bibliography.bib

@inproceedings{marks1989niche,
  title={Niche strategies: the Prisoner’s Dilemma computer tournaments revisited},
  author={Marks, Robert E},
  booktitle={JOURNAL OF EVOLUTIONARY ECONOMICS},
  year={1989},
  organization={Citeseer}
}

@article{fogel1993evolving,
  title={Evolving behaviors in the iterated prisoner's dilemma},
  author={Fogel, David B},
  journal={Evolutionary Computation},
  volume={1},
  number={1},
  pages={77--97},
  year={1993},
  publisher={MIT Press}
}

@article{franken2005particle,
  title={Particle swarm optimization approaches to coevolve strategies for the iterated prisoner's dilemma},
  author={Franken, Nelis and Engelbrecht, Andries Petrus},
  journal={IEEE Transactions on Evolutionary Computation},
  volume={9},
  number={6},
  pages={562--579},
  year={2005},
  publisher={IEEE}
}

@article{slany2007some,
  title={On some winning strategies for the Iterated Prisoner’s Dilemma, or, Mr. Nice Guy and the Cosa Nostra},
  author={Slany, Wolfgang and Kienreich, Wolfgang},
  journal={The Iterated Prisoners' Dilemma: 20 Years on},
  volume={4},
  pages={171},
  year={2007},
  publisher={World Scientific Publishing Company Incorporated}
}

@article{li2007design,
  title={How to design a strategy to win an IPD tournament},
  author={Li, Jiawei},
  journal={The iterated prisoner’s dilemma},
  volume={20},
  pages={89--104},
  year={2007}
}

@article{nowak1993strategy,
  title={A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner's Dilemma game.},
  author={Nowak, Martin and Sigmund, Karl},
  journal={Nature},
  volume={364},
  number={6432},
  pages={56},
  year={1993},
  publisher={Nature Publishing Group}
}

@article{sandholm1996multiagent,
  title={Multiagent reinforcement learning in the iterated prisoner's dilemma},
  author={Sandholm, Tuomas W and Crites, Robert H},
  journal={Biosystems},
  volume={37},
  number={1-2},
  pages={147--166},
  year={1996},
  publisher={Elsevier}
}

@inproceedings{ashlock2006training,
  title={Training function stacks to play the iterated prisoner's dilemma},
  author={Ashlock, Daniel},
  booktitle={Computational Intelligence and Games, 2006 IEEE Symposium on},
  pages={111--118},
  year={2006},
  organization={IEEE}
}

@inproceedings{ashlock2006changes,
  title={Changes in prisoner’s dilemma strategies over evolutionary time with different population sizes},
  author={Ashlock, Wendy and Ashlock, Daniel},
  booktitle={Evolutionary Computation, 2006. CEC 2006. IEEE Congress on},
  pages={297--304},
  year={2006},
  organization={IEEE}
}

@article{ashlock2008fingerprinting,
  title={Fingerprinting: Visualization and automatic analysis of prisoner's dilemma strategies},
  author={Ashlock, Daniel and Kim, Eun-Youn},
  journal={IEEE Transactions on Evolutionary Computation},
  volume={12},
  number={5},
  pages={647--659},
  year={2008},
  publisher={IEEE}
}

@article{ashlock2009fingerprint,
  title={Fingerprint analysis of the noisy prisoner's dilemma using a finite-state representation},
  author={Ashlock, Daniel and Kim, Eun-Youn and Ashlock, Wendy},
  journal={IEEE Transactions on Computational Intelligence and AI in Games},
  volume={1},
  number={2},
  pages={154--167},
  year={2009},
  publisher={IEEE}
}

@inproceedings{ashlock2013impact,
  title={The impact of varying resources available to iterated prisoner's dilemma agents},
  author={Ashlock, Daniel and Kim, Eun-Youn},
  booktitle={Foundations of Computational Intelligence (FOCI), 2013 IEEE Symposium on},
  pages={60--67},
  year={2013},
  organization={IEEE}
}

@inproceedings{ashlock2014shaped,
  title={Shaped prisoner's dilemma automata},
  author={Ashlock, Wendy and Ashlock, Daniel},
  booktitle={Computational Intelligence and Games (CIG), 2014 IEEE Conference on},
  pages={1--8},
  year={2014},
  organization={IEEE}
}

@inproceedings{sudo2015effects,
  title={Effects of ensemble action selection with different usage of player's memory resource on the evolution of cooperative strategies for iterated prisoner's dilemma game},
  author={Sudo, Takahiko and Goto, Kazushi and Nojima, Yusuke and Ishibuchi, Hisao},
  booktitle={Evolutionary Computation (CEC), 2015 IEEE Congress on},
  pages={1505--1512},
  year={2015},
  organization={IEEE}
}

@inproceedings{barlow2015varying,
  title={Varying decision inputs in Prisoner's Dilemma},
  author={Barlow, Lee-Ann and Ashlock, Daniel},
  booktitle={Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2015 IEEE Conference on},
  pages={1--8},
  year={2015},
  organization={IEEE}
}


@inproceedings{vassiliades2010multiagent,
  title={Multiagent reinforcement learning in the iterated prisoner's dilemma: fast cooperation through evolved payoffs},
  author={Vassiliades, Vassilis and Christodoulou, Chris},
  booktitle={Neural Networks (IJCNN), The 2010 International Joint Conference on},
  pages={1--8},
  year={2010},
  organization={IEEE}
}

@article{franken2005particle,
  title={Particle swarm optimization approaches to coevolve strategies for the iterated prisoner's dilemma},
  author={Franken, Nelis and Engelbrecht, Andries Petrus},
  journal={IEEE Transactions on Evolutionary Computation},
  volume={9},
  number={6},
  pages={562--579},
  year={2005},
  publisher={IEEE}
}

@article{Lee2015,
    author = {Lee, Christopher and Harper, Marc and Fryer, Dashiell},
    doi = {10.1371/journal.pone.0120625},
    file = {:Users/vince/Dropbox/Mendeley/2015/2015 - Lee, Harper, Fryer - The Art of War Beyond Memory-one Strategies in Population Games.pdf:pdf},
    issn = {1932-6203},
    journal = {Plos One},
    mendeley-groups = {manuscripts/An-open-reproducible-framework-for-the-study-of-the-iterated-prisoners-dilemma},
    number = {3},
    pages = {e0120625},
    title = {{The Art of War: Beyond Memory-one Strategies in Population Games}},
    howpublished = {http://dx.plos.org/10.1371/journal.pone.0120625},
    volume = {10},
    year = {2015}
}

@article{knight2016open,
  title={An Open Framework for the Reproducible Study of the Iterated Prisoner’s Dilemma},
  author={Knight, Vincent and Campbell, Owen and Harper, Marc and Langner, Karol and Campbell, James and Campbell, Thomas and Carney, Alex and Chorley, Martin and Davidson-Pilon, Cameron and Glass, Kristian and others},
  journal={Journal of Open Research Software},
  volume={4},
  number={1},
  year={2016},
  publisher={Ubiquity Press}
}

@book{Nowak,
    author = {Nowak, Martin A},
    doi = {10.1086/523139},
    isbn = {0674023382},
    issn = {0033-5770},
    pmid = {242996800028},
    publisher = {Cambridge: Harvard University Press},
    title = {{Evolutionary Dynamics: Exploring the Equations of Life.}}
}

@misc{axelrodproject,
  author       = {The Axelrod project developers},
  title        = {Axelrod-Python/Axelrod: v2.13.0},
  month        = jun,
  year         = 2017,
  doi          = {10.5281/zenodo.801749},
  howpublished          = {https://doi.org/10.5281/zenodo.801749}
}


@misc{axelrodproject2.2,
  author       = {The Axelrod project developers},
  title        = {Axelrod-Python/Axelrod: v2.2.0},
  month        = dec,
  year         = 2016,
  doi          = {10.5281/zenodo.211828},
  howpublished          = {https://doi.org/10.5281/zenodo.211828}
}

@article{Moran1957,
	author = {Moran, P.A.P.},
	number = {April},
	pages = {60--71},
	title = {{Random Processes in Genetics}},
	year = {1957}
}

@article{Li2014,
	author = {Li, Jiawei and Kendall, Graham and Member, Senior},
	number = {X},
	pages = {1--8},
	title = {{The effect of memory size on the evolutionary stability of strategies in iterated prisoner ' s dilemma}},
	volume = {X},
	year = {2014}
}

@article{Axelrod1980a,
    author = {Axelrod, R.},
    journal = {Journal of Conflict Resolution},
    mendeley-groups = {manuscripts/Playing-Games,software{\_}projects/Axelrod,manuscripts/An-open-reproducible-framework-for-the-study-of-the-iterated-prisoners-dilemma},
    number = {1},
    pages = {3--25},
    title = {{Effective Choice in the Prisoner's Dilemma}},
    volume = {24},
    year = {1980}
}

@article{Baek2016,
	author = {Baek, Seung Ki and Jeong, Hyeong-chai and Hilbe, Christian and Nowak, Martin A},
	doi = {10.1038/srep25676},
	journal = {Nature Publishing Group},
	pages = {1--13},
	publisher = {Nature Publishing Group},
	title = {{Comparing reactive and memory- one strategies of direct reciprocity}},
	howpublished = {http://dx.doi.org/10.1038/srep25676},
	year = {2016}
}


@article{Bendor1993,
	author = {Bendor, Jonathan},
	journal = {The Journal of Conflict Resolution},
	number = {4},
	pages = {709--734},
	title = {{Uncertainty and the Evolution of Cooperation}},
	volume = {37},
	year = {1993}
}

@article{Wu1995,
	author = {Wu, Jianzhong and Axelrod, Robert},
	journal = {The Journal of Conflict Resolution},
	number = {1},
	title = {{How to Cope with Noise in the Iterated Prisoner's Dilemma}},
	volume = {39},
	year = {1995}
}

@article{Nowak1993,
	abstract = {The Prisoner's Dilemma is the leading metaphor for the evolution of cooperative behaviour in populations of selfish agents, especially since the well-known computer tournaments of Axelrod and their application to biological communities. In Axelrod's simulations, the simple strategy tit-for-tat did outstandingly well and subsequently became the major paradigm for reciprocal altruism. Here we present extended evolutionary simulations of heterogeneous ensembles of probabilistic strategies including mutation and selection, and report the unexpected success of another protagonist: Pavlov. This strategy is as simple as tit-for-tat and embodies the fundamental behavioural mechanism win-stay, lose-shift, which seems to be a widespread rule. Pavlov's success is based on two important advantages over tit-for-tat: it can correct occasional mistakes and exploit unconditional cooperators. This second feature prevents Pavlov populations from being undermined by unconditional cooperators, which in turn invite defectors. Pavlov seems to be more robust than tit-for-tat, suggesting that cooperative behaviour in natural situations may often be based on win-stay, lose-shift.},
	author = {Nowak, M and Sigmund, K},
	doi = {10.1038/364056a0},
	isbn = {0028-0836},
	issn = {0028-0836},
	journal = {Nature},
	number = {6432},
	pages = {56--58},
	pmid = {8316296},
	title = {{A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner's Dilemma game.}},
	volume = {364},
	year = {1993}
}

@article{hunter2007matplotlib,
  title={Matplotlib: A 2D graphics environment},
  author={Hunter, John D},
  journal={Computing In Science \& Engineering},
  volume={9},
  number={3},
  pages={90--95},
  year={2007},
  publisher={IEEE}
}

@article{walt2011numpy,
  title={The NumPy array: a structure for efficient numerical computation},
  author={Walt, St{\'e}fan van der and Colbert, S Chris and Varoquaux, Gael},
  journal={Computing in Science \& Engineering},
  volume={13},
  number={2},
  pages={22--30},
  year={2011},
  publisher={IEEE}
}

@inproceedings{mckinney2010data,
  title={Data structures for statistical computing in python},
  author={McKinney, Wes and others},
  booktitle={Proceedings of the 9th Python in Science Conference},
  volume={445},
  pages={51--56},
  year={2010},
  organization={van der Voort S, Millman J}
}

@article{Ashlock2006,
	author = {Ashlock, Wendy and Ashlock, Daniel},
	isbn = {0780394879},
	mendeley-groups = {software{\_}projects/Axelrod},
	pages = {1001--1008},
	title = {{Changes in Prisoner ' s Dilemma Strategies Over Evolutionary Time With Different Population Sizes}},
	year = {2006}
}

@article{Hilbe2017,
    author = {Hilbe, Christian and Martinez-Vaquero, Luis A. and Chatterjee, Krishnendu and Nowak, Martin A.},
    doi = {10.1073/pnas.1621239114},
    issn = {0027-8424},
    journal = {Proceedings of the National Academy of Sciences},
    pages = {201621239},
    title = {{Memory- {\textless}i{\textgreater}n{\textless}/i{\textgreater} strategies of direct reciprocity}},
    howpublished = {http://www.pnas.org/lookup/doi/10.1073/pnas.1621239114},
    year = {2017}
}

@InCollection{Kuhn2017,
	author       =	{Kuhn, Steven},
	title        =	{Prisoner's Dilemma},
	booktitle    =	{The Stanford Encyclopedia of Philosophy},
	editor       =	{Edward N. Zalta},
	howpublished =	{\url{https://plato.stanford.edu/archives/spr2017/entries/prisoner-dilemma/}},
	year         =	{2017},
	edition      =	{Spring 2017},
	publisher    =	{Metaphysics Research Lab, Stanford University}
}

@article{Stewart2012,
    author = {Stewart, Alexander J. and Plotkin, Joshua B.},
    title = {Extortion and cooperation in the Prisoner’s Dilemma},
    volume = {109},
    number = {26},
    pages = {10134-10135},
    year = {2012},
    doi = {10.1073/pnas.1208087109},
    howpublished = {http://www.pnas.org/content/109/26/10134.short},
    eprint = {http://www.pnas.org/content/109/26/10134.full.pdf},
    journal = {Proceedings of the National Academy of Sciences}
}

@misc{Prison1998,
  author       = {LIFL},
  title        = {PRISON},
  year         = 2008,
  howpublished          = {http://www.lifl.fr/IPD/ipd.frame.html}
}

@article{Adami2013,
	abstract = {Zero-determinant strategies are a new class of probabilistic and conditional strategies that are able to unilaterally set the expected payoff of an opponent in iterated plays of the Prisoner's Dilemma irrespective of the opponent's strategy (coercive strategies), or else to set the ratio between the player's and their opponent's expected payoff (extortionate strategies). Here we show that zero-determinant strategies are at most weakly dominant, are not evolutionarily stable, and will instead evolve into less coercive strategies. We show that zero-determinant strategies with an informational advantage over other players that allows them to recognize each other can be evolutionarily stable (and able to exploit other players). However, such an advantage is bound to be short-lived as opposing strategies evolve to counteract the recognition.},
	archivePrefix = {arXiv},
	arxivId = {arXiv:1208.2666v4},
	author = {Adami, Christoph and Hintze, Arend},
	doi = {10.1038/ncomms3193},
	eprint = {arXiv:1208.2666v4},
	isbn = {2041-1723 (Electronic)$\backslash$r2041-1723 (Linking)},
	issn = {2041-1723},
	journal = {Nature communications},
	keywords = {Biological Evolution,Computer Simulation,Game Theory,Humans,Memory,Mutation,Mutation: genetics,Probability},
	number = {1},
	pages = {2193},
	pmid = {23903782},
	publisher = {Nature Publishing Group},
	title = {{Evolutionary instability of zero-determinant strategies demonstrates that winning is not everything.}},
	howpublished = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3741637{\&}tool=pmcentrez{\&}rendertype=abstract},
	volume = {4},
	year = {2013}
}

@article{Mathieu2015,
	author = {Mathieu, Philippe and Delahaye, Jean-Paul},
	isbn = {9781450337717},
	issn = {15582914},
	journal = {14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015)},
	keywords = {agent,game theory,group strategy,ipd,iterated prisoner,memory,opponent identification,s behaviour,s dilemma},
	pages = {1665--1666},
	title = {{New Winning Strategies for the Iterated Prisoner's Dilemma (Extended Abstract)}},
	year = {2015}
}

@article{Kraines1989,
  title={Pavlov and the prisoner's dilemma},
  author={Kraines, David and Kraines, Vivian},
  journal={Theory and decision},
  volume={26},
  number={1},
  pages={47--79},
  year={1989},
  publisher={Springer}
}

@article{Li2011,
    author = {Li, Jiawei and Hingston, Philip and Member, Senior and Kendall,
              Graham},
    number = {4},
    pages = {348--360},
    title = {{Engineering Design of Strategies for Winning Iterated Prisoner ' s
             Dilemma Competitions}},
    volume = {3},
    year = {2011}
}

@inproceedings{Berg2015,
  title={The importance of mechanisms for the evolution of cooperation},
  author={van den Berg, Pieter and Weissing, Franz J},
  booktitle={Proc. R. Soc. B},
  volume={282},
  number={1813},
  pages={20151382},
  year={2015},
  organization={The Royal Society}
}

@article{Andre2013,
  year={2013},
  title={Iterated Prisoner’s Dilemma-An extended analysis},
  author={Carvalho, Andre LC and Rocha, Honovan P and Amaral, Felipe T and Guimaraes, Frederico G}
}

@book{Axelrod1984,
  title={The evolution of cooperation},
  author={Axelrod, Robert M},
  year={2006},
  publisher={Basic books}
}

@article{Axelrod1980,
  title={Effective choice in the prisoner's dilemma},
  author={Axelrod, Robert},
  journal={Journal of conflict resolution},
  volume={24},
  number={1},
  pages={3--25},
  year={1980},
  publisher={Sage Publications Sage CA: Los Angeles, CA}
}

@article{Ashlock2008,
  title={Fingerprinting: Visualization and automatic analysis of prisoner's dilemma strategies},
  author={Ashlock, Daniel and Kim, Eun-Youn},
  journal={IEEE Transactions on Evolutionary Computation},
  volume={12},
  number={5},
  pages={647--659},
  year={2008},
  publisher={IEEE}
}

@article{Axelrod1980b,
    abstract = {This study reports and analyzes the results of the second round
                of the computer tournament for the iterated Prisoner's Dilemma.
                The object is to gain a deeper understanding of how to perform
                well in such a setting. The 62 entrants were able to draw
                lessons from the results of the first round and were able to
                design their entries to take these lessons into account. The
                results of the second round demonstrate a number of subtle
                pitfalls which specific types of decision rules can encounter.
                The winning rule was once again TIT FOR TAT, the rule which
                cooperates on the first move and then does what the other player
                did on the previous move. The analysis of the results shows the
                value of not being the first to defect, of being somewhat
                forgiving, but also the importance of being provocable. An
                analysis of hypothetical alternative tournaments demonstrates
                the robustness of the results.},
    author = {Axelrod, R.},
    doi = {10.1177/002200278002400301},
    isbn = {00220027},
    issn = {0022-0027},
    journal = {Journal of Conflict Resolution},
    number = {3},
    pages = {379--403},
    title = {{More Effective Choice in the Prisoner's Dilemma}},
    volume = {24},
    year = {1980}
}

@article{Axelrod1980b,
    abstract = {This study reports and analyzes the results of the second round
                of the computer tournament for the iterated Prisoner's Dilemma.
                The object is to gain a deeper understanding of how to perform
                well in such a setting. The 62 entrants were able to draw
                lessons from the results of the first round and were able to
                design their entries to take these lessons into account. The
                results of the second round demonstrate a number of subtle
                pitfalls which specific types of decision rules can encounter.
                The winning rule was once again TIT FOR TAT, the rule which
                cooperates on the first move and then does what the other player
                did on the previous move. The analysis of the results shows the
                value of not being the first to defect, of being somewhat
                forgiving, but also the importance of being provocable. An
                analysis of hypothetical alternative tournaments demonstrates
                the robustness of the results.},
    author = {Axelrod, R.},
    doi = {10.1177/002200278002400301},
    isbn = {00220027},
    issn = {0022-0027},
    journal = {Journal of Conflict Resolution},
    number = {3},
    pages = {379--403},
    title = {{More Effective Choice in the Prisoner's Dilemma}},
    volume = {24},
    year = {1980}
}

@article{Hilbe2013,
  title={Adaptive dynamics of extortion and compliance},
  author={Hilbe, Christian and Nowak, Martin A and Traulsen, Arne},
  journal={PloS one},
  volume={8},
  number={11},
  pages={e77886},
  year={2013},
  publisher={Public Library of Science}
}

@inproceedings{Beaufils1997,
  title={Our meeting with gradual, a good strategy for the iterated prisoner’s dilemma},
  author={Beaufils, Bruno and Delahaye, Jean-Paul and Mathieu, Philippe},
  booktitle={Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems},
  pages={202--209},
  year={1997}
}

@article{Tzafestas2000,
	author = {Tzafestas, E},
	journal = {From Animals to animals: Proceedings of the 6th International Conference on the Simulation of Adaptive Behavior {(SAB-2000)}},
	pages = {334--340},
	title = {{Toward adaptive cooperative behavior}},
	volume = {2},
	year = {2000}
}

@article{Ashlock2015,
  title={Multiple Opponent Optimization of Prisoner’s Dilemma Playing Agents},
  author={Ashlock, Daniel and Brown, Joseph Alexander and Hingston, Philip},
  journal={IEEE Transactions on Computational Intelligence and AI in Games},
  volume={7},
  number={1},
  pages={53--65},
  year={2015},
  publisher={IEEE}
}

@article{Press2012,
	abstract = {The two-player Iterated Prisoner's Dilemma game is a model for both sentient and evolutionary behaviors, especially including the emergence of cooperation. It is generally assumed that there exists no simple ultimatum strategy whereby one player can enforce a unilateral claim to an unfair share of rewards. Here, we show that such strategies unexpectedly do exist. In particular, a player X who is witting of these strategies can (i) deterministically set her opponent Y's score, independently of his strategy or response, or (ii) enforce an extortionate linear relation between her and his scores. Against such a player, an evolutionary player's best response is to accede to the extortion. Only a player with a theory of mind about his opponent can do better, in which case Iterated Prisoner's Dilemma is an Ultimatum Game.},
	author = {Press, William H and Dyson, Freeman J},
	doi = {10.1073/pnas.1206569109},
	isbn = {1091-6490 (Electronic)$\backslash$n0027-8424 (Linking)},
	issn = {1091-6490},
	journal = {Proceedings of the National Academy of Sciences of the United States of America},
	keywords = {Game Theory,Humans,Models, Psychological,Prisoners},
	number = {26},
	pages = {10409--13},
	pmid = {22615375},
	title = {{Iterated Prisoner's Dilemma contains strategies that dominate any evolutionary opponent.}},
	howpublished = {http://www.pnas.org/content/109/26/10409.abstract},
	volume = {109},
	year = {2012}
}

@article{Axelrod1995,
  title={How to cope with noise in the iterated prisoner's dilemma},
  author={Wu, Jianzhong and Axelrod, Robert},
  journal={Journal of Conflict resolution},
  volume={39},
  number={1},
  pages={183--189},
  year={1995},
  publisher={Sage Periodicals Press 2455 Teller Road, Thousand Oaks, CA 91320}
}

@article{Mittal2009,
	abstract = {In this paper, we present a new paradigm of searching optimal strategies in the game of iterated prisoner's dilemma (IPD) using multiple-objective evolutionary algorithms. This method is more useful than the existing approaches, because it not only produces strategies that perform better in the iterated game but also finds a family of nondominated strategies, which can be analyzed to decipher properties a strategy should have to win the game in a more satisfactory manner. We present the results obtained by this new method and discuss substrategies found to be common among nondominated strategies. The multiobjective treatment of the IPD problem demonstrated here can be applied to other similar game-playing tasks.},
	author = {Mittal, Shashi and Deb, Kalyanmoy},
	doi = {10.1109/TEVC.2008.2009459},
	isbn = {1424404649},
	issn = {1089778X},
	journal = {IEEE Transactions on Evolutionary Computation},
	keywords = {Evolutionary algorithms,Games,Multiobjective optimization,Prisoner's dilemma},
	number = {3},
	pages = {554--565},
	title = {{Optimal strategies of the iterated prisoner's dilemma problem for multiple conflicting objectives}},
	volume = {13},
	year = {2009}
}

@article{Li2009,
 abstract = {In recent iterated prisoner's dilemma tournaments, the most
             successful strategies were those that had identification
             mechanisms. By playing a predetermined sequence of moves and
             learning from their opponents' responses, these strategies managed
             to identify their opponents. We believe that these identification
             mechanisms may be very useful in evolutionary games. In this paper
             one such strategy, which we call collective strategy, is analyzed.
             Collective strategies apply a simple but efficient identification
             mechanism (that just distinguishes themselves from other
             strategies), and this mechanism allows them to only cooperate with
             their group members and defect against any others. In this way,
             collective strategies are able to maintain a stable population in
             evolutionary iterated prisoner's dilemma. By means of an invasion
             barrier, this strategy is compared with other strategies in
             evolutionary dynamics in order to demonstrate its evolutionary
             features. We also find that this collective behavior assists the
             evolution of cooperation in specific evolutionary environments.},
    author = {Li, Jiawei and Kendall, Graham},
    doi = {10.1162/evco.2009.17.2.257},
    isbn = {1063-6560},
    issn = {1063-6560},
    journal = {Evolutionary Computation},
    keywords = {Biological Evolution,Computer Simulation,Game
                Theory,Genetic,Models,Statistical},
    number = {2},
    pages = {257--274},
    pmid = {19413490},
    title = {{A strategy with novel evolutionary features for the iterated
             prisoner's dilemma.}},
    howpublished = {http://www.ncbi.nlm.nih.gov/pubmed/19413490},
    volume = {17},
    year = {2009}
}

@book{kendall2007iterated,
  title={The iterated prisoners' dilemma: 20 years on},
  author={Kendall, Graham and Yao, Xin and Chong, Siang Yew},
  volume={4},
  year={2007},
  publisher={World Scientific}
}

@article{Nachbar1992,
  title={Evolution in the finitely repeated prisoner's dilemma},
  author={Nachbar, John H},
  journal={Journal of Economic Behavior \& Organization},
  volume={19},
  number={3},
  pages={307--326},
  year={1992},
  publisher={Elsevier}
}

@misc{Eckhart2015,
  author       = {Eckhart Arnold},
  title        = { CoopSim v0.9.9 beta 6},
  year         = 2015,
  howpublished = {https://github.com/jecki/CoopSim/}
}

@inproceedings{Ashlock2006b,
  title={Changes in prisoner’s dilemma strategies over evolutionary time with different population sizes},
  author={Ashlock, Wendy and Ashlock, Daniel},
  booktitle={Evolutionary Computation, 2006. CEC 2006. IEEE Congress on},
  pages={297--304},
  year={2006},
  organization={IEEE}
}

@article{Gaudesi2016,
  title={Exploiting evolutionary modeling to prevail in iterated prisoner’s dilemma tournaments},
  author={Gaudesi, Marco and Piccolo, Elio and Squillero, Giovanni and Tonda, Alberto},
  journal={IEEE Transactions on Computational Intelligence and AI in Games},
  volume={8},
  number={3},
  pages={288--300},
  year={2016},
  publisher={IEEE}
}

@inproceedings{Ashlock2014,
  title={The evolution of exploitation},
  author={Ashlock, Wendy and Tsang, Jeffrey and Ashlock, Daniel},
  booktitle={Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on},
  pages={135--142},
  year={2014},
  organization={IEEE}
}

@misc{PD2017,
  author       = {Unknown},
  title        = {www.prisoners-dilemma.com},
  year         = 2017,
  howpublished          = {http://www.prisoners-dilemma.com/}
}

@article{Banks1990,
  title={Repeated games, finite automata, and complexity},
  author={Banks, Jeffrey S and Sundaram, Rangarajan K},
  journal={Games and Economic Behavior},
  volume={2},
  number={2},
  pages={97--117},
  year={1990},
  publisher={Elsevier}
}

@article{Frean1994,
  title={The prisoner's dilemma without synchrony},
  author={Frean, Marcus R},
  journal={Proceedings of the Royal Society of London B: Biological Sciences},
  volume={257},
  number={1348},
  pages={75--79},
  year={1994},
  publisher={The Royal Society}
}

@article{Robson1990,
  title={Efficiency in evolutionary games: Darwin, Nash and the secret handshake},
  author={Robson, Arthur J},
  journal={Journal of theoretical Biology},
  volume={144},
  number={3},
  pages={379--396},
  year={1990},
  publisher={Elsevier}
}

@inproceedings{Au2006,
  title={Accident or intention: that is the question (in the Noisy Iterated Prisoner's Dilemma)},
  author={Au, Tsz-Chiu and Nau, Dana},
  booktitle={Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems},
  pages={561--568},
  year={2006},
  organization={ACM}
}

@misc{dojo,
  author       = {Marc Harper and
                  Vince Knight and
                  Martin Jones and
                  Georgios Koutsovoulos},
  title        = {Axelrod-Python/axelrod-dojo: V0.0.2},
  month        = jul,
  year         = 2017,
  doi          = {10.5281/zenodo.832282},
  howpublished          = {https://doi.org/10.5281/zenodo.832282}
}

@article{turing1950computing,
  title={Computing machinery and intelligence},
  author={Turing, Alan M},
  journal={Mind},
  volume={59},
  number={236},
  pages={433--460},
  year={1950},
  publisher={JSTOR}
}

@article{moriarty1999evolutionary,
  title={Evolutionary algorithms for reinforcement learning},
  author={Moriarty, David E and Schultz, Alan C and Grefenstette, John J},
  journal={J. Artif. Intell. Res.(JAIR)},
  volume={11},
  pages={241--276},
  year={1999}
}

@article{imran2013overview,
  title={An overview of particle swarm optimization variants},
  author={Imran, Muhammad and Hashim, Rathiah and Khalid, Noor Elaiza Abd},
  journal={Procedia Engineering},
  volume={53},
  pages={491--496},
  year={2013},
  publisher={Elsevier}
}

@article{Bendor1991,
	author = {Bendor, Jonathan and Kramer, Roderick M and Stout, Suzanne},
	doi = {10.1177/0022002791035004007},
	issn = {0022-0027},
	journal = {Journal of Conflict Resolution},
	keywords = {*Cooperation,*Prisoners Dilemma Game,*White Noise,30,3020,3020 Group and Interpersonal Processes,Human},
	number = {4},
	pages = {691--719},
	pmid = {10334},
	title = {{When in doubt\dots: Cooperation in a noisy prisoner's dilemma}},
	volume = {35},
	year = {1991}
}

@article{Stephens2002,
	author = {Stephens, D W and McLinn, C M and Stevens, J R},
	doi = {10.1126/science.1078498},
	isbn = {1095-9203 (Electronic)$\backslash$n0036-8075 (Linking)},
	issn = {00368075},
	journal = {Science (New York, N.Y.)},
	number = {5601},
	pages = {2216--2218},
	pmid = {12481142},
	title = {{Discounting and reciprocity in an Iterated Prisoner's Dilemma.}},
	volume = {298},
	year = {2002}
}

@article{hunter2007matplotlib,
  title={Matplotlib: A 2D graphics environment},
  author={Hunter, John D},
  journal={Computing In Science \& Engineering},
  volume={9},
  number={3},
  pages={90--95},
  year={2007},
  publisher={IEEE}
}

@inproceedings{mckinney2010data,
  title={Data structures for statistical computing in python},
  author={McKinney, Wes and others},
  booktitle={Proceedings of the 9th Python in Science Conference},
  volume={445},
  pages={51--56},
  year={2010},
  organization={van der Voort S, Millman J}
}

@article{walt2011numpy,
  title={The NumPy array: a structure for efficient numerical computation},
  author={Walt, St{\'e}fan van der and Colbert, S Chris and Varoquaux, Gael},
  journal={Computing in Science \& Engineering},
  volume={13},
  number={2},
  pages={22--30},
  year={2011},
  publisher={IEEE}
}

@misc{data,
  author       = {Vincent Knight and Marc Harper},
  title        = {{Data for: Reinforcement Learning Produces Dominant Strategies for the Iterated Prisoner's Dilemma}},
  month        = jul,
  year         = 2017,
  doi          = {10.5281/zenodo.832287},
  howpublished          = {https://doi.org/10.5281/zenodo.832287}
}