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原文連結
論文資訊
- 類型：已發表論文
- 日期：2001
摘要
Recent research has shown the importance of time horizons in models of learning in finance. The dynamics of how 智能體s adjust to believe that the world around them is stationary may be just as crucial in the convergence to a rational expectations equilibrium as getting parameters and model specifications correct in the learning process. This paper explores the process of this 演化 in learning and time horizons in a simple 智能體-based financial market. The results indicate that, although the simple model structure used here replicates usual rational expectations results with long-horizon 智能體s, the route to evolving a 族群 of both long- and short-horizon 智能體s to long horizons alone may be difficult. Furthermore, 族群s with both short-and long-horizon 智能體s increase return variability, and leave pattern
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