To kill a kangaroo: understanding the decision to pursue high-risk/high-gain resources

Abstract

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 twocomponent 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 lowrisk/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.

Is Palaeolithic cave art consistent with costly signalling theory? Lascaux as a test case

“…Costly signally theory (henceforth CST) has been invoked to explain various forms of non-optimised, unnecessary or 'wasteful' behaviours, including particularly high-risk and/or low-gain hunting observed among modern hunter-gatherers (e.g. Jones et al 2013. Bliege Bird andPower 2015), footbinding, monumental architecture, and expensive public rituals such as funerary feasts among various small-scale societies (Smith and Bliege Bird 2000) ii .…”

Disturbance, Complexity, Scale: New Approaches to the Study of Human–Environment Interactions

“…Therefore, both on-encounter returns and daily trip returns (including search and travel time to and from the foraging locale) tend to be more variable: There is a higher risk of complete harvest failure and a higher chance of coming home empty-handed, but also a higher chance of a harvest bonanza (see . Whereas larger animals may provide big bonanzas on average, when the variance in reward is taken into account, such bonanzas may actually provide less utility than a lower mean return on less variable prey ( Jones et al 2013). In general, foraging portfolios dominated by smaller or less mobile types of prey tend to be associated with lower intra-and interindividual variance than those dominated by larger prey.…”

“…Due to high encounter rates and the high chances of pursuit success, sand monitors are a staple food resource, the harvest of which can be adjusted to need on a daily basis (Codding et al 2010). Variance discounting models show that although sand monitor hunting has lower mean returns than kangaroo hunting, its lower variance and higher chances of success make it more useful for consumption than kangaroo hunting ( Jones et al 2013). In the context of a hybrid economy that includes some reliance on purchased goods, sand monitor hunting, unlike kangaroo hunting, responds to economic scarcity: It has been shown that more women hunt significantly more often when money is short (Scelza et al 2014).…”

Pursuit

“…The large game focused strategy had a higher mean return rate but resulted in successful kills in only 4 of the 10 rounds played and thus carried a higher risk of failure per round. How foragers perceive or incorporate risk into their prey choice decisions is an active area of research in behavioral ecology (e.g., Jones et al, 2013) and Pursuit could be used to study this topic.…”

“…These models can be used to make predictions about the behavior of any animal that makes decisions about where and what to eat. Researchers have employed these models in the analysis of human foraging behavior (Bird, Smith, & Bird, 2001;Hill, Kaplan, Hawkes, & Hurtado, 1987;Jones, Bird, & Bird, 2013;Koster, 2008;Sosis, 2002;Wood & Marlowe, 2014). Such studies have successfully predicted human behavior with simple assumptions of adaptive decision making, and especially when such predictions fail, helped illuminate the additional factors that influence foraging behavior.…”