Editing Social learning theory (section)

=== Algorithm for computer optimization ===
In modern field of computational intelligence, the social learning theory is adopted to develop a new computer optimization algorithm, the social learning algorithm.<ref name=":0">{{Cite journal|url = http://www.ai.sysu.edu.cn/GYJ/sla/sla_smc14.pdf|title = From the Social Learning Theory to a social learning algorithm for global optimization|last = Gong|first = Yue-Jiao|date = 2014-10-11|journal = Systems, Man and Cybernetics}}</ref> Emulating the observational learning and reinforcement behaviors, a virtual society deployed in the algorithm seeks the strongest behavioral patterns with the best outcome. This corresponds to searching for the best solution in solving optimization problems. Compared with other bio-inspired global optimization algorithms that mimic natural evolution or animal behaviors, the social learning algorithm has its prominent advantages. First, since the self-improvement through learning is more direct and rapid than the evolution process, the social learning algorithm can improve the efficiency of the algorithms mimicking natural evolution. Second, compared with the interaction and learning behaviors in animal groups, the social learning process of human beings exhibits a higher level of intelligence. By emulating human learning behaviors, it is possible to arrive at more effective optimizers than existing swarm intelligence algorithms. Experimental results have demonstrated the effectiveness and efficiency of the social learning algorithm, which has in turn also verified through computer simulations the outcomes of the social learning behavior in human society.<ref name=":0" />

Another example is the [[social cognitive optimization]], which is a population-based metaheuristic optimization algorithm. This algorithm is based on the [[social cognitive theory]], simulating the process of individual learning of a set of agents with their own memory and their social learning with the knowledge in the social sharing library. It has been used for solving [[continuous optimization]], [[integer programming]], and [[combinatorial optimization]] problems.

There also several [[mathematical models of social learning]] which try to model this phenomenon using probabilistic tools.