The Economics of Hunter-gatherer

8/15/2019 The Economics of Hunter-gatherer

1/24

Viewpoint: The economics of hunter-

gatherer societies and the evolution of human characteristicsArthur J. Robson Department of Economics, Simon Fraser

UniversityHillard S. Kaplan Department of Anthropology, University of

New Mexico

Abstract. We argue for attention to the evolutionary origins of economic behaviour.Going beyond this, we argue that the economy of hunting and gathering was thecontext in which evolution shaped human characteristics that underlie modern eco-nomic behaviour. We first reconsider the basic biological question of why aging occursat all. We then illustrate the usefulness of considering foraging economics by asking whyit is biologically advantageous for humans to live long after their reproductive career is

over. Further, we argue that foraging economics would have led to the simultaneousexaggeration of intelligence and of longevity that is characteristic of humans. JELclassification: A12, J10

L’e´ conomie des socie ´ té s de chasseurs-cueilleurs et l’e ´ volution des traits humains. Lesauteurs sugge r̀ent qu’on doit porter plus d’attention aux origines e v́olutionnaires ducomportement e ćonomique. Poussant un pas plus loin, ils sugge r̀ent que l’e ćonomiedes chasseurs-cueilleurs a e t́e ́ le contexte où l’e v́olution a conforme ́ les traits humainsqui sous-tendent le comportement e ćonomique moderne. D’abord, ils re éxaminent laquestion biologique de base a ̀ savoir pourquoi il y a vieillissement. Ensuite, ils illustrentl’utilite ́ d’une e ćonomie des activite ś de cueillette, chasse et engrangement en posant la

question a ̀savoir pourquoi il est biologiquement avantageux pour les humains de bienvivre apre s̀ que leur pe ŕiode de reproduction active est termine é. Finalement, ils sug-gè rent que ce type d’é conomie aurait entraı̂ né simultané ment une surestimation del’intelligence et de la longe v́ite ́– deux traits des humains.

Robson was supported by the Social Sciences Research Council of Canada and theCanada Research Chairs program; Kaplan by the National Science Foundation and theNational Institutes on Aging. Email: [email protected]

Canadian Journal of Economics / Revue canadienne d’Economique, Vol. 39, No. 2May / mai 2006. Printed in Canada / Imprimé au Canada

0008-4085 / 06 / 375–398 / Canadian Economics Association


2/24

1. Introduction

Serious consideration of the evolutionary origins of human economic behaviour

is likely to pay handsome dividends. Our evolutionary history is inherentlyfascinating and logically must underpin relevant human characteristics.Recently, however, a number of empirical regularities have been found thatcast doubt on the validity of conventional economic theory. These regularitieshave inspired a bewildering variety of conflicting alternative theories, many of which are limited in scope and ad hoc. We believe, then, that the mostimportant potential contribution from considering the evolutionary origins of economic behaviour is that it can provide a disciplined way of winnowingdown and unifying the current array of models of economic behaviour. An

evolutionary perspective holds out the hope of modifying and extendingeconomic theory while retaining its appealing generality and consistency.

Progress can be made by considering in the abstract how biologicalevolution shaped attitudes to risk, for example (see, e.g., Robson 1996a, b).However, an evolutionary approach can be sharpened by considering thecircumstances prevailing during our evolutionary history. For most of thetwo million years since the advent of the genus Homo , that is, we have huntedand gathered. This foraging lifestyle put an unusually heavy emphasis onintelligence and social organization. We hunted, for example, using a myriadof techniques that were customized to a corresponding myriad of situations,and many of these techniques involved cooperative behaviour. The cognitivecomplexity of economic production in foraging societies entailed a dramaticinitial increase in the rate of output as individuals age, owing to the effects of learning-by-doing, in particular. It is then not surprising that foraging societiesare also characterized by huge intergenerational transfers from adults tochildren. Thus, we take foraging economies with such characteristics as the contextin which natural selection operated to form modern human demographic andeconomic characteristics.

A thesis of this paper is therefore that the usual implicit causal relationshipbetween economics and biology should be partially reversed. That is, wheneconomists think of biological evolution (which admittedly is not very often), itmight be as providing the effectively exogenous backdrop against which moderneconomic phenomena play out. This paper argues instead that the economicsof archaic societies was the crucible in which biological evolution formedhuman demographic and economic characteristics. That is, if we peek behindthe backdrop, there is not only evolutionary biology, but economics as well.

2. The evolved life history of a skill-intensive and social species: an overview

In many ways, we human beings are ideal biological organisms to study. Welive and have lived in a diverse array of environments; we know our ages andour reproductive history; we can report directly on relevant phenomena; and

376 A.J. Robson and H.S. Kaplan


3/24

we leave historical records. For example, we certainly know much more aboutaging in humans than we do about aging in any other mammal (with thepossible exception of laboratory rats and mice).

The processes underlying human aging stand as testaments to the forcesshaping life span evolution among living creatures in general. More specifi-cally, research with the few remaining people that still practise a traditionalhunting and gathering lifestyle is providing critical information about the lifehistories of our ancestors and the selection pressures that acted on them.Humans lived as hunter-gatherers for most of our 2 million years of evolu-tionary history. Modern hunter-gatherers, who are affected by global socio-economic forces, are not living replicas of our stone age past. Yet, in spite of their variable historical, ecological, and political conditions, foraging peoples

exhibit remarkable similarities, especially in the realms of development andaging. This suggests that natural selection has produced a characteristic lifehistory for our species, a life history that is quite distinct from that of our closestliving primate relatives.

When we compare humans with other creatures, our brains and attendantmental abilities stand out. The human brain is about three times as large asthat of a chimpanzee, our closest living relative and arguably the most intelligentnon-human species. Any attempt to account for our unique evolutionary pathwill also have to explain why human brains became so large and why webecame such good learners.

In addition to our large brain size, humans are also distinctive because of our very long lives. Comparing the life span of people living in remote societieswithout Western medicine to that of wild chimpanzees is particularly revealing.Overall, such humans have an average life span that is about twice that of chimpanzees in the wild. In greater detail: the risk of dying is high duringinfancy for both humans and chimpanzees, but then decreases rapidly. Forchimpanzees the lowest point – about 3% per year – is reached at about age 13(the age of first reproduction for females) and increases sharply after that. Incontrast, mortality among people still living as hunter-gatherers drops to amuch lower point – about 0.5% per year – and remains low without muchincrease between about 15 and 40 years of age. Human mortality rates then riseslowly, followed by a rapid jump only in the 60s and 70s.

As a result, about 60% of hunter-gatherer children survive to adulthood,compared with 35% of chimpanzees. Chimpanzees also have a much shorteradult life span than humans. From the age when they first reproduce,chimpanzees live an additional 15 years, on average, compared with 38 moreyears among human foragers. It is important to note that very few femalechimpanzees even reach a post-reproductive phase, whereas women spend morethan a third of their adult life in such a phase. Fewer than 10% of chimpanzeeslive to age 40, but more than 15% of hunter-gatherers make it to age 70.

Thus, delayed aging and long adult life spans appear to be evolved char-acteristics of our species. Adult mortality risks are remarkably uniform across

Economics of hunter-gatherer societies 377


4/24

human societies without access to western medicine. Although the ancestors of the aboriginal peoples of South America, Africa, and New Guinea divergedmore than 10,000 years ago, the risk of dying at each age is very similar from

society to society, and not that different from that in Europe prior to modernmedicine.It helps to examine our primate ancestors in order to understand the

evolution of our larger brains and longer lives. As the primate order evolvedand different species radiated across the world’s warmer areas, there was aseries of four ‘grade shifts’ that increased ‘encephalization’ – brain size relativeto body size – and increased longevity.

The first grade shift began about 60 million years ago with the evolution of prosimians, who probably lived longer because living in trees afforded them

more safety. The second grade shift came about 35 million years ago with theevolution of the monkey lineage. It involved a huge increase in both brain sizeand life span. The development of the ape lineage – represented today bychimpanzees, gorillas, and orangutans – was the third major grade shift.Apes can live almost twice as long as most monkeys and have much biggerbrains, even after adjusting for their larger body size. The fourth grade shiftoccurred with the divergence of the hominid line, particularly the advent of thegenus Homo about 2 million years ago.

This coevolution of brains and life spans makes sense when one considersthat natural selection depends on both costs and benefits. Brains are verycostly: humans use about 65% of base metabolism for the maintenance andgrowth of the brain during the first year of life. Brains resulting from naturalselection must provide benefits that pay for their costs. If the benefits providedby the brain were outweighed by its cost, individuals with smaller brains wouldleave more descendants than those with larger brains, and the average brainsize of the population would shrink.

Since learning transforms present experiences into better future performance,we can see brain development – especially development of the cerebral cortex(which expanded disproportionately during primate evolution) – as an investmentin the future. During primate evolution, brain size appeared to increase as thelearning required by the feeding niche grew more intense. The advent of monkeys, for example, involved a shift from the prosimians’ smell- andhearing-based insect eating to a reorganized sensory system with binocular,colour vision able to find many different plant foods, captured by dexteroushands and manipulated through hand-eye coordination. Because monitoringand exploiting the fruits and leaves of different trees demanded more learn-ing, brain size increased.

During the third shift, apes adopted a diet that emphasized ripe fruits, requiringeven more environmental monitoring and more complex extractive techniques.These higher prerequisites for learning mean apes take longer than monkeys tobecome competent foragers. For example, even though chimpanzees canprovide most of their own caloric needs by age five, juveniles under age



5/24

seven are unable to utilize and make tools for termite fishing and therefore stillrely upon their mothers for protein, even after weaning. Orangutans alsodepend upon their mothers for about seven years. Today, as conservation-

minded scientists attempt to reintroduce captive-born orangutans into theforest, they are discovering how much learning must take place before thesecreatures are able to survive on their own.

During our evolutionary history, humans pushed learning-intensive feedingtechniques to the extreme. Evidence from a wide variety of sources shows thathumans became specialized in the consumption of calorie-dense, low-fibrefoods rich in protein and fat. Although there is considerable variation acrosssocieties depending on their ecology, modern foragers all differ considerably indiet from chimpanzees. The majority of forager diets is meat, accounting for

about 60% of calories. In contrast, chimpanzees obtain only about 2% of theirfood energy from hunted foods.

The next most important food category for foragers is resources such asinsects, roots, nuts, seeds, and difficult-to-extract plant parts such as palm fibreor growing shoots. These resources tend to be embedded in a protectivecontext, such as underground or in hard shells. Such extracted foods makeup about 30% of the forager diet, as opposed to 3% among chimpanzees. Incontrast to such difficult-to-acquire resources, collected foods such as fruits,leaves, flowers, and other easily accessible plant parts form, on average, 95%of the chimpanzee diet but only 8% of the human forager diet. The data thussuggest that humans specialize in rare but nutrient-dense resource packages – such as meat, roots, or nuts – whereas chimpanzees specialize in easily attainablebut less nutritionally dense plant parts.

Owing to the learning-intensive nature of their feeding niche, hunter-gathererstake even longer than apes to become competent foragers. Whereas chimpanzeescan meet most of their caloric needs by age five, human foragers produce fewercalories than they consume for close to twenty years! In fact, the total calories thathuman parents must provide for their offspring each year increases from birth toabout age 14, as children grow but remain unproductive.

Yet however burdensome child rearing may sometimes seem to hunter-gatherer parents, this long period of dependence pays off in the long run.Human adults are much more productive than chimpanzees. Net production(the surplus after one’s own food consumption is taken into account) climaxesat about 1,750 calories per day for human adults, compared with 250 for adultfemale chimpanzees, although human foragers generally do not reach that levelof productivity until about age 45.

Between ages 20 and 40, human hunters go through the equivalent of graduate school and a period of on-the-job training. This is because humans,unlike other predators, rely more on knowledge than on physical prowess. Forexample, in one group of South American hunter-gatherers, the Ache, menknow that the paca (a large rodent) lives in burrows with a main entrance andup to seven hidden escape hatches. When jaguars attempt to dig a paca out, it



6/24

can run away unscathed through one of its alternative exits. But rarely does itfool an Ache hunter, who calls the other hunters to the hole while quietly andsystematically searching for each escape route. When the other hunters arrive,

a younger, less-skilled hunter is given the task of ramming a large log into themain entrance. Simultaneously, each of the remaining hunters dives on top of his respective escape hatch while his hands form a noose, allowing the fleeingpaca’s head through his open arms only to grab its throat and suffocate it.

Feeding niches that demand such high levels of learning and cooperationshould select not only for bigger brains, but also for increased longevity. This isbecause the brain costs a great deal early in life, providing benefits only later inlife. To illustrate the importance of longevity to a learning-intensive feedingniche, consider what would happen if a human forager group suffered the same

mortality rates that chimpanzees do. Humans depend on their parents for along time, creating a large calorie deficit that they only gradually pay backduring their highly productive middle adulthood years. However, less than10% of chimpanzees live to the age at which human productivity peaks. With achimpanzee life span, a human forager group would then necessarily experiencea precipitous decline in numbers.

The flows of food and other services – both within and among families – that support this long period of dependence are also particular to humans.Unlike our ape relatives, such as chimpanzees, who give females the entireburden of feeding and caring for infants, humans cooperate in this activity,practising a sexual division of labour, a practice that appears to have ancientroots. Although the details vary from society to society, all existing hunter-gatherers and peoples who depend on a mix of foraging and farming world-wide collaborate in raising children. Typically, the male contribution to this joint endeavour focuses on food supply, mainly from hunting but with somegathering as well, while women participate in a wider mix of activities such asgathering, food preparation, and direct child care.

Why is a division of labour so fundamental to the human way of life?After all, women are physically capable of being adept hunters and some-times do so when it is necessary. However, since traditional human huntingis so learning intensive, it pays to hunt only if one spends many years doingso. If women were to hunt only when they were not pregnant or nursing,they might get less food from hunting than from gathering because of theirlack of experience.

Males probably became the primary provider of food when hunted animalsbecame an important part of human diets. The protein and fat from meatalong with the carbohydrate obtained from plant foods created a balanceddiet. Although the proportion of food provided by men and women variesacross hunter-gatherer societies, men provide, on average, about twice as manycalories and seven times as much protein as women. After taking into accounttheir own consumption, women supply only 3% of the calories to offspringwhile men provide the remaining 97%.



7/24

This assistance from men enables women to focus their energy on providinghigh-quality child care, resulting in almost double the survival rate for humanchildren compared with that of chimpanzee offspring. In addition to behav-

iour, women’s physiology is consistent with an evolutionary history of exten-sive male parental investment. Unlike other primates, human females lowertheir metabolic rates during pregnancy and store fat, which is permitted onlyby receiving provisions. Human female foragers also tend to work less duringlactation, unlike female chimpanzees, who heighten mortality risk by workingmore in this circumstance.

This extensive cooperation between hunter-gatherer men and women makesevolutionary sense only once the reproductive performance of spouses islinked. Even though divorce is common in many hunter-gatherer societies,

marriages stabilize once children are born. The long period of dependence onparents means that, at any one time, most parents are raising several dependentoffspring of different ages. This puts pressure on couples to stay together.Those who divorce and remarry while raising children frequently argue withnew spouses over the division of resources among their joint children.Avoiding those conflicts is an additional incentive for a couple to stay togetherand have all or most of their children together.

In fact, although men are physiologically capable of reproducing through-out their lives, most men undergo ‘behavioral menopause.’ Among Achehunter-gatherers, for example, if a couple had at least two children together,the woman’s last birth was the same as her husband’s last child in 90% of cases.

Indeed, the phenomenon of menopause, whether physiological or behav-ioural, is puzzling from a biological perspective. That is, how could humanshave evolved to outlive their biological usefulness? One suggestion is thatmenopause evolved because women would leave more genetic descendants byhelping as a grandmother than by continuing to reproduce. As women age, thereasoning goes, their pregnancies are less likely to succeed and they are morelikely to die in childbirth. Indeed, among foragers, there are no ‘golden’retirement years; both grandmothers and grandfathers spend significant timehelping to raise children. Although older people switch to less physicallydemanding and more knowledge-intensive activities as they age, they stillwork very hard until death.

This paper examines some of the issues arising here in greater detail, utiliz-ing our published and unpublished work, as follows. Given the apparentadvantages of longevity, we begin by re-examining a basic question applicableto a wide range of species, but to humans in particular: Why do we age at all?We then ask questions that focus more on the human case. These questionsalso illustrate how the economics of hunting and gathering was the context inwhich biological evolution shaped human demography: Why is fertility con-centrated towards the middle of life? Why do women live after menopause?Finally, we come back, in particular, to the major themes of this overview:



8/24

Why are we so smart? Why do we live so long? Why might these last twoattributes be linked together?

3. Why do we age? Why is fertility concentrated towards the middle of life?

From a biological point of view, aging refers to an eventual increase inmortality. But why does aging occur? How can it be evolutionarily optimalfor us to be programmed to first invest heavily in physical growth (while alsolearning productive skills), only to subsequently decay and senesce? It isimportant to appreciate in this connection that recent empirical findingsimply that aging in this sense may not be inevitable. 1

Robson and Kaplan (2005) show this issue can be addressed by applyingeconomic analysis of intertemporal allocation of scarce resources. Organismsgrow in order to produce more energy that is then available for additionalgrowth, maintenance, and reproduction. Natural selection chooses the lifehistory with the maximum rate of population increase. 2

There are two important related problems in evolutionary theories of aging(Key references: Charlesworth 2000; Hamilton 1966; Kirkwood 1990;Medawar 1952; Rose 1991; Williams 1957). First, existing evolutionary theoriesfail to provide an integrated model explaining both the typical decrease inmortality with age during the first phase of life and the subsequent typical risein mortality during the second phase. Second, this existing formal theory doesnot explain the extended periods of survival after reproduction has ceased, asobserved in humans (and in a few other species).

Inspired by the available data for hunter gatherers on mortality, bodyweight, and output, as sketched in figure 1, we suggest as an answer to thefirst problem that (a) growth in the quantity of somatic tissue helps to drive theincreasing productivity during the first phase of life and this results in anendogenous decline in mortality, and (b) Deterioration in the functional effi-ciency of somatic tissue drives the decline in productivity later in life, and this,in turn, drives an endogenous increase in mortality.

The second problem is: Why do we survive after fertility has fallen to zero?This essentially asks: Why do we not age faster? The existence of this phenom-enon in humans is demonstrated by the data on hunter-gatherer fertility andmortality in figure 1. The economic explanation for this phenomenon that weadopt relies upon the huge intergenerational transfers that are also observedin these societies. (Data on cumulative expected net energy production forhunter-gatherers in figure 2 below demonstrate the importance of these inter-generational transfers.)

1 For example, aging is negligible in some species of tree that live for several millennia (Finch1998). It is particularly revealing that humans age at about half the rate of chimpanzees(Kaplan et al. 2000), despite the close evolutionary relationship of the two species.

2 Stearns (1992) presents the evolutionary theory of life history.



9/24

Our theory hypothesizes that each individual’s somatic capital is character-ized by both its quantity and its quality. The quantity of capital is the numberof cells. There is initial investment in quantity, so cell number increasesirreversibly up to some age, but it is thereafter constant, capturing the ‘deter-minate growth’ pattern of humans. Cell quality, as measured by productiveefficiency, is also capable of improvement by energetic investment. Withoutsuch investment, however, cell quality depreciates over time.

Our approach to somatic quality is based on what is now known about thebiochemistry of cellular aging. Research shows that metabolic activity in thecell inevitably produces various harmful biochemical by-products, includingreactive oxygen species, or ROS, for example. These by-products are impli-cated in aging; For example, ROS is implicated in some serious neurologicaldisease. (See Love and Jenner 1999). However, such aging is not inevitable.That is, it is possible to remove such by-products and hence reduce or evenprevent aging. Consider, for example, the ‘nematode’ worm C. elegans , one of biology’s favorite experimental organisms. This has a genetic variant in which

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35 40 45 50 55 60 65Age

P r o d

u c t

i o n , W

e i g

h t a n

d F e r

t i l i t y

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0.11

0.12

0.13

0.14

M o r t a l

i t y

Production

Weight

Female Fertility

Mortality

FIGURE 1 Standardized production, standardized weight, standardized female fertility, and

mortality, all by ageSOURCES: The data on production and mortality are derived from Kaplan et al. (2000) andrepresent the average production and age-specific mortality of males and females by age amongthree foraging groups: the Ache of Paraguay, the Hiwi of Venezuela, and the Hadza of Tanzania.Data on weight are derived from unpublished measures of Ache males and females made byKaplan, along with coworkers Kim Hill, A. Magdalena Hurtado, and K. Hawkes. Data on fertilityare based upon the Ache of Paraguay from Hill and Hurtado (1996), those on the !Kung of Botswana from Howell (1979)



10/24

particular relatively large molecules are produced within each cell, which thenbind to the small harmful by-product molecules. The entire molecular combin-ation can then be purged from the cell. The interesting fact is that this genetic

variant, although typically present, is not common in the wild population (SeeMcElwee et al. 2004). The issue then must be: Is somatic quality maintenancetoo expensive to be worthwhile?

We propose a new theory of aging in which such quality maintenance isexpected to be too costly, as follows. Human beings, and many other species,are characterized by a segregation of the ‘germ line’ and the somatic line. Thatis, the cells that form the body of each individual are separate from the sex cellsof that individual – the eggs for females or the sperm for males. Thus, forexample, mutations arising from the replication of somatic cells during the

growth of the individual are not transmitted to offspring via the sex cells.In our model, the cost of investment in quality depends on the quantity of

cells, because each cell is subject to deterioration and has its own maintenancecosts. It may then be evolutionarily optimal to generate a high level of initialquality, but then to let it fall with age. This is because the quality of therelatively small number of cells in the germ line can be maintained cheaply,while the quality of the large soma achieved after growth would be much moreexpensive to maintain.

Indeed, we assume, as a reasonable limiting case, that the quality of thegerm line can be maintained costlessly. Thus the plans for a new individual canbe maintained without cost, implying that the initial quality of such a newindividual is high. It is relative to this alternative of producing a small buthigh-quality new individual that the cost of maintaining the quality of a full-grown individual is too high.

Our model also then explains why mortality rates are U-shaped, a commonfeature of a wide variety of species. That is, we explain not only why mortalityrates eventually rise, or why aging occurs, but why mortality rates initially fallduring infancy and childhood. In our model, optimal investments in mortalityreduction respond to the overall value of remaining life. As expected net futureproduction increases as a result of somatic growth, the optimal investment inmortality reduction then increases and mortality falls. As expected net futureproduction declines later in life, owing to decreased somatic quality, optimalinvestment in mortality reduction decreases and mortality rises.

3.1. A two-period exampleIt is impossible to illustrate all of the important theoretical issues here in a two-period example. Merely to obtain U-shaped mortality, for example, requires atleast four periods. Indeed, to obtain aging as an optimal outcome requires anunlimited number of periods, since to impose a terminal date guarantees agingnear that date. A key aspect of the model, however, concerns the dependenceof the cost of quality maintenance on the quantity of somatic capital. That



11/24

quality maintenance is thus discouraged by the optimal accumulation of somatic capital can be illustrated by a two-period example as follows.

Suppose that gross energy output of an individual is given by

F ðK ; QÞ ¼ aK bK 2 þ eQ fQ2; where a; b; e; f > 0;

K is the quantity of somatic capital and Q is its quality. An individual lives twoperiods and is endowed with somatic capital with initial quantity K 0 and initialquality Q0 . Suppose the energy cost of producing each individual is C 0 , aconstant, so that the reproductive technology is constant returns to scale.(This simplifying assumption cannot be made in the general model below if this is to yield a determinate pattern of fertility.) Suppose that individuals havefixed probability p of surviving from age 0 to age 1. This abstracts from an

important property of the general model that mortality is endogenous.At t ¼ 0, the individual invests v0 0 in the quantity of somatic capital and

w0 0 in the quality of this capital. The quantity of capital at t ¼ 1 is thenK 1 ¼ K 0 þ v0 ; the quality at t ¼ 1 is Q 1 ¼ Q0 Q 0 þ w0 , for some 2 (0, 1).There is no depreciation in the quantity of somatic capital, in line with its inter-pretation as the number of cells; but quality tends to depreciate, in line with itsinterpretation discussed above. The energy cost of v0 is v0 ; that of w0 is K 1 w0 .The latter cost function builds in a greater cost of quality maintenance for a largersoma, where the most appropriate soma is that from the next period, K 1 . (Thisissue does not arise in the general continuous time formulation sketched below.)

Consider, now, a large population in steady-state growth at rate r, say,so that the age distribution is constant. The ratio of old to young is then( p(s)/(1 þ r)). That is, there are fewer old individuals because there is someprobability of death in the interim, but also because there were fewer in thebirth cohort one period ago, given a positive growth rate.

What population growth rates are then economically feasible in the longrun? It is assumed that the energy flow is food, for example, which cannot bestored but is also not wasted. Since the steady state is then feasible if and onlyif it is possible for the adults to cover the deficits of the children, it follows that

C 0 ¼ F 0 ðK 0 þ I 0Þw0 I 0 þ pF ðK 0 þ v0; Q 0 Q 0 þ w0Þ

1 þ r ;

where F 0 ¼ F (K 0 , Q0 ) is fixed. Thus, the growth rate of the population is

1 þ r ¼ pF ðK 0 þ v0; Q 0 Q 0 þ w0Þ

ðK 0 þ I 0Þw0 þ I 0 þ C 0 F 0where ðK 0 þ I 0Þw0 þ I 0 þ C 0 F 0

> 0;which follows, for example, if C 0 > F 0 .The most evolutionarily successful type must then maximize this expression

for 1 þ r over choice of v0 and w0 . For simplicity, suppose, furthermore, thatthis maximized growth rate is zero. Indeed, humans must have had essentially



12/24

a zero growth rate over the 2 million years of our evolutionary history.(However, it is easy to allow a positive growth rate.)

Assuming an interior solution for v0 , the first-order conditions are then

F K ðK 1; Q 1ÞF ðK 1; Q 1Þ

¼

v0 þ C 0 F 0and

F Q ðK 1; Q 1ÞF ðK 1; Q 1Þ

K 1v0 þ C 0 F 0

;

with equality if w0 > 0. Equivalently,

pF K ðK 1; Q 1Þ ¼ a 2bK 1 ¼ and pF Q ðK 1; Q 1Þ ¼ e 2 fQ1 K 1;

with equality if w0 > 0. It follows that

K 1 ¼ a

2b ; so that ; if a

2b > e; then w0 ¼ 0:That is, the optimal strategy is to increase the quantity of somatic capital, eventhough such an increase renders the maintenance of quality suboptimal. It isnot that quality is forced to decline; it is a prediction of the model. For thatmatter, it may well be feasible to maintain quality, although a type that did sowould experience a a lower growth rate.

Indeed, if investment in quantity v0 is simply constrained to be zero, and K 0and Q 0 are small enough, then it will be optimal to invest in quality, w0 . That is,

if v0 ¼ 0; but e > K 0 and e K 0

2 f > Q 0 Q 0;

then the optimal

Q 1 ¼ e K 0

2 f ¼ Q 0 Q 0 þ w0 > Q 0 Q 0:

From an economic point of view, the form of the cost of investment in qualityimplies that the quantity and quality of somatic capital are essentially sub-

stitutes: an increase in the quantity of somatic capital reduces the attractivenessof investing in its quality. A key source of the evident asymmetry betweenquantity and quality is that initial quality is relatively high but initial quantityis low.

3.2. A sketch of a general theoryConsider now a sketch of the general model of Robson and Kaplan (2005).Greater generality is required in order to obtain U-shaped mortality as well asa fertility profile that is at first constant at zero, then rises, then falls, finallybecoming constant at zero again.

A key aspect of this general model is a non-linear cost of fertility. Thisimplies that the optimal time profile of fertility is determinate and has arealistic shape. Suppose, then, that the gross energy production rate of anorganism is given by the concave function G(K , Q). Again, K is the somatic



13/24

capital of the individual and Q is the quality of this somatic capital. Fertility isnow s and the energy flow net of the cost of fertility is given by the concavefunction F (G, s). This function is increasing in G, decreasing in s and

F Gs (G, s) < 0, so that reproduction is cheaper, at the margin, the greater theamount of gross energy available. For simplicity, individuals here reproduceparthenogenetically, by means of virgin birth.

The rate of augmentation of the capital stock at age t is denoted by v, where

dK dt

¼ v 2 ½0; v :

The quantity of somatic capital does not depreciate, as is consistent with thedata. The energy cost of increasing the capital stock at rate v is v.

Also for simplicity, investment in capital is confined to an initial range of ages. That is, there exists t* 0 such that

v ¼ v; for all t 2 ½0; t*v ¼ 0; for all t > t*:

Thus, the choice of the investment profile of the quantity of somatic capitalreduces to the choice of t* 0.

The evolution of the quality of the somatic stock, Q, is given by

dQdt

¼ w Q :

Here, is the rate of decline of quality of the stock in the absence of discre-tionary maintenance. The contribution of discretionary maintenance to off-setting or reversing this decline is w, and the energy cost of this is wd (K ),where d is increasing in K . That is, a given reduction in the rate of decline of quality is more energetically costly, the greater the quantity of the somaticstock involved. This reflects the energy economics of removing metabolicby-products, for example.

Another major component of the model that was absent in the example isendogenous mortality. If is the rate of mortality and p is the probability of survival, then these are connected by the identity

1 p

dpdt

¼ :

However, mortality is subject to natural selection, where the energetic cost of mortality is e( ), a decreasing and convex function. Consider the operation

of the immune system, for example. The larger the number of antibodies of agiven type, the better protected one is against the corresponding disease; thewider the spectrum of types of antibody, the greater the variety of diseases towhich one has immunity. But the larger the army of antibodies maintained, thegreater the metabolic cost.



14/24

Suppose, now, that the population is in a steady-state growth equilibrium,with growth rate r. The Euler-Lotka equation, a foundation of demography,must hold (see, e.g., Charlesworth 1994, chap. 1). That is,

Z 10 e rt psdt ¼ 1:In addition, it follows from the above that the energy flow surplus for an

individual at age t is

F v wd e:

This surplus is not constrained to be non-negative. Rather, intergenerationaltransfers can be made freely, a reasonable abstraction for the human case. 3

With such transfers, but given that energy in the form of food cannot bestored, economic feasibility requires that the total energy excess generated bythe old covers the total energy deficits of the young, at any given point in time,so that the steady-state ‘budget balance condition’ is

Z 1

0e rt pðF v wd eÞdt 0:

This, then, is a new foundation of our approach, a foundation based oneconomics rather than demography.

This is an idealization of hunter-gatherer society – that is, despite itspossibly having only 20–30 people, it is assumed that the steady-state agestructure has been attained. Individuals in each group of hunter-gatherers arealso assumed to be related to one another. This provides the basis for ananswer to the question: Why do the current old not simply renege on theobligation to make large transfers to the current young, even if these oldindividuals themselves were once the beneficiaries of such transfers? That is,from a biological point of view, individuals have a strong incentive to transferresources to the young if these individuals are close relatives genetically. Theproximate emotional mechanism driving such behaviour is parental love.

The basic evolutionary problem is then to maximize the growth rate rsubject to the differential equations governing K , Q, and p, the Euler-Lotkaequation, and the budget balance constraint.

The main result of Robson and Kaplan (2005) is that it may be optimal toinvest in somatic capital during an initial growth phase. Nevertheless, at thesame time it may be optimal never to counteract the decline in the quality of somatic capital. Furthermore, fertility is hump-shaped and is zero at the

beginning and again at the end of life. Finally, mortality is U-shaped with aminimum before the end of the growth phase, so that mortality has the shapeseen in the majority of species.

3 Kaplan and Hill (1985) provide evidence on food sharing and examine its causal basis.



15/24

The present theory can be shown to imply that mortality is governed mostlyby narrow biological considerations, based on fertility, when young. At theother end of life, however, where fertility is eventually zero, mortality is

determined by purely economic considerations, based on expected net energyproduction. It is therefore evolutionarily optimal to live beyond the age atwhich fertility falls to zero, because expected net energy production remainspositive there. The model then accounts for life beyond menopause.

It is worth emphasizing that it is generally feasible to maintain a value of lifethat is constant and hence mortality that is also constant. However, this is notoptimal, and a type that did this would not maximize the population growthrate. We grow old because the marginal cost of not growing old, of keepingmortality constant, exceeds the marginal benefit. This is in the light of the

option of using the pristine germ line ‘blueprints’ to create another individual,and is despite the enormous biological and economic costs that are incurred inraising such an individual.

At the same time, the Robson and Kaplan model can be generalized so thatit pays to eventually arrest the decline in the quality of somatic capital. That is,since the marginal product of quality rises as quality falls, eventually it may beoptimal to invest appropriately in somatic quality. This would produce aplateau in output and hence in mortality. Although such an individual wouldnot live forever, since her mortality would remain positive, she would not agebeyond a certain point, in the present biological sense. This is of interest, sinceit has been claimed that such a mortality plateau exists in the human data inadvanced old age. 4

3.3. A note on the literatureOur model is best seen as a more attractive formulation of Kirkwood’s ‘dis-posable soma’ theory of aging (see, e.g., Kirkwood 1990, 1999). Kirkwoodargues that repair of somatic tissue must be optimized by natural selection. Atsome point, greater returns will be obtained through reproduction thanthrough repair. Perhaps, then, optimal repair is less than complete, and thesoma deteriorates with age, ultimately being replaced by descendants. Thesegregation of the somatic and germ lines permits the degradation of theformer.

However, there are key differences in the detailed explanations of agingbetween Kirkwood’s model and ours. In Kirkwood’s model, in contrast toours, it is simply impossible to make the mortality rate fall over time andprohibitively expensive at the margin to keep it constant. 5 This assumption of

4 See Vaupel (1997). Indeed, there is sketchy evidence for a decrease in mortality in advancedold age. The obvious alternative explanation for such a phenomenon is that individuals arenot homogeneous. Hence, since individuals with higher mortality rates tend to be winnowedout of the population first, this reduces the mortality rates of the remainder.

5 Sozou and Seymour (2004), show that relaxing this assumption implies that constantmortality may be optimal.



16/24


17/24

Kaplan and Robson (2002) and Robson and Kaplan (2003) focus upon amore specific and accessible question than that concerning the origin of theintelligence of humans, namely: Why might the extreme intelligence and long-

evity of humans be expected to go together?Such an association holds quite generally in a cross-section sense (Allman,McLaughlin, and Hakeem 1993). Multiple regression with data on primates,for example, shows that brain weight has a statistically significant effect onlongevity, even when body weight is also included as an explanatory variableand the effect of body weight on brain weight is allowed for. Humans have abrain that is roughly three times as big as that of chimpanzees, in particular,and live about twice as long, despite being only slightly heavier.

Furthermore, this association between intelligence and longevity is a salient

feature of our evolutionary history. This history, in the first place, has featureddramatic increases in brain size, with no similar clear trend in body size. Theencephalization quotient, EQ, a measure of brain size relative to body size, hasincreased dramatically. 7

In the second place, the evidence concerning the longevity of our evolu-tionary ancestors is necessarily less direct. However, evidence on the sequenceof eruption of teeth, for example, suggests that Australopithecus had a life spancomparable to that of a modern chimpanzee, much less, then, than that of modern humans; intermediate species have intermediate life spans (Smith,1991). Thus, human evolutionary history involves a large increase in brainsize and an apparent substantial concurrent increase in longevity.

A key part of an explanation of this association is that investment in thebrain is a more roundabout biological strategy than investment in the body.This is illustrated by the productivity profile in figure 1. That is, the costs of alarge brain are even more concentrated towards the start of life than is the costof physical growth overall, but the returns as hunting productivity, for exam-ple, do not peak until about age 35, well beyond the age of physical maturity.

Using both productivity and mortality data, it is possible to make evenmore vivid the resulting huge intertemporal trade-off in hunter-gatherer lifehistory. Figure 2 plots cumulative production net of consumption: transfers,that is, weighted by the probability of survival, as a function of age, for allavailable hunter-gatherer societies. It then takes almost until age 50 to breakeven and the ultimate payback is relatively small.

This also demonstrates the essential way in which intelligence and mortalityare intertwined. That is, if this calculation is performed using human net transfersbut chimpanzee mortality, the curve remains in substantial deficit. Giventhat chimpanzee mortality approximates the mortality of human evolutionaryancestors, this ancestral demography would have had to change to support theemergence of human intelligence and the associated economic life history.

7 See Martin (1981). Jerison (1973) provides evidence concerning the evolution of intelligence ina variety of species.



18/24


19/24

there is an initial range of ages, t, over which F (K , t) rises. The key factorleading to rising productivity is learning-by-doing. Thus, a large brain, K ,induces high levels of F (K , t), which may only become evident at ages t longafter brain growth itself has ended. Second, senescence is captured in a term-inal range of ages over which F (K , t) declines. That is, as justified by theanalysis of the previous section, aging is now built in.

Output can be used as investment, v, which augments the capital stock as

dK dt

¼ v; where v 2 ½0; v :

The upper bound on investment v represents a simple specification of anincreasing cost of investment, which is plausible in the current setting. Foranalytic simplicity, neural investment is now constrained to occur in a block of time at the beginning of life. That is, for some t 0,

vð Þ ¼ v > 0 2 ½0; tÞ0 t

:

In addition, there is assumed to be a constant initial energy cost C 0 K 0 > 0for each individual. In terms of the model of the previous section, this

–7,000,000

–6,000,000

–5,000,000

–4,000,000

–3,000,000

–2,000,000

–1,000,000

0

1,000,000

2,000,000

0 5 10 15 20 25 30 35 40 45 50 55 60 65Age

N e t

C a l o r i e s

Human Prod, Human Surv.Human Prod., Chimp. Surv.Chimp. Prod. Chimp. Surv

FIGURE 2 Cumulative expected energy production net of consumption, by ageSOURCE: This is adapted from Kaplan et al. (2001), to which reference should be made for datasources.



20/24

assumption is equivalent to assuming that the cost of fertility is linear andindependent of the level of gross energy, G. That is, it is equivalent to specify-ing that F (G, s) ¼ G s. This simplifies the problem, but means that the

pattern of fertility is indeterminate.Essentially, as in the previous model, some of the energy output, s, reducesthe rate of mortality, (s). The probability of survival to age t, p(t), say, satisfies

dpðtÞdt

¼ pðtÞ ðsÞ where pð0Þ ¼ 1:

The ‘net output’ is y ¼ F (K ) v s.There is an essentially unique optimal path for s, determined by the condi-

tion that ~J (t) 0(s) ¼ 1, where

~J ðtÞ ¼ 1

pðtÞZ 1

t pð ÞðF ðK ; Þ v sÞd :

In this model, it can be shown that the maximization of the steady state growthrate, together with the requirement that this maximum growth rate be zero, canbe achieved by maximizing the undiscounted lifetime expected net output of energy excluding the cost C 0 :

~J ð0Þ ¼

Z 1

0

pðF ðK ; tÞ v sÞdt ;

where, in addition, the maximum value of ~J is equal to C 0 . Indeed, given thatoptimal neural investment ends before output peaks, optimal mortality isU-shaped, with a minimum that is no later than peak output.

4.3. Comparative staticsConsider, now, the effect of a more challenging environment, modelled by anincrease in a parameter introduced into the production function. Suppose theincrease in decreases output right after brain growth ends, but eventuallyincreases output. This represents the advent of a more cognitively demandingforaging activity. Learning this activity results in a substantial ultimateincrease in productivity, but this is predicated on investment in a large brainin the first place. In addition, such on-the-job-training may also necessitatesacrificing an occupation that is more productive at first. Suppose, however,that the overall effect of the increase in is either to raise or to hold constanttotal expected energy surplus, and also that increasing does not lower themarginal product of capital. It can then be shown that such a shift raises K andreduces mortality at every age, thus increasing longevity.

Alternatively, it can also be shown that a more favourable environment, inthe form of an exogenous reduction in mortality, also induces such shifts in K and mortality. That is, if parameterizes an additive shift in , reducing alsohas these two effects.



21/24

These comparative static results imply that human intelligence and lifeexpectancy might have co-evolved as follows. Consider the circumstanceswhen a drier climate in Africa precipitated the replacement of the African

rain forests by savanna, several million years ago. The result was to fostergreater numbers of herbivores, for example, as well as energy intensive plantresources. The skill intensity needed to exploit this new foraging niche plau-sibly raised the productivity of the brain. Greater expenditure on mortalityreduction then became worthwhile and longevity rose. In addition, greaterinvestment in the brain itself also paid off.

The skills, weapons, and social organization in early humans required forhunting must simultaneously have reduced predation on humans. This reduc-tion in exogenous mortality caused an additional decrease in endogenous

mortality. The additional reduction in overall mortality then induced anfurther increase in investment in the brain. These two effects – from increasingproductivity and from reducing extrinsic mortality – therefore reinforced oneanother.

These comparative statics results also illuminate the differences that pre-sently exist between humans and our closest living relatives, the chimpanzees,as discussed in the introduction. That is, after humans evolved to fill a niche onthe African savanna characterized by somewhat higher skill intensity and lowermortality, perhaps we continued to be positioned to fill a series of niches withyet higher skill intensities and lower mortalities, while chimpanzees remainedin a niche with relatively low skill intensity and relatively high mortality. Theabove comparative statics results are then consistent with our greater intelli-gence – our brains are about three times as large as those of chimpanzees – andour greater longevity, since we live about twice as long.

Our approach, more generally, illuminates the evolution of species with lowrates of mortality and low rates of senescence. That is, we show how theproductivity of capital affects human life history evolution. Circumstanceswhere capital is highly productive favour reduced senescence not only inhumans, but also in diverse non-human species. For example, the extremelongevity of queens in social insect colonies may reflect the productivity of various forms of capital – not only somatic capital, but also non-somaticphysical capital, the hive or nest, for example.

5. Conclusions

In this paper, we have advocated consideration of the evolutionary origins of human demographic and economic characteristics. Going beyond this, we haveillustrated how the economics of hunting and gathering societies was the anvilon which natural selection formed these innate characteristics. We have con-sidered the shaping of the basic human characteristics of mortality, fertility,and intelligence. In particular, we have explained why our mortality profile isU-shaped, but why we also live well beyond the cessation of fertility. We have



22/24

also explained why human intelligence and longevity has increased in a lock-step fashion.

Indeed, the remarkable similarities evident in figure 1, for example, between

foraging and modern economies are to be expected under our approach. Thatis, our innate capacities and preferences were adapted by natural selection tothe foraging economy. These capacities and preferences then make it optimalfor us to reproduce key features of the foraging economy within the moderneconomy.

There are a number of additional topics that we intend to investigate. In theintroduction, we discussed the sexual division of labour in foraging societies. Itwould be interesting to capture the forces inducing this division in a formalmodel. Perhaps, for example, a relatively small difference between the sexes

based on their biological roles in reproduction would be amplified by thedemands of the foraging economy. Such a model might ultimately help toaddress the question: Why do women now live longer than men?

Another important topic concerns the formation of the rate of time pre-ference as a function of age. Here, the existence of intergenerational transfershas implications for the rate of time preference of older individuals. Theprospect of imminent death of the individual would have less dramatic effectson time preference when the resources involved in the intertemporal trade-off accrue substantially to offspring.

A final ambitious aim is to consider how strategic behaviour has beenshaped by the economics of hunting and gathering. This concerns the extentthat strategic behaviour is not completely rational. Perhaps, for example,humans are ‘conditionally cooperative’ – willing to anticipate cooperation atfirst, and therefore themselves to be cooperative initially, despite also energe-tically punishing defection. Such a propensity might have arisen readily insmall groups of hunter-gatherers, in which each individual knew all othermembers, being indeed genetically related to many of them. 8

The pioneers of modern economic theory, such as Paul Samuelson, enjoyedtremendous success in applying relatively simple mathematics to provide con-vincing explanations of basic economic phenomena. However, the very successof these early pioneers means that such possibilities that still exist withinconventional economics are now few and far between. On the other hand,there is a vast, untouched expanse of territory awaiting exploration in theadjoining hinterlands of economics, biology, and anthropology. This is agolden opportunity to apply simple optimization models to explain importantphenomena in a convincing way.

8 See Seabright (2004) for vivid descriptions of such phenomena.



23/24

References

Allman, John, Todd McLaughlin, and Atiya Hakeem (1993) ‘Brain weight and life-spanin primate species,’ Proceedings of the National Academy of Sciences of the USA 90,118–22

Charlesworth, Brian (1994) Evolution in Age-Structured Populations (Cambridge:Cambridge University Press)

–– (2000) ‘Fisher, Medawar, Hamilton and the evolution of aging,’ Genetics 156, 927–31Ehrlich, Isaac, and Hiroyuki Chuma (1990) ‘A model of the demand for longevity and

the value of life extension,’ Journal of Political Economy 98, 761–82Finch, Caleb E. (1998) ‘Variations in senescence and longevity include the possibility of

negligible senescence,’ Journal of Gerontology: Biological Sciences 53A, B235–B239Grossman, Michael (1972) ‘On the concept of health capital and the demand for health,’

Journal of Political Economy 80, 223–55Hamilton, William D. (1966) ‘The moulding of senescence by natural selection,’ Journal

of Theoretical Biology 12, 12–45Hill, Kim, and Anna Magdalena Hurtado (1996) Ache Life History: The Ecology and

Demography of a Foraging People (Hawthorne, NY: Aldine)Holliday, Malcolm A. (1978) ‘Body composition and energy needs during growth,’ in

Human Growth , ed. F. Falker and J. Tanner (New York: Plenum)Howell, N. (1979) Demography of the Dobe !Kung (New York: Academic Press)Jerison, Harry J. (1973) Evolution of the Brain and Intelligence (New York: Academic

Press)Jurmain, Robert, Harry Nelson, Lynn Kilgore, and Wenda Trevathan (2000)

Introduction to Physical Anthropology (Belmont, CA: Wadsworth)

Kaplan, Hillard S., and Kim Hill (1985) ‘Food-sharing among Ache foragers: tests of explanatory hypotheses,’ Current Anthropology 26, 223–45Kaplan, Hillard S., and Arthur J. Robson (2002) ‘The emergence of humans: the

coevolution of intelligence and longevity with intergenerational transfers,’Proceedings of the National Academy of Sciences 99, 10221–226

Kaplan, Hillard S., Kim Hill, A. Magdalena Hurtado, and Jane B. Lancaster (2001)‘The embodied capital theory of human evolution,’ in Reproductive Ecology and Human Evolution , ed. P. Ellison (Hawthorne, NY: Aldine de Gruyter)

Kaplan, Hillard S., Kim Hill, Jane B. Lancaster, and Anna Magdalena Hurtado (2000)‘A theory of human life history evolution: diet, intelligence, and longevity,’Evolutionary Anthropology 9, 156–85

Kaplan, Hillard S., Jane B. Lancaster, and Arthur J. Robson (2003) ‘Embodied capitaland the evolutionary economics of the human lifespan,’ Population and DevelopmentReview 29, Supplement, 152–82

Kirkwood, Thomas B.L. (1990) ‘The disposable soma theory of aging,’ in GeneticEffects on Aging II , ed. D. Harrison (Caldwell, N.J.: Telford Press)

–– (1999) Time of Our Lives (New York: Oxford University Press)Love, Seth, and Peter Jenner (1999) ‘Oxidative stress in neurological disease,’ Brain

Pathology 9, 55–6; introduction to a symposium available at http://www.brainpath.medsch.ucla.edu/brainpath/abstracts/vol9/901/901toc.html

Martin, M.J., S.B. Hulley, W.S. Browner, L.H. Kuller, and D. Wentworth (1986)

‘Serum cholesterol, blood pressure, and mortality: implications from a cohort of 361,662 men,’ Lancet 8513, 933–6Martin, Robert D. (1981) ‘Relative brain size and basal metabolic rate in terrestrial

vertebrates,’ Nature 293, 57–60McElwee, Joshua. J., Eugene Schuster, Eric Blanc, James H. Thomas, and David Gems

(2004) ‘Shared transcriptional signature in Caenorhabditis elegans dauer larvae and



24/24

long-lived daf-2 mutants implicates detoxification system in longevity assurance,’Journal of Biological Chemistry 279, 44533–43

Medawar, Peter B. (1952) An Unsolved Problem in Biology (London: Lewis)Reader, Simon M., and Kevin N. Laland (2002) ‘Social intelligence, innovation, and

enhanced brain size in primates,’ Proceedings of the National Academy of Sciences of the USA 99, 4436–41

Robson, Arthur J. (1996a) ‘The evolution of attitudes to risk: lottery tickets and relativewealth,’ Games and Economic Behavior 14, 190–207

–– (1996b) ‘A biological basis for expected and non-expected utility,’ Journal of Economic Theory 68, 397–424

Robson, Arthus J., and Hillard S. Kaplan (2003) ‘The evolution of human longevityand intelligence in hunter-gatherer economies,’ American Economic Review 93,150–69

–– (2005) ‘Why we grow large and then grow old: economics, biology, and mortality,’Simon Fraser University Working Paper

Rose, Michael (1991) The Evolutionary Biology of Aging (Oxford: Oxford UniversityPress)

Seabright, Paul (2004) The Company of Strangers (Princeton, NJ: Princeton UniversityPress)

Smith, B. Holly (1991) ‘Dental development and the evolution of life history in homi-nidae,’ American Journal of Physical Anthropology 86, 157–74

Sozou, Peter and Rob Seymour (2004) ‘To age or not to age,’ Proceedings of the Royal Society: Biological Sciences 271, 457–63

Stearns, Stephen C. (1992) The Evolution of Life Histories (Oxford: Oxford UniversityPress)

Vaupel, James W. (1997) ‘Trajectories of mortality at advanced ages,’ in Between Zeusand Salmon: The Biodemography of Longevity , ed. K. Wachter, and C. Finch(Washington, DC: National Academy Press)

Williams, George C. (1957) ‘Pleiotropy, natural selection, and the evolution of senescence,’ Evolution 11, 398–411


The Economics of Hunter-gatherer

Documents

Transcript of The Economics of Hunter-gatherer