信息异质性如何影响预测质量？
How does informational heterogeneity affect the quality of forecasts?
摘要：
我们研究了一个玩具模型的诱导相互作用的代理人，旨在预测一个连续的，外生的随机变量E。E上的私有信息在代理之间不均匀地传播。当异质性最大时，羊群效应是首选的预测机制。然而，在这样的条件下，有效地汇总信息是困难的，即使在学习的存在，羊群效应的比率上升显着高于有效市场的期望值1和显着接近的经验观察值。我们还研究了不同的参数（交互范围，学习率，信息成本和分数记忆）可能会影响这种情况下，提高效率的困难阶段。
英文摘要：
We investigate a toy model of inductive interacting agents aiming to forecast a continuous, exogenous random variable E. Private information on E is spread heterogeneously across agents. Herding turns out to be the preferred forecasting mechanism when heterogeneity is maximal. However in such conditions aggregating information efficiently is hard even in the presence of learning, as the herding ratio rises significantly above the efficient-market expectation of 1 and remarkably close to the empirically observed values. We also study how different parameters (interaction range, learning rate, cost of information and score memory) may affect this scenario and improve efficiency in the hard phase.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
