(* Content-type: application/mathematica *)

(*** Wolfram Notebook File ***)
(* http://www.wolfram.com/nb *)

(* CreatedBy='Mathematica 6.0' *)

(*CacheID: 234*)
(* Internal cache information:
NotebookFileLineBreakTest
NotebookFileLineBreakTest
NotebookDataPosition[       145,          7]
NotebookDataLength[    195431,       3657]
NotebookOptionsPosition[    190707,       3508]
NotebookOutlinePosition[    192089,       3550]
CellTagsIndexPosition[    192046,       3547]
WindowFrame->Normal
ContainsDynamic->False*)

(* Beginning of Notebook Content *)
Notebook[{

Cell[CellGroupData[{
Cell["Lesson 4 - demonstration code", "Title",
 CellChangeTimes->{{3.4323368555625*^9, 3.4323368600625*^9}, {
   3.432915856640625*^9, 3.43291586221875*^9}, {3.432918644203125*^9, 
   3.432918649390625*^9}, 3.434126210296875*^9, {3.43412646996875*^9, 
   3.434126470515625*^9}}],

Cell[CellGroupData[{

Cell["\<\
SIMPLE OPTIMISATION MODEL + MUTUAL INVASIBILITY PLOT\
\>", "Section",
 CellChangeTimes->{{3.432925002453125*^9, 3.432925019578125*^9}, {
  3.4341262366875*^9, 3.4341262429375*^9}, {3.434127592390625*^9, 
  3.434127606109375*^9}}],

Cell[CellGroupData[{

Cell["\<\
Lack (1954) showed that in Common Swifts the proportion of young that fledge \
p as a function of clutch size C was approximately given as p(C) = \
Exp(-0.7-0.07*C^2). Here we will use a simple optimisation / adaptive \
dynamics model to determine the resulting optimal clutch size that  maximises \
the number of surviving/fledging young. \
\>", "Subsection",
 CellChangeTimes->{{3.432669383328125*^9, 3.43266938340625*^9}, {
   3.43267042596875*^9, 3.432670444484375*^9}, {3.432670479796875*^9, 
   3.43267068290625*^9}, {3.432671107015625*^9, 3.432671129375*^9}, 
   3.434126273171875*^9, 3.434126445671875*^9, {3.43413147684375*^9, 
   3.434131644125*^9}, {3.434161239871999*^9, 3.434161245637624*^9}, {
   3.434162347418874*^9, 3.434162361996999*^9}, {3.434170652543*^9, 
   3.43417067407425*^9}},
 FontWeight->"Bold"],

Cell["\<\
The proportion of young that fledge p as a function of clutch size C :\
\>", "Text",
 CellChangeTimes->{{3.432669730515625*^9, 3.432669749390625*^9}, {
  3.4326698036875*^9, 3.432669804609375*^9}}],

Cell[BoxData[
 RowBox[{
  RowBox[{
   RowBox[{"p", "[", "C_", "]"}], "=", 
   RowBox[{"Exp", "[", 
    RowBox[{
     RowBox[{"-", "0.7"}], "-", 
     RowBox[{"0.07", "*", 
      RowBox[{"C", "^", "2"}]}]}], "]"}]}], ";"}]], "Input",
 CellChangeTimes->{{3.434161108200124*^9, 3.434161133106374*^9}, {
  3.434161811543874*^9, 3.434161861981374*^9}, {3.434161899200124*^9, 
  3.434161914543874*^9}, {3.434162034809499*^9, 3.434162082575124*^9}, {
  3.434162118153249*^9, 3.434162124356374*^9}, {3.434162215215749*^9, 
  3.434162241465749*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"Plot", "[", 
  RowBox[{
   RowBox[{"p", "[", "C", "]"}], ",", 
   RowBox[{"{", 
    RowBox[{"C", ",", "1", ",", "4"}], "}"}], ",", 
   RowBox[{"PlotRange", "\[Rule]", 
    RowBox[{"{", 
     RowBox[{"0", ",", "1"}], "}"}]}], ",", 
   RowBox[{"Frame", "\[Rule]", "True"}], ",", 
   RowBox[{"FrameLabel", "\[Rule]", 
    RowBox[{"{", 
     RowBox[{
     "\"\<Clutch size c\>\"", ",", "\"\<Proportion that fledge p\>\""}], 
     "}"}]}]}], "]"}]], "Input",
 CellChangeTimes->{{3.43266961275*^9, 3.432669720015625*^9}, {
  3.43266981328125*^9, 3.432669822109375*^9}, {3.434127541109375*^9, 
  3.43412754825*^9}, {3.434161147356374*^9, 3.434161155184499*^9}}],

Cell[BoxData[
 GraphicsBox[{{}, {}, 
   {Hue[0.67, 0.6, 0.6], LineBox[CompressedData["
1:eJwV0ns803scx/ExtjU0hzkpJc7BcelBp5QuTp+v1MiDjlPuOiSLtlORbpNH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     "]]}},
  AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948],
  Axes->True,
  AxesOrigin->{1., 0},
  Frame->True,
  FrameLabel->{
    FormBox["\"Clutch size c\"", TraditionalForm], 
    FormBox["\"Proportion that fledge p\"", TraditionalForm]},
  PlotRange->{{1, 4}, {0, 1}},
  PlotRangeClipping->True,
  PlotRangePadding->{
    Scaled[0.02], Automatic}]], "Output",
 CellChangeTimes->{3.434267566640625*^9}]
}, Open  ]],

Cell["\<\
The number of surviving/fledging young as a function of clutch size C is \
therefore C*p(c).\
\>", "Text",
 CellChangeTimes->{{3.432669730515625*^9, 3.432669794453125*^9}, {
  3.4326698293125*^9, 3.43266984978125*^9}, {3.43412660190625*^9, 
  3.434126620375*^9}, {3.434126662734375*^9, 3.434126667859375*^9}, {
  3.43412672328125*^9, 3.434126774015625*^9}, {3.4341268074375*^9, 
  3.4341268534375*^9}, {3.434127372046875*^9, 3.434127375640625*^9}}],

Cell[BoxData[
 RowBox[{
  RowBox[{
   RowBox[{"nsurvivingyoung", "[", "C_", "]"}], "=", 
   RowBox[{"C", "*", 
    RowBox[{"p", "[", "C", "]"}]}]}], ";"}]], "Input",
 CellChangeTimes->{{3.434127393015625*^9, 3.434127407703125*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"Plot", "[", 
  RowBox[{
   RowBox[{"nsurvivingyoung", "[", "C", "]"}], ",", 
   RowBox[{"{", 
    RowBox[{"C", ",", "1", ",", "4"}], "}"}], ",", 
   RowBox[{"Frame", "\[Rule]", "True"}], ",", 
   RowBox[{"FrameLabel", "\[Rule]", 
    RowBox[{"{", 
     RowBox[{
     "\"\<Clutch size c\>\"", ",", "\"\<Number of surviving young\>\""}], 
     "}"}]}], ",", 
   RowBox[{"AxesOrigin", "\[Rule]", 
    RowBox[{"{", 
     RowBox[{"1", ",", "0"}], "}"}]}]}], "]"}]], "Input",
 CellChangeTimes->{{3.43266961275*^9, 3.432669720015625*^9}, {
  3.43266981328125*^9, 3.432669822109375*^9}, {3.432669892109375*^9, 
  3.432669938390625*^9}, {3.4341270046875*^9, 3.43412702096875*^9}, {
  3.434127456296875*^9, 3.4341274625*^9}, {3.43412750265625*^9, 
  3.4341275553125*^9}, {3.434149963653249*^9, 3.434149964121999*^9}, {
  3.434162149793874*^9, 3.434162158887624*^9}, {3.434162255887624*^9, 
  3.434162263481374*^9}}],

Cell[BoxData[
 GraphicsBox[{{}, {}, 
   {Hue[0.67, 0.6, 0.6], LineBox[CompressedData["
1:eJwV13k4lF0UAPAx874zsoaSJLSgohRKRe6JbEVFWZOlJFRIGy1CtiyRpYVK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     "]]}},
  AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948],
  Axes->True,
  AxesOrigin->{1, 0},
  Frame->True,
  FrameLabel->{
    FormBox["\"Clutch size c\"", TraditionalForm], 
    FormBox["\"Number of surviving young\"", TraditionalForm]},
  PlotRange->{{1, 4}, {0, 0.8049753787000293}},
  PlotRangeClipping->True,
  PlotRangePadding->{
    Scaled[0.02], 
    Scaled[0.02]}]], "Output",
 CellChangeTimes->{3.43426756671875*^9}]
}, Open  ]],

Cell["\<\
Hence, the relative fitness w of a mutant mother producing a clutch size y in \
a population where everybody produces a clutch size Z will be\
\>", "Text",
 CellChangeTimes->{{3.432669730515625*^9, 3.432669794453125*^9}, {
   3.4326698293125*^9, 3.43266984978125*^9}, {3.43412660190625*^9, 
   3.434126620375*^9}, {3.434126662734375*^9, 3.434126667859375*^9}, {
   3.43412672328125*^9, 3.434126774015625*^9}, {3.4341268074375*^9, 
   3.4341268534375*^9}, {3.43412736834375*^9, 3.434127368734375*^9}, 
   3.434160865262624*^9}],

Cell[BoxData[
 RowBox[{
  RowBox[{
   RowBox[{"w", "[", 
    RowBox[{"y_", ",", "Z_"}], "]"}], "=", 
   FractionBox[
    RowBox[{"nsurvivingyoung", "[", "y", "]"}], 
    RowBox[{"nsurvivingyoung", "[", "Z", "]"}]]}], ";"}]], "Input",
 CellChangeTimes->{{3.4326693878125*^9, 3.432669403015625*^9}, {
   3.432669501203125*^9, 3.432669519875*^9}, {3.4326695655625*^9, 
   3.43266964528125*^9}, {3.43266980771875*^9, 3.43266980803125*^9}, {
   3.432669857*^9, 3.432669873875*^9}, 3.434126623265625*^9, {
   3.43412669496875*^9, 3.4341267113125*^9}, {3.434126857015625*^9, 
   3.434126865796875*^9}, 3.43412694840625*^9, {3.43412741428125*^9, 
   3.434127420078125*^9}, {3.434160867043874*^9, 3.434160869246999*^9}}],

Cell["This gives the following mutual invasibility plot:", "Text",
 CellChangeTimes->{{3.432669730515625*^9, 3.432669794453125*^9}, {
  3.4326698293125*^9, 3.43266984978125*^9}, {3.43412660190625*^9, 
  3.434126620375*^9}, {3.434126662734375*^9, 3.434126667859375*^9}, {
  3.43412672328125*^9, 3.434126774015625*^9}, {3.4341268074375*^9, 
  3.4341268534375*^9}, {3.43412736834375*^9, 3.434127368734375*^9}, {
  3.4341298823125*^9, 3.43412989040625*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{
  RowBox[{"RegionPlot", "[", 
   RowBox[{
    RowBox[{
     RowBox[{"w", "[", 
      RowBox[{"y", ",", "Z"}], "]"}], ">", "1"}], ",", 
    RowBox[{"{", 
     RowBox[{"Z", ",", "1", ",", "4"}], "}"}], ",", 
    RowBox[{"{", 
     RowBox[{"y", ",", "1", ",", "4"}], "}"}], ",", 
    RowBox[{"Frame", "\[Rule]", "True"}], ",", 
    RowBox[{"PlotPoints", "\[Rule]", "100"}], ",", 
    RowBox[{"FrameLabel", "->", 
     RowBox[{"{", 
      RowBox[{
      "\"\<Resident clutch size Z\>\"", ",", "\"\<Mutant clutch size y\>\""}],
       "}"}]}], ",", 
    RowBox[{"LabelStyle", "\[Rule]", "Medium"}]}], "]"}], " ", 
  RowBox[{"(*", " ", 
   RowBox[{
    RowBox[{"mutual", " ", "invasibility", " ", "plot"}], " ", "-", " ", 
    RowBox[{
    "grey", " ", "regions", " ", "show", " ", "areas", " ", "where", " ", 
     "the", " ", "mutant", " ", "has", " ", "a", " ", "higher", " ", 
     "fitness", " ", "than", " ", "the", " ", "resident"}]}], " ", 
   "*)"}]}]], "Input",
 CellChangeTimes->{{3.4326215413233747`*^9, 3.4326215480577497`*^9}, {
  3.4326220279483747`*^9, 3.4326220532921247`*^9}, {3.434040196546875*^9, 
  3.43404027315625*^9}, {3.434040320125*^9, 3.43404034278125*^9}, {
  3.434040463640625*^9, 3.434040489484375*^9}, {3.43412690565625*^9, 
  3.434126972203125*^9}, {3.434127470078125*^9, 3.434127524484375*^9}, {
  3.434160874309499*^9, 3.434160878371999*^9}, {3.434161199825124*^9, 
  3.434161211293874*^9}},
 CellID->15076866],

Cell[BoxData[
 GraphicsBox[GraphicsComplexBox[CompressedData["
1:eJxk3Xl8HXW5P/B0TfeALOWiUkUpKhiQJWylZ1CCogalkqpAkSUqYFSEoiyF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   "], {{
     {RGBColor[0.798413061722744, 0.824719615472648, 0.968322270542458], 
      EdgeForm[None], GraphicsGroupBox[{PolygonBox[CompressedData["
1:eJxMnXW4lVX3tTm7ntx1DgJ2d4vYXYiIgS0iXSIgoJiAIiKKgqIoCiIqdndj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         "]], 
        PolygonBox[CompressedData["
1:eJwtmnngVcMbxm9KtlDde+65Mzck2ZdIC5KKNiRKlEIkbdJO2hclRCuilBTt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         "]]}]}, {}, {}, {}}, 
    {GrayLevel[0], Opacity[0.4], LineBox[CompressedData["
1:eJwl13fYz/Uex/GvLXtkUyGSTYqMknXLuO29R0JHuamMOunqkGM1jpTSuXJU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      "]], LineBox[CompressedData["
1:eJwl13mUVnMcx/FbM800Nc3UNJQSdUgoUmSLMlJpoc2WJW32NBmitE4pMY5I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      "]]}}],
  AspectRatio->1,
  Frame->True,
  FrameLabel->{
    FormBox["\"Resident clutch size Z\"", TraditionalForm], 
    FormBox["\"Mutant clutch size y\"", TraditionalForm]},
  LabelStyle->Medium,
  Method->{"TransparentPolygonMesh" -> True, "AxesInFront" -> True},
  PlotRange->{{1, 4}, {1, 4}},
  PlotRangeClipping->True,
  PlotRangePadding->{
    Scaled[0.02], 
    Scaled[0.02]}]], "Output",
 CellChangeTimes->{3.434267569453125*^9},
 ImageCache->GraphicsData["CompressedBitmap", "\<\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\
\>"]]
}, Open  ]],

Cell["The selection gradient for increased clutch size is given by", "Text",
 CellChangeTimes->{{3.432669730515625*^9, 3.432669794453125*^9}, {
   3.4326698293125*^9, 3.43266984978125*^9}, {3.43412660190625*^9, 
   3.434126620375*^9}, {3.434126662734375*^9, 3.434126667859375*^9}, {
   3.43412672328125*^9, 3.434126774015625*^9}, {3.4341268074375*^9, 
   3.4341268534375*^9}, {3.43412736834375*^9, 3.434127368734375*^9}, {
   3.4341298823125*^9, 3.434129921890625*^9}, {3.434130066140625*^9, 
   3.434130086796875*^9}, 3.434130898578125*^9, {3.434160887512624*^9, 
   3.434160887700124*^9}}],

Cell[BoxData[
 RowBox[{
  RowBox[{"selD", "=", 
   RowBox[{
    RowBox[{"D", "[", 
     RowBox[{
      RowBox[{"w", "[", 
       RowBox[{"y", ",", "Z"}], "]"}], ",", "y"}], "]"}], "/.", 
    RowBox[{"{", 
     RowBox[{"y", "\[Rule]", "Z"}], "}"}]}]}], ";"}]], "Input",
 CellChangeTimes->{{3.434129927203125*^9, 3.4341299458125*^9}, {
  3.434130901859375*^9, 3.434130908671875*^9}, {3.434160890200124*^9, 
  3.434160893278249*^9}, {3.434161283387624*^9, 3.434161287887624*^9}}],

Cell["\<\
The optimal clutch size that gives the maximum number of surviving/fledging \
young is reached when this selection gradient will become zero. Assuming the \
mutant type only differs slightly from the wild type (y->Z) this occurs for \
an ESS clutch size of C=2.7 : \
\>", "Text",
 CellChangeTimes->{{3.432669730515625*^9, 3.432669794453125*^9}, {
   3.4326698293125*^9, 3.43266984978125*^9}, {3.432670064265625*^9, 
   3.432670082703125*^9}, {3.43412635753125*^9, 3.434126379296875*^9}, {
   3.43412779184375*^9, 3.43412782496875*^9}, {3.434129970765625*^9, 
   3.434130026546875*^9}, {3.434130115578125*^9, 3.434130120109375*^9}, {
   3.4341309365625*^9, 3.434130954359375*^9}, 3.434160905121999*^9, 
   3.434161331512624*^9, {3.434161994059499*^9, 3.434161998575124*^9}, 
   3.434162285575124*^9}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"ess", "=", 
  RowBox[{
   RowBox[{
    RowBox[{
     RowBox[{"Solve", "[", 
      RowBox[{
       RowBox[{"selD", "\[Equal]", "0"}], ",", "Z"}], "]"}], "[", 
     RowBox[{"[", "2", "]"}], "]"}], "[", 
    RowBox[{"[", "1", "]"}], "]"}], "[", 
   RowBox[{"[", "2", "]"}], "]"}]}]], "Input",
 CellChangeTimes->{{3.4341272223125*^9, 3.43412725884375*^9}, {
   3.43412972046875*^9, 3.434129729953125*^9}, 3.4341300355*^9, {
   3.434130924328125*^9, 3.43413092634375*^9}, {3.434130957796875*^9, 
   3.434130967140625*^9}, 3.434161322465749*^9}],

Cell[BoxData["2.6726124191242437`"], "Output",
 CellChangeTimes->{{3.434127232359375*^9, 3.43412725978125*^9}, 
   3.43412973046875*^9, 3.4341300499375*^9, {3.43413096240625*^9, 
   3.434130967453125*^9}, 3.434161264090749*^9, 3.434161322996999*^9, {
   3.434161985028249*^9, 3.434162000215749*^9}, {3.434162184356374*^9, 
   3.434162204246999*^9}, 3.434162282653249*^9, 3.43426756971875*^9}]
}, Open  ]],

Cell["\<\
The following two second-order derivatives determine the stability and \
attainability of this strategy:\
\>", "Text",
 CellChangeTimes->{{3.432669730515625*^9, 3.432669794453125*^9}, {
   3.4326698293125*^9, 3.43266984978125*^9}, {3.432670064265625*^9, 
   3.432670082703125*^9}, {3.43412635753125*^9, 3.434126379296875*^9}, {
   3.43412779184375*^9, 3.43412782496875*^9}, {3.434129970765625*^9, 
   3.434130026546875*^9}, {3.434130115578125*^9, 3.434130120109375*^9}, {
   3.4341309365625*^9, 3.434130954359375*^9}, 3.434160905121999*^9, 
   3.434161331512624*^9, {3.434161398996999*^9, 3.434161431418874*^9}, {
   3.434161604559499*^9, 3.434161605684499*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"B", "=", 
  RowBox[{
   RowBox[{"D", "[", 
    RowBox[{
     RowBox[{"w", "[", 
      RowBox[{"y", ",", "Z"}], "]"}], ",", 
     RowBox[{"{", 
      RowBox[{"y", ",", "2"}], "}"}]}], "]"}], "/.", 
   RowBox[{"{", 
    RowBox[{
     RowBox[{"y", "\[Rule]", "ess"}], ",", 
     RowBox[{"Z", "\[Rule]", "ess"}]}], "}"}]}]}]], "Input",
 CellChangeTimes->{{3.434161348856374*^9, 3.434161364840749*^9}}],

Cell[BoxData[
 RowBox[{"-", "0.28`"}]], "Output",
 CellChangeTimes->{3.434161365465749*^9, 3.434162002387624*^9, 
  3.434162288840749*^9, 3.434267569734375*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"A", "=", 
  RowBox[{
   RowBox[{"D", "[", 
    RowBox[{
     RowBox[{"w", "[", 
      RowBox[{"y", ",", "Z"}], "]"}], ",", 
     RowBox[{"{", 
      RowBox[{"Z", ",", "2"}], "}"}]}], "]"}], "/.", 
   RowBox[{"{", 
    RowBox[{
     RowBox[{"y", "\[Rule]", "ess"}], ",", 
     RowBox[{"Z", "\[Rule]", "ess"}]}], "}"}]}]}]], "Input",
 CellChangeTimes->{{3.434161348856374*^9, 3.434161388559499*^9}}],

Cell[BoxData["0.28`"], "Output",
 CellChangeTimes->{{3.434161365465749*^9, 3.434161390481374*^9}, 
   3.434162004606374*^9, 3.434162290450124*^9, 3.434267570140625*^9}]
}, Open  ]],

Cell["\<\
The strategy is evolutionarily stable since B < 0 (immune to invasion by \
neighbouring types)

The strategy is convergence stable (attracting) since A > B

The strategy is capable of invading into all of its neighbouring types since \
A > 0 (invasion potential)\
\>", "Text",
 CellChangeTimes->{{3.434130767703125*^9, 3.434130789671875*^9}, {
  3.434161463559499*^9, 3.434161706840749*^9}, {3.434168325653249*^9, 
  3.434168329528249*^9}}]
}, Open  ]]
}, Open  ]],

Cell[CellGroupData[{

Cell["\<\
GAME THEORY AND INCLUSIVE FITNESS\
\>", "Section",
 CellChangeTimes->{{3.432925002453125*^9, 3.432925019578125*^9}, {
  3.4341262366875*^9, 3.4341262429375*^9}, {3.434127592390625*^9, 
  3.434127606109375*^9}, {3.43413059134375*^9, 3.43413059715625*^9}, {
  3.434131727609375*^9, 3.434131735375*^9}}],

Cell[CellGroupData[{

Cell["Hawk-dove game (mixed-strategy case)", "Subsection",
 CellChangeTimes->{{3.432669383328125*^9, 3.43266938340625*^9}, {
   3.43267042596875*^9, 3.432670444484375*^9}, {3.432670479796875*^9, 
   3.43267068290625*^9}, {3.432671107015625*^9, 3.432671129375*^9}, 
   3.434126273171875*^9, 3.434126445671875*^9, {3.43413147684375*^9, 
   3.434131644125*^9}, {3.434156911950124*^9, 3.434156920200124*^9}},
 FontWeight->"Bold"],

Cell["\<\
The fitness of an individual who plays hawk with probability y1 and interacts \
with an individual who plays hawk with probability y2 is\
\>", "Text",
 CellChangeTimes->{{3.434157185090749*^9, 3.434157268778249*^9}, {
  3.434162390325124*^9, 3.434162393075124*^9}}],

Cell[BoxData[
 RowBox[{
  RowBox[{
   RowBox[{"w1", "[", 
    RowBox[{"y1_", ",", "y2_"}], "]"}], "=", 
   RowBox[{"w0", "+", 
    RowBox[{
     RowBox[{"(", 
      RowBox[{"1", "-", "y1"}], ")"}], "*", 
     RowBox[{"(", 
      RowBox[{"1", "-", "y2"}], ")"}], "*", 
     RowBox[{"V", "/", "2"}]}], "+", 
    RowBox[{"y1", "*", 
     RowBox[{"(", 
      RowBox[{"1", "-", "y2"}], ")"}], "*", "V"}], "+", 
    RowBox[{"y1", "*", "y2", "*", 
     RowBox[{
      RowBox[{"(", 
       RowBox[{"V", "-", "C"}], ")"}], "/", "2"}]}]}]}], ";"}]], "Input",
 CellChangeTimes->{{3.4341272223125*^9, 3.43412725884375*^9}, {
  3.434129620828125*^9, 3.434129668828125*^9}, {3.434129738109375*^9, 
  3.434129742359375*^9}, {3.4341297856875*^9, 3.434129810453125*^9}, {
  3.434156959371999*^9, 3.434156991231374*^9}, {3.434162395293874*^9, 
  3.434162408684499*^9}}],

Cell["\<\
where w0 is baseline fitness, V is the value of the resource and C if the \
cost of two hawks fighting with each other.\
\>", "Text",
 CellChangeTimes->{{3.434157185090749*^9, 3.434157312918874*^9}}],

Cell["\<\
Hence, the invasion fitness of an individual who plays hawk with probability \
y in a resident population where individuals play hawk with probability Z is\
\>", "Text",
 CellChangeTimes->{{3.434157185090749*^9, 3.434157352590749*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{
  RowBox[{"w", "[", 
   RowBox[{"y_", ",", "Z_"}], "]"}], "=", 
  RowBox[{"FullSimplify", "[", 
   RowBox[{
    RowBox[{"w1", "[", 
     RowBox[{"y", ",", "Z"}], "]"}], "/", 
    RowBox[{"w1", "[", 
     RowBox[{"Z", ",", "Z"}], "]"}]}], "]"}]}]], "Input",
 CellChangeTimes->{{3.434156995981374*^9, 3.434157023871999*^9}, 
   3.434162421246999*^9, 3.434162543684499*^9}],

Cell[BoxData[
 FractionBox[
  RowBox[{"V", "+", 
   RowBox[{"2", " ", "w0"}], "+", 
   RowBox[{"V", " ", "y"}], "-", 
   RowBox[{
    RowBox[{"(", 
     RowBox[{"V", "+", 
      RowBox[{"C", " ", "y"}]}], ")"}], " ", "Z"}]}], 
  RowBox[{"V", "+", 
   RowBox[{"2", " ", "w0"}], "-", 
   RowBox[{"C", " ", 
    SuperscriptBox["Z", "2"]}]}]]], "Output",
 CellChangeTimes->{{3.434157015309499*^9, 3.434157025450124*^9}, {
  3.434162425809499*^9, 3.434162430700124*^9}, {3.434162500575124*^9, 
  3.434162544215749*^9}}]
}, Open  ]],

Cell["\<\
The selection differential for an increased probability of playing hawk is \
given by\
\>", "Text",
 CellChangeTimes->{{3.434157185090749*^9, 3.434157376621999*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"selD", "=", 
  RowBox[{
   RowBox[{"D", "[", 
    RowBox[{
     RowBox[{"w", "[", 
      RowBox[{"y", ",", "Z"}], "]"}], ",", "y"}], "]"}], "/.", 
   RowBox[{"{", 
    RowBox[{"y", "\[Rule]", "Z"}], "}"}]}]}]], "Input",
 CellChangeTimes->{{3.434157037137624*^9, 3.434157086153249*^9}, {
   3.434157149559499*^9, 3.434157162637624*^9}, 3.434162565887624*^9}],

Cell[BoxData[
 FractionBox[
  RowBox[{"V", "-", 
   RowBox[{"C", " ", "Z"}]}], 
  RowBox[{"V", "+", 
   RowBox[{"2", " ", "w0"}], "-", 
   RowBox[{"C", " ", 
    SuperscriptBox["Z", "2"]}]}]]], "Output",
 CellChangeTimes->{{3.434157069731374*^9, 3.434157087184499*^9}, {
   3.434157153809499*^9, 3.434157163621999*^9}, 3.434162551325124*^9}]
}, Open  ]],

Cell["\<\
And an ESS is reached when z*=V/C, i.e. individuals play hawk with a \
probability of V/C :\
\>", "Text",
 CellChangeTimes->{{3.434157185090749*^9, 3.434157415543874*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"ess", "=", 
  RowBox[{
   RowBox[{
    RowBox[{
     RowBox[{"Solve", "[", 
      RowBox[{
       RowBox[{"selD", "\[Equal]", "0"}], ",", "Z"}], "]"}], "[", 
     RowBox[{"[", "1", "]"}], "]"}], "[", 
    RowBox[{"[", "1", "]"}], "]"}], "[", 
   RowBox[{"[", "2", "]"}], "]"}]}]], "Input",
 CellChangeTimes->{{3.434157070684499*^9, 3.434157089075124*^9}, {
  3.434157169075124*^9, 3.434157176496999*^9}, {3.434158001778249*^9, 
  3.434158002934499*^9}, {3.434162568153249*^9, 3.434162568309499*^9}}],

Cell[BoxData[
 FractionBox["V", "C"]], "Output",
 CellChangeTimes->{{3.434157078340749*^9, 3.434157089887624*^9}, {
   3.434157169309499*^9, 3.434157177184499*^9}, 3.434158003825124*^9, 
   3.434162557059499*^9}]
}, Open  ]]
}, Open  ]],

Cell[CellGroupData[{

Cell["\<\
Inclusive fitness example:
hawk-dove game played between relatives (mixed-strategy case)\
\>", "Subsection",
 CellChangeTimes->{{3.432669383328125*^9, 3.43266938340625*^9}, {
   3.43267042596875*^9, 3.432670444484375*^9}, {3.432670479796875*^9, 
   3.43267068290625*^9}, {3.432671107015625*^9, 3.432671129375*^9}, 
   3.434126273171875*^9, 3.434126445671875*^9, {3.43413147684375*^9, 
   3.434131644125*^9}, {3.434156911950124*^9, 3.434156920200124*^9}, {
   3.434157430215749*^9, 3.434157433700124*^9}, {3.434158176121999*^9, 
   3.434158182965749*^9}},
 FontWeight->"Bold"],

Cell["\<\
But what if the two interacting individuals are relatives of each other? In \
that case we need to write down the fitnesses of both individuals separately \
and look at the consequences of individual 1 increasing its probability of \
playing hawk both on its own fitness and on the fitness of the other \
individual, weighed by relatedness.\
\>", "Text",
 CellChangeTimes->{{3.434157185090749*^9, 3.434157268778249*^9}, {
  3.434157436496999*^9, 3.434157500371999*^9}, {3.434157811075124*^9, 
  3.434157843403249*^9}, {3.434162590637624*^9, 3.434162605762624*^9}}],

Cell["\<\
The fitness of an individual who plays hawk with probability y1 and interacts \
with an individual who plays hawk with probability y2 is\
\>", "Text",
 CellChangeTimes->{{3.434157185090749*^9, 3.434157268778249*^9}, {
  3.434157436496999*^9, 3.434157506106374*^9}, {3.434157856621999*^9, 
  3.434157861168874*^9}}],

Cell[BoxData[
 RowBox[{
  RowBox[{
   RowBox[{"w1", "[", 
    RowBox[{"y1_", ",", "y2_"}], "]"}], "=", 
   RowBox[{"w0", "+", 
    RowBox[{
     RowBox[{"(", 
      RowBox[{"1", "-", "y1"}], ")"}], "*", 
     RowBox[{"(", 
      RowBox[{"1", "-", "y2"}], ")"}], "*", 
     RowBox[{"V", "/", "2"}]}], "+", 
    RowBox[{"y1", "*", 
     RowBox[{"(", 
      RowBox[{"1", "-", "y2"}], ")"}], "*", "V"}], "+", 
    RowBox[{"y1", "*", "y2", "*", 
     RowBox[{
      RowBox[{"(", 
       RowBox[{"V", "-", "C"}], ")"}], "/", "2"}]}]}]}], ";"}]], "Input",
 CellChangeTimes->{{3.4341272223125*^9, 3.43412725884375*^9}, {
  3.434129620828125*^9, 3.434129668828125*^9}, {3.434129738109375*^9, 
  3.434129742359375*^9}, {3.4341297856875*^9, 3.434129810453125*^9}, {
  3.434156959371999*^9, 3.434156991231374*^9}, {3.434157867543874*^9, 
  3.434157881684499*^9}}],

Cell["\<\
where w0 is baseline fitness, V is the value of the resource and C if the \
cost of two hawks fighting with each other.\
\>", "Text",
 CellChangeTimes->{{3.434157185090749*^9, 3.434157312918874*^9}}],

Cell["\<\
Similarly, the fitness of the social interactant will be\
\>", "Text",
 CellChangeTimes->{{3.434157185090749*^9, 3.434157268778249*^9}, {
  3.434157436496999*^9, 3.434157530950124*^9}}],

Cell[BoxData[
 RowBox[{
  RowBox[{
   RowBox[{"w2", "[", 
    RowBox[{"y1_", ",", "y2_"}], "]"}], "=", 
   RowBox[{"w0", "+", 
    RowBox[{
     RowBox[{"(", 
      RowBox[{"1", "-", "y1"}], ")"}], "*", 
     RowBox[{"(", 
      RowBox[{"1", "-", "y2"}], ")"}], "*", 
     RowBox[{"V", "/", "2"}]}], "+", 
    RowBox[{"y2", "*", 
     RowBox[{"(", 
      RowBox[{"1", "-", "y1"}], ")"}], "*", "V"}], "+", 
    RowBox[{"y1", "*", "y2", "*", 
     RowBox[{
      RowBox[{"(", 
       RowBox[{"V", "-", "C"}], ")"}], "/", "2"}]}]}]}], ";"}]], "Input",
 CellChangeTimes->{{3.4341272223125*^9, 3.43412725884375*^9}, {
  3.434129620828125*^9, 3.434129668828125*^9}, {3.434129738109375*^9, 
  3.434129742359375*^9}, {3.4341297856875*^9, 3.434129810453125*^9}, {
  3.434156959371999*^9, 3.434156991231374*^9}, {3.434157533840749*^9, 
  3.434157560246999*^9}, {3.434157897309499*^9, 3.434157931356374*^9}}],

Cell["\<\
The selection differential is now given by the inclusive fitness effect of \
individual 1 increasing its probability of playing hawk, which is given by\
\>", "Text",
 CellChangeTimes->{{3.434157185090749*^9, 3.434157376621999*^9}, {
  3.434158027856374*^9, 3.434158058778249*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"selD", "=", 
  RowBox[{
   RowBox[{"(", 
    RowBox[{
     RowBox[{"D", "[", 
      RowBox[{
       RowBox[{"w1", "[", 
        RowBox[{"y1", ",", "y2"}], "]"}], ",", "y1"}], "]"}], "+", 
     RowBox[{
      RowBox[{"D", "[", 
       RowBox[{
        RowBox[{"w2", "[", 
         RowBox[{"y1", ",", "y2"}], "]"}], ",", "y1"}], "]"}], "*", "r"}]}], 
    ")"}], "/.", 
   RowBox[{"{", 
    RowBox[{
     RowBox[{"y1", "\[Rule]", "Z"}], ",", 
     RowBox[{"y2", "\[Rule]", "Z"}]}], "}"}]}]}]], "Input",
 CellChangeTimes->{{3.434157638481374*^9, 3.434157687418874*^9}, {
   3.434157938012624*^9, 3.434157974621999*^9}, 3.434158075637624*^9}],

Cell[BoxData[
 RowBox[{
  RowBox[{
   FractionBox["1", "2"], " ", "V", " ", 
   RowBox[{"(", 
    RowBox[{"1", "-", "Z"}], ")"}]}], "+", 
  RowBox[{
   FractionBox["1", "2"], " ", 
   RowBox[{"(", 
    RowBox[{
     RowBox[{"-", "C"}], "+", "V"}], ")"}], " ", "Z"}], "+", 
  RowBox[{"r", " ", 
   RowBox[{"(", 
    RowBox[{
     RowBox[{
      RowBox[{"-", 
       FractionBox["1", "2"]}], " ", "V", " ", 
      RowBox[{"(", 
       RowBox[{"1", "-", "Z"}], ")"}]}], "-", 
     RowBox[{"V", " ", "Z"}], "+", 
     RowBox[{
      FractionBox["1", "2"], " ", 
      RowBox[{"(", 
       RowBox[{
        RowBox[{"-", "C"}], "+", "V"}], ")"}], " ", "Z"}]}], 
    ")"}]}]}]], "Output",
 CellChangeTimes->{{3.434157676137624*^9, 3.434157688543874*^9}, {
   3.434157967512624*^9, 3.434157974887624*^9}, 3.434158079762624*^9, 
   3.434162685496999*^9}]
}, Open  ]],

Cell["\<\
where D[w1, y1] measures the increase in personal reproduction as a result of \
slightly increasing ones probability of playing hawk and D[w2, y1] measures \
the decrease in reproduction (ie the cost for) the social interactant. The \
latter needs to be weighed by relatedness r.\
\>", "Text",
 CellChangeTimes->{{3.434157185090749*^9, 3.434157376621999*^9}, {
  3.434158027856374*^9, 3.434158158559499*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"ess", "=", 
  RowBox[{
   RowBox[{
    RowBox[{
     RowBox[{"Solve", "[", 
      RowBox[{
       RowBox[{"selD", "\[Equal]", "0"}], ",", "Z"}], "]"}], "[", 
     RowBox[{"[", "1", "]"}], "]"}], "[", 
    RowBox[{"[", "1", "]"}], "]"}], "[", 
   RowBox[{"[", "2", "]"}], "]"}]}]], "Input",
 CellChangeTimes->{{3.434157677309499*^9, 3.434157696496999*^9}, {
   3.434157980106374*^9, 3.434157995434499*^9}, 3.434158077418874*^9}],

Cell[BoxData[
 FractionBox[
  RowBox[{"V", "-", 
   RowBox[{"r", " ", "V"}]}], 
  RowBox[{"C", " ", 
   RowBox[{"(", 
    RowBox[{"1", "+", "r"}], ")"}]}]]], "Output",
 CellChangeTimes->{
  3.434157696778249*^9, {3.434157980512624*^9, 3.434157995903249*^9}, 
   3.434158081606374*^9, {3.434162678731374*^9, 3.434162685528249*^9}}]
}, Open  ]]
}, Open  ]],

Cell[CellGroupData[{

Cell["\<\
Calculation of reproductive value
Relative reproductive value of males and females in haplodiploids\
\>", "Subsection",
 CellChangeTimes->{{3.432669383328125*^9, 3.43266938340625*^9}, {
   3.43267042596875*^9, 3.432670444484375*^9}, {3.432670479796875*^9, 
   3.43267068290625*^9}, {3.432671107015625*^9, 3.432671129375*^9}, 
   3.434126273171875*^9, 3.434126445671875*^9, {3.43413147684375*^9, 
   3.434131644125*^9}, {3.434156911950124*^9, 3.434156920200124*^9}, {
   3.434157430215749*^9, 3.434157433700124*^9}, {3.434158176121999*^9, 
   3.434158182965749*^9}, {3.434165538465749*^9, 3.434165542887624*^9}, {
   3.434166244278249*^9, 3.434166257246999*^9}},
 FontWeight->"Bold"],

Cell["\<\
Our recurrence equations for a neutral gene were as follows:
frequency of allele in queens in next generation pf\[CloseCurlyQuote] = \
(1/2).pf + (1/2).pm
frequency of allele in males in next generation pm\[CloseCurlyQuote] = pf

If we put the gene transmission probabilities in a matrix A we get:\
\>", "Text",
 CellChangeTimes->{{3.434166267903249*^9, 3.434166322575124*^9}}],

Cell[BoxData[
 RowBox[{
  RowBox[{"A", "=", 
   RowBox[{"(", GridBox[{
      {
       RowBox[{"1", "/", "2"}], 
       RowBox[{"1", "/", "2"}]},
      {"1", "0"}
     }], ")"}]}], ";"}]], "Input",
 CellChangeTimes->{{3.434166030590749*^9, 3.434166048356374*^9}}],

Cell["\<\
The individual reproductive values rate of increase of our gene is given by \
the dominant eigenvalue of matrix A, but since we are looking at a neutral \
gene this is 1 :\
\>", "Text",
 CellChangeTimes->{{3.434166362918874*^9, 3.434166481606374*^9}, {
  3.434166939293874*^9, 3.434166941528249*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"\[Lambda]1", "=", 
  RowBox[{
   RowBox[{"Eigenvalues", "[", "A", "]"}], "[", 
   RowBox[{"[", "1", "]"}], "]"}], 
  "                                                        ", 
  RowBox[{"(*", " ", 
   RowBox[{"the", " ", "dominant", " ", "eigenvalue", " ", "\[Lambda]"}], " ",
    "*)"}]}]], "Input",
 CellChangeTimes->{3.434166093575124*^9}],

Cell[BoxData["1"], "Output",
 CellChangeTimes->{{3.434165940950124*^9, 3.434165948231374*^9}, 
   3.434166053543874*^9, 3.434166093809499*^9, 3.434166328434499*^9, 
   3.434166957012624*^9}]
}, Open  ]],

Cell["\<\
And our stable class frequences and individual reproductive values are given \
by the dominant right and dominant left eigenvector of gene transmission \
matrix A:\
\>", "Text",
 CellChangeTimes->{{3.434166362918874*^9, 3.434166463496999*^9}, {
  3.434166881840749*^9, 3.434166929575124*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"e1", "=", 
  RowBox[{
   RowBox[{"Eigenvectors", "[", "A", "]"}], "[", 
   RowBox[{"[", "1", "]"}], "]"}], 
  "                                                     ", 
  RowBox[{"(*", " ", 
   RowBox[{"equilibrium", " ", "class", " ", "frequencies", " ", 
    SubscriptBox["u", "j"]}], " ", "*)"}]}]], "Input",
 CellChangeTimes->{
  3.434166100106374*^9, {3.434166613887624*^9, 3.434166616559499*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"1", ",", "1"}], "}"}]], "Output",
 CellChangeTimes->{
  3.434165955778249*^9, 3.434166055778249*^9, 3.434166100481374*^9, 
   3.434166330387624*^9, {3.434166602387624*^9, 3.434166616762624*^9}, 
   3.434166759965749*^9, {3.434166825793874*^9, 3.434166847918874*^9}, 
   3.434166957106374*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"\[Epsilon]1", "=", 
  RowBox[{
   RowBox[{"Eigenvectors", "[", 
    RowBox[{"Transpose", "[", "A", "]"}], "]"}], "[", 
   RowBox[{"[", "1", "]"}], "]"}], "                    ", 
  RowBox[{"(*", " ", 
   RowBox[{"individual", " ", "reproductive", " ", "values", " ", 
    SubscriptBox["\[Nu]", "j"]}], " ", "*)"}]}]], "Input",
 CellChangeTimes->{
  3.434166107246999*^9, {3.434166609965749*^9, 3.434166619106374*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"2", ",", "1"}], "}"}]], "Output",
 CellChangeTimes->{
  3.434165966184499*^9, 3.434166057590749*^9, 3.434166107543874*^9, 
   3.434166332465749*^9, {3.434166606059499*^9, 3.434166619293874*^9}, 
   3.434166767434499*^9, {3.434166828590749*^9, 3.434166847965749*^9}, 
   3.434166957153249*^9}]
}, Open  ]],

Cell["\<\
I.e. females have twice the reproductive value as males (contribute twice as \
many genes to future generations as males)\
\>", "Text",
 CellChangeTimes->{{3.434166362918874*^9, 3.434166463496999*^9}, {
  3.434166881840749*^9, 3.434166929575124*^9}, {3.434167050809499*^9, 
  3.434167077653249*^9}}]
}, Open  ]]
}, Open  ]]
}, Open  ]]
},
WindowToolbars->"EditBar",
WindowSize->{800, 740},
WindowMargins->{{398, Automatic}, {Automatic, 74}},
PrivateNotebookOptions->{"ColorPalette"->{RGBColor, -1}},
ShowCellLabel->True,
ShowCellTags->False,
RenderingOptions->{"ObjectDithering"->True,
"RasterDithering"->False},
FrontEndVersion->"6.0 for Microsoft Windows (32-bit) (February 7, 2008)",
StyleDefinitions->Notebook[{
   Cell[
    StyleData[
    StyleDefinitions -> 
     FrontEnd`FileName[{"Creative", ""}, "PastelColor.nb", CharacterEncoding -> 
       "WindowsANSI"]]], 
   Cell[
    StyleData["Input"], Background -> GrayLevel[1]], 
   Cell[
    StyleData["Output"]], 
   Cell[
    StyleData["Text"], CellDingbat -> None, Background -> GrayLevel[1]], 
   Cell[
    StyleData["Item"], FontWeight -> "Bold", FontColor -> GrayLevel[0]], 
   Cell[
    StyleData["InlineFormula"], FontFamily -> "Arial", FontSize -> 12, 
    FontWeight -> "Bold", FontSlant -> "Plain", 
    FontVariations -> {"StrikeThrough" -> False, "Underline" -> False}], 
   Cell[
    StyleData["Code"], Background -> GrayLevel[1]]}, Visible -> False, 
  FrontEndVersion -> "6.0 for Microsoft Windows (32-bit) (February 7, 2008)", 
  StyleDefinitions -> "PrivateStylesheetFormatting.nb"]
]
(* End of Notebook Content *)

(* Internal cache information *)
(*CellTagsOutline
CellTagsIndex->{}
*)
(*CellTagsIndex
CellTagsIndex->{}
*)
(*NotebookFileOutline
Notebook[{
Cell[CellGroupData[{
Cell[590, 23, 278, 4, 73, "Title"],
Cell[CellGroupData[{
Cell[893, 31, 239, 5, 75, "Section"],
Cell[CellGroupData[{
Cell[1157, 40, 833, 14, 87, "Subsection"],
Cell[1993, 56, 207, 4, 45, "Text"],
Cell[2203, 62, 540, 13, 41, "Input"],
Cell[CellGroupData[{
Cell[2768, 79, 678, 17, 62, "Input"],
Cell[3449, 98, 2168, 43, 266, "Output"]
}, Open  ]],
Cell[5632, 144, 458, 8, 45, "Text"],
Cell[6093, 154, 231, 6, 41, "Input"],
Cell[CellGroupData[{
Cell[6349, 164, 929, 21, 62, "Input"],
Cell[7281, 187, 5807, 103, 261, "Output"]
}, Open  ]],
Cell[13103, 293, 536, 9, 63, "Text"],
Cell[13642, 304, 711, 14, 57, "Input"],
Cell[14356, 320, 453, 6, 45, "Text"],
Cell[CellGroupData[{
Cell[14834, 330, 1462, 34, 102, "Input",
 CellID->15076866],
Cell[16299, 366, 152557, 2514, 385, 143514, 2364, "CachedBoxData", "BoxData", \
"Output"]
}, Open  ]],
Cell[168871, 2883, 591, 8, 45, "Text"],
Cell[169465, 2893, 476, 12, 41, "Input"],
Cell[169944, 2907, 809, 13, 81, "Text"],
Cell[CellGroupData[{
Cell[170778, 2924, 563, 14, 41, "Input"],
Cell[171344, 2940, 392, 5, 40, "Output"]
}, Open  ]],
Cell[171751, 2948, 672, 11, 45, "Text"],
Cell[CellGroupData[{
Cell[172448, 2963, 421, 13, 41, "Input"],
Cell[172872, 2978, 160, 3, 40, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[173069, 2986, 421, 13, 41, "Input"],
Cell[173493, 3001, 168, 2, 40, "Output"]
}, Open  ]],
Cell[173676, 3006, 450, 11, 117, "Text"]
}, Open  ]]
}, Open  ]],
Cell[CellGroupData[{
Cell[174175, 3023, 310, 6, 75, "Section"],
Cell[CellGroupData[{
Cell[174510, 3033, 425, 6, 36, "Subsection"],
Cell[174938, 3041, 275, 5, 63, "Text"],
Cell[175216, 3048, 851, 23, 41, "Input"],
Cell[176070, 3073, 209, 4, 45, "Text"],
Cell[176282, 3079, 245, 4, 63, "Text"],
Cell[CellGroupData[{
Cell[176552, 3087, 394, 11, 41, "Input"],
Cell[176949, 3100, 514, 15, 57, "Output"]
}, Open  ]],
Cell[177478, 3118, 175, 4, 45, "Text"],
Cell[CellGroupData[{
Cell[177678, 3126, 381, 10, 41, "Input"],
Cell[178062, 3138, 341, 9, 57, "Output"]
}, Open  ]],
Cell[178418, 3150, 181, 4, 45, "Text"],
Cell[CellGroupData[{
Cell[178624, 3158, 523, 13, 41, "Input"],
Cell[179150, 3173, 212, 4, 55, "Output"]
}, Open  ]]
}, Open  ]],
Cell[CellGroupData[{
Cell[179411, 3183, 585, 11, 53, "Subsection"],
Cell[179999, 3196, 574, 9, 81, "Text"],
Cell[180576, 3207, 324, 6, 63, "Text"],
Cell[180903, 3215, 851, 23, 41, "Input"],
Cell[181757, 3240, 209, 4, 45, "Text"],
Cell[181969, 3246, 195, 4, 45, "Text"],
Cell[182167, 3252, 897, 23, 41, "Input"],
Cell[183067, 3277, 290, 5, 63, "Text"],
Cell[CellGroupData[{
Cell[183382, 3286, 661, 20, 41, "Input"],
Cell[184046, 3308, 845, 28, 55, "Output"]
}, Open  ]],
Cell[184906, 3339, 418, 7, 81, "Text"],
Cell[CellGroupData[{
Cell[185349, 3350, 451, 12, 41, "Input"],
Cell[185803, 3364, 330, 9, 57, "Output"]
}, Open  ]]
}, Open  ]],
Cell[CellGroupData[{
Cell[186182, 3379, 692, 12, 53, "Subsection"],
Cell[186877, 3393, 387, 8, 117, "Text"],
Cell[187267, 3403, 262, 9, 52, "Input"],
Cell[187532, 3414, 310, 6, 63, "Text"],
Cell[CellGroupData[{
Cell[187867, 3424, 368, 9, 41, "Input"],
Cell[188238, 3435, 190, 3, 40, "Output"]
}, Open  ]],
Cell[188443, 3441, 302, 6, 63, "Text"],
Cell[CellGroupData[{
Cell[188770, 3451, 425, 10, 41, "Input"],
Cell[189198, 3463, 332, 7, 40, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[189567, 3475, 441, 10, 41, "Input"],
Cell[190011, 3487, 332, 7, 40, "Output"]
}, Open  ]],
Cell[190358, 3497, 309, 6, 45, "Text"]
}, Open  ]]
}, Open  ]]
}, Open  ]]
}
]
*)

(* End of internal cache information *)