A Comparative Bayes tactic for mate assessment and choice
A Comparative Bayes tactic for mate assessment and choice
复制标题
用于配偶评估和选择的比较贝叶斯策略
DOI：
10.1093/beheco/7.4.451
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发表时间：
1996
影响因子：
2.4
通讯作者：
Barney Luttbeg
中科院分区：
文献类型：
--
作者：
Barney Luttbeg
择偶策略模型假设雌性在收集信息时随机遇到雄性，并且信息是完全的。对四种鸟类的实验观察表明，雌性在交配前会选择性地拜访雄性并重复拜访雄性。这表明，以前模型的假设过于严格。提出了一种信息收集和择偶的替代模型，该模型放松了随机相遇和完美信息的假设。在这个比较贝叶斯模型中，决定何时以及从谁那里收集信息是使用贝叶斯估计每个男性的质量。模型的预测结果是：（1）随着对雄性质量的初始不确定性的增加，随着信息收集成本的降低，以及雌性感知到的信号更好地代表了雄性的实际质量，配偶评估的发生率将增加：（2）当来自雄性的信号具有中等可靠性时，重复访问雄性的发生率最高;以及（3）评估哪一个雄性的决定将取决于估计的雄性的质量、关于每个雄性的质量的先前确定性、每个雄性的信号的可靠性以及评估的成本。模拟比较比较贝叶斯策略的健身结果，其他择偶策略。当评估成本较低时，比较贝叶斯策略的适应度显著高于固定阈值策略和n中最佳策略。
Models of mate choice tactics have assumed that females randomly encounter males when collecting information and the information is perfect. Empirical observations of four bird species show that females selectively visit males and repeat visits to males before mating. This suggests that the assumptions of previous models have been too restrictive. An alternative model of information gathering and mate choice, which relaxes the assumptions of random encounters and perfect information, is presented. In this Comparative Bayes model, the decision of when and from whom to collect information is made using Bayesian estimates of each male's quality. Predictions from the model are that: (1) the occurrence of mate assessment will increase as initial uncertainty about the quality of males increases, as the cost of gathering information decreases, and as the signal perceived by the female becomes a better representation of males' actual qualities; (2) the occurrence of repeat visits to males will be highest when signals from males are of medium reliability; and (3) the decision of which male to assess will depend on the estimated qualities of males, prior certainty about each male's quality, the reliability of each male's signal, and the costs of assessment. Simulations compare the fitness outcomes of the Comparative Bayes tactic to other mate choice tactics. The fitness from the Comparative Bayes tactic is significantly higher than from the fixed threshold tactic and than from the best-of-n tactic when the cost of assessment is low.