Prioritised Learning in Snowdrift-Type Games

Mathematics

JerzyA. Filar¹  SabrinaS. Streipert²  Maria Kleshnina³  Krishnendu Chatterjee³ 

[1] Centre for Applications in Natural Resource Mathematics, School of Mathematics and Physics, University of Queensland, St Lucia, QLD 4072, Australia;Department of Mathematics and Statistics, McMaster University, 1280 Main St W, Hamilton, ON L8S 4L8, Canada;Institute of Science and Technology Austria (IST Austria), Am Campus 1, 3400 Klosterneuburg, Austria;

关键词: incompetence;    evolutionary games;    learning;    cooperation;    snowdrift game;   

DOI  :  10.3390/math8111945

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown