Rupprecht Nathaniel, Vural Dervis Can
Department of Physics, University of Notre Dame, South Bend, Indiana, United States of America.
PLoS One. 2017 Oct 24;12(10):e0186785. doi: 10.1371/journal.pone.0186785. eCollection 2017.
Theoretical models of populations and swarms typically start with the assumption that the motion of agents is governed by the local stimuli. However, an intelligent agent, with some understanding of the laws that govern its habitat, can anticipate the future, and make predictions to gather resources more efficiently. Here we study a specific model of this kind, where agents aim to maximize their consumption of a diffusing resource, by attempting to predict the future of a resource field and the actions of other agents. Once the agents make a prediction, they are attracted to move towards regions that have, and will have, denser resources. We find that the further the agents attempt to see into the future, the more their attempts at prediction fail, and the less resources they consume. We also study the case where predictive agents compete against non-predictive agents and find the predictors perform better than the non-predictors only when their relative numbers are very small. We conclude that predictivity pays off either when the predictors do not see too far into the future or the number of predictors is small.
种群和群体的理论模型通常始于这样一种假设，即个体的运动受局部刺激的支配。然而，一个对支配其栖息地的规律有所理解的智能个体能够预测未来，并做出预测以便更高效地收集资源。在此，我们研究此类的一个具体模型，其中个体旨在通过尝试预测资源场的未来以及其他个体的行动来最大化其对扩散资源的消耗。一旦个体做出预测，它们就会被吸引朝着当前拥有且未来会拥有更密集资源的区域移动。我们发现，个体尝试预测未来的时间跨度越长，其预测尝试失败得就越多，消耗的资源也就越少。我们还研究了有预测能力的个体与无预测能力的个体竞争的情况，发现只有当有预测能力个体的相对数量非常少时，它们的表现才会优于无预测能力的个体。我们得出结论，当有预测能力的个体对未来的预测跨度不大或者其数量较少时，预测能力才会带来回报。
