## Figures

## Abstract

The observed cooperation on the level of genes, cells, tissues, and individuals has been the object of intense study by evolutionary biologists, mainly because cooperation often flourishes in biological systems in apparent contradiction to the selfish goal of survival inherent in Darwinian evolution. In order to resolve this paradox, evolutionary game theory has focused on the Prisoner's Dilemma (PD), which incorporates the essence of this conflict. Here, we encode strategies for the iterated Prisoner's Dilemma (IPD) in terms of conditional probabilities that represent the response of decision pathways given previous plays. We find that if these stochastic strategies are encoded as genes that undergo Darwinian evolution, the environmental conditions that the strategies are adapting to determine the fixed point of the evolutionary trajectory, which could be either cooperation or defection. A transition between cooperative and defective attractors occurs as a function of different parameters such as mutation rate, replacement rate, and memory, all of which affect a player's ability to predict an opponent's behavior. These results imply that in populations of players that can use previous decisions to plan future ones, cooperation depends critically on whether the players can rely on facing the same strategies that they have adapted to. Defection, on the other hand, is the optimal adaptive response in environments that change so quickly that the information gathered from previous plays cannot usefully be integrated for a response.

## Author Summary

The observed cooperation between genes, cells, tissues, and higher organisms represents a paradox for Darwinian evolution, because the individual success of cheating is rewarded before its long-term detrimental consequences are felt. The tension between cooperation and defection can be represented by a simple game (the “Prisoner's Dilemma”), which has been used to study the conflicts between decisions to cooperate or defect. Here, we encode these decisions within genes, and allow them to adapt to environments that differ in how well a player can predict how an opponent is going to play. We find that evolutionary paths end at strategies that cooperate if the environment is sufficiently predictable, while they end in defection in uncertain and inconsistent worlds because inconsistency favors defection over cooperation. This work shows that cooperation or defection, in populations of players that use the information from previous moves to plan future ones, can be influenced by changing the environmental parameters.

**Citation: **Iliopoulos D, Hintze A, Adami C (2010) Critical Dynamics in the Evolution of Stochastic Strategies for the Iterated Prisoner's Dilemma. PLoS Comput Biol 6(10):
e1000948.
https://doi.org/10.1371/journal.pcbi.1000948

**Editor: **Arne Traulsen, Max Planck Institute for Evolutionary Biology, Germany

**Received: **January 24, 2010; **Accepted: **September 1, 2010; **Published: ** October 7, 2010

**Copyright: ** © 2010 Iliopoulos et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Funding: **This work was supported in part by the National Science Foundation's “Frontiers in Integrative Biological Research” program grant FIBR-0527023. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

**Competing interests: ** The authors have declared that no competing interests exist.

## Introduction

The evolution of cooperation is difficult to understand within Darwinian theory [1]–[3]. Indeed, cooperation is intrinsically vulnerable to exploitation because evolution rewards individual success, while any detrimental long-term effects for the group are secondary [4], [5]. The tension between the short-term benefits of defection and the long-term benefits of cooperation has been studied using the Prisoner's Dilemma as a paradigm of social conflicts [3], [6]–[9]. Previous work has shown that cooperation can only emerge in the presence of different enabling mechanisms. The main ones are direct reciprocity [6], [10] (which can emerge when players play against each other repeatedly), spatial reciprocity [7], which is ensured if players only play neighbors on a regular grid (or more generally, on arbitrary graphs, giving rise to “network reciprocity” [11]), tag-based selection [12] (where players can recognize each other using some observable trait), kin selection [13], indirect reciprocity [14], [15] (where cooperative or altruistic acts increase a player's reputation), or group selection [16]. Social diversity, where either the payoffs or the neighborhoods vary from player to player [17], [18] can also enhance cooperation, as can “active linking” [19], [20], where players differ in the rate at which they maintain interactions with other players. Generally speaking, the co-evolution of strategies with the different enabling mechanisms can also increase cooperation [21]. In all the discussed scenarios, a player's strategy is such that they either cooperate or defect in a deterministic manner, sometimes conditionally on previous plays.

If a cooperating strategy *accidentally* defects (or a defector accidentally cooperates) the noise that is introduced in this manner can have a dramatic effect on the competition. For example, among the (deterministic) strategies that take one previous move into account in order to decide how to play, the reciprocating strategy “TFT” (Tit-for-Tat) dominates [6], but is outcompeted [22]–[24] by “Win-Stay-Lose-Shift” (WSLS), which can correct for occasional mistakes [23]. Experiments with bacteria [25] and social amoeba [26] indeed suggest that the decision to cooperate or defect (in a general sense) is stochastic, and moreover that these decisions are controlled by genetically-encoded probabilities that are evolvable [27]. Rather than assuming that noisy decisions are either due to fuzziness in perception or lack of control over one's action [22], here we allow these probabilities to be fine-tuned by adaptation in response to the environment. We find that if a player's stochastic decisions are under genetic control, then the level of uncertainty about an opponent's next move (given their previous encounter) determines whether cooperation or defection evolves. Because this uncertainty is a direct consequence of environmental conditions, we conclude that when decisions are based on previous interactions, these conditions alone are sufficient to explain the evolution of cooperation in populations. Note that the stochasticity introduced by probabilistic play controlled by genes is fundamentally different from other random effects that can be introduced into evolutionary game dynamics, such as a probability to inherit a neighbor's strategy [28], or stochastically fluctuating payoffs [29], [30], because neither of them can evolve.

In its simplest form, PD players have only two play options: cooperate (C) or defect (D). Both players are awarded a payoff R for mutual cooperation and a payoff P for mutual defection. Unequal moves award S to the cooperator and T to the defector. In standard PD [6], the values of the payoffs are constrained so that T>R>P>S and R>(S+T)/2. The first equation ensures that for a single round of play, defection is an evolutionary stable strategy [4], while the second equation ensures that reciprocation of cooperation is favored over the trading of cooperative with defective moves. In the repeated PD (iterated PD or IPD) that we study here, two players meet more than once, and can establish cooperation by means of direct reciprocity [6]. In particular, we study exclusively the IPD with *memory*, that is, where players base their decision on previous plays (except for the first move with a new opponent). The term “memory” is not meant to imply that only higher organisms can engage in such strategies. Rather, stochastic decisions can be based entirely on the levels of protein on a cell's receptor, for example, and where these protein levels are the result of a cellular “decision” at an earlier time. A simple example for such a stochastic decision in response to the decision of other cells is quorum sensing in bacteria (see, e.g., [31]). We contend that the introduction of information exchange between players (via conditional strategies) is crucial for the evolution of cooperation.

## Results

We evolve strategies in spatially-structured and well-mixed finite populations, as it is known that the evolutionary dynamics depend on population structure as well as size (small fitness differences are effectively neutral only in finite populations [32]). Evolution experiments are carried out with populations on a regular 32×32 grid with wrapping boundary conditions, where the manner of replacement determines the population structure. Players engage their eight closest neighbors exactly once every update (playing one move), for 500,000 iterations. At the end of each update, a proportion *r* (the replacement rate) of players is randomly eliminated using a Moran-like process [33], [34], establishing a finite probability of future encounters between players beyond the first [35]. For spatially-structured populations, each player marked for death is replaced by an offspring of one of his neighbors, while for well-mixed populations the entire grid of players is considered for filling the empty position. In both population types, replicating players are chosen in proportion to their fitness, defined as the accumulated score. Scores are awarded according to the standard payoff matrix of Axelrod [35] throughout, with T = 5, R = 3, P = 1, S = 0.

For memory-one strategies, each player is represented by a genotype (strategy) composed of five genes, four of which encode the conditional probabilities *P _{XY}* representing the probability that a player will cooperate, given that his last historical play was X and his opponent's response was Y, along with the unconditional probability

*P*to cooperate on the first move [24]. Each population is seeded with the “random” genotype where each of the five probabilities is set to 0.5. At each replication event, genes are subject to a per-gene mutation rate

_{C}*μ*, replacing that gene's probability to cooperate with a uniformly distributed number between 0 and 1.

For each evolutionary run, we record the genotype as well as phenotype (play statistics π_{CC}, π_{CD}, π_{DC}, and π_{DD}, given by the fraction of that type of play among all plays) for each organism on the line of descent (LOD) [36]. The LOD is generated by randomly selecting a genotype at the end of each run and tracing back its ancestry to the seeding genotype. Compared to the previously discovered deterministic memory-one strategies [37], our genetic implementation leads to the evolution of novel and drastically different successful strategies, depending on mutation rate, replacement rate, and population structure. None of the 32 deterministic strategies ever appear on the LOD, but instead, strategies evolve that are either cooperative or defective, depending on the experimental setting. Using the LOD averaged over 80 runs (see Fig. S1), we can obtain a consensus genotype for the particular experiment by averaging all genotypes in the latter half of this average LOD, removing any influence from the starting conditions (see Methods).

The consensus genotype for spatially-structured populations at low mutation and replacement rates is that of a cooperative strategy (*P _{C}*,

*P*,

_{CC}*P*,

_{CD}*P*,

_{DC}*P*= 0.647, 0.989, 0.234, 0.318, 0.448), as is evident from a commitment to exchange C plays (i.e.,

_{DD}*P*≈1) and a tendency to cooperate on the first move. By having a low

_{CC}*P*probability this strategy maintains a low tolerance to opponent defection and displays an unwillingness to be exploited. Maintaining a

_{CD}*P*value close to 0.5 with a slight bias towards defection, the consensus genotype expresses indifference in propagating defection but willingness to return to cooperation, a behavior not previously seen among stochastic strategies [24]. When faced with defective play, the strategy will acquire a deficit in lifetime payoff, which can be offset by exploiting naïve cooperators (and occasionally similar strategies) as indicated by a low

_{DD}*P*probability. Consensus strategies for cooperation in well-mixed populations, as well as defectors in both population structures (that appear at high mutation and replacement rates) are listed in Table S1 and described in Text S1.

_{DC}In order to monitor the evolution of strategies, we reduce strategy space by performing a principal component analysis (PCA) of the probabilities on the average LOD obtained from 80 runs at mutation rate *μ* = 0.5% and replacement rate *r* = 1%, and use these components to display the average trajectory at other mutation rates as well. For the spatially-structured population the first two principal components explain 83% and 10% of the variance, respectively (see Methods). Within the two-dimensional window defined by these principal components, we can also mark the location of some well-known strategies (see Fig. 1). We find that evolutionary trajectories obtained from the average LOD move towards a fixed point defined by a consensus genotype (see Methods) that represents the dominant strategy in the particular regime, while the actual genotypes on the LOD form a cloud in strategy space around the consensus that defines the strategy *attractor*. Strategies form clouds around this attractor because in a genetic implementation of IPD, the selective pressures acting on genes depend on the population a player finds himself in. For example, the DD gene in a cooperating population will begin to drift, only to return to its adaptive value when an invasion of defectors reinstates the selective pressure. Similarly, the CD and DC genes are under weakened selection in spatial populations because they are only expressed at the boundaries of homogeneous clusters.

All trajectories start at the same point (START), and move towards the strategy marked by ‘END’. Several well-known strategies provide landmarks in strategy space: TFT: (*P _{CC}*,

*P*,

_{CD}*P*,

_{DC}*P*) = (1,0,1,0), ALLC = (1,1,1,1), ALLD = (0,0,0,0), WSLS = (1,0,0,1), GTFT = (1,0.333,1,0), START = (0.5,0.5,0.5,0.5). All experiments shown are run in a spatially-structured environment at replacement rate

_{DD}*r*= 1%. Trajectories for well-mixed populations are shown in Fig. S3. (

**A**) Evolution of the average LOD for

*μ*= 0.5%. RC marks the consensus genotype (see Methods) of this trajectory, while RAND marks the consensus genotype at

*μ*= 50%, when the population drifts neutrally. This attractor is not the same as ‘END’ because that genotype lies past the most recent common ancestor of the population. (

**B**) Trajectory for

*μ*= 2.5%, close to the critical mutation rate. (

**C**) Trajectory for

*μ*= 5%. ‘RD’ marks the consensus genotype for these parameters. (

**D**) Trajectories emanating from 16 deterministic strategies (at

*μ*= 0.5%) suggest that the fixed point is unique. Blue symbols: start, red dots: end points. Symbols: ⧫: TFT, ▪: ALLC, ★: ALLD, ▴: WSLS.

**A–D**use principal components of trajectory shown in

**A**.

The path in strategy space along the average LOD depends strongly on the mutation rate, and shows a qualitative switch—reminiscent of a phase transition—from the cooperative attractor RC (Fig. 1A) to the defecting attractor RD (Fig. 1C) at a critical value (Fig. 1B), as the mutation rate is increased. Studying the trajectories that emanate from the 16 (ignoring the first gene) deterministic strategies (Fig. 1D) suggests that the evolutionary fixed points are unique attractors for a given environment. We characterize the attractors with an *order parameter m* generated from the average play frequencies:(1)which is the normalized difference between frequencies of cooperative and defective play, averaged over the genotypes on the LOD after equilibration (see Methods). This parameter crosses zero at a critical mutation rate (Fig. 2A), indicating a transition from cooperative to defective strategies. We find that a transition can also be forced by changing the replacement rate *r*, as well as other parameters discussed below.

(**A**) The order parameter *m* defined in Eq. [1] as a function of the mutation rate for a spatially-structured and a well-mixed population, obtained from play statistics averaged over 80 independent runs each (see Text S2). Errors are two standard errors. (**B**) Qualitative phase diagram as a function of *μ* and *r* for spatially-structured populations, where light grey indicates cooperation and black indicates defection. (**C**) Phase diagram for well-mixed populations. Both phase diagrams with quantitative levels of cooperation are shown in Fig. S4.

We can study the evolution of cooperation by plotting the order parameter Eq. [1] as a function of *r* and *μ* in a *phase diagram* that shows that both low replacement rate and low mutation rate lead to cooperation (Fig. 2B), but that the cooperative phase is much smaller for well-mixed populations (Fig. 2C). As *μ* approaches 0.5 (a per-genome mutation rate of 2.5 mutations per replication event), both the spatially-structured and the well-mixed populations begin to drift randomly, signaling that selection has become incapable of maintaining the genetic information. This transition is likely a *quasispecies delocalization* [38], but is smooth rather than abrupt owing to the small genome size [39]. That all strategies occur with equal frequencies in the population when taking the limit of very high mutation rate has been noted before [40].

Previous studies have only investigated small slices of this phase diagram by varying the average number of rounds between players [6] (for deterministic strategies) or varying the mutation rate in analytic calculations and numeric simulations of an infinitely iterated Markov process [11], [40], concluding that cooperation is favored in spatially-structured population but not in well-mixed ones [41]. The phase diagram suggests instead that both cooperation and defection are possible in either population structure, but that the parameter range that facilitates cooperation in well-mixed populations is more restricted.

As the order parameter Eq. [1] is obtained from play statistics that represent the phenotype of players, we may ask how this transition is reflected in the genotype instead. The consensus genotype shows a marked decrease of the *P _{CC}* probability as mutation rate increases, with clear differences between strategies in spatial (Fig. 3A) versus well-mixed (Fig. 3B) scenarios, as has been noted before [24]. At the critical mutation rate (Fig. 3, dashed vertical lines), the probability to cooperate after CC equals the probability to defect after DD. Thus, the consensus genotypes mirror the play statistics obtained to define the critical point.

Mean of probabilities of the consensus genotype as a function of mutation rate (*r* = 1%). Colored areas represent the variance of the probability distribution, and reflect the strength of selection. (*P _{C}* is omitted because it drifts neutrally, see Methods). Vertical lines drawn at the critical mutation rate. (

**A**) Spatially-structured. (

**B**) Well-mixed.

## Discussion

Cooperation is inherently more risky than defection because it forgoes a guaranteed return (P) with the expectation of a benefit (R), rather than keeping the guaranteed return hoping for a windfall (T). This risk is mitigated if the uncertainty about receiving the benefit is reduced. For example, spatial reciprocity allows kin strategies to preferentially play each other (because kin place offspring close to themselves) thus increasing trust. In our model, an increase in mutation rate decreases the probability that kin play the same strategy (because mutations change the strategy of kin), and thus increases the uncertainty about the identity of the strategy a player will face. An increased replacement rate has a similar effect, as increasing *r* shortens the average number of plays that a pair engages in, and this again decreases the probability to face a kin strategy (mutated or not). Previously, a general theory for the evolution of cooperation has been proposed [42],[43] that posits that positive assortment between a player's genotype and the opponent's phenotype is sufficient to promote cooperation, using arguments that ultimately recapitulate Queller's [44] extension of Hamilton's rule.

In our experiments with stochastic conditional strategies, the assortment between a player's genotype and an opponent's phenotype is generated via the evolution of conditional interactions between the players [45], i.e., their ability to base their decisions on information about past behavior. In a sense, evolutionary adaptation creates this assortment by forging a “model” of the environment (in terms of the probabilities *P _{XY}*) that is adapted to the phenotype given by the play frequencies π

*. For example, the cooperative fixed point represents a strategy that cooperates with cooperators, retaliates against defectors, but also forgives mistakes. Thus, it models an environment where cooperators dominate, errors happen, and sometimes defectors try to invade.*

_{XY}More uncertain environments reduce the accuracy of the model, thereby reducing positive assortment, leading to reduced cooperation. Can changing environmental conditions then drive a population from a cooperating to a defecting phenotype and vice versa? In Fig. 4, we show the order parameter of an adapting population on the line of descent where we changed the mutation rate abruptly from one favoring defection to one favoring cooperation, and back. We see that the population responds quickly (in terms of evolutionary time) and predictably to the changes.

Order parameter *m* as a function of update time for an experiment with five changes in mutation rate, starting with a type adapted to a high mutation rate of 5% (defection regime). We show the order parameter for the average LOD of 80 runs with the same regime of mutation rate changes. The population reacts to a changed mutation rate quickly, and settles around the fixed point appropriate for that mutation rate, indicated in the figure.

If consistent environments enable cooperative behavior of strategies that rely on “sensing” their environment, we should also be able to influence the critical mutation rate (where cooperation turns into defection) by changing other parameters that affect uncertainty. For example, it is possible to increase player memory so that the last *two* moves by both players are taken into account to make decisions about cooperation or defection. In this case, player strategies are encoded in 21 genes, which can be used to predict future moves. As expected, the critical mutation rate is pushed to higher genomic mutation rates *μL* (where *L* is the number of genes) for memory 2 (Fig. 5A), and even higher for memory 3 (data not shown). Another source of unpredictability is the maximal strategy uncertainty given by the Shannon entropy [46] of the genome. In the present implementation, the probabilities that affect player decisions are coarse-grained to a resolution of 32,768 different alleles for each probability, or 15 bits of entropy per gene. Decreasing this resolution decreases the uncertainty generated by mutations. Fig. 5B shows the dependence of the order parameter on mutation rate for coarse-grainings of strategy space down to 1 bit (the deterministic strategies). In this limit, the critical mutation rate (for 1% replacement) is pushed towards *μ* = 10%, implying that higher mutation rates result in defective play even though cooperation is expected [7], [47]. Thus, obtaining more information about the environment, for example by basing decisions on more than one past move, increases the amount of information that a player can use to model the environment, and therefore gives rise to a more close assortment between genotype and opponent phenotype, which increases cooperation.

(**A**) Phase transition for populations playing with memories of different size as a function of genomic mutation rate *μL*, where *L* = 5 for memory-one strategies (D1, blue line) and *L* = 21 for memory-two strategies (D2, pink line). (**B**) Phase transition for environments with different resolutions of strategy space, from 15 bits per gene to 1 bit per gene (deterministic strategies). Colors as in legend.

A framework where evolutionary game theory is implemented via genes that are under mutation and selection could also be used to predict how manipulation of the environment will affect the evolutionary fixed point in other systems. For example, defection has been observed in a number of biological systems whose dynamics can be described by a PD payoff matrix [48], [49]. It is tempting to imagine that these systems can be coaxed into cooperation if mutation rate or turnover rate can be manipulated (as is shown in Fig. 4).

Evolution can be viewed as a process in which organisms increase their fit to the world by acquiring information about their environment [36], [50]. Via this process, genomes become correlated to their environment, that is, genotypes that are adapted to their niche covary with the niche's character. Clearly, such a covariance is greatly enhanced if organisms can sense their environment, and thus base their decisions appropriately on the context. Therefore, we can expect that the evolution of sensory circuits that inform decisions should ultimately lead to a sufficient amount of covariance so that cooperation is expected according to Queller's rule [44] (unless environments are so *inherently* uncertain that they must remain uninformative to any player). If this is indeed true, then it appears that cooperation does not need to be added as a “third fundamental principle of evolution beside mutation and natural selection” as was suggested before [9], because it is a consequence of evolution.

## Methods

### Population dynamics

The payoff for different moves was kept constant at Axelrod's values for all simulations:At each update, every player on the 32×32 grid (with wrapping boundary conditions) plays each of its neighbors exactly once. Upon birth, each player begins by consulting its *P _{C}* gene for each opponent, and one of the four conditional genes thereafter, depending on its own play and the opponent's response. Players are selected for removal randomly with a probability given by the replacement rate

*r*, giving rise to overlapping generations (asynchronous updating) [51], [52]. As long as the player and its opponent are not replaced, they continue to consult their conditional genes to make decisions, so the replacement rate determines the average length of play history between two players (if a player's partner is replaced, the partner is greeted by consulting the unconditional gene). For most replacement rates, the first gene is consulted so rarely that it drifts neutrally, with a mean around 0.5 and a variance of 1/12, as expected for a uniformly distributed random variable bounded by zero and one. As a consequence, we often do not show any statistics for this gene.

To implement well-mixed populations using our grid structure, we only changed the identity of the pool used for replacing individuals marked for death, thus keeping the rest of the dynamics consistent. For structured populations, the eight neighbors of the marked individual are candidates for replication, with a probability proportional to their fitness given by their lifetime accumulated score. For well-mixed populations, the pool is given by all 1,023 remaining strategies in the population (in a Moran process, it is not usual for the individual to be marked for death to be included in the candidates for replication), but each strategy still plays eight neighbors. The player to be replaced, on the other hand, is chosen randomly among all 1,024 players in the population, irrespective of population structure or fitness. After replication, a genotype is mutated with a probability *μ*, which is the mean number of mutations per gene per individual, implemented as a Poisson process. For most of the results in this study, the gene's probabilities are coarse-grained to 15 bits, which means that the probabilities are chosen from among 2^{15} = 32,768 possible values, representing the number of possible alleles at that locus. This resolution affects the critical mutation rates as shown in Fig. 5B, but increasing the resolution past 15 bits does not (data not shown). Because the mutation probabilities are thought to represent the decision of entire pathways of perhaps hundreds of genes, they should not be compared to per-nucleotide mutation rates.

### Line of descent and consensus genotypes

Rather than collecting population averages of plays, we instead study the evolution of strategies by following the line of descent (LOD) of player genotypes for each replicate run. The LOD is obtained by choosing a random player at the end of the run and following its direct ancestors backwards to the first genotype [36]. Fig. S2A shows a typical sequence of genotypes, while Fig. S2B shows the play statistics for the same LOD. The population average of play statistics for the same experiment is shown for comparison in Fig. S2C. Average lines of descent and average play statistics along the line of descent can be created by averaging, for each update, the probabilities of the genotypes as well as the probabilities of play, of the organism on the LOD of each of the 80 replicates at that update. Fig. S1A shows such an average genetic LOD, while Figure S1B and C shows the average play statistic on the line of descent for two different mutation rates. The latter two figures show that the average play statistics converge towards evolutionary fixed points that we term the *consensus genotype*, but that the time to achieve this fixed point depends on the mutation rate. The consensus genotype for each set of replicates is obtained by averaging the second half of the average genetic LOD minus the last 50,000 updates, which removes most or all of the transient and also the variance due to picking random genotypes as the originators of the LOD. Indeed, because the LOD splits at the most recent common ancestor (MRCA) of the population at the end of the run, the LOD past the MRCA is not necessarily representative of the evolutionary dynamics (as seen for example in Fig. S1B.) Discarding the last 50,000 updates truncates the LOD to genotypes before the MRCA for almost all runs. Using the MRCA genotype instead of the consensus genotype as representative of the fixed point does not change the results.

### Principal component analysis

We create the evolutionary trajectories in Fig. 1 and Fig. S3 by performing a principal component analysis of the set of probabilities (*P _{CC}*,

*P*,

_{CD}*P*,

_{DC}*P*) from all of the 500,000 data points on the average genetic LOD of the 80 replicates at mutation rate

_{DD}*μ*= 0.5% and replacement rate

*r*= 1%, for both the spatially-structured and the well-mixed population, respectively. Because the first gene (

*P*) is consulted so rarely it drifts almost neutrally and is for that reason omitted from the PCA. Including it does not significantly affect the four other principal components (data not shown). For the spatially structured population we obtain

_{C}*PC*

_{1}= (−0.86, 0.192, −0.055, −0.47) and

*PC*= (−0.348, 0.442, −0.065, 0.824). These components explain 83% and 10% of the variance respectively. For the well-mixed population, the principal components are

_{2}*PC*

_{1}= (−0.714, 0.132, −0.162, −0.668) and

*PC*= (−0.393, 0.54, 0.646, 0.37), explaining 86% and 7% of the variance, respectively. To depict the evolutionary trajectories at higher mutation rate (panels B and C in Fig. 1 and panels B–D in Fig. S3), we keep the principal components obtained with the low mutation rate strategies so that the landmarks given by the common deterministic strategies such as TFT (Tit-for-Tat), WSLS (Win-Stay-Lose-Shift), ALL-C, and ALL-D remain at the same positions. These fixed components are also used to plot the location of the consensus genotype at mutation rate 0.5% (RC, the “robust cooperator”), and the consensus genotype at mutation rate 5% (RD, the “robust defector”). The consensus strategies RC and RD for spatially-structured and well-mixed populations are different, and described in the supplementary text below. Using the principal components implied by the average LOD obtained at 5% mutation rate (defecting attractor) instead does not change the nature of the results (data not shown).

_{2}## Supporting Information

### Figure S1.

Average gene probabilities and play statistics. LOD gene probabilities (*P _{XY}*) and play statistics (

*π*) for a spatially-structured population, averaged over 80 experiments (500,000 updates each), at different

_{XY}*μ*and fixed

*r*(1%).

*P*and

_{C}*π*are omitted because

_{C}*P*drifts almost neutrally (see Methods). (A) Average gene probabilities recorded at

_{C}*μ*= 1%. (B) Play statistics recorded at

*μ*= 0.1%. (C) Play statistics recorded at

*μ*= 2%.

https://doi.org/10.1371/journal.pcbi.1000948.s001

(0.25 MB TIF)

### Figure S2.

LOD and population genotypes and phenotypes. Single run LOD gene probabilities (*P _{XY}*) and play statistics (

*π*), as well as population average play statistics, for a spatially-structured population at

_{XY}*μ*= 1% and

*r*= 1%.

*P*and

_{C}*π*are omitted because

_{C}*P*drifts neutrally (see Methods). (A) LOD gene probabilities. (B) LOD play statistics. (C) Average population play statistics.

_{C}https://doi.org/10.1371/journal.pcbi.1000948.s002

(0.41 MB TIF)

### Figure S3.

Evolutionary trajectories and attractors for well-mixed populations. All trajectories start at the same point (‘START’), and move towards the strategy marked by ‘END’. Several well-known strategies provide landmarks in strategy space: ‘TFT’: (*P _{CC}*,

*P*,

_{CD}*P*,

_{DC}*P*) = (1,0,1,0), ‘ALLC’ = (1,1,1,1), ‘ALLD’ = (0,0,0,0), WSLS = (1,0,0,1), GTFT = (1,0.333,1,0), START = (0.5,0.5,0.5). All experiments shown are run at replacement rate

_{DD}*r*= 1% for well-mixed populations. (A), Evolution of the average LOD for

*μ*= 0.5%. RC marks the consensus genotype of this trajectory (described in supplementary text S1). This attractor is not the same as ‘END’ because that genotype lies past the most recent common ancestor of the population. (B) Trajectory for

*μ*= 1.5%, close to the critical mutation rate. (C) Trajectory for

*μ*= 5%. ‘RD’ marks the consensus genotype for these parameters. (D) Trajectory for

*μ*= 50%. ‘RAND’ marks the consensus genotype for these parameters.

https://doi.org/10.1371/journal.pcbi.1000948.s003

(0.14 MB TIF)

### Figure S4.

Quantitative phase transition diagrams as a function of *μ* and *r*. Coloring is applied according to the order parameter (*m*) with dark red to black indicating defection (*m*<−0.2), light yellow to light orange indicating cooperation (*m*>0.2) and orange indicating a transition regime of equal cooperation and defection (0.2≥*m*≥−0.2). White colored areas contain no recorded data. (A) Spatially-structured environment. (B) Well-mixed environment.

https://doi.org/10.1371/journal.pcbi.1000948.s004

(0.46 MB TIF)

### Table S1.

Consensus genotypes for different mutation rates and population structures. Mean probabilities for each gene averaged over 80 average LODs, with variance in brackets. SS: spatially-structured population, WM: well-mixed population, COOP: cooperator, DEFEC: defector.

https://doi.org/10.1371/journal.pcbi.1000948.s005

(0.04 MB DOC)

### Text S1.

Description of consensus strategies.

https://doi.org/10.1371/journal.pcbi.1000948.s006

(0.03 MB DOC)

## Acknowledgments

We thank Bjørn Østman and Sharmila Kopanathi for discussions, as well as Richard Lenski and Claus Wilke for comments on the manuscript.

## Author Contributions

Conceived and designed the experiments: DI AH CA. Performed the experiments: DI AH CA. Analyzed the data: DI AH CA. Contributed reagents/materials/analysis tools: DI AH CA. Wrote the paper: DI AH CA.

## References

- 1.
Maynard Smith J, Szathmáry E (1995) The Major Transitions in Evolution. Oxford: Freeman. J. Maynard SmithE. Szathmáry1995The Major Transitions in EvolutionOxfordFreeman
- 2.
Frank SA (1998) Foundations of Social Evolution. Princeton, , NJ: Princeton University Press. SA Frank1998Foundations of Social EvolutionPrinceton, , NJPrinceton University Press
- 3.
Dugatkin LA (1997) Cooperation Among Animals: An Evolutionary Perspective. Princeton, , NJ: Oxford University Press. LA Dugatkin1997Cooperation Among Animals: An Evolutionary PerspectivePrinceton, , NJOxford University Press
- 4.
Maynard Smith J (1982) Evolution and the Theory of Games. Cambridge, UK: Cambridge University Press. J. Maynard Smith1982Evolution and the Theory of GamesCambridge, UKCambridge University Press
- 5. Nowak MA, Sigmund K (1990) The evolution of stochastic strategies in the Prisoner's Dilemma. Acta Appl Math 20: 247–265.MA NowakK. Sigmund1990The evolution of stochastic strategies in the Prisoner's Dilemma.Acta Appl Math20247265
- 6. Axelrod R, Hamilton W (1981) The evolution of cooperation. Science 211: 1390–1396.R. AxelrodW. Hamilton1981The evolution of cooperation.Science21113901396
- 7. Nowak M, May R (1992) Evolutionary games and spatial chaos. Nature 359: 826–829.M. NowakR. May1992Evolutionary games and spatial chaos.Nature359826829
- 8. Nowak M, Sigmund K (1992) Tit for tat in heterogeneous populations. Nature 355: 250–253.M. NowakK. Sigmund1992Tit for tat in heterogeneous populations.Nature355250253
- 9. Nowak M (2006) Five rules for the evolution of cooperation. Science 314: 1560–1563.M. Nowak2006Five rules for the evolution of cooperation.Science31415601563
- 10. Trivers R (1971) The evolution of reciprocal altruism. Q Rev Biol 46: 35–57.R. Trivers1971The evolution of reciprocal altruism.Q Rev Biol463557
- 11. Ohtsuki H, Hauert C, Lieberman E, Nowak M (2006) A simple rule for the evolution of cooperation on graphs and social networks. Nature 441: 502–505.H. OhtsukiC. HauertE. LiebermanM. Nowak2006A simple rule for the evolution of cooperation on graphs and social networks.Nature441502505
- 12. Riolo R, Cohen M, Axelrod R (2001) Evolution of cooperation without reciprocity. Nature 414: 441–443.R. RioloM. CohenR. Axelrod2001Evolution of cooperation without reciprocity.Nature414441443
- 13. Hamilton W (1964) The genetical evolution of social behaviour. I. J Theor Biol 7: 17–52.W. Hamilton1964The genetical evolution of social behaviour.I. J Theor Biol71752
- 14. Nowak M, Sigmund K (2005) Evolution of indirect reciprocity. Nature 437: 1291–1298.M. NowakK. Sigmund2005Evolution of indirect reciprocity.Nature43712911298
- 15. Nowak M, Sigmund K (1998) The dynamics of indirect reciprocity. J Theor Biol 194: 561–574.M. NowakK. Sigmund1998The dynamics of indirect reciprocity.J Theor Biol194561574
- 16. Traulsen A, Nowak M (2006) Evolution of cooperation by multilevel selection. Proc Natl Acad Sci U S A 103: 10952–10955.A. TraulsenM. Nowak2006Evolution of cooperation by multilevel selection.Proc Natl Acad Sci U S A1031095210955
- 17. Santos FC, Santos MD, Pacheco JM (2008) Social diversity promotes the emergence of cooperation in public goods games. Nature 454: 213–216.FC SantosMD SantosJM Pacheco2008Social diversity promotes the emergence of cooperation in public goods games.Nature454213216
- 18. Perc M, Szolnoki A (2008) Social diversity and promotion of cooperation in the spatial prisoner's dilemma game. Phys Rev E Stat Nonlin Soft Matter Phys 77: 011904.M. PercA. Szolnoki2008Social diversity and promotion of cooperation in the spatial prisoner's dilemma game.Phys Rev E Stat Nonlin Soft Matter Phys77011904
- 19. Pacheco J, Traulsen A, Nowak M (2006) Active linking in evolutionary games. J Theor Biol 243: 437–443.J. PachecoA. TraulsenM. Nowak2006Active linking in evolutionary games.J Theor Biol243437443
- 20. Pacheco JM, Traulsen A, Nowak MA (2006) Coevolution of strategy and structure in complex networks with dynamical linking. Phys Rev Lett 97: 258103.JM PachecoA. TraulsenMA Nowak2006Coevolution of strategy and structure in complex networks with dynamical linking.Phys Rev Lett97258103
- 21. Perc M, Szolnoki A (2010) Coevolutionary games-A mini review. Biosystems 99: 109–125.M. PercA. Szolnoki2010Coevolutionary games-A mini review.Biosystems99109125
- 22. Nowak M (1990) Stochastic strategies in the prisoner's dilemma. Theor Popul Biol 38: 93–112.M. Nowak1990Stochastic strategies in the prisoner's dilemma.Theor Popul Biol3893112
- 23. Nowak M, Sigmund K (1993) A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner's Dilemma game. Nature 364: 56–58.M. NowakK. Sigmund1993A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner's Dilemma game.Nature3645658
- 24. Brauchli K, Killingback T, Doebeli M (1999) Evolution of cooperation in spatially structured populations. J Theor Biol 200: 405–417.K. BrauchliT. KillingbackM. Doebeli1999Evolution of cooperation in spatially structured populations.J Theor Biol200405417
- 25. Travisano M, Velicer GJ (2004) Strategies of microbial cheater control. Trends Microbiol 12: 72–78.M. TravisanoGJ Velicer2004Strategies of microbial cheater control.Trends Microbiol127278
- 26. Strassmann JE, Zhu Y, Queller DC (2000) Altruism and social cheating in the social amoeba Dictyostelium discoideum. Nature 408: 965–967.JE StrassmannY. ZhuDC Queller2000Altruism and social cheating in the social amoeba Dictyostelium discoideum.Nature408965967
- 27. Khare A, Santorelli LA, Strassmann JE, Queller DC, Kuspa A, et al. (2009) Cheater-resistance is not futile. Nature 461: 980-U240.A. KhareLA SantorelliJE StrassmannDC QuellerA. Kuspa2009Cheater-resistance is not futile.Nature461980-U240
- 28. Szabo G, Toke C (1998) Evolutionary prisoner's dilemma game on a square lattice. Phys Rev E 58: 69–73.G. SzaboC. Toke1998Evolutionary prisoner's dilemma game on a square lattice.Phys Rev E586973
- 29. Traulsen A, Rohl T, Schuster HG (2004) Stochastic gain in population dynamics. Phys Rev Lett 93: 028701.A. TraulsenT. RohlHG Schuster2004Stochastic gain in population dynamics.Phys Rev Lett93028701
- 30. Perc M (2006) Coherence resonance in a spatial prisoner's dilemma game. New J Phys 8: 14424–14429.M. Perc2006Coherence resonance in a spatial prisoner's dilemma game.New J Phys81442414429
- 31. Miller MB, Bassler BL (2001) Quorum sensing in bacteria. Annu Rev Microbiol 55: 165–199.MB MillerBL Bassler2001Quorum sensing in bacteria.Annu Rev Microbiol55165199
- 32. Nowak M, Sasaki A, Taylor C, Fudenberg D (2004) Emergence of cooperation and evolutionary stability in finite populations. Nature 428: 646–650.M. NowakA. SasakiC. TaylorD. Fudenberg2004Emergence of cooperation and evolutionary stability in finite populations.Nature428646650
- 33.
Moran PAP (1962) The Statistical Processes of Evolutionary Theory. Oxford: Clarendon Press. PAP Moran1962The Statistical Processes of Evolutionary TheoryOxfordClarendon Press
- 34.
Nowak MA (2006) Evolutionary Dynamics. Cambridge, , MA: Harvard University Press. MA Nowak2006Evolutionary DynamicsCambridge, , MAHarvard University Press
- 35.
Axelrod R (1984) The Evolution of Cooperation: Basic Books. R. Axelrod1984The Evolution of Cooperation: Basic Books
- 36. Lenski RE, Ofria C, Pennock RT, Adami C (2003) The evolutionary origin of complex features. Nature 423: 139–144.RE LenskiC. OfriaRT PennockC. Adami2003The evolutionary origin of complex features.Nature423139144
- 37.
Axelrod R (1987) Evolving new strategies: The evolution of strategies in the iterated prisoner's dilemma. In: Davis L, editor. Genetic Algorithm and Simulated Annealing: Morgan Kaufman. pp. 32–41.R. Axelrod1987Evolving new strategies: The evolution of strategies in the iterated prisoner's dilemma.L. DavisGenetic Algorithm and Simulated Annealing: Morgan Kaufman3241
- 38. Eigen M, Schuster P (1977) Hypercycle - Principle of Natural Self-Organization. A. Emergence of Hypercycle. Naturwissenschaften 64: 541–565.M. EigenP. Schuster1977Hypercycle - Principle of Natural Self-Organization. A. Emergence of Hypercycle.Naturwissenschaften64541565
- 39. Nowak M, Schuster P (1989) Error Thresholds of Replication in Finite Populations Mutation Frequencies and the Onset of Mullers Ratchet. J Theor Biol 137: 375–395.M. NowakP. Schuster1989Error Thresholds of Replication in Finite Populations Mutation Frequencies and the Onset of Mullers Ratchet.J Theor Biol137375395
- 40. Antal T, Traulsen A, Ohtsuki H, Tarnita C (2009) Mutation-selection equilibrium in games with multiple strategies. J Theor Biol 258: 614–622.T. AntalA. TraulsenH. OhtsukiC. Tarnita2009Mutation-selection equilibrium in games with multiple strategies.J Theor Biol258614622
- 41. Nowak MA, Tarnita CE, Antal T (2010) Evolutionary dynamics in structured populations. Philos Trans R Soc Lond B Biol Sci 365: 19–30.MA NowakCE TarnitaT. Antal2010Evolutionary dynamics in structured populations.Philos Trans R Soc Lond B Biol Sci3651930
- 42. Fletcher JA, Zwick M (2007) The evolution of altruism: Game theory in multilevel selection and inclusive fitness. J Theor Biol 245: 26–36.JA FletcherM. Zwick2007The evolution of altruism: Game theory in multilevel selection and inclusive fitness.J Theor Biol2452636
- 43. Fletcher JA, Doebeli M (2009) A simple and general explanation for the evolution of altruism. Proc R Soc Lond B Biol Sci 276: 13–19.JA FletcherM. Doebeli2009A simple and general explanation for the evolution of altruism.Proc R Soc Lond B Biol Sci2761319
- 44. Queller DC (1985) Kinship, reciprocity and synergism in the evolution of social behavior. Nature 318: 366–367.DC Queller1985Kinship, reciprocity and synergism in the evolution of social behavior.Nature318366367
- 45. Fletcher JA, Zwick M (2006) Unifying the theories of inclusive fitness and reciprocal altruism. American Naturalist 168: 252–262.JA FletcherM. Zwick2006Unifying the theories of inclusive fitness and reciprocal altruism.American Naturalist168252262
- 46.
Cover TM, Thomas JA (1991) Elements of Information Theory. New York: John Wiley. TM CoverJA Thomas1991Elements of Information TheoryNew YorkJohn Wiley
- 47. Lindgren K, Nordahl M (1994) Evolutionary dynamics of spatial games. Physica D 75: 292–309.K. LindgrenM. Nordahl1994Evolutionary dynamics of spatial games.Physica D75292309
- 48. Turner PE, Chao L (1999) Prisoner's dilemma in an RNA virus. Nature 398: 441–443.PE TurnerL. Chao1999Prisoner's dilemma in an RNA virus.Nature398441443
- 49. Frick T, Schuster S (2003) An example of the prisoner's dilemma in biochemistry. Naturwissenschaften 90: 327–331.T. FrickS. Schuster2003An example of the prisoner's dilemma in biochemistry.Naturwissenschaften90327331
- 50. Adami C, Ofria C, Collier TC (2000) Evolution of biological complexity. Proc Natl Acad Sci U S A 97: 4463–4468.C. AdamiC. OfriaTC Collier2000Evolution of biological complexity.Proc Natl Acad Sci U S A9744634468
- 51. Huberman B, Glance N (1993) Evolutionary games and computer simulations. Proc Natl Acad Sci U S A 90: 7716–7718.B. HubermanN. Glance1993Evolutionary games and computer simulations.Proc Natl Acad Sci U S A9077167718
- 52. Newth D (2009) Asynchronous Iterated Prisoner's Dilemma. Adapt Behav 17: 175–183.D. Newth2009Asynchronous Iterated Prisoner's Dilemma.Adapt Behav17175183