and displays several of the anomalies observed in real markets. It develops a rich psychology of divergent beliefs that don't converge over time. Expectational rules such as “if the market is trending up, predict a 1% price rise” that appear randomly in the population of hypotheses can become mutually reinforcing: If enough investors act on these, the price will indeed go up. Thus subpopulations of mutually reinforcing expectations arise, agents bet on these (therefore technical trading emerges), and this causes occasional bubbles and crashes. Our artificial market also shows periods of high volatility in prices, followed randomly by periods of low volatility. This is because if some investors discover new profitable hypotheses, they change the market slightly, causing other investors to also change their expectations. Changes in beliefs therefore ripple through the market in avalanches of all sizes, causing periods of high and low volatility. We conjecture that actual financial markets, which show exactly these phenomena, lie in this complex regime."
"increasing returns problems in economics are best seen as dynamic processes with random events and natural positive feedbacks—as nonlinear stochastic processes"
"The common finding that economic structures can crystallize around small events and lock in is beginning to change policy in all of these areas toward an awareness that governments should avoid both extremes of coercing a desired outcome and keeping strict hands off, and instead seek to push the system gently toward favored structures that can grow and emerge naturally. Not a heavy hand, not an invisible hand, but a nudging hand."
"Within this computerized market, we found two phases or regimes. If parameters are set so that our artificial agents update their hypotheses slowly, the diversity of expectations collapses quickly into homogeneous rational ones. The reason is that if a majority of investors believes something close to the rational expectations forecast, then resulting prices will validate it, and deviant or mutant predictions that arise in the population of expectational models will be rendered inaccurate. Standard finance theory, under these special circumstances, is upheld. But if the rate of updating of hypotheses is increased, the market undergoes a phase transition into a complex regime and displays several of the anomalies observed in real markets. It develops a rich psychology of divergent beliefs that don't converge over time. Expectational rules such as “if the market is trending up, predict a 1% price rise” that appear randomly in the population of hypotheses can become mutually reinforcing: If enough investors act on these, the price will indeed go up. Thus subpopulations of mutually reinforcing expectations arise, agents bet on these (therefore technical trading emerges), and this causes occasional bubbles and crashes. Our artificial market also shows periods of high volatility in prices, followed randomly by periods of low volatility. This is because if some investors discover new profitable hypotheses, they change the market slightly, causing other investors to also change their expectations. Changes in beliefs therefore ripple through the market in avalanches of all sizes, causing periods of high and low volatility. We conjecture that actual financial markets, which show exactly these phenomena, lie in this complex regime."
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