{"type":"journal_article","has_accepted_license":"1","date_updated":"2023-08-22T13:25:45Z","volume":8,"publication_identifier":{"eissn":["22277390"]},"isi":1,"_id":"8789","doi":"10.3390/math8111945","author":[{"id":"4E21749C-F248-11E8-B48F-1D18A9856A87","last_name":"Kleshnina","first_name":"Maria","full_name":"Kleshnina, Maria"},{"first_name":"Sabrina","full_name":"Streipert, Sabrina","last_name":"Streipert"},{"last_name":"Filar","first_name":"Jerzy","full_name":"Filar, Jerzy"},{"orcid":"0000-0002-4561-241X","first_name":"Krishnendu","full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","last_name":"Chatterjee"}],"abstract":[{"text":"Cooperation is a ubiquitous and beneficial behavioural trait despite being prone to exploitation by free-riders. Hence, cooperative populations are prone to invasions by selfish individuals. However, a population consisting of only free-riders typically does not survive. Thus, cooperators and free-riders often coexist in some proportion. An evolutionary version of a Snowdrift Game proved its efficiency in analysing this phenomenon. However, what if the system has already reached its stable state but was perturbed due to a change in environmental conditions? Then, individuals may have to re-learn their effective strategies. To address this, we consider behavioural mistakes in strategic choice execution, which we refer to as incompetence. Parametrising the propensity to make such mistakes allows for a mathematical description of learning. We compare strategies based on their relative strategic advantage relying on both fitness and learning factors. When strategies are learned at distinct rates, allowing learning according to a prescribed order is optimal. Interestingly, the strategy with the lowest strategic advantage should be learnt first if we are to optimise fitness over the learning path. Then, the differences between strategies are balanced out in order to minimise the effect of behavioural uncertainty.","lang":"eng"}],"article_number":"1945","publication":"Mathematics","publication_status":"published","department":[{"_id":"KrCh"}],"acknowledgement":"This work was supported by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement #754411, the Australian Research Council Discovery Grants DP160101236 and DP150100618, and the European Research Council Consolidator Grant 863818 (FoRM-SMArt).\r\nAuthors would like to thank Patrick McKinlay for his work on the preliminary results for this paper.","article_processing_charge":"No","month":"11","date_created":"2020-11-22T23:01:24Z","citation":{"mla":"Kleshnina, Maria, et al. “Prioritised Learning in Snowdrift-Type Games.” <i>Mathematics</i>, vol. 8, no. 11, 1945, MDPI, 2020, doi:<a href=\"https://doi.org/10.3390/math8111945\">10.3390/math8111945</a>.","ama":"Kleshnina M, Streipert S, Filar J, Chatterjee K. Prioritised learning in snowdrift-type games. <i>Mathematics</i>. 2020;8(11). doi:<a href=\"https://doi.org/10.3390/math8111945\">10.3390/math8111945</a>","short":"M. Kleshnina, S. Streipert, J. Filar, K. Chatterjee, Mathematics 8 (2020).","ieee":"M. Kleshnina, S. Streipert, J. Filar, and K. Chatterjee, “Prioritised learning in snowdrift-type games,” <i>Mathematics</i>, vol. 8, no. 11. MDPI, 2020.","chicago":"Kleshnina, Maria, Sabrina Streipert, Jerzy Filar, and Krishnendu Chatterjee. “Prioritised Learning in Snowdrift-Type Games.” <i>Mathematics</i>. MDPI, 2020. <a href=\"https://doi.org/10.3390/math8111945\">https://doi.org/10.3390/math8111945</a>.","ista":"Kleshnina M, Streipert S, Filar J, Chatterjee K. 2020. Prioritised learning in snowdrift-type games. Mathematics. 8(11), 1945.","apa":"Kleshnina, M., Streipert, S., Filar, J., &#38; Chatterjee, K. (2020). Prioritised learning in snowdrift-type games. <i>Mathematics</i>. MDPI. <a href=\"https://doi.org/10.3390/math8111945\">https://doi.org/10.3390/math8111945</a>"},"external_id":{"isi":["000593962100001"]},"project":[{"call_identifier":"H2020","name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425"},{"_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","grant_number":"863818","name":"Formal Methods for Stochastic Models: Algorithms and Applications","call_identifier":"H2020"}],"status":"public","oa":1,"file_date_updated":"2020-11-23T13:06:30Z","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","ddc":["000"],"language":[{"iso":"eng"}],"file":[{"checksum":"61cfcc3b35760656ce7a9385a4ace5d2","date_created":"2020-11-23T13:06:30Z","relation":"main_file","file_name":"2020_Mathematics_Kleshnina.pdf","success":1,"access_level":"open_access","date_updated":"2020-11-23T13:06:30Z","file_id":"8797","creator":"dernst","content_type":"application/pdf","file_size":565191}],"intvolume":"         8","scopus_import":"1","quality_controlled":"1","title":"Prioritised learning in snowdrift-type games","oa_version":"Published Version","issue":"11","tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"day":"04","article_type":"original","year":"2020","publisher":"MDPI","ec_funded":1,"date_published":"2020-11-04T00:00:00Z"}