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### Notes Literature.md

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# Notes Literature
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### Small Groups and long memories promote cooperation
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- Opening paragraph has a good reference for applications
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- More references about memory studies, 22-25.
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- Intro, second to last paragraph is a good paragraph
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- Ref 31 for the new figure in the SI with the defective and cooperative Nash
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- they do this weird transformation
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- then because the payoffs in the public's good game only depend on the amount of cooperation
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they focus on memory counting strategies.
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- they then get a different system for representing the counting strategies.
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- This new system has 5 components.
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- For them counting strategies lead to a higher cooperation rates,
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because of the volume and because it's easier for them to invade other populations.
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### Reactive learning strategies for iterated games
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- They look at what they call reactive learning strategies, they use as an
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input the actions of the co-player to decide on an action. Now since these
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strategies learn as the game proceeds then they are considered long memory
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strategies. They show that there is a subset that is memory-one, reactive-1
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specifically. So I am assuming you can map these to any reactive-n strategy
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is you really tried.
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The main result of the paper is to show that the payoffs of a memory-1 strategy
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are constrained when playing against these reactive learning players.
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.gitignore

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data/*
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matlab_data/*
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checks/*
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*txt
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*csv
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notebooks/DefectingNash/
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src/evolution_simulations/ErrorGrid.zip
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src/evolution_simulations/ErrorGrid/
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src/evolution_simulations/ErrorSim.zip
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src/evolution_simulations/ErrorSim/
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src/evolution_simulations/ErrorSim_2/
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notebooks/DefectingNashCounting/
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# Folder for CH to keep track of the history
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oldfiles/
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# CH's Mathematica calculations
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# CH's Mathematica and Matlab calculations
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mathematica/
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matlab/
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FirstRevision/Cover letter.docx

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