Woods Institute for Environment, Stanford University, Stanford, CA 94305-2034, USA.
Proc Biol Sci. 2013 Jul 24;280(1767):20131210. doi: 10.1098/rspb.2013.1210. Print 2013 Sep 22.
In this paper, we attempt to understand hunter-gatherer foraging decisions about prey that vary in both the mean and variance of energy return using an expected utility framework. We show that for skewed distributions of energetic returns, the standard linear variance discounting (LVD) model for risk-sensitive foraging can produce quite misleading results. In addition to creating difficulties for the LVD model, the skewed distributions characteristic of hunting returns create challenges for estimating probability distribution functions required for expected utility. We present a solution using a two-component finite mixture model for foraging returns. We then use detailed foraging returns data based on focal follows of individual hunters in Western Australia hunting for high-risk/high-gain (hill kangaroo) and relatively low-risk/low-gain (sand monitor) prey. Using probability densities for the two resources estimated from the mixture models, combined with theoretically sensible utility curves characterized by diminishing marginal utility for the highest returns, we find that the expected utility of the sand monitors greatly exceeds that of kangaroos despite the fact that the mean energy return for kangaroos is nearly twice as large as that for sand monitors. We conclude that the decision to hunt hill kangaroos does not arise simply as part of an energetic utility-maximization strategy and that additional social, political or symbolic benefits must accrue to hunters of this highly variable prey.
本文试图利用期望效用框架理解在能量回报的均值和方差均存在差异的猎物的觅食者觅食决策。我们表明，对于能量回报的偏态分布，风险敏感觅食的标准线性方差折扣（LVD）模型可能会产生相当误导的结果。除了给 LVD 模型带来困难之外，狩猎回报的偏态分布也给估计期望效用所需的概率分布函数带来了挑战。我们提出了一种使用觅食回报的两分量有限混合模型的解决方案。然后，我们使用基于西澳大利亚个体猎人狩猎高风险/高收益（丘陵袋鼠）和相对低风险/低收益（沙监视器）猎物的焦点跟踪的详细觅食回报数据。使用混合模型估计的两种资源的概率密度，结合以最高回报为特征的边际效用递减的理论合理效用曲线，我们发现，尽管丘陵袋鼠的平均能量回报几乎是沙监视器的两倍，但沙监视器的预期效用大大超过了丘陵袋鼠。我们得出结论，猎捕丘陵袋鼠的决定并非仅仅是能量效用最大化策略的一部分，对于这种高度可变的猎物的猎人来说，必须有额外的社会、政治或象征利益。
