You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#s1}
============

Cooperation is the basis for complex organizational structures in biological as well as social systems. Nevertheless, understanding the emergence and stability of cooperative behavior in the context of Darwinian selection remains a challenge to date. The dilemmas of cooperation are usually tackled within the framework of evolutionary game theory [@pone.0021787-Hofbauer1]--[@pone.0021787-Sigmund1]. Although several mechanism allowing for the evolution of cooperation have already been identified [@pone.0021787-Nowak2], the resolution of social dilemmas and the closely related avoidance of the "tragedy of the commons" [@pone.0021787-Hardin1] is still considered an open problem. The prisoner\'s dilemma game [@pone.0021787-Axelrod1], in particular, has attracted considerable attention in the past three decades [@pone.0021787-Doebeli1]--[@pone.0021787-Perc1], and to date it is widely consider as a paradigmatic example for the tensions between social welfare and individual interests [@pone.0021787-Santos1]--[@pone.0021787-Perc3]. Cooperation and defection are the two strategies that are at the heart of the prisoner\'s dilemma game. In general, while cooperators sacrifice some of their personal fitness for the benefit of the society, defectors succumb to the temptations and take full advantage of them. The prisoner\'s dilemma captures this situation by means of the following payoffs: mutual cooperation yields the reward , mutual defection leads to punishment , and the mixed choice gives the cooperator the sucker\'s payoff and the defector the temptation . The payoff ranking thus satisfies . In the iterated prisoner\'s dilemma game the assumption that the mutual cooperation yields the highest collective income imposes another constraint, namely . This makes it clear that the rational (selfish) action is to defect, and according to the fundamental principles of Darwinian selection, cooperation extinction is inevitable. Full defection is indeed the only stable Nash equilibrium for the prisoner\'s dilemma game in well-mixed populations.

Since the seminal paper by Nowak and May [@pone.0021787-Nowak3], however, we know that this may not be the case for spatial interactions. Although not universally applicable [@pone.0021787-Hauert1], spatial reciprocity is recognized as a potent promoter of cooperative behavior, even more so on complex networks [@pone.0021787-Abramson1]--[@pone.0021787-Perc4] (for a comprehensive review see [@pone.0021787-Szab1]). Other prominent mechanism promoting cooperation are kin selection [@pone.0021787-Hamilton1], direct and indirect reciprocity [@pone.0021787-Nowak4]--[@pone.0021787-Brandt2], as well as group selection [@pone.0021787-Dugatkin1]--[@pone.0021787-Szolnoki3], to name but a few.

Inspired by previous works on this subject, we here introduce particle swarm optimization [@pone.0021787-Kennedy1]--[@pone.0021787-Eberhart2] to the players engaging in the prisoner\'s dilemma game on a square lattice [@pone.0021787-Szab5], with the aim of investigating its impact on the evolution of cooperation. However, we abandon the commonly considered assumption that the players can choose only between the two pure strategies, namely to either cooperate or to defect. Real-life situations are often more complex than that, and indeed there is a lot of gray between the black and white extremes. Motivated by this fact, we here consider stochastic strategies, such that the cooperativeness of each players is determined by . returns full cooperation, while returns full defection. These are the two extremes recovered from our present setup. Between , however, there exists a continuous set of strategies that can be considered either as predominantly cooperative (if ) or predominantly defective (if ). Moreover, while the evolution of strategies is traditionally performed by means of different strategy adoption (or updating) rules (see [@pone.0021787-Szab1] for a comprehensive review), we here take a much less explored avenue, namely by considering the aforementioned particle swarm optimization as the driving force behind strategy evolution. The particle swarm optimization algorithm is based on a simplified social model that is tightly tied to the theory of swarming [@pone.0021787-Kennedy1]--[@pone.0021787-Eberhart2]. A traditional analogy is a swarm of bees searching for a food source. In this analogy, each bee (considered here as a particle) makes use of its own memory as well as the knowledge obtained by the swarm as a whole, to find the best available food source. Particle swarm optimization can also be considered as being representative for multidimensional search (for example to find an optimum of a utility function). Typically, a number of simple entities (the "particles") is randomly positioned in the search space, and to each a velocity vector is assigned, which is subsequently used to update the current position of each particle in the swarm. Each particle then proceeds by evaluating the objective function at its current location, and finally to determining its movement through the search space by combining some aspects of the history of its own current as well as other potentially optimal locations with those of one or more members of the swarm. Thus, the process makes use of the memory of each particle, as well as the knowledge gained by the swarm as a whole. The next iteration takes place after all the particles have moved once. Eventually the swarm, like a flock of birds collectively foraging for food, is likely to move closer to an optimum of the utility function. Accordingly, the particles (bees, birds, players) therefore should have a tendency to fly towards better and better areas over the course of the search process.

Here we focus specifically on introducing the particle swarm optimization algorithm to the strategy updating process in the stochastic strategy prisoner\'s dilemma game on the square lattice. In agreement with the above described general concept, each individual is assigned a variable from the unit interval determining its level of cooperativeness (or willingness to cooperate). Likewise, a velocity vector is assigned to every player. Following this initialization, each player makes use of its own memory (i.e., keeping score of the most profitable individual strategy in the past), as well as use of the knowledge gained by the swarm (i.e., the nearest neighbors) as a whole, to find the best available strategy for itself and the society. In particular, the particle swarm optimization algorithm makes use of the velocity vector to update the current strategy of each player in the swarm. In this sense our study can be considered related to previous works investigating the effects of mobility on the evolution of cooperation [@pone.0021787-Vainstein1]--[@pone.0021787-Jiang1], although it relies on an essentially different algorithm. The outline of the latter is as follows: 1) Start with a set of strategies (i.e., cooperation probabilities ) that are initially uniformly distributed in the interval. 2) Calculate a velocity vector for each strategy in the swarm. 3) Update the strategy of each agent, using its previous value and the updated velocity vector. 4) Go to step and repeat until convergence. All the details of this setup are described in the [Methods](#s4){ref-type="sec"} section, while here we proceed with presenting the main results.

Results {#s2}
=======

We start by presenting the average level of cooperation, defined as where is the system size and runs over all the players in the population, in dependence on the temptation to defect for different values of (for the definition see the [Methods](#s4){ref-type="sec"} section) in [Fig. 1](#pone-0021787-g001){ref-type="fig"}. Expectedly, the average level of cooperation decreases as increases for all . However, while for the cooperative behavior dies out completely at high values of , for the average level of cooperation hovers comfortably over , even when the maximal limit is reached. For intermediate and low values of , however, small values of may yield overall higher average levels of cooperation. It is thus intriguing to find that the introduced particle swarm optimization in the strategy updating, fine-tuned by means of the parameter , can be responsible for the emergence of cooperative behavior across the whole span of defection temptation values, as well as for its dominance at low values of . More precisely, two regimes can be differentiated. For intermediate and high values of are actually detrimental for the evolution of cooperation, while for the higher the the higher the stationary level of cooperative behavior. These results make it clear that low (e.g., ) strongly support the cooperation level for small , up to , whereas high are much better suited for cooperation to evolve under this dynamics in strongly defection-prone environments. At this point we argue that for , when players imitate their best past actions rather than the best players in the swarm (see [Methods](#s4){ref-type="sec"} for details), the proposed strategy updating rule warrants the most significant benefits to cooperative behavior if looking at the entire range of values, thus in turn resolving the prisoner\'s dilemma.

![Average level of cooperation in dependence on for different values of .\
It can be observed that while imitating the best performing player in the swarm () might be beneficial at low temptations to defect, imitating personal success () is definitively better for the evolution of cooperation in strongly defection-prone environments. Each data point is an average of the final outcome (stationary state) of the game over independent realizations. Lines connecting the symbols are just to guide the eye.](pone.0021787.g001){#pone-0021787-g001}

In order to obtain an understanding of these results, we first systematically analyze the impact of on the final distribution of strategies in the whole population for various values of , as depicted in [Fig. 2](#pone-0021787-g002){ref-type="fig"}. Note that for the distribution of strategies is very monotonous, while for much more diversity is inferable. Both observations are virtually independent of . Since the parameter determines the tendency of every player to either adopt the most profitable strategy in its past actions () or the strategy of the most successful player in its neighborhood (), these results can be understood very well. In particular, for individuals are strongly inclined to imitate the best-performing strategies in the swarm, irrespective of their personal experience in the past. This narrow-sightedness inevitably results in strongly polarized distributions, as only either pure cooperators or pure defectors are the ones most likely to have the overall highest payoffs. Note that this is because the payoffs are directly scaled by (see [Methods](#s4){ref-type="sec"}). Conversely, for the situation is very different since players will focus on their own past actions and learn from them in order to arrive at the best possible strategy. This has the advantage that, unlike for , here only the immediate neighborhood is explicitly taken into account. For high values of local considerations are obviously much more important than for low values of . In the latter case, the nearest neighbors can much easily be neglected since the environment on its own is not strongly favorable for defectors, and hence cooperators can prevail even if overlooking the detailed distribution of strategies in their immediate neighborhood. An additional advantage of small , however, is that by focusing only (or predominantly) on the best-performing players in the swarm, the average level of cooperativeness can be maximized more efficiently (as evidenced by results presented in [Fig. 1](#pone-0021787-g001){ref-type="fig"}). But if the temptation to defect is strong the strictly local considerations are much more important, as proper adaptation is then crucial for cooperators to survive. Accordingly, for high values of higher yield better results (higher average level of cooperation) by exploiting effectively the whole array of available strategies to respond properly (*locally* properly) to invading defectors. At low values of , however, these locally optimal adaptations (warranted by ) might be less effective than the more globally inspired actions (warranted by ).

![Distribution of strategies in the whole population, as obtained for different combinations of and .\
It can be observed that for the nature of the stochastic strategy prisoner\'s dilemma game is essentially completely overridden by the selfish drive of players to reach the highest current payoffs in the swarm, in turn virtually completely transforming the game to its two-strategy \[only (full defection) or (full cooperation) strategies are present in the population\] version. Conversely, for the full spectrum of available strategies is exploited to arrive at the final stationary state. Note that the horizontal axis displays the willingness to cooperate (defining the strategy of every player), while the vertical axis depicts the probability that this strategy is present in the population. Depicted results are averages of the final outcome (stationary state) over independent realizations.](pone.0021787.g002){#pone-0021787-g002}

These conclusions can be corroborated further by examining characteristic snapshots of strategy and velocity distributions for key combinations of and , as presented in [Figs. 3](#pone-0021787-g003){ref-type="fig"} and [4](#pone-0021787-g004){ref-type="fig"}. Focusing first on the distribution of strategies in [Fig. 3](#pone-0021787-g003){ref-type="fig"}, it can be inferred that for , where only the most successful strategies within the whole swarm can spread rapidly due to the workings of the particle swarm optimization algorithm, the strategy distribution becomes very monotonous, leading to the isolation of homogeneous groups of players characterized either by or , respectively. This holds irrespective of , only that for strong temptations to defect the clusters of strongly cooperative players become rarer. Note that in this parameter region the here studied stochastic strategy prisoner\'s dilemma game actually becomes strikingly similar to the classical two-strategy spatial prisoner\'s dilemma game [@pone.0021787-Nowak3], [@pone.0021787-Szab5], where the clustering of cooperators is the main driving force prohibiting the full dominance of defectors. Conversely, for , where the particle swarm optimization algorithm is driven by the past experience of every individual player (rather than the swarm as a whole), highly heterogeneous kaleidoscopes appear, and it is indeed this diversity that warrants a high level of cooperativeness even by strong temptations to defect. In particular, snapshots in the bottom panel of [Fig. 3](#pone-0021787-g003){ref-type="fig"} indicate that many clusters consist of a small amount of players with a high cooperation level (i.e., close to ), surrounded by players with comparatively lower values. This in turn implies that not the clustering itself is crucial for the sustenance of cooperation, but actually the aggregation of such clusters itself, which enables the players with higher cooperation level to survive the evolutionary process. Note that the high cooperation level within clusters provides surrounding individuals with a safe source of benefits that are sufficient to resist the invasion of predominantly defective (i.e., close to ) players. The particle swarm optimization algorithm thus spontaneously generates the diversity needed for cooperation to survive at high , much by means of the same mechanism that was reported previously for manually introduced heterogeneous states [@pone.0021787-Perc5]. Of course, players located in the interior of such clusters enjoy the benefits of mutual cooperation and are therefore able to survive despite the constant exploitation by defectors, yet this positive effect is additionally amplified by the diversity and the hierarchical local structures that give additional strength to the cooperative strategy, while at the same time provide no benefits for defectors.

![Characteristic spatial distributions of strategies, as obtained for different combinations of and .\
As concluded from results depicted in [Fig. 2](#pone-0021787-g002){ref-type="fig"}, for low values of only the two "extreme" strategies (with rare exceptions) are adopted, while for high values of the whole array of available strategies comes into play. Moreover, it is interesting to observe that values of yield the well-known clustering of cooperators [@pone.0021787-Nowak3] on the square lattice, while the snapshots for seem to have these feature somewhat less pronounced, although still clearly inferable (note that the distinction of clusters is somewhat difficult due to the continuous array of possible strategies). This suggests that, besides the clustering of cooperators, additional mechanisms may underlie the survival of cooperators at high temptations to defect and within the present setup. The color encoding, as depicted right, indicates the values of for each individual player.](pone.0021787.g003){#pone-0021787-g003}

![Characteristic spatial distributions of velocities, as obtained for different combinations of and .\
Top row depicts results for , while bottom row features results for . Irrespective of , it can be observed that for the whole population essentially becomes a swarm in that the velocities of all players are much the same and close to zero. The fact that the prevailing velocity is close to zero simply reflects that the stationary state has been reached by means of adaptive, locally-inspired and slow strategy changes (which are, however, very effective even if the temptations to defect are strong). For , however, only isolated clusters can be considered to act as swarms, while the majority of players cannot be associated with any kind of group dynamics and is simply caught in the futile pursuit for the highest, yet for the majority unattainable, payoffs. These results indicate that swarming is an important agonist that promotes cooperation at high temptations to defect (see results presented in [Fig. 1](#pone-0021787-g001){ref-type="fig"}). The color encoding, as depicted right, indicates the values of for each individual player, where was chosen sufficiently large such that the stationary state of the game has been reached. Importantly, we note that for the stationary state has in fact been reached, although at a given instance in time the average velocity in the population might be different from zero.](pone.0021787.g004){#pone-0021787-g004}

Moreover, by examining the characteristic distributions of velocities presented in [Fig. 4](#pone-0021787-g004){ref-type="fig"}, we can obtain further insight with regards to the evolution of the strategies and their adaptation. Note that by means of Eqs. (1) and (2) (see the [Methods](#s4){ref-type="sec"} section), the two quantities are strongly interdependent. For , even though the snapshots are taken in the stationary state (where the average level of cooperation is stable), the majority of players will have the velocity very different from (although on average over time and space it is virtually zero, thus assuring the stationary state being reached). This indicates that players will constantly try to reach the currently maximal payoff in the swarm, despite the fact that for the majority this will be unattainable. The locally high velocity values also indicate that the evolutionary process at low values of is quite violent and fast, with the population therefore unable to cope with high temptations to defect. Conversely, for the situation is very different. Here the majority of players will adapt their strategy very slowly to the changing local influences, which yields the velocity profile for every player being very close to zero. These conclusions are valid practically irrespective of for the two considered values of , but the average level of cooperation is in fact very much different. While individually optimal past strategies in the particle swarm optimization algorithm yield a slow but stable and very effective response even to severe defector attacks, population-wide (or swarm-wide) pursuit for extraordinary benefits proves insufficiently effective to sustain cooperative behavior at high values. The latter approach, however, may be superior at low temptations to defect, where local considerations are not so vital, and where the pursuit of individual benefits can be successful even if driven by globally-inspired fast and bold actions.

Discussion {#s3}
==========

In sum, we have studied the impact of particle swarm optimization on the evolution of cooperation in the stochastic strategy spatial prisoner\'s dilemma game. The strategy updating was guided by the particle swarm optimization algorithm, using as input the individual memory of every player (i.e., keeping score of the most profitable individual strategy in the past) as well as the knowledge gained by the swarm (i.e., the nearest neighbors) as a whole. By means of extensive simulations, we found that cooperative behavior can prevail in large regions of the parameter space defining the stochastic strategy prisoner\'s dilemma game, thus effectively leading to the resolution of the dilemma in favor of pro-social behavior. In particular, we have demonstrated that imitating the most profitable strategy in the swarm may lead to full dominance of cooperation at moderate temptations to defect, while imitating the best individual actions in the past may lead to the survival of cooperative behavior even if the environment is strongly prone to defection. We have also investigated the actual strategy configurations in the population as well as pertaining spatial distributions of strategies and velocities, for which we have found to be closely tied to the setup of the particle swarm optimization algorithm, and in fact instrumental for the understanding of the observed promotion of the evolution of cooperation. We hope that our work will offer new ways of ensuring cooperation in situations constituting a social dilemma, and that it will be an inspiration for future research when considering the very interesting combination of intelligent algorithms and evolutionary games.

Methods {#s4}
=======

We consider an evolutionary stochastic strategy prisoner\'s dilemma game on a square lattice, consisting of players with nearest-neighbor interactions and periodic boundary conditions. Initially the strategies of all players are drawn randomly from uniformly distributed values of in the interval, whereby determines the cooperativeness of each individual (or the willingness to cooperate). While returns full cooperation and returns full defection, between there exists a continuous set of strategies that can be considered either as being predominantly cooperative (if ) or predominantly defective (if ), hence constituting a stochastic strategy version of the prisoner\'s dilemma game.

Players interact pairwise with all their nearest neighbors, thereby receiving payoffs that can be summarized succinctly by the rescaled payoff matrixwhere and define the level of cooperativeness of players and , respectively. This setup entails as the only free parameter determining the temptation to defect, but it is well-known that the essence of the prisoner\'s dilemma game is thereby left intact [@pone.0021787-Nowak3].

The stochastic strategy prisoner\'s dilemma game is iterated forward in time using a synchronous Monte Carlo updating scheme. First, each player accumulates its payoff by playing the game with all four of its nearest neighbors. Subsequently, players have to decide what strategy they will adopt in the next round (i.e., what will their new be), which we here determine by means of the particle swarm optimization algorithm. Its implementation is simple and intuitive, as follows. Initially, at time step , all players are assigned the same velocity . For each following , the velocity vector of every player is updated according toand the strategy follows directly aswhere in Eq. (1) is the most profitable strategy of player in all its past actions, whereas is the best performing strategy in the swarm (here considered to be composed of the four nearest neighbors). The parameter determines the tendency of every player to either adopt the most profitable strategy in its past actions or the current strategy of the most successful player within the swarm. In particular, implies that the player will definitely imitate its past best action, i.e., the strategy that in the past yielded the highest payoff. On the other hand, implies that the player will copy the strategy of the currently best performing player in its neighborhood. Intermediate values of interpolate linearly between these two extremes. Besides the temptation to defect , is here considered as the second crucial system parameter.

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

**Funding:**This work was supported by the National Nature Science Foundation of China (grant numbers 60974064 and 60736022) and the Specialized Research Fund for the Doctoral Program of Higher Education (grant number 20060001013). MP acknowledges support from the Slovenian Research Agency (grant number Z1-2032). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

[^1]: Performed the experiments: JZ CZ TC MP. Analyzed the data: JZ CZ TC MP. Contributed reagents/materials/analysis tools: JZ CZ TC MP. Wrote the paper: JZ CZ TC MP.