Sherratt T N, Roberts G
Department of Biological Sciences, University of Durham, U.K.
J Theor Biol. 1998 Jul 7;193(1):167-77. doi: 10.1006/jtbi.1998.0703.
In this paper we present a resource-explicit Donor-Receiver model for reciprocally altruistic interactions that obeys the defining inequalities of the Prisoner's Dilemma. In our model, individuals vary in the quantity of resource they invest when cooperating (termed "generosity") and they have the freedom to opt out of interactions with potential partners on the basis of their past experiences with these players (termed "choosiness"). Dynamic optimal solutions were found using a genetic algorithm in which the decision rules (cooperate or defect), generosity when cooperating, and choosiness exhibited by individuals when deciding to opt out, were all coded on genes held on two separate chromosomes. Through this genetic algorithm, individuals that had alleles which resulted in greatest success at playing our modified Prisoner's Dilemma left more offspring. When the benefit of receiving a unit resource exceeded the cost of giving, then generous cooperative behaviour tended to emerge within the population, even when the alleles of all the individuals in the starting population were set to defect. When the probability of individuals re-encountering one another was increased, individuals not only cooperated more, but they developed greater generosity. However, as the ratio of the benefits received to costs expended increased above 1, individuals in this model remained highly cooperative but their median generosity decreased significantly. In contrast to earlier studies using genetic algorithms, the extra potential for cheating afforded by asymmetrical degrees of generosity meant that genuinely cooperative behaviour did not emerge in the equivalent round-robin tournament in which individuals were not able to exercise partner preference.
在本文中,我们提出了一种资源明确的捐赠者-接受者模型,用于相互利他的互动,该模型遵循囚徒困境的定义不等式。在我们的模型中,个体在合作时投入的资源数量(称为“慷慨程度”)各不相同,并且他们可以根据过去与这些潜在伙伴的交往经验自由选择退出与他们的互动(称为“挑剔程度”)。我们使用遗传算法找到了动态最优解,其中决策规则(合作或背叛)、合作时的慷慨程度以及个体在决定退出时表现出的挑剔程度,都编码在两条独立染色体上的基因中。通过这种遗传算法,那些拥有能在我们修改后的囚徒困境游戏中取得最大成功的等位基因的个体留下了更多后代。当获得单位资源的收益超过给予的成本时,即使初始群体中所有个体的等位基因都设定为背叛,群体中也往往会出现慷慨的合作行为。当个体再次相遇的概率增加时,个体不仅合作得更多,而且变得更加慷慨。然而,当获得的收益与付出的成本之比超过1时,该模型中的个体仍然高度合作,但他们的平均慷慨程度显著下降。与早期使用遗传算法的研究不同,慷慨程度的不对称所带来的额外作弊可能性意味着,在个体无法行使伙伴偏好的等效循环赛中,真正的合作行为不会出现。