O R I G I NA L A RT I C L E doi:10.1111/j.1558-5646.2010.01117.x

EVOLUTIONARY GAMES IN WRIGHT’S ISLAND MODEL: KIN SELECTION MEETS EVOLUTIONARY GAME THEORY Hisashi Ohtsuki1,2,3 1

PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan

2

Department of Value and Decision Science, Tokyo Institute of Technology, 2-12-1-W9-35 O-okayama, Meguro,

Tokyo 152-8552, Japan 3

E-mail: [email protected]

Received June 12, 2010 Accepted August 10, 2010 This article studies evolutionary game dynamics in Wright’s infinite island model. I study a general n × n matrix game and derive a basic equation that describes the change in frequency of strategies. A close observation of this equation reveals that three distinct effects are at work: direct benefit to a focal individual, kin-selected indirect benefit to the focal individual via its relatives, and the cost caused by increased kin competition in the focal individual’s natal deme. Crucial parameters are the coefficient of relatedness between two individuals and its analogue for three individuals. I provide a number of examples and show when the traditional inclusive fitness measure is recovered and when not. Results demonstrate how evolutionary game theory fits into the framework of kin selection. KEY WORDS:

Coefficient of relatedness, identity by descent, inclusive fitness, matrix game, spatial structure.

Evolutionary game theory is the study of frequency-dependent selection. It was invented by Maynard-Smith and Price (1973) and has been drawing wide attention for decades as a powerful theoretical tool to investigate interactions among organisms (Maynard Smith 1982). Examples include sex ratio theory (Hamilton 1967; Boomsma and Grafen 1991; West 2009), animal contests (Maynard-Smith and Price 1973; Maynard Smith 1982), and evolution of social behavior (Frank 1998). Among a variety of models proposed so far, games in spatially structured populations have been intensively investigated (Nowak and May 1992; Ohtsuki et al. 2006; Szab´o and F´ath 2007). In these models, locally limited dispersal is often assumed. As Hamilton (1964) noted in his original paper, population viscosity resulting from limited dispersal increases genetic relatedness among neighbors, thus facilitates the operation of kin selection. In fact, there is a growing interest in analyzing, or sometimes reanalyzing, models of social evolution in terms of relatedness and inclusive fitness  C

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to obtain a general and unifying understanding of the system (Rousset 2004; Lehmann and Keller 2006a; West et al. 2007a,b). Those two methodologies, evolutionary game theory and kin selection theory, have developed for decades with influencing each other. For example, the concept of evolutionarily stable strategy (ESS) (Maynard-Smith and Price 1973) is of central importance and widely used in kin selection models as well (Taylor and Frank 1996). Nevertheless, there is still a debate over the applicability of these two theories. For example, it has been argued that nonadditivity of social interaction (or termed synergistic effect) is not captured well by inclusive fitness hence kin selection theory applies only to a special class of game models (Fletcher and Doebeli 2006; Taylor and Nowak 2007; van Veelen 2009; Nowak et al. 2010; Traulsen 2010). In contrast, other authors argue that nonadditivity can be studied in the framework of kin selection (Lehmann and Keller 2006b; Gardner et al. 2007). In my view, one reason for this discrepancy is because those two fields have

C 2010 The Society for the Study of Evolution. 2010 The Author(s). Evolution  Evolution 64-12: 3344–3353

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traditionally adopted different approaches. Evolutionary game theory usually explores games described by payoff matrices and investigates dynamic properties of the system (Hofbauer and Sigmund 1998, 2003) whereas it pays relatively little attention to its precise genetic background. Kin selection theory, on the other hand, usually assumes weak selection and/or additivity of several effects (Taylor 1996) but pays particular attention to the underlying genetic structure of the model (Rousset 2004) to explain allele-frequency dynamics. The main goal of this article is to provide a synthetic theoretical framework to demonstrate that these two theories are not mutually exclusive but rather compatible with each other. In particular, I will show that genetic association between two individuals is not enough but that genetic association between three individuals is sufficient to solve the nonadditivity issue. Through several examples, I will show how evolutionary game theory fits into the framework of kin selection theory. A good starting point for evolutionary game theorists is to consider a general two-person game in a matrix form, which is described by the following payoff matrix: ⎛ 1 .. . A= k .. . n

1

a ⎜ 11 ⎜ .. ⎜ . ⎜ ⎜ ⎜ ak1 ⎜ ⎜ . ⎜ .. ⎝ an1

···



··· .. .

a1 .. .

··· .. .

ak .. .

· · · an

···

n

⎞ · · · a1n ⎟ .. ⎟ .. . . ⎟ ⎟ ⎟ . · · · akn ⎟ ⎟ .. ⎟ .. . . ⎟ ⎠ ···

In this article, I provide a basic equation that describes the change in frequency of strategies in a general n × n game played in a spatially subdivided population that follows Wright’s infinite island model. From the viewpoint of population genetics, an individual’s strategy in my model is encoded by one of the n alleles at a single locus. Thus, my model directly corresponds to a one-locus n-allele model in population genetics. I demonstrate generality of the approach here by showing how several previous results follow from the basic equation. I also interpret my result in terms of inclusive fitness. It generalizes existing inclusive fitness in two ways. First, the framework presented here can easily deal with more than two strategies (=alleles). Second, triplet genetic association as well as traditional dyadic genetic association appears in the generalized inclusive fitness. This article is structured as follows. In next section, I explain my model and present the main result of the article. In subsequent three sections, I provide a number of examples to clarify the meaning of each term of the main equation and show how previously known results follow from it. Last section concludes the article.

Model (1)

ann

The game consists of n pure strategies, each labeled from 1 to n. Each entry of the payoff matrix (1) represents the payoff of a row player matched with a column player. In other words, a player with strategy k matched with a player with strategy  obtains ak as his payoff. In evolutionary game models, payoffs affect one’s fitness, and individuals with higher payoffs are more favored by natural selection. There is a vast literature on evolutionary game dynamics of matrix games (Weibull 1995; Hofbauer and Sigmund 1998; Nowak 2006). A good starting point for kin selection theorists is to adopt Wright’s island model (Wright 1931), where a population is subdivided into islands (hereafter called demes). In each generation some fraction of juveniles migrates to other demes, whereas the other juveniles remain in their natal deme. Such a pattern of limited dispersal eventually leads to positive genetic association among individuals in the same deme. Wright’s island model has been very intensively studied in population genetics as a basic model to study the effect of spatial segregation on drift and selection (see, e.g., Rousset 2004 and Wakeley 2008). Thus exploring game interactions in Wright’s island model should provide a baseline understanding of the role that spatial structure plays in evolutionary game dynamics.

I consider an infinitely large population of asexually reproducing haploid individuals that follow Wright’s island model, where in each of nd (→ ∞) many demes fosters exactly N (≥2) adults. Individuals (or, players) have the following life cycle. Step (1). Each adult plays the matrix-form game described by (1) with its (N − 1) neighbors and obtains a game payoff. Step (2). Each adult produces a large number of juveniles that is proportional to its fecundity, which is given by 1 + δ(average payoff per interaction), and dies. The parameter 0 < δ  1 is called intensity of selection. Step (3). Each juvenile independently either remains in its natal deme with probability (1 − m) or disperses to another randomly chosen deme with probability m (throughout the paper I always assume m > 0). Step (4). In each deme, juveniles compete for N vacant spots on an equal basis. Winners occupy the vacancy and losers die. The strategy of individual j in deme i is represented by an (n) (k) n-dimensional vector, pij = (p(1) ij , . . . , pij ). The element pij takes the value of either one or zero and represents the use of strategy k by player j in deme i; it is one if the corresponding individual adopts strategy k in the game (1), otherwise zero. It is called indicator variable. Several types of averages can be calculated, such as the average strategy in deme i, the average strategy in

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Table 1.

Table 1.

Symbols used in this article.

Continued.

Symbol

Definition

Symbol

Definition

A ak

Payoff matrix Payoff of a k-strategist matched with an -strategist.  = n=1 p () ak  = n=1 p () ak   = n, =1 p () p ( ) a  = n=1 p () a

R3RN

Probability with which three individuals randomly sampled from the same deme, with replacing the first individual but without replacing the second one, are identical by descent Probability with which three individuals randomly sampled from the same deme each time with replacement are identical by descent Additive effect of the focal individual with strategy k on its own payoff Additive effect of the focal individual’s opponent with strategy  on the focal individual’s payoff Inclusive fitness of a player with strategy k Fitness of individual j in deme i Average fitness in the whole population n d  N (= n d1N i=1 j=1 wi j = 1)

ak• a•k a•◦ a••

R3RR

BH CH CP DH DP Ei,j [ · ] fij fi f −i

Benefit of receiving help Cost of help Cost of harming (or “punishment”) Synergistic effect of mutual help Cost of receiving harming (or “punishment”) Average over i and j Fecundity effect of player j in deme i N Average fecundity effect in deme i (= N1 j=1 f ij ) Average fecundity effect in the population  excluding deme i (= n d1−1 ind=1, i  =i f i  )

i, i j, j , j k, ,  m N n nd p(k) ij

Subscripts for demes Subscripts for individuals Subscripts for strategies Migration rate Deme size Number of strategies Number of demes (→∞) Indicator of player j in deme i using strategy k (true=1, false=0) (n) Vector (p(1) ij , . . . , pij ) Frequency of strategy k in deme i excluding  player j (= N 1−1 Nj =1, j  = j pij(k) )

pij p(k) i,−j pi,−j p(k) i

(n) Vector (p(1) i,−j , . . . , pi,−j ) N (k) Frequency of strategy k in deme i (= N1 j=1 pij )

pi p(k)

(n) Vector (p(1) i , . . . , pi ) Frequency of strategy k in the whole population n d  N (k) (= n d1N i=1 j=1 pij )

p  p(k) (p (k) )δ R2

Vector (p(1) , . . . , p(n) ) Change in p(k) after one step of update First-order effect of δ on p(k) Probability with which two individuals randomly sampled from the same deme without replacement are identical by descent Probability with which two individuals randomly sampled from the same deme with replacement are identical by descent Probability with which three individuals randomly sampled from the same deme each time without replacement are identical by descent

R2R

R3

Continued

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uk v WIF(k) wi j w δ ω

Intensity of selection Probability with which another round occurs in the repeated game

deme i excluding individual j, or the average strategy in the whole population. They are denoted by pi , pi,−j , and p, respectively. Table 1 summarizes symbols used in this article. The average payoff per interaction, which I also call fecundity effect in the following, of individual j in deme i is calculated as f ij =

n



( ) pij() pi,− j a .

(2)

, =1

The derivation of equation (2) is as follows: the focal individual uses strategy  with probability p() ij (it is either one or zero) and its random opponent in the same deme uses strategy  with  ) probability p( i,−j , in which case the focal player obtains the payoff, a . Similarly to above, several averages of fecundity effect can be calculated. For example, the average fecundity effect in deme i and the average fecundity effect in the whole population excluding deme i are, respectively, denoted by fi and f −i (see Table 1). For the island model, the fitness of individual j in deme i is given by

1 + δf ij wij = (1 − m) (1 − m)(1 + δf i ) + m(1 + δf −i )

nd 1 + δf ij 1 =1 m + n d − 1 i  =1 (1 − m)(1 + δf i  ) + m(1 + δf −i  ) i  =i

+δ ( f ij − f −i ) − (1 − m)2 +

m2 ( f i − f −i ) + o(δ). nd − 1 (3)

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Here o(δ) represents terms of higher order than δ. The first and second terms of w ij in equation (3) represent the numbers of focal individual’s offspring that survive to adulthood in and outside its natal deme, respectively (see also Lehmann et al. 2007b). Note that the average fitness in the whole population is always w = 1. Of particular interest is the change in frequency of strategies. The change in p(k) , which is the population-wide frequency of individuals with strategy k, after one generation is described by the Price equation (Price 1970), as      p (k) = Ei, j (wij − w) pij(k) − p (k) = Ei, j (wij − w) pij(k) , (4) where Ei,j [·] represents the average over all demes (i) and all individuals (j). Substituting equations (2) and (3) into equation (4) yields equations (S1.1) and (S1.2) in the Appendix S1. By taking the infinite-deme limit (nd → ∞), we obtain the main result (see Appendix S1 for detailed calculations)   p (k) = δ p (k) δ + o(δ),

(5a)

where the first-order effect of selection is  (k)   p δ = p (k) (ak• − a•◦ ) + R2 (akk − ak• − a•• + a•◦ )  − (1 − m)2 R2R (ak• + a•k − 2a•◦ )  + R3RN (akk − ak• − a•k − a•• + 2a•◦ ) . (5b) Here, I have invented the following convenient notations for several types of average payoffs: ak• ≡ a•◦ ≡

n

p () ak ,

=1 n , =1

a•k ≡

n

p () p ( ) a ,

R2 = (1 − m)2 R2R ,

a•• ≡

n

p () a .

R2R =

(6)

(7)

where RR2 is the probability with which two individuals randomly sampled from the same deme with replacement are identical by descent, which is given by

p () ak ,

=1 

relatedness between two different individuals in the same deme (see Appendix S1). Similarly, R3 is the probability with which three individuals randomly sampled from the same deme, each time without replacement, are identical by descent. Note that R3 is well defined for N ≥ 3. I propose to call R3 coefficient of triplet relatedness because it matches with a natural extension of the coefficient of relatedness to three individuals (see Appendix S1). To distinguish R2 from R3 , I will occasionally call R2 coefficient of pair relatedness in the following. The assumption of weak selection (i.e., δ  1) guarantees that those quantities can be calculated under neutrality, δ = 0, for the use in equation (5) (see Appendix S1). Let us derive those IBD probabilities. The following calculations employ several approaches developed in literature (Kirkpatrick et al. 2002; Whitlock 2002; Ajar 2003; Roze and Rousset 2003, 2005; Lehmann et al. 2007b; Roze and Rousset 2008; Gardner and West 2010), especially the one in Roze and Rousset (2008). For the infinite island model, because the probability with which two individuals descend from the same deme is (1 − m)2 , R2 is calculated from

1 N −1 + R2 . N N

(8)

Similarly, the probability with which three individuals descend from the same deme is (1 − m)3 , so R3 satisfies

=1

Each symbol, • or ◦, represents a frequency-weighted average of payoffs over the corresponding subscript. Different colors represent sums over different subscripts. The first expression in equation (6) is the average payoff of k-strategists in a (hypothetical) well-mixed population, the second one is the average payoff of opponents of k-strategists in a well-mixed population, the third one is the population-wide average payoff in a well-mixed population, and the fourth one is the average payoff in a population if everyone was allowed to play a game only with oneself. It is easy to see that adding the same constant to all entries of the payoff matrix does not alter equation (5b). Key parameters in equation (5) are R2 and R3 , and their variants, RR2 and RRN 3 , each of which represents a probability of identity by descent(IBD). More specifically, R2 is defined as the probability with which two individuals randomly sampled from the same deme without replacement are identical by descent. Note that individuals are “identical by descent” if they share a common ancestor. This probability agrees with the coefficient of

R3 = (1 − m)3 R3RR ,

(9)

where RRR 3 is the probability with which three individuals randomly sampled from the same deme, each time with replacement, are identical by descent, which is given by R3RR =

1 3(N − 1) (N − 1)(N − 2) + R2 + R3 . 2 2 N N N2

(10)

Finally I define another IBD probability, denoted by RRN 3 , which is the probability with which the three individuals chosen in the following manner are identical by descent: we randomly sample a first individual from the population, then we randomly sample a second individual from the same deme as that of the first one with replacement of the first individual (i.e., the second individual can be the same as the first one), and finally we randomly sample a third individual from the same deme as that of the first and second ones without replacement of the second individual (i.e., the third individual can be the same as the first one but can never be the

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Additive Payoffs and Relationship with Inclusive Fitness

1.0

relatedness, R

0.8

ADDITIVE PAYOFFS

Let us study a special case in which the payoff ak is additively determined by the focal individual(FI)’s behavior (uk ) and its opponent’s behavior (v  ), as ak = uk + v  . A typical example of such an additive game is a Prisoner’s Dilemma game with the following payoff structure:

0.6 0.4 0.2 0.0 0.0

0.2

0.4 0.6 dispersal rate, m

0.8

1.0

Figure 1.

Five coefficients of relatedness that appear in the present article, against dispersal rate, m. I set N = 5. Black (from RR the bottom): R2 and RR2 . Gray (from the bottom): R3 , RRN 3 , and R3 .

same as the second one). It is easy to see that RRN 3 is given by 2 N −2 R2 + R3 . N N

R3RN =

(11)

Note that equations (10) and (11) are valid even for N = 2, as R3 vanishes from those expressions. Solving equations (7–10) for R2 and R3 leads to R2 =

(1 − m)2 . N − (N − 1)(1 − m)2

(12a)

D



C

D

BH − CH

−CH

BH

0

⎞ ⎠,

(1 − m)3 [N + 2(N − 1)(1 − m)2 ] [N − (N − 1)(1 − m)2 ][N 2 − (N − 1)(N − 2)(1 − m)3 ] (N ≥ 3).

(12b)

RR for the infinite island model. Coefficients RR2 , RRN 3 , and R3 are obtained in a similar manner (see Fig. 1). The reason why I have not explicitly substituted those values in equation (5) is because the form of equation (5) remains valid for variants of the island model as well. For example, suppose that the following step is added after (step 4) in the players’ life-cycle:

Step (5). All adults are globally reshuffled. In this alternative model, it is obvious that players in the same deme are not identical by descent any more, because there are infinitely many demes. In this case, R2 = 0 and R3 = 0 should be used instead. A trivial example is when dispersal is global (i.e., m = 1). In this case, R2 = 0 holds and we obtain 

p (k)

 δ

= p (k) (ak• − a•◦ ),

(13)

which shows a formal similarity to replicator equations (Taylor and Jonker 1978; Hofbauer and Sigmund 1998).

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(14)

where strategies C and D, respectively, represent cooperator and defector. The parameters BH and CH , respectively, represent the benefit and the cost of help. It is easy to confirm that ak = uk + v  holds in this example with u C = −CH (helping costs CH ), u D = 0 (not helping is costless), vC = BH (helping yields BH to a recipient) and vD = 0 (not helping yields no benefit). For such additive games, ak• − a•◦ = u k − u •

(15a)

akk − ak• − a•• + a•◦ = vk − v•

(15b)

ak• + a•k − 2a•◦ = (u k + vk ) − (u • + v• )

and R3 =

A=

C



akk − ak• − a•k − a•• + 2a•◦ = 0,

(15c) (15d)

 hold, where the following notations are used: u • = n=1 p () u  n and v• = =1 p () v . Equation (15) is quite useful in understanding the role of the four payoff-relevant terms in equation (5) (see also Appendix S2 for an intuition behind these four terms). Equation (15a) represents the FIs benefit through strategy k, relative to its population average. Equation (15b) represents the benefit that the opponent of the FI gains through strategy k, relative to its population average. Equation (15c) represents the sum of benefits to the FI and its opponent through strategy k, relative to its population average. Finally, equation (15d) represents the synergistic effect to the FI and its opponent through strategy k, relative to its population average. Appendix S2 shows that for a general, not necessarily additive, payoff matrix, equation (15d) represents deviation from additive games, hence it is null here. Substituting equation (15) in equation (5) yields   (k)  p δ = p (k) (u k − u • ) + R2 (vk − v• )  (16) −(1 − m)2 R2R (u k + vk − u • − v• ) . Notably, the coefficient of triplet relatedness (with/without replacement of the first/second individual), RRN 3 , is absent in equation (16). For the island model R2 = (1 − m)2 RR2 holds, so equation

E VO L U T I O NA RY G A M E S I N W R I G H T ’ S I S L A N D M O D E L

(16) is further simplified to 

p (k)

 δ

= p (k) (1 − R2 )(u k − u • ).

(17)

Observe that the FIs effect on its opponent’s payoff, vk , does not appear in equation (17). This finding generalizes Taylor’s (1992) classical result that the benefit of help is exactly canceled out in Wright’s island model thus unconditional cooperation never evolves unless the cost of help is negative (CH < 0 in (14)). A general conclusion here is that whenever the payoff matrix is additive in the sense of ak = uk + v  , the strategy that achieves the largest uk (the most beneficial strategy to actors) will eventually dominate the population in Wright’s infinite island model. INCLUSIVE FITNESS FOR ADDITIVE GAMES

When n = 2 (i.e., there are two strategies in the game), equation (16) is rewritten as  p (1) δ = p (1) (1 − p (1) )   × (u 1 − u 2 ) + R2 (v1 − v2 ) − (1 − m)2 R2R (u 1 + v1 − u 2 − v2 ) .    

(1) =WIF

(18)

The underbraced expression, W (1) IF , in equation (18) is the traditional inclusive fitness of a player with strategy 1 (strictly speaking it is the inclusive fitness effect in the sense of Hamilton (Hamilton 1964; Grafen 2006) but throughout the article I simply call it inclusive fitness). The first term, (u1 − u2 ), is the FIs direct fecundity benefit through strategy 1 relative to strategy 2. The second term, R2 (v 1 − v 2 ), represents the FIs kin-selected indirect fecundity benefit via its relatives, to whom the FI is related by R2 , through strategy 1 relative to strategy 2. The third term represents the effect of increased kin competition; with probability (1 − m)2 , two juveniles produced in the FIs deme, each of whom is related to the FI on average by RR2 , remain in their natal deme, and compete for survival. The increase in competition among relatives caused by strategy 1 relative to strategy 2 is represented by (u1 + v 1 ) − (u2 + v 2 ). The relationship R2 = (1 − m)2 RR2 for the island model further simplifies the inclusive fitness to W (1) IF = (1 − R2 ) (u1 − u2 ). The viewpoint provided above is quite useful, because the expression inside the braces of equation (16) can be interpreted as a generalization of inclusive fitness for n-strategy (n-allele) models: it represents the inclusive fitness of a player with strategy k. In this generalized inclusive fitness, the term (uk − u• ) is the FIs direct benefit through strategy k, the term R2 (v k − v • ) is the FI’s kin-selected indirect benefit via its relatives through strategy k, and the remaining term − (1 − m)2 RR2 (uk + v k − u• − v • ) is the FI’s direct and indirect cost caused by the increased competition among its relatives. The terms − u• and − v • suggest that these effects should always be compared with their population averages.

Traditional inclusive fitness in a two-strategy model is frequencyindependent because p(1) (1 − p(1) ) is already factored out as in (18), whereas the expression inside the braces of equation (16) is generally frequency-dependent.

Demes of Size Two Another interesting example where RRN 3 vanishes from the general expression, equation (5), is when N = 2. In this case, RRN 3 = R2 holds. For example, for n = 2 (i.e., two-strategy game) we obtain (p (1) )δ = p (1) (1 − p (1) )WIF(1) , where WIF(1) = (a12 − a22 ) + R2 (a21 − a22 )   + R2 + (1 − R2 ) p (1) (a11 − a12 − a21 + a22 )

1 + R2 − (1 − m)2 (a12 + a21 − 2a22 ) 2   + R2 + (1 − R2 ) p (1) (a11 − a12 − a21 + a22 ) . (19) Again W (1) IF is the inclusive fitness of a player with strategy 1. It is frequency-dependent unless a11 − a12 − a21 + a22 = 0 holds (i.e., the payoff matrix is additive). This condition is sometimes called equal gains from switching (Nowak and Sigmund 1990). As an example, let us study a Prisoner’s Dilemma game with a synergistic effect (Queller 1984, 1985), given by

A=

C D

⎛ ⎝

C

D

BH − CH + DH

−CH

BH

0

⎞ ⎠,

(20)

where the new parameter DH represents the synergistic effect of mutual help on fecundity. For this game we obtain   WIF(C) = −CH + R2 BH + R2 + (1 − R2 ) p (C) DH − (1 − m)2

  1 + R2 (C) × DH , (BH − CH ) + R2 + (1 − R2 ) p 2 (21) which exactly reproduces the result of Lehmann and Keller (2006b) (their eq. 13). The first term in equation (21) is the direct cost that a focal cooperator pays, the second term is the kinselected benefit that the focal cooperator receives, the third term is the synergistic effect weighted by the probability with which the focal cooperator meets another cooperator, and the fourth term with square brackets is the effect of increased kin competition.

Full Equation So far I have shown when the payoff matrix is additive or when N = 2 the coefficient of triplet relatedness disappears from the basic equation, equation (5). When neither of these conditions

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holds, however, the coefficient of triplet relatedness, RRN 3 , comes into play. Recently triplet genetic association has been considered in many works (Ajar 2003; Roze and Rousset 2003, 2004, 2009; Gardner et al. 2007; Ladret and Lessard 2007; Lehmann et al. 2007b; Reuter et al. 2008; Gardner and West 2010). Because traditional inclusive fitness theory has mainly paid attention to the role of dyadic genetic association, it is of great interest to see the effect of coefficient of triplet relatedness on evolutionary game dynamics. EXAMPLES

As a first example, let us study the evolutionary model of strong reciprocators by Lehmann et al. (2007b). There are two strategies in the game, strong reciprocator (S) and defector (D). The game consists of two stages. In the first stage of the game, strong reciprocators help its (N − 1) neighbors. Each act of help costs the actor the fecundity cost of CH , while it yields the fecundity benefit of BH to the recipient. Defectors neither pay nor give anything. In the second stage, strong reciprocators harm (or “punish”) those who do not help in the first stage. Each act of harming costs the actor the fecundity cost of CP but incurs the fecundity loss of DP to the victim. Defectors do nothing in the second stage. The payoff matrix of this game is given by

⎛ S − CH S B H ⎝ A= D BH − DP 

⎞  D BH − CH −CH − CP ⎠ = BH 0  

 0 −CP + . −DP 0   

−CH 0



first stage

(22)

Applying the main result to equation (22) yields (p (S) )δ = p (S) (1 − p (S) )WIF(S) , where   WIF(S) = − CH − (1 − p (S) )CP + p (S) DP   + R2 BH + (1 − p (S) )CP − p (S) DP    −(1 − m)2 R2R BH − CH − (1 − 2 p (S) )(CP + DP )  + R3RN (1 − 2 p (S) )(CP + DP ) . (23) This expression is exactly the same as that of Lehmann et al. (2007b) (their eq. A13). As a second example, I explore invasion conditions of strategies. In particular, I study a game with two strategies (n = 2) and derive the invasion condition of strategy 1 in the population of strategy 2. When strategy 1 is rare, the symbols • and ◦ in equation (5) can be replaced with the subscript of the dominant

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p (1)

 δ

∝ (a12 − a22 ) + R2 (a11 − a12 )  −(1 − m)2 R2R (a12 + a21 − 2a22 )  + R3RN (a11 − a12 − a21 + a22 ) > 0

(24)

is the invasion condition of strategy 1. In fact, it is not difficult to see that the equivalent condition has already been derived by Gardner and West (2010) (their equation (A15); they use rP and sP (corresponding to my RR2 and RRR 3 ) in their expression so it looks different). As a third example, I study a game with more than two strategies. Consider a repeated game played by unconditional cooperators (C), unconditional defectors (D), and Tit-for-Tat (TFT) players (T ) (Axelrod and Hamilton 1981). Players repeatedly interact with a fixed partner. One game interaction is called a round. After every round, another round can occur with probability 0 < ω < 1. Unconditional cooperators always help. Unconditional defectors do nothing. TFT players help in the first round. After the first round TFT players mimic whatever their opponent did in the previous round. As before, the benefit and the cost of help are given by BH and CH , respectively. Thus, the payoff matrix of this game is, up to the common factor of (1 − ω)−1 , given by



second stage

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strategy, 2, and therefore

C



⎜ A= D⎜ ⎝ T

C

D

T

BH − CH

−CH

BH − CH

BH

0

BH − CH

−(1 − ω)CH



⎟ (1 − ω)BH ⎟ ⎠. (25) BH − CH

I study the evolutionary dynamics of the game (25) played in the Wright’s island model. For simplicity, let us assume BH > CH > 0. In the absence of limited dispersal (i.e., m = 1), the domain of frequencies of the three strategies, ( p (C) , p (D) , p (T ) ), where TFT is favored by natural selection (i.e., p (T ) > 0) exists if and only if BH /CH > 1/ω (Fig. 2a; see also Brandt and Sigmund 2006). The introduction of limited dispersal (i.e., 0 < m < 1) widens this domain if and only if BH 2−ω > CH ω

(26)

holds (Fig. 2b; see Appendix S3 for detailed calculations). Therefore, depending on details of the parameters, limited dispersal either promotes or inhibits the evolution of direct reciprocity. Interestingly, the condition (26) agrees with the condition known as risk dominance (Harsanyi and Selten 1988); if (26) holds TFT risk dominates unconditional defection, and otherwise the opposite holds.

E VO L U T I O NA RY G A M E S I N W R I G H T ’ S I S L A N D M O D E L

A

tion. Moreover, through using strategy k, the FI can cause an extra increase in kin competition in its natal deme, only when the FI is related to its game-opponent and the synergistic effect, (akk − ak• − a•k − a•• + 2a•◦ ), is produced, and this increase in competition becomes a kin-selected indirect cost to the FI only when juveniles produced in the FI’s natal deme are related to the FI. This event occurs with probability R3RN .

TFT

Discussion C B

C

D TFT

D

Phase diagrams of indirect reciprocity game, (25): C = unconditional cooperator, D = unconditional defector, and TFT = Tit-for-tat player. The C-TFT edge consists of a continuum of fixed

Figure 2.

points. There is bistability on the D-TFT edge; the open circle represents an unstable equilibrium. The frequency of TFT-players increases in the white region, whereas it decreases in the gray one. I set BH /C H = 5/4, ω = 0.9 and N = 5. (A) In the absence of limited dispersal (m = 1). (B) In the presence of limited dispersal (m = 0.05). Because BH /C H > (2 − ω)/ω holds in this example, the white region is wider than that in panel (A). See the main text.

TOWARD A GENERALIZED INCLUSIVE FITNESS

As we have seen in several examples, equation (5b) is a candidate for generalized inclusive fitness in many aspects. First, it deals with more than two strategies (or alleles). Second, it incorporates synergistic effects between two players. Even if games are not additive, equation (5b), with triplet genetic association, RRN 3 , correctly predicts the direction of evolution. In the main result, equation (5b), the term (ak• − a•◦ ) represents the direct benefit to the FI through strategy k. Similarly, the term R2 (akk − ak• − a•• + a•◦ ) represents the kin-selected indirect benefit to the FI through strategy k via its relatives, to whom the FI is related by R2 . The remaining term with the factor − (1 − m)2 represents the direct and indirect costs to the FI owing to increased competition in its natal deme. Through using strategy k, the FI increases the competition in its natal deme by (ak• + a•k − 2a•◦ ). Juveniles produced in the FI’s deme, to whom the FI is related by R2R on average, suffer from the increased competi-

I have studied evolutionary dynamics of games played in the Wright’s infinite island model. My results demonstrate that the change in frequency of strategies is described by the equation that includes the coefficient of pair relatedness, R2 , as well as the coefficient of triplet relatedness, R3 , (and their variants). Traditional inclusive fitness theory has often focused on the case in which the payoff (or fecundity) is additively determined by behaviors of two interactants. I have clarified that under such a scenario R3 does not appear and therefore that all we need is genetic correlations between two individuals. In particular, when the model describes the competition between two strategies (biallelic model), one can reproduce a frequency-independent inclusive fitness. For more general nonadditive games, however, the coefficient of triplet relatedness appears in the calculation unless the deme size is N = 2, thus genetic correlations among three individuals must be taken into account. A primary reason why the coefficient of triplet relatedness, R3 , appears in my basic equation is because one’s fitness is affected by results of the game between two other players in the same deme. Because my model is based on a two-person game, we need to take into account the genetic association between an FI and two other players engaged in a game in the same deme, which leads to triplet relatedness. Similarly, for a game model based on an L-person game, what matters is the genetic association between an FI and L other players engaged in a game, and therefore it is easy to expect that a coefficient of (L + 1)-tuple relatedness, namely RL+1 , should appear in the evolutionary dynamics. For a systematic way to compute those higher-order coefficients I refer to the Appendix of Gardner and West (2010). My approach here is so-called “direct fitness approach” (Taylor and Frank 1996; Frank 1998) or “neighbor-modulated approach” (see West et al. 2007b), where one writes down an individual fitness, wij , and sees how it is affected by the focal and other individuals. I have shown that my basic equation can be interpreted as a generalized inclusive fitness. The inclusive fitness comprises direct benefit to the FI, kin-selected indirect benefit to the FI via its relatives, and direct and indirect costs to the FI caused by increased kin competition in its natal deme. Recently it has been argued that inclusive fitness can deal with only a special class of games or games with two strategies (Taylor and Nowak 2007; Nowak et al. 2010). In this article, I have shown

EVOLUTION DECEMBER 2010

3351

HISASHI OHTSUKI

that for additive games genetic association between two individuals is sufficient to describe evolutionary game dynamics. I have also shown that for nonadditive games we need up to triplet genetic association to study the full dynamics unless a special condition is met (i.e., the deme size is N = 2). The theoretical framework provided in this article extends traditional inclusive fitness to incorporate triplet relatedness such that we can study a general matrix-form game with any number of strategies played in the Wright’s island model. As a good example, I provide in Appendix S4 the full equation of evolutionary dynamics of a nonadditive Prisoner’s Dilemma game, because this game has drawn particularly intense attention for decades (Axelrod and Hamilton 1981; Axelrod 1984; Queller 1984, 1985, 1992; Nowak and May 1992; Fletcher and Zwick 2006; Lehmann and Keller 2006b; Nowak 2006; Wenseleers 2006; Gardner et al. 2007; Taylor and Nowak 2007; Traulsen 2010). It is now clear that triplet genetic association solves the long-lasting nonadditivity issue. Players’ life cycle is a critical factor that affects evolutionary game dynamics. In my model, adults play games. An interesting alternative is the model recently proposed by Gardner and West (2010), in which each adult produces the same but a large number of juveniles and those juveniles play games before dispersal. In this case, payoffs in the game are interpreted as survivorship of juveniles. In Appendix S5, I have studied this alternative model and obtained a basic equation that shows a formal similarity to the original one. I have also investigated the effect of overlapping generations (Taylor and Irwin 2000; Irwin and Taylor 2001) on evolutionary game dynamics in Appendix S6. I have assumed that each individual adopts one of n-strategies and that the strategy is inherited true by offspring. The present framework corresponds to a one-locus n-allele model in population genetics, where each of the n-alleles encodes a strategy in the game. A two-locus, or multilocus game model (Rousset and Roze 2007; Lehmann et al. 2009) is one possible extension, especially when the game under study consists of two or more stages, as in the strong-reciprocator game given by equation (22). I have explored this line of extension in Appendix S7. Relationship between my central result with group selection (or multilevel selection) is of great theoretical importance (Grafen 1984; Queller 1992; Lehmann et al. 2007a). In terms of group selection, demes in the island model is viewed as a higher unit of selection whereas individuals are deemed as a lower one, which leads to the idea of multilevel selection. Based on a mathematical decomposition of the Price equation (Price 1972), I have clarified the between-group component of the selective force and its withingroup component in Appendix S8. To conclude the article, I have studied evolutionary game dynamics played in the Wright’s infinite island model. We have seen that many of previously known important results immediately follow from my main equation. My results clarify the relationship

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between evolutionary game theory and kin selection theory, and link methodologies in these two fields. ACKNOWLEDGMENTS The author thanks Y. Kobayashi, P. D. Taylor, and J. Y. Wakano for valuable discussions. The author also thanks editors and two anonymous reviewers for their helpful suggestions. LITERATURE CITED Ajar, E. 2003. Analysis of disruptive selection in subdivided populations. BMC Evol. Biol. 3:22. Axelrod, R. 1984. The evolution of cooperation. Basic Books, New York. Axelrod, R., and W. D. Hamilton. 1981. The evolution of cooperation. Science 211:1390–1396. Boomsma, J. J., and A. Grafen. 1991. Colony-level sex ratio selection in the eusocial Hymenoptera. J. Evol. Biol. 44:383–407. Brandt, H., and K. Sigmund. 2006. The good, the bad and the discriminator— errors in direct and indirect reciprocity. J. Theor. Biol. 239:183–194. Fletcher, J. A., and M. Doebeli. 2006. How altruism evolves: assortment and synergy. J. Evol. Biol. 19:1389–1393. Fletcher, J. A., and M. Zwick. 2006. Unifying the theories of inclusive fitness and reciprocal altruism. Am. Nat. 168:252–262. Frank, S. A. 1998. Foundations of social evolution. Princeton Univ. Press, Princeton, NJ. Gardner, A., and S. A. West. 2010. Greenbeards. Evolution 64:25–38. Gardner, A., S. A. West, and N. Barton. 2007. The relation between multilocus population genetics and social evolution. Am. Nat. 169:207–226. Grafen, A. 1984. Natural selection, kin selection and group selection. Pp. 62–84 in Krebs, J. R. and N. B. Davies, eds. Behavioural ecology: an evolutionary approach. Blackwell Scientific Publications, Oxford. Grafen, A. 2006. Optimization of inclusive fitness. J. Theor. Biol. 238:541– 563. Hamilton, W. D. 1964. The genetical evolution of social behaviour, I & II. J. Theor. Biol. 7:1–52. ———. 1967. Extraordinary sex ratios. Science 156:477–488. Harsanyi, J. C., and R. Selten. 1988. A general theory of equilibrium selection in games. MIT Press, Cambridge. Hofbauer, J., and K. Sigmund. 1998. Evolutionary games and population dynamics. Cambridge Univ. Press, Cambridge, U.K. ———. 2003. Evolutionary game dynamics. B. Am. Math. Soc. 40:479–519. Irwin, A. J., and P. D. Taylor. 2001. Evolution of altruism in steppingstone populations with overlapping generations. Theor. Popul. Biol. 60:315– 325. Kirkpatrick, M., T. Johnson, and N. Barton. 2002. General models of multilocus evolution. Genetics 161:1727–1750. Ladret, V., and S. Lessard. 2007. Fixation probability for a beneficial allele and a mutant strategy in a linear game under weak selection in a finite island model. Theor. Popul. Biol. 72:409–425. Lehmann, L., and L. Keller. 2006a. The evolution of cooperation and altruism. A general framework and classification of models. J. Evol. Biol. 19:1365–1378. ———. 2006b. Synergy, partner choice and frequency dependence: their integration into inclusive fitness theory and their interpretation in terms of direct and indirect fitness effects. J. Evol. Biol. 19:1426–1436. Lehmann, L., L. Keller, S. A. West, and D. Roze. 2007a. Group selection and kin selection: two concepts but one process. Proc. Natl. Acad. Sci. USA 104:6736–6739. Lehmann, L., F. Rousset, D. Roze, and L. Keller. 2007b. Strong-reciprocity or strong-ferocity? A population genetic view of the evolution of altruistic punishment. Am. Nat. 170:21–36.

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Lehmann, L., M. W. Feldman, and F. Rousset. 2009. On the evolution of harming and recognition in finite panmictic and infinite structured populations. Evolution 63, 2896–2913. Maynard Smith, J. 1982. Evolution and the theory of games. Cambridge Univ. Press, Cambridge, U.K. Maynard Smith, J., and G. R. Price. 1973. The logic of animal conflict. Nature 246:15–18. Nowak, M. A. 2006. Evolutionary dynamics. Harvard Univ. Press, Cambridge, MA. Nowak, M. A., and R. M. May. 1992. Evolutionary games and spatial chaos. Nature 359:826–829. Nowak, M. A., and K. Sigmund. 1990. The evolution of stochastic strategies in the Prisoner’s dilemma. Acta Appl. Math. 20:247–265. Nowak, M. A., C. E. Tarnita, and T. Antal. 2010. Evolutionary dynamics in structured populations. Phil. Trans. R. Soc. B 365:19–30. Ohtsuki, H., C. Hauert, E. Lieberman, and M. A. Nowak. 2006. A simple rule for the evolution of cooperation on graphs and social networks. Nature 441:502–505. Price, G. R. 1970. Selection and covariance. Nature 227:520–521. ———. 1972. Extension of covariance selection mathematics. Ann. Hum. Genet. 35:485–490. Queller, D. C. 1984. Kin selection and frequency-dependence—a game theoretic approach. Biol. J. Linn. Soc. 23:133–143. ———. 1985. Kinship, reciprocity and synergism in the evolution of social behavior. Nature 318:366–367. ———. 1992. Quantitative genetics, inclusive fitness, and group selection. Am. Nat. 139:540–558. Reuter, M., L. Lehmann, and F. Guillaume. 2008. The spread of incompatibility-inducing parasites in sub-divided host populations. BMC Evol. Biol. 8:134. Rousset, F. 2004. Genetic structure and selection in subdivided populations. Princeton Univ. Press, Princeton, NJ \ J. Rousset, F., and D. Roze. 2007. Constraints on the origin and maintenance of genetic kin recognition. Evolution 61:2320–2330. Roze, D., and F. Rousset. 2003. Selection and drift in subdivided populations: a straightforward method for deriving diffusion approximations and applications involving dominance, selfing and local extinctions. Genetics 165:2153–2166. ———. 2004. The robustness of Hamilton’s rule with inbreeding and dominance: kin selection and fixation probabilities under partial sib mating. Am. Nat. 164:214–231.

———. 2005. Inbreeding depression and the evolution of dispersal rates: a multilocus model. Am. Nat. 166:708–721. ———. 2008. Multilocus models in the infinite island model of population structure. Theor. Popl. Biol. 73:529–542. ———. 2009. Strong effects of heterosis on the evolution of dispersal rates. J. Evol. Biol. 22:1221–1233. Szabø, G., and G. F´ath. 2007. Evolutionary games on graphs. Phys. Rep. 446:97–216. Taylor, P. D. 1992. Altruism in viscous populations—an inclusive fitness model. Evol. Ecol. 6:352–356. ———. 1996. Inclusive fitness arguments in genetic models of behaviour. J. Math. Biol. 34:654–674. Taylor, P. D., and S. A. Frank. 1996. How to make a kin selection model. J. Theor. Biol. 180:27–37. Taylor, P. D., and A. J. Irwin. 2000. Overlapping generations can promote altruistic behavior. Evolution 54:1135–1141. Taylor, P. D., and L. B. Jonker. 1978. Evolutionary stable strategies and game dynamics. Math. Biosci. 40:145–156. Taylor, C., and M. A. Nowak. 2007. Transforming the dilemma. Evolution 61:2281–2292. Traulsen, A. 2010. Mathematics of kin- and group-selection: formally equivalent? Evolution 64:316–323. van Veelen, M. 2009. Group selection, kin selection, altruism and cooperation: when inclusive fitness is right and when it can be wrong. J. Theor. Biol. 259:589–600. Wakeley, J. 2008. Coalescent theory: an introduction. Roberts & Company Publishers, Greenwood Village, Colorado. Weibull, J. 1995. Evolutionary game theory. MIT Press, Cambridge. Wenseleers, T. 2006. Modelling social evolution: the relative merits and limitations of a Hamilton’s rule-based approach. J. Evol. Biol. 19:1419– 1422. West, S. A. 2009. Sex allocation. Princeton Univ. Press, Princeton, NJ. West, S. A., A. S. Griffin, and A. Gardner. 2007a. Evolutionary explanations for cooperation. Curr. Biol. 17:R661–R672. ———. 2007b. Social semantics: altruism, cooperation, mutualism, strong reciprocity and group selection. J. Evol. Biol. 20:415–432. Whitlock, M. C. 2002. Selection, load and inbreeding depression in a large metapopulation. Genetics 160:1191–1202. Wright, S. 1931. Evolution in Mendelian populations. Genetics 16:97–159.

Associate Editor: S. West

Supporting Information The following supporting information is available for this article: Appendix S1. Appendix S2. Appendix S3. Appendix S4. Appendix S5. Appendix S6. Appendix S7. Appendix S8.

Deriving the main result. Decomposition of payoffs. Evolutionary dynamics of direct reciprocity game. Evolutionary dynamics of Prisoner’s Dilemma game. Juveniles before dispersal play games. Overlapping generations. Two-locus model. Relationship with group selection.

Supporting Information may be found in the online version of this article. Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

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