Individual versus social learning: Evolutionary analysis in a fluctuating environment
Individual versus social learning: Evolutionary analysis in a fluctuating environment
复制标题
DOI：
10.1537/ase.104.209
复制
发表时间：
1996-07-01
影响因子：
0.7
通讯作者：
Kumm, J
中科院分区：
文献类型：
--
作者：
Feldman, MW;Aoki, K;Kumm, J
提出了一个社会和个体学习的单倍体无性遗传模型。一种基因型的动物，个体学习者（IL），在当前环境中表现最佳，除了由于学习错误而产生的固定成本外，在该环境中具有最佳适应性。另一种基因型的动物是社会学习者（SL），每一种动物都从上一代随机复制一个个体。然而，社会学习者的表型取决于它模仿的对象。如果它复制了IL或行为正确的SL，它就具有“正确”的表型SLC。不同的环境波动模型对SL动物的出现频率有不同的影响。无限状态环境是这样的，当它改变时，它永远不会恢复到早期的状态。如果每一代人都在改变，社会学习就永远不会成功。然而，如果环境改变的一代之后是L-1代环境停滞，并且l大于或等于3，则某些适应度集确实允许社会学习的维持。对于随机波动的环境和循环的双态环境，也有类似的结果。在第二种模型中，每只动物都能以概率L单独学习。我们研究这种概率在无限状态环境中的演化稳定性。当一代的变化之后是L-1代的停滞，可以找到适应度参数，产生一个进化稳定的非零概率的社会学习。在所有的模型中，环境变化的可能性越大，社会学习就越难进化。
A model for haploid asexual inheritance of social and individual learning is proposed. Animals of one genotype, individual learners (IL), behave optimally for the current environment and, except for a fixed cost due to learning errors, have the optimal fitness in that environment. Animals of the other genotype are social learners (SL) each of whom copies a random individual from the previous generation. However, the phenotype of a social learner depends on whom it copies. If it copies an IL or a correctly behaving SL, it has the ''correct'' phenogenotype, SLC. Otherwise, its behavior is wrong and we call its phenogenotype SLW.Different models for the environmental fluctuation produce different dynamics for the frequency of SL animals. An infinite state environment is such that when it changes, it never reverts to an earlier state. If it changes every generation, social learning can never succeed. If, however, a generation in which the environment changes is followed by L-1 generations of environmental stasis and l greater than or equal to 3, some fitness sets do allow the maintenance of social learning. Analogous results are shown for a randomly fluctuating environment, and for cyclic two-state environments.In a second type of model, each animal can learn individually with probability L. We examine the evolutionary stability properties of this probability in the infinite state environment. When a generation of change is followed by L-1 generations of stasis, fitness parameters can be found that produce an evolutionarily stable nonzero probability of social learning. In all of the models treated, the greater the probability of environmental change, the more difficult it is for social learning to evolve.