The Glass is Half-Full: Overestimating the Quality of a Novel Environment is Advantageous
The Glass is Half-Full: Overestimating the Quality of a Novel Environment is Advantageous
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DOI：
10.1371/journal.pone.0034578
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发表时间：
2012-04-03
期刊：
影响因子：
3.7
通讯作者：
Avgar, Tal
中科院分区：
文献类型：
--
作者：
Berger-Tal, Oded;Avgar, Tal
根据最优觅食理论，觅食决策是基于觅食者对其环境质量的当前估计。然而，在一个新的环境中，觅食者并不拥有关于环境质量的信息，并且可能基于有偏见的估计来做出决定。我们用一个简单的模拟模型表明，当面对不同环境中的不确定性时，高估环境的质量(做一个乐观主义者)比低估环境质量要好得多，因为乐观的动物因为更高的探索率而更快地学习环境的真正价值。此外，我们表明，当动物有能力记住资源补丁的位置和质量时，由于探索的好处，对环境的正向偏向估计比无偏估计会产生更高的适应度收益。我们的研究展示了直接从最优觅食理论得出的不完全信息的简单觅食模型如何产生有充分证据的探索动物的复杂空间使用模式。
According to optimal foraging theory, foraging decisions are based on the forager's current estimate of the quality of its environment. However, in a novel environment, a forager does not possess information regarding the quality of the environment, and may make a decision based on a biased estimate. We show, using a simple simulation model, that when facing uncertainty in heterogeneous environments it is better to overestimate the quality of the environment (to be an "optimist") than underestimate it, as optimistic animals learn the true value of the environment faster due to higher exploration rate. Moreover, we show that when the animal has the capacity to remember the location and quality of resource patches, having a positively biased estimate of the environment leads to higher fitness gains than having an unbiased estimate, due to the benefits of exploration. Our study demonstrates how a simple model of foraging with incomplete information, derived directly from optimal foraging theory, can produce well documented complex space-use patterns of exploring animals.