Cooperation in an Uncertain World: For the Maasai of East
Africa, Need-Based Transfers Outperform Account-Keeping
in Volatile Environments
&Rolando de Aguiar
Published online: 3 May 2016
#The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract Using an agent-based model to study risk-pooling
in herder dyads using rules derived from Maasai osotua (Bum-
bilical cord^) relationships, Aktipis et al.(2011) found that
osotua transfers led to more risk-pooling and better herd sur-
vival than both no transfers and transfers that occurred at
frequencies tied to those seen in the osotua simulations.
Here we expand this approach by comparing osotua-style
transfers to another type of livestock transfer among Maasai
known as esile (Bdebt^). In osotua, one asks if in need, and one
gives in response to such requests if doing so will not threaten
one’s own survival. In esile relationships, accounts are kept
and debts must be repaid. We refer to these as Bneed-based^
and Baccount-keeping^systems, respectively. Need-based
transfers lead to more risk pooling and higher survival than
account keeping. Need-based transfers also lead to greater
wealth equality and are game theoretically dominant to
Keywords Need-based transfers .Account-keeping
transfers .Risk pooling .Herd survival outcomes .Maasai .
People everywhere have to deal with both predictable and
unpredictable risks to their livelihoods. From modern day
humans grappling with uncertainties about health and em-
ployment, to Maasai pastoralist managing large herds in the
face of potential drought, disease and theft, risk management
is an important adaptive problem for humans around the
world. Humans use many strategies to manage risk, including
risk retention (accepting risk and absorbing losses), risk avoid-
ance (reducing dependence on high variability outcomes), risk
reduction (lowering the probability of or size of losses) and
risk transfer (moving risk from one party to another)
(Dorfman 2007). Risk transfer is of particular interest to social
scientists because it is the only one of these methods that
requires cooperation. One common form of risk transfer is
risk-pooling (also referred to as risk-sharing). Here we use
two different types of livestock transfer found among
Maasai pastoralists in East Africa to examine whether some
rules regarding such transfers lead to more risk-pooling and
more effective risk management than others.
We build upon Aktipis et al.(2011), which used an
agent-based model to examine the impact of stock friend-
ships on risk-pooling and herd survival among East African
pastoralists. That model was based specifically on the rules
underlying Maasai stock friendships, which they refer to by
their word for umbilical cord: osotua. The rules that govern
osotua relationships are straightforward: Ask only if you
are in need and only for what is needed, and give if you
are able to do so without threatening your own survival.
The model showed that pairs of herders that follow the
rules of osotua had herds that survive longer in the face
of occasional shocks than pairs that either engage in no
livestock exchange or that exchange livestock probabilisti-
cally at rates derived from the osotua simulations. In this
article, we expand our understanding of dyadic livestock
exchange among Maasai by comparing osotua transfers
with transfers Maasai refer to as esile, which translates
simply as Bdebt^(Mol 1996).
Arizona State University, Tempe, AZ 85281, USA
Rutgers University, New Brunswick, NJ, USA
Hum Ecol (2016) 44:353–364
Maasai and other Maa-speaking pastoralists engage in sev-
eral different types of livestock transfers, each of which fol-
lows a different set of rules. In addition to bridewealth pay-
ments, osotua, and esile, these include enkitaaroto, in which
animals are put in someone else’s herd but without a transfer
of ownership, ketaaro or elipa, in which a milking cow is lent
to a household in need, aitogaroo, in which a bull is lent for
breeding purposes, and keitapashaki, in which animals are
exchanged immediately, usually so that an individual can ob-
tain a steer for ceremonial purposes (Perlov 1987:173–188).
Here we focus solely on osotua and esile, which have very
different underlying logic and rules. When osotua partners
transfer livestock, no debt is created, and it is inappropriate
to talk about either debt or payment. Osotua partners have
obligations to help one another, but the flow of goods and
services between them does not need to be even roughly bal-
anced over time (Cronk 2007). In transfers following the rules
of esile, debt and repayment are of the essence. Esile means
debt, and repayment is expected in the form of an animal at
least as valuable if not more so than the one given. The repay-
ment is referred to as elaata, which means to set free or untie a
knot (Perlov 1987:184). If a debtor fails to repay, his creditor
has the option of forgiving the debt but then referring to him
henceforth as BPasile^: One whose debt I have forgiven. This
type of construction, in which the prefix Bpa^is used to indi-
cate what a person has given or received, is common in Maa,
but it is normally used in a positive way. For example, a man
refers to his father-in-law as BPakiteng,^meaning Bcow re-
ceiver.^The use of the term BPasile^essentially serves as a
mild public reproach to those who fail to repay their debts. If
debts are not repaid before the debtor dies, they are passed on
to his heirs.
In principle, risk pooling could be accomplished via a va-
riety of different resource transfer rules. Here we focus on two
rules that can lead to the pooling of risk: osotua and esile.
Because many pastoralists other than the Maasai also have
rules that are equivalent to osotua and esile (Almagor 1978;
Bollig 1998,2010; Dyson-Hudson 1966; Flannery et al.1989;
Gulliver 1955), in an effort to generalize our terminology we
will henceforth refer to these not by their Maa labels but rather
as Bneed-based transfers^and Baccount keeping.^Here we
examine the underlying logic of both account keeping and
need-based transfers and use an agent-based model to com-
pare them in terms of their ability to enhance survival in vol-
atile environmental conditions.
This model is adapted from Aktipis et al.’s(2011) agent-based
model of risk pooling among Maasai pastoralists. That model
was constructed to examine herd survival in volatile ecologi-
cal conditions characteristic of East African pastoralism (Dahl
and Hjort 1976; Homewood 2008). Here, in addition to incor-
porating account-keeping rules, we also generalize this model
by exploring a wider variety of ecological conditions. All
parameter values and assumptions about resource volatility
were initially drawn from Aktipis et al.(2011) and Dahl and
Hjort (1976), but were then varied to investigate our questions
We used Netlogo software to model a population of two
actors, each with a herd of finite size. Each actor represented a
household/family of approximately six individuals and began
with a herd of 70. Although Maasai and other Maa-speaking
pastoralists keep a variety of different types of livestock (cat-
tle, goats, sheep, donkeys, and, in some arid areas, camels), in
an effort to keep our model simple and tractable we refer
simply to Bstock.^Given that the Maasai economy is domi-
nated by cattle, it would make the most sense for the reader to
think of the Bstock^in our model as cattle. During each time
period, each actor’s resource stock grew or shrank at a rate
normally distributed around a mean of 3.4 %, a typical annual
growth rate (Dahl and Hjort 1976). Maximum herd size was
600, a realistic maximum herd size for an average sized house-
hold. During each period there was a chance that each herd
would suffer a loss through drought or disease. As in Aktipis
et al.(2011), we also ran additional simulations in which we
varied the volatility size from zero to 50 % and the volatility
rate from zero to 20 %. Based on estimates of a family’s
caloric needs and productivity in the dry season (Dahl and
Hjort 1976), we set the minimum size of a viable herd at 64.
Although this is high compared to the living standards of some
Maa-speaking pastoralists (e.g., Cronk 2004), the exact num-
ber is not important so long as it is consistent across all con-
ditions. Importantly, this figure is also consistent with Aktipis
et al.(2011)(Fig.1and Appendix).
We then simulated account-keeping-based and need-based
livestock transfers between individuals. In order to establish a
baseline for comparison, we first ran simulations in which no
transfers occurred. For runs involving the transfer of livestock,
we simulated interactions among individuals of the same type
as well as individuals of different types.
Below we describe the algorithms underlying need-based
transfers and account keeping. Account keeping requires the
tracking of debt and credit over time while need-based trans-
fers require individuals only to know their own resource
Need-based transfer rules were implemented as follows
(consistent with Aktipis et al.2011):
1. Need-based asking rule: Individuals ask their partners
for livestock only if their current holdings are below the
asking threshold (i.e., the minimum stock size of 64).
2. Need-based giving rule: Individuals give what is asked,
but not so much as to put their herds below the giving
threshold (also the minimum stock size of 64).
354 Hum Ecol (2016) 44:353–364
Account-keeping rules were implemented as follows:
1. Account-keeping payback rule:
a. If livestock have been previously transferred
from the partner to the actor and the actor has
enough to pay back without going below sus-
tainability threshold (resource min), the actor
‘pays back’livestock to his partner according
to the actor’srepayment probability
2. Account-keeping partner credit check rule:
a. Checks whether partner is in good standing, which
includes not having exceeded tolerated delay or credit
size (when applicable)
3. Account-keeping asking rule:
a. As with the need-based transfer asking rule, individ-
uals ask their partners for livestock if their current
herd size is below the sustainability threshold of 64.
4. Account-keeping giving rule:
a. Response to partner- If a request is made, actors give
if two conditions are met:
i. If no debt remains from a previous request and part-
had not existed for longer than tolerated delay)
ii. The amount transferred cannot exceed the credit
size extended to the partner
We then compared the performance of pairs of need-based
transfer individuals with other types of pairs including no
exchange pairs, account-keeping pairs and mixed pairs.
Additional details regarding the model schedule, parameter
values and model design can be found in the appendix.
Survival of Need-Based-Transfer Pairs vs.
We compared median survival of pairs of need-based transfer
individuals, account-keeping individuals and no-exchange in-
dividuals under the ecological conditions specified in the
model description. We found that pairs of need-based transfer
individuals had higher rates of herd survival than account-
1. Stock grows
Herds increase in size
according to growth rate
Herds decrease in size
by volatility size every
volatility rate years
3. Requests made
Requests are made
according to account-
keeping or need-based
according to account-
keeping or need-based
5. Check viability
If holdings are below
herd min for two
consecutive years, the
individual is no longer
Fig. 1 Overview of model
schedule. The full model schedule
is included in the appendix
Hum Ecol (2016) 44:353–364 355
keeping pairs or pairs in which partners did not transfer re-
sources (Fig. 2).
Correlations of Survival Durations Within Pairs
In order to better understand whether individuals pool risk
more effectively under need-based transfer rules than under
account-keeping rules, we compared the median herd survival
experienced by one individual in a simulation to that experi-
enced by the other individual in the same simulation under
four different conditions: (1) no exchange, (2) two account-
keeping agents, (3) one account-keeping and one need-based
transfer agent, and (4) two need-based transfer agents (Fig. 3).
We found no correlation between survival durations when
individuals did not make transfers, but highly significant cor-
relations between the fates of the individuals when they did
transfer livestock. Specifically, when both individuals used
need-based transfer rules, correlations of their survival were
higher (ρ=0.54) than when both individuals used account-
keeping rules (ρ= 0.40). This indicates that, in addition to
being more effective than account-keeping at keeping live-
stock alive, need-based transfers also lead to a tighter yoking
of the fates of the two parties in the risk-pooling relationship.
Effects of Varying Environmental Volatility on Survival
In our baseline model, the likelihood and severity of losses
were normally distributed around 30 % and 10 %, respective-
ly. Given that the frequency of droughts in parts of East Africa
has been increasing (Homann et al.2008;Richéet al.2009),
we also looked at what happens when we vary the volatility
size and volatility rate across simulations for pairs of account-
keeping individuals and pairs of need-based transfer individ-
uals (Fig. 4). When volatility is very low, all individuals sur-
vive and when it is very high all individuals die. At all values
located between those two extremes, need-based transfer in-
dividuals survive longer than account-keeping individuals.
Effects of Generosity on Survival
In the osotua system, need-based transfers in their ideal form
are always generous, i.e., individuals always give if they are
asked and can do so without going below their own sustain-
ability thresholds. We investigated whether the success of the
need-based transfer rule was critically reliant on having 100 %
generosity (Fig. 5). We varied the generosity of need-based
transfer individuals and account-keeping individuals to com-
pare the viability of these strategies. For need-based transfer
0 25 50 75 100
Median proportion of herds surviving
Fig. 2 Need-based transfer pairs
(red) show higher overall survival
than account-keeping pairs (blue).
Both need-based transfers and
account keeping have higher
survival than pairs in which no
exchange occurs (green)
Fig. 3 Pairs of need-based transfer individuals (bottom right) show
greater correlations of herd survival durations than account-keeping
pairs, no-exchange pairs or mixed pairs. Spearman’s rank correlations,
n= 10,000 per condition: no-transfer condition ρ=−0.01, n.s.; account-
keeping ρ=0.40, p≈0; heterogeneous strategies ρ=0.45, p≈0; need-
based transfers ρ=0.54,p≈0. p-values for all transfer conditions < 10
356 Hum Ecol (2016) 44:353–364
individuals, generosity was the likelihood of giving if asked
and able. For account-keeping individuals generosity was the
likelihood of giving to a partner in good standing if asked. We
found (1) that neither the account-keeping rule nor the need-
based transfer rule consistently outperforms no exchange un-
less generosity is at least 80 % and (2) that, when generosity is
high, pairs using the need-based transfer rule outperformed
pairs using the account-keeping rule. Without generosity,
need-based transfers are no more successful than account
Survival in Mixed Pairs
Under ecological conditions characterized by resource volatil-
ity, pairs using a need-based transfer strategy can outperform
pairs using an account-keeping strategy. The relationship be-
tween the need-based transfer and account-keeping strategies
is similar to the Stag Hunt Game (Skyrms 2003). Both strat-
egies are coordination points, but two individuals using the
need-based transfer strategy are likely to survive longer than
two individuals using the account-keeping strategy (Fig. 6).
Fig. 4 a Need-based transfers
outperform account keeping most
clearly when volatility size is
between 20-30 % of total
holdings. bSimilarly, need-based
transfers have the greatest
advantage over account keeping
when volatility rate is between 5-
10 % per year. Shaded regions
represent 95 % confidence
Fig. 5 Need-based transfer pairs
outperform account-keeping pairs
only when generosity is 80 % or
higher. Generosity is the
likelihood of giving if asked by
one’s partner and one is able
keeping). Shaded regions
represent 95 % confidence
Hum Ecol (2016) 44:353–364 357
The situation is thus a coordination problem that can be solved
if all the individuals know both that there is a solution and that
everyone involved also knows the solution (Chwe 2001;
Cronk and Leech 2013). In the real world, common knowl-
edge about need-based transfers can easily be provided by
sharing norms such as the osotua concept that focus on the
recipient’s need rather than on debt and repayment.
In our model, stochastic growth and volatility of resources
create wealth inequality. Inequality is often measured using
the Gini coefficient (Gini 1912), which varies from 0 (when
wealth holdings are equal) to 1 (when one person controls all
the wealth). For comparative purposes, the highest Gini coef-
ficient in the world is currently 0.632 (Lesotho), and lowest is
0.230 (Sweden). The figure for the United States is 0.45
(Central Intelligence Agency 2013). We find that need-based
transfer individuals have the lowest inequality compared to all
other individuals (Fig. 7). Wealth inequality is highest in the
absence of wealth transfers (.576), followed by account-
keeping individuals (.516), and need-based transfer
individuals (.418). The Gini coefficient for need-based transfers
(.418) is similar to that found for a sample of pastoralist societies
(.42 ± .05) in a recent analysis of wealth inequality in societies
with subsistence economies (Borgerhoff Mulder et al.2009).
Inspired by the Maasai osotua system, Aktipis et al.(2011)
used an agent-based model to test whether a need-based trans-
fer rule leads to risk pooling and enhanced survival in volatile
ecological conditions. Here we extended this model to test
whether need-based transfer rules also outperform account-
keeping in risky environments. We found that need-based
transfers lead to greater risk pooling, longer survival and
greater wealth equality than account-keeping, though both
strategies outperformed scenarios involving no transfers of
Need-Based Transfers and Balanced Reciprocity
Reciprocity has been an important topic in economic anthro-
pology since the days of Malinowski (1922), Mauss (1967),
and Polanyi (1957). A common framework for understanding
different types of reciprocity is Sahlins’(1965)trichotomyof
generalized, balanced, and negative reciprocity. Balanced rec-
iprocity corresponds fairly closely with what we have been
calling Baccount keeping.^We chose Baccount keeping^over
balanced reciprocity in order to emphasize and capture the
importance of debt and repayment in Maasai esile transfers.
Our focus has been on the contrasts between account-keeping
reciprocity and need-based transfers. Both of these patterns
may be adaptive responses to individual needs that are
Need-based 30, 30 23, 29.5
Account keeping 29.5, 23 25, 25
Fig. 6 Payoffs in terms of survival at 50 years (in %) for individual and
partner using each rule in ecologically realistic environmental conditions.
The need-based transfer strategy is dominant to the account-keeping
Fig. 7 Stable differences in
inequality exist between transfer
strategies. Individuals engaging in
no transfers (green)havethe
highest level of inequality,
followed by account-keeping
individuals (blue) and then dyads
composed of need-based transfer
individuals (red). The straight line
(gray) shows perfect equality
358 Hum Ecol (2016) 44:353–364
asynchronous among individuals. When individuals’needs all
occur at the same time, neither account-keeping nor need-
based transfers beyond close kin would likely be adaptive.
But when needs arise asynchronously, it may make good
adaptive sense for those with resources to transfer them to
those without. The difference between need-based transfers
and account-keeping becomes apparent when we consider
the predictability of the needs in question. When needs are
both asynchronous and highly predictable, account-keeping
makes sense. If I know that I will always be in need on
Tuesday and you know that you will always be in need on
Friday, we can easily set up a system of balanced, tit-for-tat,
account-keeping reciprocity that benefits us both. But if needs
are not only asynchronous but also unpredictable, need-based
transfers may, as our model suggests, make more adaptive
sense than account-keeping. In short, when information about
future needs is good, account-keeping may reign, while need-
based transfers may prevail when such information is poor or
nonexistent, as it was in our model. In developed economies,
unpredictable needs are dealt with largely via formal insurance
markets. Insurance companies overcome the problem of poor
information about each individual’s needs by gathering large
amounts of data about needs (rates of car accidents, home
fires, etc.) of many people. That allows them to focus on the
accuracy of data about needs in the aggregate rather than on
the inaccuracy of data regarding each individual’sneeds,
which in turns allows them to set rates and engage in
account-keeping exchanges with their clients (Levy 2012).
Need-Based Transfers and Generalized Reciprocity
In Sahlins’generalized reciprocity, sharing is indiscrim-
inate and widespread. The same idea is also captured by
Fiske’sBcommunal sharing^(Fiske 1991). Generalized
reciprocity and communal sharing are good descriptions
of how resources are often shared within households
and within hunter-gatherer bands (e.g., Howell 2010;
Woodburn 1998;Price1975). However, neither is an
accurate description of osotua relationships or other
stock friendship relationships among pastoralists. Such
relationships are characterized not by indiscriminate
and widespread sharing but rather by limited contractual
commitments between individual livestock owners. The
advantage of the phrase Bneed-based transfers^is that it
captures both of these patterns, which focuses our atten-
tion on the fact that risk-pooling results from both of
them. This, in turn, provides a link between the study
of need-based transfers and the existing literatures on
risk-pooling in both anthropology (Bird and Bird 1997;
Bliege Bird et al.2002; Cashdan 1985; Gurven et al.
2000; Gurven and Hill 2009,2010; Wiessner 1982;
Winterhalder 1986) and economics (e.g., Barr and
Genicot 2008; Fafchamps and Lund 2003).
Computational Simplicity of Need-Based Transfers
In addition to its effectiveness in pooling risk, need-
based transfer systems also have low cognitive load.
Need-based transfer rules are simple: Ask if you need,
give if you can. The rules underlying a tit-for-tat,
account-keeping strategy can be straightforward in a
Prisoner’s Dilemma framework (Axelrod 1984), but they
prove much more complex in the more realistic situation
we sought to model. In contrast to the simple rules
followed by the need-based transfer agents, the
account-keeping agents must follow a complex set of
rules regarding such issues as credit, debt and repay-
ment. Account-keeping agents use memory of their past
transactions with other agents, whereas need-based
transfer agents simply need to keep track of whether
their own resource holdings are above their survival
threshold and occasionally calculate whether they can
afford to help a partner in need. The low cognitive
requirements of need-based transfer systems suggest that
they may have predated account-keeping in our species’
evolutionary history and could be more phylogenetically
widespread than systems requiring account-keeping.
Cheating and the Evolution of Cooperation
Organisms who live socially have the ability to manage risk
through risk pooling, enabling them to live in more challeng-
ing ecological conditions by sharing resources during times of
need. However, as with other explanations for the evolution of
cooperation, the problem of cheating must be addressed.
The vast literature on cheater detection and cheater
suppression has largely been motivated by solving the
problem of cheating in the context of account-keeping
interactions (Cosmides and Tooby 1992; Van Lier et al.
2013). Cheating in the context of need-based transfer is
different, however, from cheating in an account-keeping
system. In an account-keeping system, cheating is typi-
cally a matter of not repaying one’sdebts.Inaneed-
based transfer system, unbalanced accounts do not con-
stitute cheating. Rather, cheating in a need-based trans-
fer system involves asking for help when one is not in
need or refusing to give when one is able. These dif-
ferent criteria for what constitutes cheating are another
important way in which need-based transfers differ from
Need-based transfers can be conceptualized as a form
of decentralized and informal insurance. One of the
problems that can arise when individuals have insurance
in general is the problem of moral hazard, i.e., when
individuals become more prone to take risks and act
less carefully because they do not bear all the costs
associated with a bad outcome. In this sense, the moral
Hum Ecol (2016) 44:353–364 359
hazard problem may be at the heart of another way of
cheating in a need-based transfer system: if being re-
sponsible and careful is costly, one can cheat by being
lazy and taking unwise risks, knowing that one will
receive help if the outcome is a severe loss or catastro-
phe. Interestingly, in the Maasai osotua system, individ-
uals are also expected to act with responsibility, restraint
and respect in how they handle their herds (Cronk
2007). In other words, they are expected to behave in
such a way that may help to solve the moral hazard
problem and minimize this type of cheating that is pos-
sible in systems of need-based transfers.
Another way of suppressing cheating is for individ-
uals to carefully choose partners with whom they enter
into need-based transfer relationships. Partner choice is
one way of enhancing assortment of cooperators with
one another, and it can be realized through both simple
and complex rules for choosing and maintaining rela-
tionships (Aktipis 2004,2006,2011; Barclay 2013;
Barclay and Willer 2007;Nesse2009;Noeand
Hammerstein 1994). Among the Maasai, need-based
transfer relationships are taken very seriously and are
said to be unbreakable once formed. Partner choice
mechanisms could be at work in relationship formation
if individuals can evaluate others through observing
their behavior or reputations before entering into rela-
tionships in which they are committed to help.
Choosing need-based transfer partners that have comple-
mentary risk profiles (i.e., independence of shocks) and
responsible practices has high stakes. Discerning partner
choice is therefore likely to play an important role in
the viability of need-based transfer systems.
Need-based transfer systems require that individuals
stay committed to helping a partner if that partner is
unlucky; however, there may be conditions under which
it is acceptable to terminate a need-based transfer rela-
tionship. These conditions may allow for dissolving re-
lationships with greedy, stingy or irresponsible individ-
uals (though not unfortunate ones). Relationship disso-
lution rules could thus provide another partner choice
mechanism that could reduce cheating in need-based
transfer systems. This is a research question that we
will address in future fieldwork and modeling.
Finally, cheating in need-based transfer systems may
be made difficult simply by the public nature of certain
kinds of wealth. For example, among foragers, the same
kinds of foods that are the most variable from day to
game animals) are also the ones that are the most dif-
ficult to conceal. Among Maasai and other pastoralists,
wealth primarily takes the form of livestock, whose vis-
ibility may make it difficult for anyone to feign either
need or an inability to help. Despite the visibility of
livestock, it would in principle be possible to hide one’s
wealth by taking advantage of practices such as
enkitaaroto, a Maasai system in which animals are put
in someone else’s herd but without a transfer of owner-
ship. We have livestock census data from two East
African pastoralist societies, the Mukogodo Maasai of
Kenya (Cronk 1989,2004) and the Karimojong of
Uganda. In both cases, the correlation between herders’
apparent wealth, defined as the numbers of animals in
their herds regardless of who really owns them, and
actual wealth, defined as the number of animals that
they actually own regardless of whose herd they happen
to be in, is too high for this kind of cheating to be a
problem (Mukogodo Maasai: Pearson’sr=0.984,
p< 0.01, N= 183; Karimojong: Pearson’s r = 0.968,
p<0.01, N= 44). In systems where resources can be
hidden or individuals are otherwise unable to evaluate
the resource holdings of others, cheating in need-based
transfer systems is likely to be a larger problem.
Effectively managing risk and uncertainty are recurring adap-
tive problems across human societies. One way of managing
the risks associated with life in volatile ecologies is to pool
risk with others. Here we show that need-based transfers out-
perform account-keeping rules and can be effective even when
implemented in computationally simple terms.
Acknowledgments This material is based upon work supported by the
National Science Foundation under Grant No. SES-0345945 to Arizona
State University’s Decision Center for a Desert City (DCDC) and Grant
No. BCS-1324333 to Cronk and Iyer, National Institute of Health Grant
No. F32 CA144331, and a grant from the John Templeton Foundation.
We thank the Institute for Advanced Study in Princeton, the Center for
Theological Inquiry in Princeton, the Center for Advanced Study in the
Behavioral Sciences at Stanford University and the Wissenschaftskolleg
in Berlin. We would also like to thank participants in the National
Evolutionary Synthesis Center catalysis meeting for Synthesizing the
Evolutionary and Social Science Approaches to Human Cooperation,
the members of the Human Generosity Project and the members of the
Cronk and Aktipis lab groups. Any opinions, findings, conclusions, or
recommendations expressed in this material are those of the authors and
do not necessarily reflect the views of the National Science Foundation
(NSF), the National Institute of Health (NIH), or the John Templeton
Compliance with Ethical Standards
Funding This material is based upon work supported by the National
Science Foundation under Grant No. SES-0345945 to Arizona State
University’s Decision Center for a Desert City (DCDC) and Grant No.
BCS-1324333 to Cronk and Iyer, National Institute of Health Grant No.
F32 CA144331, and a grant from the John Templeton Foundation to
Aktipis and Cronk.
Conflict of interests The authors declare that they have no conflicts of
360 Hum Ecol (2016) 44:353–364
Model Description The model description offered below fol-
lows the standardized ODD protocol for describing individual and
agent based models (Grimm et al.2006) and is based on Aktipis
Purpose Here we use an agent-based model of wealth transfers
within ecologically realistic conditions to investigate the viability
of two sets of cooperative rules: one characterized by account
keeping and the other characterized by risk pooling norms of
need-based transfers. We then investigate how these two rules
affect overall resource stock survivorship and the variability of
survivorship within dyads.
State Variables and Scales In this model time is represented
discretely. Space is not explicitly modeled. Resource stock
growth dynamics and volatility are implemented with global
variables while the resource stock size and giving/asking rules
are agent variables (Table 1). During each time period, agents
execute the commands described in the schedule.
Process Overview and Scheduling This model proceeds in
discrete time steps, and entities execute procedures according to the
1. For each actor, resource stocks change in size:
a. Resource stocks increase in size according to growth rate
b. Resource stocks decrease in size by volatility size (as a percent of
total holdings) according to volatility rate
c. If resource stock size is above resource stock max it is set to resource
d. Resource stock size is rounded to nearest integer
2. Requests are made:
&If giving is need-based, requests are made if resource stock size
is below resource stock min
&If giving is account-keeping-based, requests are made if re-
source stock size is below resource stock min
3. Transfers are made:
&If giving is need-based, requests are fulfilled to the extent possible
keeping the resource stock size of the giver above resource stock min
&If giving is account-keeping-based
–If resources have been previous transferred from the partner to
the actor, the actor transfers net received resources to their
partner according to repaym ent prob
–If a new request was made, actors give if two conditions are met
The debt has not existed for longer than tolerate d delay
The amount transferred cannot exceed the credit size extended
to the partner.
&All actors update net received to reflect transfers
4. Actors removed from the population if two consecutive rounds occur
where resources holdings are below resource stock min.
Tabl e 1 Overview of state
variables associated with each
Entity State variable Description
Global •Growth rate Amount by which resource stocks grow each year
•Volatility rate Likelihood of a negative event (e.g., drought)
•Volatility size Decrease in resource stock size resulting from negative
•Min. resource stock
The minimum viable resource stock size
•Max. resource stock
The maximum resource stock that can be maintained
Agents •Resource stock size
Number of resources in agent’s resource stock
Number of resources received minus given to partner
•Asking threshold The threshold below which agents ask for resources
•Generosity The likelihood of giving if asked and able (need-based)
or giving to partner in good standing (account-keeping)
Need-based only •Giving threshold The threshold below which agents will no longer give
•Credit size The amount of credit granted to partner
•Tolerated delay The number of periods an agent will tolerate not being
repaid by partner before placing partner in bad standing
•P good standing If partner is in good standing, means that they have
not exceeded tolerated delay in past transfers
•Repayment probability The likelihood of repaying partner during each time period
Hum Ecol (2016) 44:353–364 361
5. Age of actors incremented by 1
Emergence In this model, risk pooling emerges from interactions
Prediction Agents in this model lack the ability to predict outcomes
of future environmental variability or future social interactions. They do
not integrate information across time periods.
Sensing Agents receive requests from their interaction partners and
are able to examine theirown resource holdings before fulfilling requests.
Interaction Agents interact by making and fulfilling requests for
Stochasticity Resource stock growth and environmental volatility
both have stochastic components.
Observation Reported data are averaged from 10,000 runs.
Simulations were run until both agents were removed from the population
(i.e., dropped below the viability threshold for more than 2 consecutive
Initialization All runs were initialized according to default
parameters in the table below (Table 2).
Input In order to make our model of the Maasai pastoral system as
realistic as possible, the following parameter values and assumptions
about resource dynamics were based on existing scholarship (Dahl and
Growth rate We used a 3.4 % growth rate with an SD of 2.53 based
on Dahl and Hjort’s(1976:66) estimate the growth rate in Bnormal^con-
ditions to be 3.4 %, with a maximum possible growth rate of roughly
11 % and a minimum of approximately −6 % (in the diminishing resource
stocks example). Dahl and Hjort estimates are based on both empirical
evidence and analytical modeling.
Resource stock size Initial resource stock sizes in our model were
70, with a minimum of 64 and a maximum of 600. These values were
derived from Dahl and Hjort (1976:178) who state that a resource stock of
64 resources is sufficient to sustain a reference family. Resource stock
sizes described in the text range from 60–100 cows and resource stocks
larger than 600 are not considered viable (Dahl and Hjort 1976:158).
Volatility We used a volatility rate of .1, meaning that on average a
disaster (e.g., drought or disease) occurred every 10 years. In our model,
this disaster reduced the resources resource stock by 30 % on average,
with a SD of 10 %. Dahl and Hjort (1976:114–130) note that these disas-
ters occur approximately every 10–12 years based on empirical data, and
that the population decline (during disasters that occur every 10 years)
should not bemore than approximately 28 %, based on analytical models.
Tabl e 2 Initial and default values
for all variables Entity State variable Initial/Default value Units
Global •Growth rate 3.4 (SD: 2.53) % current resource stock
•Volatility rate 10 % per year
•Volatility size 30 (SD: 10) % of current resource stock
•Min. resource stock size 64 Number resources
•Max. resource stock size 600 Number resources
Agents •Resource stock size 70 Number resources
•Net received 0 Number resources
•Asking threshold 64 Number resources
•Generosity 100 % likelihood
Need-based only •Giving threshold 64 Number resources
Account-keeping only •Credit size 5 Number resources
•Tol era ted d el a y 5 Year s
•P good standing Yes Yes/No
•Repayment probability 100 % likelihood
362 Hum Ecol (2016) 44:353–364
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a link
to the Creative Commons license, and indicate if changes were made.
Aktipis, C. A. (2004). Know When to Walk Away: Contingent
Movement and the Evolution of Cooperation in Groups. Journal of
Theoretical Biology 231(2): 249–260.
Aktipis, C. A. (2006). Recognition Memory and the Evolution of
Cooperation: How Simple Strategies Succeed in an Agent-Based
World. Adaptive Behavior 14: 239–247.
Aktipis, C. A. (2011). Is Cooperation Viable in Mobile Organisms?
Simple Walk Away Rule Favors the Evolution Of Cooperation In
Groups. Evolution and Human Behavior 32(4): 263–276 doi:10.
Aktipis, C. A., Cronk, L., and de Aguiar, R. (2011). Risk-Pooling and
Herd Survival: An Agent-Based Model of a Maasai Gift-Giving
System. Human Ecology 39(2): 131–140.
Almagor, U. (1978). Pastoral Partners: Affinity and Bond Partnership
Among the Dassanetch of South-West Ethiopia. Manchester Univ.
Axelrod, R. (1984). The Evolution of Cooperation. Basic Books, New York.
Barclay, P. (2013). Strategies for Cooperation in Biological Markets,
Especially for Humans. Evolution and Human Behavior 34(3):
Barclay, P., and Willer, R. (2007). Partner Choice Creates Competitive
Altruism in Humans. Proceedings of the Royal Society B:
Biological Sciences 274(1610): 749–753.
Barr, A., and Genicot, G. (2008). Risk sharing, commitment, and infor-
mation: An experimental analysis. Journal of the European
Economic Association 6(6): 1151–1185.
Bird, R. L. B., and Bird, D. W. (1997). Delayed Reciprocity and Tolerated
Theft: The Behavioral Ecology of Food-Sharing Strategies. Current
Anthropology 38(1): 49–78 doi:10.1086/204581.
Bliege Bird, R., Bird, D. W., Smith, E. A., and Kushnick, G. C. (2002).
Risk and Reciprocity in Meriam Food Sharing. Evolution and
Human Behavior 23(4): 297–321.
Bollig, M. (1998). Moral economy and self-interest: Kinship, friendship,
and exchange among the Pokot (NW Kenya). In Schweizer, T., and
White, D. (eds.), Kinship, Networks, and Exchange. Cambridge
University Press, Cambridge. Cambridge University Press,
Cambridge, pp. 137–157.
Bollig, M. (2010). Risk Management in a Hazardous Environment: A
Comparative Study of Two Pastoral Societies (Vol. 2). Springer,
Clark, G., et al. (2009). Intergenerational Wealth Transmission and
the Dynamics of Inequality in Small-Scale Societies. Science
Cashdan, E. A. (1985). Coping with Risk: Reciprocity Among the
Basarwa of Northern Botswana. Man:454–474.
Central Intelligence Agency. (2013). The World Factbook. 2014, from
Chwe, M. S.-Y. (2001). Rational Ritual: Culture, Coordination, and
Common Knowledge. Princeton University Press, Princeton, N.J.
Cosmides, L., and Tooby, J. (1992). In Barkow, J. H., Cosmides, L., and
Tooby, J. (eds.), CognitiveAdaptations for Social Exchange. Oxford
University Press, New York, pp. 163–228.
Cronk, L. (1989). The Behavioral Ecology of Change Among the
Mukogodo of Kenya. Ph.D. dissertation, anthropology,
Northwestern University, Evanston.
Cronk, L. (2004). From Mukogodo to Maasai: Ethnicity and cultural
change in Kenya. Westview Press, Boulder.
Cronk, L. (2007). The Influence of Cultural Framing on Play in the Trust
Game: A Maasai Example. Evolution and Human Behavior 28(5):
Cronk, L., and Leech, B. L. (2013). Meeting at Grand Central:
Understanding the Social and Evolutionary Roots of Cooperation,
Princeton University Press.
Dahl, G., and Hjort, A. (1976). Having Herds: Pastoral Herd Growth and
Household Economy. Dept. of Social Anthropology, University of
Dorfman, M. S. (2007). Introduction to Risk Management and Insurance,
Dyson-Hudson, N. (1966). Karimojong Politics. Clarendon, Oxford.
Fafchamps, M., and Lund, S. (2003). Risk-sharing networks in rural
Philippines. Journal of Development Economics 71: 261–287.
Fiske, A. P. (1991). Structures of Social Life: The Four Elementary Forms
of Human Relations. Free Press, New York.
Flannery, K., Marcus, J., and Reynolds, R. (1989). The Flocks of the
Wamani: A Study of Llama herders on the Punas of Ayacucho,
Peru. Academic, New York.
Gini, C. (1912). Variabilità e mutabilità. In P. E., and Salvemini T (eds.),
Reprinted in Memorie di metodologica statistica. Libreria Eredi
Virgilio Veschi, Rome (1955).
Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J.,
Goss-Custard, J., Grand, T., Heinz, S., and Huse, G. (2006). A
Standard Protocol For Describing Individual-Based and Agent-
Based Models. Ecological Modeling 198: 115–126.
Gulliver, P. (1955). The Family Herds: A Study of Two Pastoral Tribes in
East Africa: the Jie and Turkana. International Library of Sociology
and Social Reconstruction.
Gurven, M., Allen-Arave, W., Hill, K., and Hurtado, M. (2000). BIt’sa
Wonderful Life^: Signaling Generosity Among the Ache of
Paraguay. Evolution and Human Behavior 21(4): 263–282.
Gurven, M., and Hill, K. (2009). Why Do Men Hunt? Current
Anthropology 50(1): 51–74.
Gurven, M., and Hill, K. (2010). Moving Beyond Stereotypes of
Men’s Foraging Goals. Current Anthropology 51(2): 265–
Homann, S., Rischkowsky, B., Steinbach, J., Kirk, M., & Mathias, E.
(2008). Towards endogenous livestock development: Borana pasto-
ralists' responses to environmental and institutional changes. Human
Ecology 36(4): 503–520.
Homewood, K. (2008). Ecology of African Pastoralist Societies. Oxford.
Howell, N. (2010). Life Histories of the Dobe !Kung. University of
California Press, Berkeley.
Levy, J. (2012). Freaks of Fortune: The Emerging World of Capitalsim
and Risk in America. Harvard University Press, Cambridge.
Malinowski, B. (1922). Argonauts of the Western Pacific. Routledge,
Mauss, M. (1967). The gift: Forms and functions of exchange in archaic
societies. Norton, Trans. Ian Cunnison. New York.
Mol, F. (1996). Maasai Language and Culture Dictionary. Maasai Centre,
Nesse, R. M. (2009). Runaway Social Selection for Displays of Partner
Value and Altruism. In Verplaetse, J., De Schrijver, J., Vanneste, S.,
and Braeckman, J. (eds.), The Moral Brain. Springer, Netherlands,
Noe, R., and Hammerstein, P. (1994). Biological Markets: Supply and
Demand Determine the Effect of Parner Choice in Cooperation,
Mutualism and Mating. Behavioral Ecology and Sociobiology 35:
Hum Ecol (2016) 44:353–364 363
Perlov, D. C. (1987). Trading for influence: The social and cultural eco-
nomics of livestock marketing among the highland Samburu of
Northern Kenya. Ph.D. dissertation, anthropology, University of
California - Los Angeles.
Polanyi, K. (1957). The great transformation. Beacon Press, Boston.
Price, J. A. (1975). Sharing: The integration of intimate economies.
Anthropologica 17(1): 3–27.
Riché, B., Hachileka, E., Awuor, C. B., & Hammill, A. (2009). Climate-
related vulnerability and adaptive capacity in Ethiopia’s Borana and
Somali communities. IISD report.
Sahlins, M. (1965). On the sociology of primitive exchange. In Banton,
M. (ed.), The Relevance of Models for Social Anthropology.
Tavistock. Reprinted in Stone Age Economics, London (1972,
Skyrms, B. (2003). The Stag Hunt and the Evolution of Social Structure:
Cambridge University Press.
Van Lier, J., Revlin, R., and De Neys, W. (2013). Detecting Cheaters
Without Thinking: Testing the Automaticity of the Cheater
Detection Module. PLoS One 8(1): e53827 doi:10.1371/journal.
Wiessner, P. (1982). Risk,reciprocity and social influences on! Kung San
economics. In Leacock, E., and Lee, R. (eds.), Politics and History
in Band Societies (Vol. 61). Cambridge University Press,
Cambridge, pp. 61–84.
Winterhalder, B. (1986). Diet Choice, Risk, and Food Sharing in a
Stochastic Environment. Journal of Anthropological Archaeology
Woodburn, J. (1998). Sharing is not a form of exchange: An
analysis of property-sharing in immediate-return hunter-gath-
Renewing the Anthropological Tradition. Cambridge University
Press, Cambridge, pp. 48–63.
364 Hum Ecol (2016) 44:353–364
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at