Resource Abundance Promotes the Evolution of
Public Goods Cooperation
Brian D. Connelly

Michigan State University
East Lansing, MI 48824, USA

connel42@cse.msu.edu

Benjamin E. Beckmann

Michigan State University
East Lansing, MI 48824, USA

beckma24@msu.edu

Philip K. McKinley

Michigan State University
East Lansing, MI 48824, USA

mckinley@cse.msu.edu

ABSTRACT

1.

Understanding the evolution of cooperation as part of anevolutionary stable strategy (ESS) is a difficult problem thathas been the focus of much work. The associated costs ofcooperation may lower the fitness of an organism below thatof its non-cooperating counterpart, allowing the more fit organism to persist and outcompete the cooperator. Insight
into these behaviors can help provide a better understanding of many aspects of the natural world, as well as provide
future avenues for fighting disease.
In this study, we use digital evolution to examine how
the abundance of a required resource affects the cooperative production of a public good in an adverse environment.
Evolutionary computation is an excellent tool for examining
these problems, as it offers researchers complete access to organisms and total control over their environment. We findthat stable cooperation can occur in otherwise competitiveenvironments at discrete levels corresponding to the availability of a required resource. When resource levels are low,organisms focus solely on competitive behaviors. However,once resource levels cross a critical threshold, cooperation
persists in populations. Further, this cooperation occurs inpatches, where it is most likely to benefit relatives. Finally,
we find that in some cases this cooperative behavior allows
organisms to increase their competitive abilities as well.

Insight into the evolution of cooperative strategies is critical in gaining an understanding of many fundamental aspects of life, including the emergence of multicellularity [25]and the evolution of pathogenesis and antibiotic resistance[7]. Cooperative acts incur some cost on the actor whileproviding some benefit to a recipient. In public goods cooperation, this benefit is placed into the environment, whereanyone may take advantage of it. Upon first glance, thetraditional “survival of the fittest” view of evolution would
select against such behaviors, as other organisms that do not
pay this cost would have higher individual fitness.
One mechanism allowing selection to favor these costly cooperative acts is kin selection [10], whereby organisms seek
to maximize their inclusive fitness, which considers both thefitness of that particular organism and the fitness of its kin,which share either common ancestry or behavior. Because
these organisms have some genetic similarity, the reproductive successes of an organism’s kin benefit the proliferation ofthat organism’s genes as well. This mechanism is captured
in Hamilton’s equation, shown in Equation 1, which statesthat cooperation will be selected for when the costs (c) of
the act are less than the benefits (b) conferred upon the recipient multiplied by the genetic relatedness (r) between thepair [10]. Therefore, cooperation is more likely to occur inenvironments with spatial structure, where organisms tend
to interact primarily with close relatives.

Categories and Subject Descriptors
H.4 [Information Systems Applications]: Miscellaneous;
F.1.1 [Computation by Abstract Devices]: Models ofComputation—Self-modifying machines

INTRODUCTION

c<r∗b

(1)

Due to the difficulties in quantifying the effects of interactions in higher organisms, as well as their use of additional mechanisms such as reciprocity and punishment,cooperation at this fundamental level is increasingly studied using populations of microorganisms such as bacteria
and yeast [24]. The short generation times of these organisms make evolutionary-scale studies feasible. Computational models of these systems are also frequently used,providing researchers complete environmental control andaccess to each organism [6, 8, 19].
One area in which cooperative behaviors are critical is in
the formation and maintenance of biofilms [15], which are
aggregates of microorganisms connected within a matrix ofextracellular polymeric substance (EPS) [7]. This “stickiness” allows organisms in biofilms to cohabitate in larger
densities and resist being flushed out of their environment.
In addition, biofilms play a large role in antibiotic resistance,and can render bacteria 1,000 times less likely to be affectedby antibiotics [22]. Because this self-produced EPS resource

General Terms
Experimentation, Theory

Keywords
Artificial life, cooperation, public goods, digital evolution,
virtual biofilms, structured populations

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GECCO’10, July 7–11, 2010, Portland, Oregon, USA.
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143is a public good in this system, selection can produce organisms that take advantage of its benefits without producing
it themselves (cheaters). The presence of such cheaters has
been observed to significantly weaken biofilms [20].
Indeed, this observation is the basis for current researchin “anti-infective” treatments of bacterial infections. In one
recent study, mutants that do not produce or respond to acooperative signaling molecule were introduced into infections in mice, resulting in a significant decrease in virulence
and a corresponding increase in host survival [21]. Similarly, the use of benign or more manageable strains thatcan outcompete pathogenic strains has been considered [4].
As a further step, researchers have also proposed employing“Trojan Horse” techniques, whereby a competitive mutant
strain containing beneficial behaviors is introduced [4]. In
order for such treatments to be effective (and not harmful tothe host), an intimate knowledge of the social interactions
occurring in bacterial communities is needed.
These interactions depend not only on the organisms andcommunities themselves, but also on the environment inwhich they live. Often, the costs and benefits are driven
by environmental factors such as disturbance, spatial limitations, and the availability of resource, which is examinedin this work. Environmental disturbance, such as grazing
by a higher trophic level species, can both promote and hinder cooperation. It has been observed that cooperation is
maximized at intermediate levels of disturbance, where thebalance between costs and benefits is positive [2]. This is because at low disturbance, there is less need for cooperative
acts, so their associated benefits are small. At high disturbance, organisms are forced to focus on self-preserving behaviors, making the costs of cooperation prohibitively high.
In addition, the density of populations supported by theenvironment may drive selection for cooperation. Specifically, as groups grow to carrying capacity, increased competition among kin may hinder the evolution of cooperation
through increased costs [12, 18]. In facilitation (interspecific cooperation), it is hypothesized that cooperation may
allow organisms to increase their “realized niche”, thus enabling larger and more diverse communities [5]. Here, cooperative acts allow a species to survive in environments thatwould otherwise be uninhabitable, either environmentally or
through competition.
The availability of a required resource often plays a largerole in determining evolutionary outcomes. As resource becomes more abundant, the relative costs of the cooperativeact decrease, allowing organisms to perform cooperative acts
in addition to their self-preserving and competitive activities. When that resource becomes more scarce, however, organisms may be forced to focus solely on their own interests.
Wet bench experiments have shown this relationship [3] aswell as a link between resource availability and diversification [9].
The study presented here examines this relationship between available environmental resource and cooperation throughthe production of a public good. Populations of digital organisms evolved in digital environments with different resource availabilities, and we examined their ability to persist
in an adverse environment through cooperation. In agreement with previous studies of natural organisms [3], we findthat the frequency of cooperative organisms increased withavailable resource. Further, our study revealed two interesting features of this cooperation not reported previously.

144

First, that populations cooperate at discrete levels–those inrich environments cooperate with similar levels of effort regardless of resource abundance, while those in poor environments cooperate consistently less. Secondly, we find thatsuccess within these two distinct groups remains closely tied
to competition. These findings provide additional insight
into the environmental conditions under which cooperation
occurs and may be used to help drive the development oftreatments that target similar behaviors in disease.

2.

METHODS

For this study, we used the Avida software platform to
study evolution in populations of self-replicating digital organisms [17]. As depicted in Figure 1, each organism consisted of a circular list of assembly-like instructions (its “genome”),
which were executed sequentially on virtual hardware allocated to that organism. This virtual hardware comprised
three 32-bit registers, two stacks, and a CPU, which had
four heads that controlled the execution flow of the organism’s genome and aided in self-replication. The instruction
set available to organisms was Turing complete, so organisms could feasibly perform any possible calculation. Eachorganism resided independently in its own cell in a 100x100cell bounded lattice, where each cell had eight neighbors.
When an organism replicated, it created a copy of itself andplaced this offspring into a random cell in the neighborhood,
replacing any organism residing in that cell. By choosing
neighboring cells, we limited dispersal and increased thelikelihood that interactions would be among kin. Because
an organism can be killed by new offspring, these environments presented a pressure to replicate quickly. Typicallyin Avida, organisms can replicate more often by shortening
their genomes through mutations or by executing instructions more quickly.

inc
get-idhallochdiv
rtrvm
rotaterrotater

Registers
Stacks
GS
AX BX
CPU
LS
CX
sendmsg
Heads
rtrvm
rotatelCell
if-less
Flow Instrh-div
Interface
getid
Write
Read
getid
inc

Figure 1: Representation of a digital organism (top)in an Avida population (bottom) [11]As in other studies using Avida [14], organisms competedin this environment by completing logic tasks, which conferred a merit bonus. An organism with more merit received
proportionally more CPU cycles, enabling it to execute its

An organism replicated by first allocating additional spacein their genome to create a copy of itself. It then copied instructions from its genome into this new area line by line.
Once this process was completed, the genome was cleaved,
and the new organism was placed into a neighboring cell.
As an organism copied its genome, instructions were modified with a probability of 0.0075, and as the organism divided, instructions were added or deleted with a probabilityof 0.05. Populations were seeded with a single organism
capable only of replication. Any additional behaviors were
evolved through mutations introduced during the replication
process.

Table 1: Logic tasks that could be completed by organisms. Upon completion, an organism was given
the corresponding merit reward. The completion of
OR NOT was not rewarded, but resulted in the production of beneficial public good at the organism’s
cell.
Task
NOT
NAND
AND
OR
AND NOTNORXOR
EQU
OR NOT

Input
A
A, B
A, B
A, B
A, B
A, B
A, B
A, B
A, B

Output¬A¬(A ∧ B)A∧B
A∨B
A ∧ ¬B, ¬A ∧ B¬(A ∨ B)(A ∧ ¬B) ∨ (¬A ∧ B)
(A ∧ B) ∨ (¬A ∧ ¬B)A ∨ ¬B, ¬A ∨ B

Merit Bonus
2
2
4
8
8
16
16
32
0

2.1

genome faster and thus spread in the environment. These
rewards were given at most once per task completed. In order to complete a task, an organism first had to execute the
IO instruction, which placed randomly-generated numbers
into its registers. The organism was then required to execute the instructions necessary to complete the task, place
the result in the correct output register, and issue an additional IO instruction. A full list of the tasks available andtheir rewards is listed in Table 1.
In addition, an organism required a sufficient amount ofa resource in order to complete each task. This resource
flowed evenly into each cell in the environment and dissolved
at a rate of 1% per update (Avida’s unit of time, further explained later). In this study, the inflow rates were varied,
ranging from extremely limiting (equilibrium of 1 unit percell) to effectively unlimited (equilibrium of 100 units percell), where organisms were unable to complete tasks quickly
enough to deplete the resource. For this work, each task consumed 1 unit of this resource. However, tasks had varying
metabolic costs, or the number of instructions required fortheir completion, as is shown in Figure 2.
(a)
BLAH
(b)
OR
NOT
(c)
EQU
BLAH
(b)
OR
NOT
(c)
EQU
IO
(c)
something(d) IO
IO(a)(a)
BLAH
(b)
OR
NOT
(c)
EQU
IO
IO
nop-c
nand
IO

IO
nop-c
nand
nand
IO

IO
nop-c
push
nand
swap
nand
swap
nop-a
pop
nand
swap
nop-c
nand
IO

Virtual Biofilms

We adopted the concept of a virtual biofilm from previous
work [6] as the model for our experiments. As in nature,
an organism’s membership in a virtual biofilm provided itwith some benefit that it could not easily obtain in a freeor planktonic state. In our digital environment, being in a
biofilm allowed organisms to survive a periodic “kill” event
similar to grazing by higher organisms in nature.
To create a virtual biofilm, organisms completed the OR
NOT task, which resulted in one unit of a public good being
placed into the environment at their cell. This cooperative
resource can be likened to EPS, enzymes, or other beneficial
products commonly observed in biofilms. Unlike the otheravailable tasks, OR NOT did not confer a merit reward (seeTable 1); however, the product that it produced could enable
that organism or its kin to avoid being killed, as discussed
shortly. We therefore define cooperators as organisms who
performed this task at some point during their lifetime, whilecheaters focused solely on rewarded tasks.
A kill event periodically selected a focal cell at random,and each cell in a neighborhood comprising cells within a 5cell radius (121 cells in total, or 1.21% of the environment)
was examined. If the mean level of public good in these cells
was below a threshold of 3 units, all organisms residing inthat region were killed. This public good both decayed at
1% and diffused into neighboring cells at 1% per update. In
this environment, cheaters could exploit both the diffusionof public good from neighboring cooperator cells as well as
the kill event’s consideration of the mean public good levelwithin a neighborhood to benefit from the production byneighbors while refraining from producing the public good.
Starting from a single seed organism, populations evolvedin environments with different resource abundances for 50,000
updates. These abundances corresponded to equilibrium celllevels of 1, 2, 4, 8, 10, 20, 40, 60, 80, and 100 units per cell.
During each update, each organism executed 30 instructions
on average. Those organisms with higher merit executed
proportionally more instructions per update than those withless. Between approximately 6,000 and 8,000 generations
were observed in each of these runs. For each configuration,
30 independent populations were evolved. Each of theseruns started with a different random seed, allowing each to
explore different evolutionary paths. The kill event was executed every update starting with update 1,000, which thengave organisms a 1.21% chance of residing within a selected
region.

IO
nop-c
push
nand
swap
nand
swap
nop-a
pop
nand
swap
nop-c
nand
push
pop
nop-c
nand
IO

Figure 2: Sample genome segments required to complete several tasks given adequate resources. (a) The
NAND task can be completed with 5 instructions, while(b) OR NOT requires 6. More difficult tasks such as (c)XOR and (d) EQU require 15 and 19 instructions, respectively.

3.

RESULTSAs observed in studies of bacterial populations [3], thefraction of cooperators in our digital populations increased145with increasing resource abundance. In each of the different
environments examined in this study, however, some populations were unable to escape the kill event in the earlystages. This occurred when a population was small and had
not had sufficient time to evolve public good production.
The results reported here correspond with the populations
that were able to run for the allotted time. For each data
set shown in Figures contained in this section, the number ofcompleted runs is indicated in each plot. Additionally, plotsshow data from a representative subset of the completed
runs in order to improve readability.
Improved Response to the Kill Event—The fractions of organisms selected and killed by the kill event isshown in Figure 3. Here, organisms in high resource environments were able to produce enough public good so thatapproximately 70-80% of organisms successfully avoided being killed, while organisms in lower resource environments
produced significantly less public good and were killed approximately 70% of the time. Interestingly, there is no clear
relationship between the amount of resource available andthe fraction of organisms that were able to avoid being killed.
In fact, there is little difference in kill rates among environments with stable resource levels of 40 units per cell andabove (in 3 out of 4 environments, however, these differences are significant; p < 0.002). There is also little difference among those in environments with levels of 10 units
per cell and below (3 out of 5 runs were significantly different; p < 10−6 ). These two qualitative groups, however,are significantly different (p < 10−6 ) from each other, whichindicates a clear difference in behaviors. This could be indicative of a critical resource level, below which cooperation
is not favored, but above which cooperation occurs at relatively similar levels. This observation matches Hamilton’s
equation (Equation 1), which indicates whether or not cooperation can occur under a given set of conditions. In thiscase, organisms have found it beneficial to cooperate in environments with sufficient resource; however, an increase incooperative behavior has not proven to be more beneficial,
so all populations maintain this behavior with the minimum
required investment.
Increasing Levels of Public Good Produced—The
observed survival in these adverse environments is due toabove-threshold levels of public good existing in selected regions. The amount of this product created within these
populations is shown in Figure 4. The populations that we
observed to be most successful at preventing being killedwere able to produce a mean per-cell level of resource well
above the 3-unit threshold, while those in lower resource
environments produced significantly less. As before, the differences within each of these two groups are minimal, indicating that there appears to be a level of required resource
below which the cooperative act is too costly. Populationsin the environment with a resource level of 20 units per cellwere significantly different than these groups (p < 10−6 ),indicating their level may be nearer to this critical value.
Increased Abundance of Cooperators—In populations that produce levels of public good well above the killthreshold, there is a large potential for cheaters to emergethat take advantage of this surplus. Figure 5 plots the fraction of cooperators present in the different populations. We
define cooperators as organisms that perform the publicgood-producing task at least once during their lifetime, whilecheaters focused solely on rewarded tasks. We see that pop-

Fraction of Organisms Killed

1
0.9
0.8
0.7
0.6
0.5
0.4
1 (N=30)
4 (N=29)
10 (N=27)
20 (N=23)
40 (N=23)
100 (N=23)

0.3
0.2
0.1
0

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Time (Update)

5
4

x 10

Figure 3: Fraction of organisms in different resource environments residing in the selected region
that were killed by the kill event. As available resource increased, more organisms resided in cellswith above-threshold public good levels, indicatingthat organisms were completing more cooperative
tasks. Data are averaged over all completed runs
(number is indicated in legend).

Per Cell Public Good Level9
1 (N=30)
4 (N=29)
10 (N=27)
20 (N=23)
40 (N=23)
100 (N=23)

8
7
6
5
4
3
2
1
0

0

0.5

1

1.5

2

2.5

3

Time (Update)

3.5

4

4.5

5
4

x 10

Figure 4: Per-cell level of public resource in runs
with differing resource levels in environments with
the neighborhood kill event. Organisms in high resource environments produced levels of public resource per cell that were above the kill threshold of
3.0. Data are averaged over all completed runs.

146ulations are comprised of more cooperators when resource
is abundant. This could be an indication of the decreased
relative costs of cooperation in high resource environments:
Organisms continue to compete through their completionof rewarded tasks, while cooperation remains as a common
feature. Although a significant increase in the fraction ofcooperators is observed in rich environments, cooperators
remained less frequent than cheaters.
1
1 (N=30)
4 (N=29)
10 (N=27)
20 (N=23)
40 (N=23)
100 (N=23)

Fraction of Cooperators

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

0

0.5

1

1.5

2

2.5

3

Time (Update)

3.5

4

4.5

5

Figure 6: Distribution of public resource in a typical high-resource environment at update 45,000.
White indicates cells with above-threshold levels
while black indicates cells with below-threshold levels. Videos showing this distribution over an entire
run are available on our website1 .

4

x 10

Figure 5: Fraction of cooperators in populations
with different resource levels. Cooperators are defined as organisms that completed at least one OR
NOT task, which produced public resource. Data areaveraged over all completed runs.

1

Public Good Level

Skewed Distribution of Public Goods—One explanation for both the abundance of cheaters and the productionof public good well above the threshold is that cooperators
are producing enough resource to support their entire populations. In this case, we would expect to see a fairly uniform
distribution of this resource throughout the environment.
Figures 6 and 7, however, show that this is not the case,and that both cooperators and public good abundance exist mostly in patches, which is common among cooperative
systems with spatial structure [16]. Here at update 45,000,
the mean per-cell level is above the kill threshold, yet 36%
of cells are below this threshold due to the 58% cheaters inthis population. These patches are continually attacked bycheaters, who reproduce more quickly and take over cells.
However, once large clusters of cheaters form, they become
susceptible to the kill event when the public resource decays and they can no longer take advantage of cooperatingneighbors. These newly-vacated cells then become available
to cooperators, and these oscillations continue. Figure 8
shows these dramatic oscillations between cooperators andcheaters within a selected 5x5-cell region during evolution
in a typical high-resource environment. Although mean percell resource levels continue to rise in resource-rich environments, spatial constraints prohibit cheaters from invading
and simply surviving from the resource produced earlier.
Videos capturing these dynamics are available on our website1 .

90

90

80

80

70

70

60

60

50

50

40

40

30

30

20

20

10

10

0

0

1000

2000

3000

4000

0

5000

Cell

6000

7000

8000

9000

10000
1

Figure 7: Distribution of public resource among cells
in a typical high-resource environment at update
45,000. We see that a small number of cells havepublic resource levels much higher than others. The
inclusion of such cells in any target area is likely togreatly assist in preventing the kill event from succeeding. In this 100x100-cell environment without
periodic boundary conditions, cells in the first row
were labeled 0-99, cells in the second 100-199, andso on.

http://www.cse.msu.edu/thinktank

147

1
Cooperators
Cheaters

0.8

6

0.6
0.5
0.4
0.3
0.2
0.1
0

0

0.5

1

1.5

2

2.5

3

Time (Update)

3.5

4

4.5

5
4

x 10

4

1 (N=30)
4 (N=29)
10 (N=27)
20 (N=23)
40 (N=23)
100 (N=23)

5

4

3

2

1

Figure 8: Oscillating fractions of cooperators andcheaters present within a selected 5x5-cell region in
one sample population in a high-level resource environment.

0

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Time (Update)

5
4

x 10

Figure 9: Number of rewarded tasks performed byorganisms in environments with different resource
levels.
Organisms in low-resource environments
were limited, while organisms in high-resource environments were able to continually improve. Data
are averaged over all completed runs.

Overall Increase in Task Completion–In addition to
avoiding the kill event, organisms also competed for spacein these environments. Therefore, they faced pressure tocomplete rewarded tasks, enabling them to execute and reproduce more quickly. Figure 9 plots the number of rewarded tasks completed in different environments. Here
we see that organisms in low-resource environments completed these tasks roughly in proportion to the level of resource available, indicating that this resource was limiting.
Those organisms in high-resource environments, however,completed roughly the same number of tasks regardless of
the level in their environments. In these environments, resource was not a limiting factor. Figure 10 shows the numberof public-good-producing tasks completed. Here, the trends
are similar among high-resource environments, and populations in low resource environments show a consistently-low
cooperative effort. Overall, populations completed this cooperative task an order of magnitude less often than the
rewarded tasks, indicating that competition remains the primary driving force in these environments.
Finally, Figure 11 plots the total number of tasks completed. We can see that in all populations, the competitive
behaviors make up a much larger portion of the total numberof tasks completed. However, those populations with intermediate resource availability and where cooperation occurs(e.g. equilibrium resource levels of 20 and 40) seemed tocomplete more tasks overall than would be predicted if thetrends seen among lower-resource environments continued.
This could indicate that cooperation enabled these populations to be more successful at competing in the task environment.

4.

x 10

0.7

Rewarded Task Count

Fraction of Organisms

0.9

Cooperative Task Count

6000
1 (N=30)
4 (N=29)
10 (N=27)
20 (N=23)
40 (N=23)
100 (N=23)

5000

4000

3000

2000

1000

0

0

0.5

1

1.5

2

2.5

3

Time (Update)

3.5

4

4.5

5
4

x 10

Figure 10: Number of cooperative, public-goodproducing tasks performed by populations in environments with different resource levels. Data areaveraged over all completed runs.

CONCLUSIONSThis study has demonstrated in digital populations a phenomenon reported in natural organisms: the relationshipbetween the availability of a required resource and the fraction of cooperation observed in populations. We saw that

148

4

7

1 (N=30)
4 (N=29)
10 (N=27)
20 (N=23)
40 (N=23)
100 (N=23)

6

Total Task Count

tors. Although this surplus made these cooperators susceptible to invasion by cheaters, we found that cooperation was
still able to persist in the populations.
In considering the relationship between cooperation andresource availability, many additional questions remain. We
have seen the effect at the population level, but how does
the evolution of an individual’s behavior change in different
environments? Perhaps some level of restraint from cheating
can be evolved at the genetic level as has been observed[23]. Additionally, plasticity [13] can play a large role in
the investment an individual makes in cooperation based on
the state of the environment and may significantly alter thepercentage of cooperators that exist in populations, given afluctuating environment.
Similarly, if the required resource is non-renewable andwill eventually be depleted, it would be interesting to investigate the conditions under which populations evolve restraint
in its consumption or can instead shift their dependence toanother resource [1].

x 10

5

4

3

2

1

0

0

0.5

1

1.5

2

2.5

3

Time (Update)

3.5

4

4.5

5
4

x 10

Figure 11: Total number of tasks, both rewarded
and cooperative, performed by organisms in environments with different resource abundances. Data
are averaged over all completed runs.

5.

ACKNOWLEDGMENTS

We are grateful for the help and insight provided by Christopher Waters, Tracy Teal, and Luis Zaman. This work was supported in part by National Science Foundation grants CNS-0915885,
CCF-0820220, and CNS-0751155; by U.S. Army Grant W911NF08-1-0495; and by a Quality Fund Grant from Michigan State
University.

as available resource increased, populations produced more
public good, enabling them to stave off being killed by a periodic event. Interestingly, however, we observed this relationship between resource and fraction of cooperation to bediscrete. Above certain levels, organisms in all resource environments cooperated at a consistent level, while cooperation
was consistently low below this critical level. We speculatethat this critical level is where the costs of the cooperative
behavior become less than the benefits provided. Further
investigation of environments with resource levels near thisdramatic change in behavior could yield insight into where
this threshold exists and the true cost and benefit structuresin these worlds. The presence of this critical resource level
lends itself nicely to future investigation into anti-infective
treatments. Following [21], this critical level could potentially be used to determine the amount of non-cooperative
mutants to introduce to an infection in order to fall below
the critical resource level and suppress cooperation and its
negative effects.
This work also measured the competitive, rewarded tasks
performed by organisms in addition to the cooperative ones,
which is rarely done in computational models of these behaviors. We found that even in populations in which stablecooperative behaviors existed, organisms performed significantly more competitive tasks than cooperative ones. This
result indicates that even in environments where competition dominates, cooperation can still emerge and may in fact
enable a species or strain to become more competitive than a
non-cooperating one. We observed this as cooperative populations in intermediate resource environments completed
more tasks than would be expected based on other populations.
We also observed large surpluses in the level of public goodin populations with significant fractions of cooperators, potentially supporting a large fraction of cheaters. We found,however, that the majority of this public good resided inclusters of cells which corresponded to clusters of coopera-

6.

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