{"license":"https://creativecommons.org/licenses/by/4.0/","date_created":"2023-01-16T10:02:51Z","external_id":{"pmid":["35700167"],"isi":["000843626800031"]},"publisher":"Public Library of Science","isi":1,"oa":1,"file_date_updated":"2023-01-30T11:28:13Z","date_updated":"2023-08-04T10:27:08Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"ec_funded":1,"keyword":["Computational Theory and Mathematics","Cellular and Molecular Neuroscience","Genetics","Molecular Biology","Ecology","Modeling and Simulation","Ecology","Evolution","Behavior and Systematics"],"publication_identifier":{"eissn":["1553-7358"]},"acknowledgement":"This work was supported by the European Research Council (https://erc.europa.eu/)\r\nCoG 863818 (ForM-SMArt) (to K.C.), and the European Research Council Starting Grant 850529: E-DIRECT (to C.H.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","quality_controlled":"1","department":[{"_id":"KrCh"}],"volume":18,"article_processing_charge":"No","type":"journal_article","issue":"6","doi":"10.1371/journal.pcbi.1010149","article_number":"e1010149","status":"public","article_type":"original","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","intvolume":"        18","publication":"PLOS Computational Biology","project":[{"call_identifier":"H2020","grant_number":"863818","name":"Formal Methods for Stochastic Models: Algorithms and Applications","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E"}],"publication_status":"published","year":"2022","file":[{"success":1,"access_level":"open_access","content_type":"application/pdf","checksum":"31b6b311b6731f1658277a9dfff6632c","date_created":"2023-01-30T11:28:13Z","file_name":"2022_PlosCompBio_Schmid.pdf","creator":"dernst","relation":"main_file","date_updated":"2023-01-30T11:28:13Z","file_size":3143222,"file_id":"12460"}],"language":[{"iso":"eng"}],"month":"06","citation":{"ieee":"L. Schmid, C. Hilbe, K. Chatterjee, and M. Nowak, “Direct reciprocity between individuals that use different strategy spaces,” <i>PLOS Computational Biology</i>, vol. 18, no. 6. Public Library of Science, 2022.","apa":"Schmid, L., Hilbe, C., Chatterjee, K., &#38; Nowak, M. (2022). Direct reciprocity between individuals that use different strategy spaces. <i>PLOS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1010149\">https://doi.org/10.1371/journal.pcbi.1010149</a>","short":"L. Schmid, C. Hilbe, K. Chatterjee, M. Nowak, PLOS Computational Biology 18 (2022).","chicago":"Schmid, Laura, Christian Hilbe, Krishnendu Chatterjee, and Martin Nowak. “Direct Reciprocity between Individuals That Use Different Strategy Spaces.” <i>PLOS Computational Biology</i>. Public Library of Science, 2022. <a href=\"https://doi.org/10.1371/journal.pcbi.1010149\">https://doi.org/10.1371/journal.pcbi.1010149</a>.","ista":"Schmid L, Hilbe C, Chatterjee K, Nowak M. 2022. Direct reciprocity between individuals that use different strategy spaces. PLOS Computational Biology. 18(6), e1010149.","ama":"Schmid L, Hilbe C, Chatterjee K, Nowak M. Direct reciprocity between individuals that use different strategy spaces. <i>PLOS Computational Biology</i>. 2022;18(6). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1010149\">10.1371/journal.pcbi.1010149</a>","mla":"Schmid, Laura, et al. “Direct Reciprocity between Individuals That Use Different Strategy Spaces.” <i>PLOS Computational Biology</i>, vol. 18, no. 6, e1010149, Public Library of Science, 2022, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1010149\">10.1371/journal.pcbi.1010149</a>."},"abstract":[{"lang":"eng","text":"In repeated interactions, players can use strategies that respond to the outcome of previous rounds. Much of the existing literature on direct reciprocity assumes that all competing individuals use the same strategy space. Here, we study both learning and evolutionary dynamics of players that differ in the strategy space they explore. We focus on the infinitely repeated donation game and compare three natural strategy spaces: memory-1 strategies, which consider the last moves of both players, reactive strategies, which respond to the last move of the co-player, and unconditional strategies. These three strategy spaces differ in the memory capacity that is needed. We compute the long term average payoff that is achieved in a pairwise learning process. We find that smaller strategy spaces can dominate larger ones. For weak selection, unconditional players dominate both reactive and memory-1 players. For intermediate selection, reactive players dominate memory-1 players. Only for strong selection and low cost-to-benefit ratio, memory-1 players dominate the others. We observe that the supergame between strategy spaces can be a social dilemma: maximum payoff is achieved if both players explore a larger strategy space, but smaller strategy spaces dominate."}],"oa_version":"Published Version","date_published":"2022-06-14T00:00:00Z","title":"Direct reciprocity between individuals that use different strategy spaces","author":[{"full_name":"Schmid, Laura","last_name":"Schmid","id":"38B437DE-F248-11E8-B48F-1D18A9856A87","first_name":"Laura","orcid":"0000-0002-6978-7329"},{"full_name":"Hilbe, Christian","last_name":"Hilbe","orcid":"0000-0001-5116-955X","first_name":"Christian","id":"2FDF8F3C-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Chatterjee","orcid":"0000-0002-4561-241X","first_name":"Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","full_name":"Chatterjee, Krishnendu"},{"full_name":"Nowak, Martin","first_name":"Martin","last_name":"Nowak"}],"pmid":1,"scopus_import":"1","day":"14","has_accepted_license":"1","ddc":["000","570"],"_id":"12280"}