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Application 1: The evolution of worker reproduction\ \>", "Title", TextAlignment->Left, FontFamily->"Arial", FontSize->40, FontWeight->"Bold", FontSlant->"Italic", FontColor->RGBColor[0.500008, 0, 0.500008], Background->GrayLevel[1]], Cell[TextData[{ "Accompanying paper: \nFrank, S.A. 1995a. Mutual policing and repression of \ competition in the evolution of cooperative groups. ", StyleBox["Nature ", FontSlant->"Italic"], "377: 520-522." }], "Text", FontSize->16], Cell[CellGroupData[{ Cell["Purpose :", "Section", FontSize->25], Cell[TextData[{ "Many papers in the social insect literature have pointed out that since \ workers are related more to sons than to nephews, any worker should gain from \ producing male offspring. But how many workers should breed within a colony? \ Clearly, a rare laying worker within a colony would be at an advantage \ relative to a nonlaying one, but what if all workers took on a reproductive \ role? In that case, the colony would perish as a result of a dearth of \ foragers. Here we will derive the ESS probability for a worker to switch to a \ reproductive role, using the model of Frank (1995, 1996) as a basis. To \ simplify the matter we will only model the evolution of worker reproduction \ under queenless condition.\nFrank's model (1995, 1996) was not specifically \ formulated to solve the worker reproduction problem (in fact worker \ reproduction is not even mentioned in the paper), instead it is framed around \ the more general concept of a ", StyleBox["tragedy of the commons", FontSlant->"Italic"], ". The reasoning is as follows. Any individual in the group will maximise \ its success relative to others by reproducing more (or with higher \ probability) than average. But since all individuals are selected to do this, \ it will eventually cause all individuals in the group to reproduce at a \ higher rate than can be sustained, hence the ", StyleBox["\"tragedy of the commons\"", FontSlant->"Italic"], ". Frank then analyses two situations. The first (", StyleBox["\"self restraint model\"", FontSlant->"Italic"], ", Ratnieks sometimes uses the terms ", StyleBox["\"self policing\"", FontSlant->"Italic"], ") is where individuals can control their own probability to breed but can \ not control the behaviour of others. The second (", StyleBox["\"policing model\"", FontSlant->"Italic"], ") is where individuals also have the possibility to prevent others from \ breeding.\nIn terms of our worker reproduction problem, this corresponds to \ the situations where workers can either (1) control their own probability to \ become a reproductive worker, but not influence the fate of others, or (2) \ also inhibit others from becoming reproductive workers." }], "Text", FontSize->16] }, Open ]], Cell[CellGroupData[{ Cell["Assumptions :", "Section", FontSize->25], Cell["\<\ Throughout Frank assumes that the optimum for the group is to have very few \ reproductive individuals, and that group efficiency is a linear decreasing \ function of the proportion of selfish (i.e. breeding) individuals in the \ group. This may be quite reasonable for the worker reproduction problem, \ since a few workers are enough to produce all the male eggs and mainly \ foragers are needed to rear these into adults.\ \>", "Text", FontSize->16] }, Open ]], Cell[CellGroupData[{ Cell["2.1 Self-restraint model", "Section", FontSize->25], Cell["\<\ Parameters: y = probability with which a focal individual takes on a breeding role z = average probability with which individuals start to breed within the \ colony g = genotype of a focal individual\ \>", "Text", FontSize->18], Cell["\<\ Step 1: Write individual reproductive success w as a function of your own \ breeding probability (y) and the average probability with which individuals \ breed in the colony (z)\ \>", "Definition", FontSize->16], Cell["Frank (1995) uses the equation ", "Text", FontSize->14], Cell[BoxData[ \(\(w = \((y/z)\)*\((1 - z)\);\)\)], "Input"], Cell["\<\ because the chance that any reared egg is yours is y/z, but the chance that \ it will be reared into adulthood is 1-z (with z=1 there are no foragers, so \ that no eggs can be reared). If you find this equation hard to understand you \ could think of absolute fitness w as being the product of relative individual \ success and group productivity, with relative individual success given by \ y/z, and group (colony) productivity given by 1-z (i.e. the total number of \ males reared over the colony's lifetime).\ \>", "Text", FontSize->14], Cell["\<\ Step 2: analyse when the regression of fitness on genotype is positive\ \>", "Definition", FontSize->16], Cell["\<\ A higher probability of becoming a reproductive worker is favoured when\ \>", "Text", FontSize->14], Cell[CellGroupData[{ Cell[BoxData[ \(\(\(dwdg\)\(=\)\(FullSimplify[ Dt[w, g] /. {Dt[z, g] -> r, Dt[y, g] -> 1, Dt[k, g] \[Rule] 0, y -> z}]\)\(\ \ \ \ \ \)\)\)], "Input"], Cell[BoxData[ \(\(-\(\(\(-1\) + r + z\)\/z\)\)\)], "Output"] }, Open ]], Cell["is greater than zero.", "Text", FontSize->14], Cell["\<\ Note that, as in the hawk-dove game, the costs and benefits of becoming a \ reproductive worker again depend on what everyone else does in the colony. \ E.g., the personal benefit of becoming a reproductive worker (D[w,y]=(1-z)/z) \ is very large if everyone else decides not to reproduce ((1-z)/z then \ approaches plus infinity), but tends towards zero as more workers become \ reproductive. Similarly, the personal cost of a high average level of worker \ reproduction (D[w,z]) depends on the reproductive decisions of all other \ workers in the colony. However, while in the hawk-dove game it would still \ have been possible to write down costs and benefits straight away, this would \ have been very challenging in this case. The derivative dz/zg (D[z,g]), mentioned by Frank (1995) to equal \ relatedness, deserves some explanation. Before we had relatedness to be the \ slope of partner phenotype on actor genotype, but in this case z is the \ average phenotype of any worker in the colony, including the actor itself. \ That is, suppose you would have a colony with only one worker. In that case z \ would be equal to y, and dz/zg would equal 1. Therefore, dz/zg is not just \ pairwise genetic relatedness, but average relatedness to any individual in \ the group, including self (Frank 1996). Formally, with group size N, r = (1/N) + r'.(N-1)/N , where r' is pairwise genetic relatedness (see also Q&A question 6).\ \>", "Text", FontSize->14], Cell["Step 3: Solve to yield the ESS", "Definition", FontSize->16], Cell["\<\ Near the ESS, the selection equation above should equal 0, so that the ESS is \ given by \ \>", "Text", FontSize->14], Cell[CellGroupData[{ Cell[BoxData[ \(ESS = FullSimplify[Solve[dwdg == 0, z]]\)], "Input"], Cell[BoxData[ \({{z \[Rule] 1 - r}}\)], "Output"] }, Open ]], Cell[TextData[{ StyleBox["Conclusion:", FontSize->18, FontWeight->"Bold"], "\nThe ESS probability with which workers should start to breed = 1-r\n\ E.g., 25% of all workers should start laying male eggs when their mother \ queen is singly mated, 50% when she is doubly mated, etc... (assuming a \ linear cost function)\nIf you find it hard to understand why 25% reproductive \ workers is the ESS with single mating, then it is also possible to look at \ the problem from a classical inclusive fitness angle :\nAt the ESS the\n\t\ Selfish direct fitness benefits due to own male production (r = 0.5) =\n\t\t\ 0.25 \t\t(chance of becoming a reproductive worker)\n\t\t* (1-0.25)\t(chance \ that any egg laid can be reared into an adult) \n\t\t* 0.5\t\t(relatedness to \ sons)\n\t\t= 3/32\n\t\n\tInclusive fitness costs due to reduced production of \ nephews (r = 0.375) =\n\t\t0.25\t\t(25% fewer nephews are reared into adults)\ \n\t\t* 0.375\t\t(relatedness to nephews)\n\t\t= 3/32 \n\nTherefore, at the \ ESS the selfish benefits of worker laying exactly balance the inclusive \ fitness costs due to reduced nephew production, which is what had to be \ shown. Simple inclusive fitness arguments are often useful for interpreting \ results in intuitive ways. However, simple IF analysis is often unreliable \ for more complex problems. In fact, for asymmetrical games, a direct fitness \ perspective is the only correct way to handle the problem. " }], "Text", FontSize->14] }, Closed]], Cell[CellGroupData[{ Cell["2.2 Social policing model", "Section", FontSize->25], Cell[CellGroupData[{ Cell["2.2.1 ANALYTICAL SOLUTION", "Subsection", FontSize->19], Cell["\<\ Parameters: y = probability with which a focal individual takes on a breeding role z = average probability with which individuals start breeding within the \ group p = individual investment in a policing trait that inhibits others to breed P = average individual investment in policing within the group b = focal individual's genotype as the breeding locus \[Pi] = focal individual's genotype at the policing locus c = individual cost of policing\ \>", "Text", FontSize->18], Cell["\<\ Step 1: write individual reproductive success w as a function of individual \ (yB) and average (zB) breeding probability and individual (yP) and average \ (zP) investment in policing \ \>", "Definition", FontSize->16], Cell["\<\ Many different equations are possible, depending on assumptions, but this is \ the equation that Frank used (I am not entirely convinced that it is \ appropriate for the worker reproduction problem, but let's use it anyway):\ \>", "Text", FontSize->14], Cell[BoxData[{ \( (*\ Relative\ Individual\ success\ *) \n\(Ind = P - c*p + \((1 - P)\)*y/z;\)\n\n (*\ Group\ success\ *) \), "\n", \(\(Grp = 1 - \((1 - P)\) z;\)\n\n (*\ Direct\ fitness\ of\ a\ focal\ individual\ *) \), "\n", \(\(w = Ind*Grp;\)\)}], "Input"], Cell["\<\ Step 2: analyse when the regression of fitness on genotype is positive\ \>", "Definition", FontSize->16], Cell["\<\ We now have 2 equations: one specifying selection for breeding, the other \ specifying selection for policing, i.e. we are analysing a 2-locus model. But \ since each of the selection equations is dependent on each other, they will \ need to be simultaneously maximised to provide the joint ESS. \ \>", "Text", FontSize->14], Cell["\<\ This is the equation that says when more workers should reproduce :\ \>", "Text", FontSize->14], Cell[CellGroupData[{ Cell[BoxData[ \(dWdb[r_, c_, z_, p_] = \((D[w, y] + D[w, z]*r)\) /. {y \[Rule] z, P \[Rule] p}\)], "Input"], Cell[BoxData[ \(\(\((1 - p)\)\ \((1 - \((1 - p)\)\ z)\)\)\/z + r\ \((\((\(-1\) + p)\)\ \((1 - c\ p)\) - \(\((1 - p)\)\ \((1 - \((1 - p)\)\ \ z)\)\)\/z)\)\)], "Output"] }, Open ]], Cell["\<\ where D[w,y] is the benefit of breeding and -D[w,z] is the cost of having \ other workers breed in the colony\ \>", "Text", FontSize->14], Cell["\<\ (we assume that the breeding and policing locus are not linked, so that dP/db \ and dp/db can both be set to zero)\ \>", "Text", FontSize->14], Cell["\<\ This is the equation that says when workers should inhibit others from \ reproducing (police) :\ \>", "Text", FontSize->14], Cell[CellGroupData[{ Cell[BoxData[ \(dWd\[Pi][r_, c_, z_, p_] = \((D[w, p] + D[w, P]*r)\) /. {y \[Rule] z, P \[Rule] p}\)], "Input"], Cell[BoxData[ \(\((1 - c\ p)\)\ r\ z - c\ \((1 - \((1 - p)\)\ z)\)\)], "Output"] }, Open ]], Cell["\<\ (we again assume that the breeding and policing locus are not linked,so that \ dz/d\[Pi] and dy/d\[Pi] can be set to zero)\ \>", "Text", FontSize->14], Cell["Step 3: Joint maximisation to provide the joint ESS", "Definition", FontSize->16], Cell["\<\ Joint maximisation of these 2 equations is difficult, but as a first start \ let us check when a policing trait can invade from a sitation where all \ individuals are reproducing with optimum probability (breeding \ probability=1-r, see Section 2.1 above). For this, set the probability of \ breeding (z*=0) to 1-r and the level of policing p* to 0 in the selection \ equation for policing, and see when an increase in policing is favoured. As it turns out, this is when \ \>", "Text", FontSize->14], Cell[CellGroupData[{ Cell[BoxData[ \(dWd\[Pi][r, c, 1 - r, 0] > 0\)], "Input"], Cell[BoxData[ \(\(-c\)\ r + \((1 - r)\)\ r > 0\)], "Output"] }, Open ]], Cell["i.e. when c < 1-r. ", "Text", FontSize->14], Cell[TextData[{ StyleBox["Conclusion :", FontSize->18, FontWeight->"Bold"], "\nWhen the cost of policing c < 1-r, policing is favoured. Otherwise, the \ self restraint and no policing optimum will be reached (Section 2.1). Below \ it is checked graphically that whenever policing invades it will also tend to \ be maximally favoured. This can best be done using field plots.\nThe \ intuitive explanation for this result is that unrestrained breeding will \ reduce the net group output by a fraction 1-r, whereas policing can restore \ group output at a cost c (the cost of policing). If policing can restore \ group efficiency (i.e. resolve the ", StyleBox["tragedy of the commons", FontSlant->"Italic"], ") at a lesser cost, it will be favoured. Low relatedness will cause large \ costs due to excessive, unrestrained breeding, and is therefore conducive to \ the evolution of social policing (Frank 1995). \nWith respect to worker \ reproduction this means that if policing occurs in queenless condition (e.g. \ workers that force other workers to work rather than to reproduce) it will \ cause fewer workers to reproduce than is predicted by the self restraint \ model (Section 2.1). For example, less than 25% of the workers for single \ mating or less than 50% for double mating, perhaps just a few (which is the \ colony optimum)." }], "Text", FontSize->14] }, Closed]], Cell[CellGroupData[{ Cell["2.2.2 GRAPHICAL SOLUTION", "Subsection", FontSize->19], Cell["\<\ As in section 1.2, we can also check graphically how an ESS may be reached.\ \>", "Text", FontSize->14], Cell[BoxData[{ \( (*\ load\ the\ PlotField\ package\ to\ enable\ you\ to\ draw\ field\ plots\ \ and\ the\ ImplicitPlot\ package\ to\ allow\ you\ to\ make\ plots\ of\ the\ \ equilibrium\ lines\ *) \[IndentingNewLine]<< Graphics`PlotField`\), "\[IndentingNewLine]", \(<< Graphics`ImplicitPlot`\)}], "Input"], Cell[CellGroupData[{ Cell[TextData[StyleBox["Case 1 : c > 1-r \n(r=c=0.75)", FontSize->17]], "Subsubsection"], Cell[BoxData[ \(\(\(\(rex = 0.75\) \)\(;\)\(\(cex = 0.75\) \)\(;\)\(\ \ \ \ \ \ \)\( (*\ Some\ example\ values\ for\ r\ and\ c\ *) \)\)\)], "Input"], Cell[BoxData[ \(\(fieldplot = PlotVectorField[{dWd\[Pi][rex, cex, z, p], \ dWdb[rex, cex, z, p]}, \n\ \ \ \ \ \ \ \ \ \ \ \ \ {p, \ 0, \ 1}, \ {z, \ 0, \ 1}, PlotPoints -> 17];\)\)], "Input"], Cell[BoxData[ \(\(equillines = ImplicitPlot[{dWd\[Pi][rex, cex, z, p] \[Equal] 0, \ dWdb[rex, cex, z, p] \[Equal] 0}, {p, 0, 1}, {z, 0, 1}, PlotStyle \[Rule] {RGBColor[0, 0, 1], RGBColor[1, 0, 0]}, DisplayFunction \[Rule] Identity];\)\)], "Input"], Cell[BoxData[ \(\(\(ESS = Graphics[{\[IndentingNewLine]{RGBColor[1, 0, 0], Disk[{0, 1 - rex}, 0.02]}, \[IndentingNewLine]{RGBColor[1, 0, 0], Text["\", {0.07, 1 - rex - 0.04}]}, \[IndentingNewLine]}];\)\(\ \ \ \ \ \)\)\)], \ "Input"], Cell["\<\ Now display the whole lot together (field plot, equilibrium lines, ESS). As you can see, with c > 1-r, the system tends to the self restraint optimum \ with the ESS probability to become a reproductive worker = 1-r = 0.25 and no \ policing :\ \>", "Text", FontSize->14], Cell[CellGroupData[{ Cell[BoxData[ \(Show[{fieldplot, equillines, ESS}, AspectRatio -> 1, Frame -> True, PlotRange -> {{0, 1}, {0, 1}}, FrameLabel -> {"\", "\"}]\)], "Input"], Cell[GraphicsData["PostScript", "\<\ %! %%Creator: Mathematica %%AspectRatio: 1 MathPictureStart /Mabs { Mgmatrix idtransform Mtmatrix dtransform } bind def /Mabsadd { Mabs 3 -1 roll add 3 1 roll add exch } bind def %% Graphics %%IncludeResource: font Courier %%IncludeFont: Courier /Courier findfont 10 scalefont setfont % Scaling calculations 0 1 0 1 [ [.2 -0.0125 -9 -9 ] [.2 -0.0125 9 0 ] [.4 -0.0125 -9 -9 ] [.4 -0.0125 9 0 ] [.6 -0.0125 -9 -9 ] [.6 -0.0125 9 0 ] [.8 -0.0125 -9 -9 ] [.8 -0.0125 9 0 ] [1 -0.0125 -3 -9 ] [1 -0.0125 3 0 ] [ 0 0 -0.125 0 ] [.5 -0.0125 -26 -21.5625 ] [.5 -0.0125 26 -9 ] [-0.0125 .2 -18 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Cell[CellGroupData[{ Cell["2.3 Questions & Answers", "Section", FontSize->25], Cell[CellGroupData[{ Cell["\<\ 1. Whenever policing is favoured, will this necessarily lead to a more \ efficient running of a society than if it is not?\ \>", "Subsection", FontFamily->"Times New Roman", FontSize->16, FontWeight->"Plain", FontVariations->{"CompatibilityType"->0}], Cell[TextData[StyleBox["Yes, it should, although animals would also waste a \ lot of energy on mutual inhibition of each other's selfishness (e.g. on \ aggression, see the c parameter in the model).", FontSlant->"Italic"]], "Text"] }, Closed]], Cell[CellGroupData[{ Cell["\<\ 2. When a social insect colony loses its queen it seems likely that not all \ workers are totipotent and can switch to a reproductive role (this should be \ true especially for foragers with regressed ovaries). Also, the workers that \ do become reproductive may be able to both lay eggs and do some foraging. How \ could this be accomodated in the model?\ \>", "Subsection", FontFamily->"Times New Roman", FontSize->16, FontWeight->"Plain", FontVariations->{"CompatibilityType"->0}], Cell[TextData[StyleBox["If t is the fraction of totipotent workers then a \ fraction (1-t) might keep foraging. This tends to reduce the costs to colony \ function, making a higher level of breeding favourable from the perspective \ of the totipotent workers. To take this into account in Model 1, let colony \ success be (1-z(1-t)) instead of just (1-z). By the same methods as above, \ this yields an ESS probability of becoming a reproductive worker of \ (1-r)/(1-t) :", FontSlant->"Italic"]], "Text", FontSize->16], Cell[CellGroupData[{ Cell[BoxData[{ \(\(w = \((y/z)\)*\((1 - z*\((1 - t)\))\);\)\), "\[IndentingNewLine]", \(\(dwdg = Dt[w, g] /. {Dt[y, g] \[Rule] 1, Dt[z, g] \[Rule] r, Dt[t, g] \[Rule] 0, y \[Rule] z};\)\), "\[IndentingNewLine]", \(mESS[r_, t_] = \(\(\(Solve[dwdg \[Equal] 0, z]\)[\([1]\)]\)[\([1]\)]\)[\([2]\)]\)}], "Input"], Cell[BoxData[ \(\(\(-1\) + r\)\/\(\(-1\) + t\)\)], "Output"] }, Open ]], Cell[TextData[StyleBox["For example, if half of the workers are totipotent at \ the time the queen is lost (t=0.5), then 50% of them should become \ reproductives instead of just 25% if all are totipotent (this assumes single \ mating). \nIf reproductive workers can spend a fraction f of their time \ foraging, then, again costs to colony function would not be as severe. \ Formally, colony success would become (1-z.(1-f)) rather than just (1-z). So \ this affects the ESS in the same way as above. ", FontSlant->"Italic"]], "Text", FontSize->16] }, Closed]], Cell[CellGroupData[{ Cell["\<\ 3. Above we assumed that workers become reproductives with some ESS \ probability in the event the queen is lost. In other words, the ESS is what \ is often called a mixed ESS. Alternatively, it could be that the ESS reflects \ how each worker will divide its time between reproduction and foraging over \ its entire lifetime. In that case, the ESS reflects a continuous ESS. Does \ the model apply to both situations? And how does this relate to the level of \ skew predicted by Model 1? How does policing interact with the predicted \ level of reproductive skew? What are the more general implications for skew \ theory? \ \>", "Subsection", FontFamily->"Times New Roman", FontSize->16, FontWeight->"Plain", FontVariations->{"CompatibilityType"->0}], Cell[TextData[StyleBox["The continuous and mixed ESS situation are very \ similar, and it seems reasonable to assume that the fitness function for each \ case is identical. Therefore, the model adequately predicts the right ESS for \ each situation. In terms of reproductive skew, the probabilistic (mixed ESS) \ model predicts high skew in high relatedness groups (just a few workers \ should lay eggs), but low skew in low relatedness groups (many workers should \ lay eggs). But the continuous ESS model would cause each worker to contribute \ equally to male production irrespective of relatedness, i.e. it predicts low \ skew under all circumstances. So even though these two situations are \ theoretically almost identical they lead to very different skew predictions. \ \nPolicing will tend to increase skew since it should cause fewer individuals \ in the group to reproduce than is dictated by their own inclusive fitness \ preferences. But since policing is favoured especially at low relatedness, it \ makes the opposite prediction as above: it should favour high skew at low \ relatedness (due to policing) and low skew at high relatedness (due to the \ absence of policing and limited self restraint). \nAll this means that any \ attempt to simply relate skew to relatedness patterns is probably in vain - \ any pattern is possible depending on the specific biology. We already know \ this from the long series of theoretical papers on skew theory, but it is \ also easy to see from the simple models above. Message: don't worry about \ skew theory but construct a specific model for the problem at hand. And if \ necessary, provide intuitive explanations in terms of Hamilton's rule - the \ only truly fundamental principle in social evolution theory. ", FontSlant->"Italic"]], "Text", FontSize->16] }, Closed]], Cell[CellGroupData[{ Cell["\<\ 4. Suppose that group success is not a decreasing linear but a decreasing \ concave or a convex function of the proportion of selfish individuals within \ the group. How would this affect the predictions of the model?\ \>", "Subsection", FontFamily->"Times New Roman", FontSize->16, FontWeight->"Plain", FontVariations->{"CompatibilityType"->0}], Cell[TextData[{ StyleBox["With a concave colony productivity function unrestrained breeding \ would reduce group output more than with a linear cost function. The \ consequence would be that in Model 1, the ESS probability of breeding would \ be lower than with a linear cost function. The inverse is true for a convex \ productivity function. To formalise this, let group success be G=", FontSlant->"Italic"], Cell[BoxData[ \(TraditionalForm\`\((1 - z)\)\^k\)], FontSlant->"Italic"], StyleBox[" instead of just (1-z) in Model 1 (k=1, >1 and <1 then \ correspond to a linear, a concave and a convex cost function). By the same \ methods as above, this yields an ESS probability of becoming a reproductive \ worker of z*=(1-r)/(1+r.(k-1)) :", FontSlant->"Italic"] }], "Text", FontSize->16], Cell[CellGroupData[{ Cell[BoxData[{ \(\(w = \((y/z)\)*\((1 - z)\)\^k;\)\), "\[IndentingNewLine]", \(\(dwdg = FullSimplify[ Dt[w, g] /. {Dt[y, g] \[Rule] 1, Dt[z, g] \[Rule] r, Dt[k, g] \[Rule] 0, y \[Rule] z}];\)\), "\[IndentingNewLine]", \(mESS[r_, k_] = \(\(\(Solve[dwdg \[Equal] 0, z]\)[\([2]\)]\)[\([1]\)]\)[\([2]\)]\)}], "Input"], Cell[BoxData[ \(\(-\(\(\(-1\) + r\)\/\(1 - r + k\ r\)\)\)\)], "Output"] }, Open ]] }, Closed]], Cell[CellGroupData[{ Cell["\<\ 5. Frank discusses his model in rather broad terms, arguing that it might be \ relevant to explain cooperation among slime mould cells, among homologous \ chromosomes within diploid cells (Mendelism), etc... But do you think the \ assumptions of the model really warrant such a broad discussion? E.g. think \ about the slime mould case. What fraction of slug cells should develop into \ spore rather than stalk cells according to Model 1? But how realistic is it \ that an average cells' success is a linear decreasing function of the \ proportion of spore forming cells? And what about meiotic drive?\ \>", "Subsection", FontFamily->"Times New Roman", FontSize->16, FontWeight->"Plain", FontVariations->{"CompatibilityType"->0}], Cell[TextData[{ StyleBox["Slime moulds: Frank assumes that the group optimum is near zero \ allocation to reproduction. This is probably an OK assumption for the worker \ reproduction problem and for the level of queen production in swarming social \ insects (see section 3). But for slime moulds it seems likely that some \ intermediate allocation to spores is the group optimum. A case in point is \ that even when slime moulds are composed of single clones (so that cell-cell \ relatedness is 1), 20% of all cells become stalk cells and 80% become spores. \ ", FontSlant->"Italic"], "\n", StyleBox["Mendelism/Meiotic drive: driving and cooperative genes typically \ reach a pure strategy equilibrium; Frank models a mixed/continuous ESS. \ Wenseleers & Ratnieks (submitted) analyses meiotic drive as a social \ interaction using a generalised version of Hamilton's rule; alternatively, \ the 2-player pure strategy analysis of Section 1.2 could be used. ", FontSlant->"Italic"] }], "Text", FontSize->16] }, Closed]], Cell[CellGroupData[{ Cell["\<\ 6. Hammerstein (1995) commented on Frank's study, saying that the policing \ model wasn't quite convincing at explaining cooperation in low relatedness \ groups, because at least in genetically unrelated groups no individual should \ care about the reproduction of others in the group (since there could be no \ inclusive fitness gains). What did Hammerstein overlook?\ \>", "Subsection", FontFamily->"Times New Roman", FontSize->16, FontWeight->"Plain", FontVariations->{"CompatibilityType"->0}], Cell[TextData[StyleBox["Policing increases overall group productivity, and \ since the policing individual makes up a fraction 1/N of the total group (if \ N is group size), policing will have a 1/N selfish benefit (cf. r = (1/N) + \ r'.(N-1)/N with r'=pairwise genetic relatedness). Even in genetically \ unrelated groups, therefore, policing is selected for, even though the motive \ is then ultimately selfish. For the same reason, a slight degree of restraint \ is favoured in unrelated groups. In D.S. Wilson's terminology (Wilson 1990), \ the 1/N selfish component of restrained breeding causes weak altruism within \ social groups (Pepper 2000 discusses the matter more fully). Although the 1/N \ component can probably be neglected for the worker reproduction problem \ (colony sizes are typically large), it may be important in other contexts. \ For example, no parent-offspring conflict over the exploitation rate of \ parental resources is expected if parents have only one offspring (N=1, it \ will then only compete with itself). ", FontSlant->"Italic"]], "Text", FontSize->16] }, Closed]], Cell[CellGroupData[{ Cell["\<\ 7. In what sense does the type of policing assumed in Frank's model differ \ from Ratnieks' (1988) concept of \"worker policing\" (mutual inhibition of \ worker laying under queenright condition)? What are the commonalities?\ \>", "Subsection", FontFamily->"Times New Roman", FontSize->16, FontWeight->"Plain", FontVariations->{"CompatibilityType"->0}], Cell[TextData[StyleBox["In Frank's model, policing is favoured because it \ increases group output. This has inclusive fitness benefits in case group \ members are related (e.g. increased production of nephews) or a 1/N selfish \ benefit in case group members are unrelated (if N is group size). Worker \ policing as envisaged by Ratnieks (1988) is selected for when workers are \ related more to the males they kill (nephews) than to the males they spare \ (brothers). Here policing can have no direct selfish benefits. Common to all \ policing models is that they address the question of when individuals are \ selected to inhibit each other's selfish tendencies (i.e. when is what I do \ not OK for others to do). ", FontSlant->"Italic"]], "Text", FontSize->16] }, Closed]], Cell[CellGroupData[{ Cell["\<\ 8. How could the model be extended so that it also applies to the evolution \ of worker laying under queenright condition (hint: first take a look at \ Section 3 on caste conflict)? Should the predicted proportion of reproductive \ workers be higher or lower? And if worker policing is allowed for, could it \ be that it is favoured at any queen mating frequency, rather than just at \ queen mating frequencies higher than two, as predicted by Ratnieks (1988)?\ \>", "Subsection", FontFamily->"Times New Roman", FontSize->16, FontWeight->"Plain", FontVariations->{"CompatibilityType"->0}], Cell[TextData[StyleBox["Haven't done this yet, but the predicted level of \ worker laying should be lower than under queenless condition, because worker \ reproduction would also reduce the number of queens reared, i.e. it would \ have additional inclusive fitness costs. The idea that worker reproduction \ would reduce colony productivity would still apply, and therefore it seems \ likely that if policing can be performed at low cost, it would be favoured at \ any queen mating frequency (although for a different reason than envisaged by \ Ratnieks (1988), see Question 7). ", FontSlant->"Italic"]], "Text", FontSize->16] }, Closed]], Cell[CellGroupData[{ Cell["\<\ 9. Tragedies of the commons have been extensively discussed in economics \ (e.g. livestock owners that overexploit common grazing land to increase \ revenues relative to others using the same piece of land, see e.g. Hardin \ 1968). What are the formal differences between calculating the best rational \ strategy in economics and calculating an ESS in biology? Have concepts such \ as relatedness any meaning in economics? \ \>", "Subsection", FontFamily->"Times New Roman", FontSize->16, FontWeight->"Plain", FontVariations->{"CompatibilityType"->0}], Cell[TextData[StyleBox["George Price was perhaps among the first evolutionary \ biologists to see the strong similarities between tragedies of the commons in \ economics and some problems of group selection in biology (Frank 1995b p. \ 386). The differences mainly pertain to what is assumed to be maximised: \ utility in economics, fitness in biology. Economic models are also purely \ phenotypic models, i.e. instead of gene frequency change they investigate \ phenotypic strategy change. Much more could probably be learned by explicit \ carry over of concepts between both fields. For example, phenotypic versions \ of relatedness (e.g. Queller 1984) have sometimes been described as measuring \ \"better than average information about a social interactant's \ behaviour/strategy\" (Frank 1997). Such a relatedness coefficient does have a \ meaning in economics: if anyone has better than average information about the \ likely behaviour of others (i.e. r>0), then that would tend to favour \ strategies that benefit the global economy (a Pareto optimal outcome) (for \ more on this, see Aumann 198; Skyrms 1994, 1996).Currently, economists often \ use evolutionary rather than classical game theory on the premise that even \ though strategies may not necessarily be heritable, the succesful ones are \ copied (imitated) more often by others.", FontSlant->"Italic"]], "Text", FontSize->16] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell["2.4 References", "Section", FontSize->25], Cell[TextData[{ "Aumann, R. J. 1987. Correlated equilibrium as an expression of Bayesian \ rationality. ", StyleBox["Econometrica", FontSlant->"Italic"], " 55: 1-18.\nFrank, S.A. 1995a. Mutual policing and repression of \ competition in the evolution of cooperative groups. ", StyleBox["Nature ", FontSlant->"Italic"], "377: 520-522.\nFrank, S.A. 1995b. George Price's contributions to \ evolutionary genetics. ", StyleBox["Journal of Theoretical Biology ", FontSlant->"Italic"], "175: 373-\n388.\nFrank, S.A. 1996. Policing and group cohesion when \ resources vary. ", StyleBox["Animal Behaviour ", FontSlant->"Italic"], "52: 1163-1169.\nFrank, S.A. 1998. ", StyleBox["Foundations of Social Evolution. ", FontSlant->"Italic"], "Princeton University Press, Princeton, New Jersey.\nHammerstein, P. 1995. \ A twofold tragedy unfolds. ", StyleBox["Nature ", FontSlant->"Italic"], "377: 478.\nHardin, G. 1968. The tragedy of the commons. ", StyleBox["Science ", FontSlant->"Italic"], "162: 1243-1244.\nPepper, J. W. 2000. Relatedness in trait group models of \ social evolution. ", StyleBox["Journal of Theoretical Biology", FontSlant->"Italic"], " 206: 355-68.\nQueller, D.C. 1984. Kin selection and frequency-dependence \ - a game theoretic approach. ", StyleBox["Biological Journal of the Linnean Society ", FontSlant->"Italic"], "23: 133-143\nRatnieks, F.L.W. 1988. Reproductive harmony via mutual \ policing by workers in eusocial Hymenoptera. ", StyleBox["American Naturalist ", FontSlant->"Italic"], "132: 217-236.\nSkyrms, B. 1994. Darwin meets the logic of decision - \ Correlation in evolutionary game theory. ", StyleBox["Philosophy of Science", FontSlant->"Italic"], " 61: 503-528.\nSkyrms, B. 1996. ", StyleBox["Evolution of the Social Contract.", FontSlant->"Italic"], " Cambridge University Press, Cambridge.\nWenseleers, T. & Ratnieks, F.L.W. \ Towards a general theory of conflict: the sociobiology of mendelian \ segregation. Submitted.\nWilson, D. S. 1990. Weak altruism, strong group \ selection. 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