The social learning process plays a key role in the emergence of collective intelligence in our society. The recent development of computational frameworks with cognitive modeling has enabled us to mathematically track how people combine their personal experiences and social information. In this article, we first present several variations of social learning processes and the situations in which social learning can be beneficial for each individual. Next, we outline a game-theoretic dilemma that arises from the interdependence between individuals who constitutes a group. As in the “tragedy of the commons” in social dilemmas, rational self-interested individuals could exploit others’ exploratory findings through social learning while behaving as a free-rider in information search. We review how groups of individuals can overcome this challenge and achieve collective intelligence. Finally, we demonstrate how large collectives of rational individuals may spread inaccurate information on the Internet and cause unpredictability in society, such as the diffusion of false information or information cascade. We discuss two possible ways to counter such unintended maladaptive problems in a large-scale society: nudges and algorithmic backups. There have been many works that shed light on how various nudge techniques can mitigate the madness of crowds. Although these efforts are certainly helpful, we argue that interventions based on deeper understanding about algorithms of human decision making may provide more fundamental aid to prevent the spread of false information in our society.
