The ideal free distribution when the resource is variable
The ideal free distribution when the resource is variable
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资源可变时的理想免费分配
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
H. Hakoyama
中科院分区：
文献类型：
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作者：
H. Hakoyama
在理想自由分配（IFD）模型的基础上，考虑两种纳入决策信息不确定性的随机模型，研究连续输入条件下资源供给率变异性对IFD的影响。在不确定信息模型中，竞争者无法追踪供给率的变化，也无法利用供给率的预期或之前的收益进行决策。两个子模型都预测均值匹配，其中每个补丁的平均竞争者数量与补丁中的平均供应率成正比。在完美信息模型中，竞争对手不断了解和追踪环境状况。数值预测取决于补丁之间资源差异的相对大小。当好补丁中的资源方差比差补丁中的资源方差大得多时，它预测均值不匹配；当每个补丁的供给率方差较小且与补丁中供给率的平均值成正比时，预测均值匹配；当较差补丁中的资源方差大于（或等于）良好补丁中的资源方差时，它预测均值过度匹配。这些结果表明，在资源供给率随机的条件下，澄清决策信息的不确定性假设和资源方差类型对于IFD检验的重要性。版权所有2003。
On the basis of the ideal free distribution (IFD) model, two stochastic models that incorporate the uncertainty of the information used for decision making were considered to investigate the effects of the variability in the resource supply rate on the IFD under continuous input conditions. In the uncertain-information model, competitors cannot trace the variation of the supply rate and use the expectation of the supply rate or previous payoffs for decision making. Both submodels predict matching of means, in which the average number of competitors for each patch is proportional to the average supply rate in the patch. In the perfect-information model, competitors continuously know and trace the environment conditions. Numerical predictions depend on the relative size of the resource variance between patches. When the resource variance in the good patch is sufficiently larger than that in the poor patch, it predicts undermatching of means; when the variance of the supply rate for each patch is small and proportional to the average of the supply rate in the patch, it predicts matching of means; and when the resource variance in the poor patch is larger than (or equal to) that in the good patch, it predicts overmatching of means. These results indicate the importance of clarifying the assumption on the uncertainty in information for decision making and the type of the resource variance for the test of the IFD under conditions where the resource supply rate is stochastic. Copyright 2003.