Integrating Different Perspectives on Social Learning

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Social learning is the cornerstone of human culture, underlying important social processes such as spread of beliefs, social norms, and collective decision making. A number of disciplines investigate how individuals learn from each other, often asking similar questions but being unaware of promising methods and established results in other fields. For instance, conformity has been studied in evolutionaryanthropology, animal learning, statistical physics, social psychology, political science, and other fields; but there are few cross-citations between their often overlapping findings. This workshop will bring together leading researchers studying social learning within different fields. Besides theoretical integration and progress, the workshop will inform pressing current questions on the circumstances that promote or restrict social learning. Effective social learning is important for human culture, but the same processes can also lead to spread of erroneous beliefs, damaging social norms, and faulty collective decision making. Understanding the factors that promote or inhibit social learning processes also has important management implications. Many different research communities investigate how individuals learn from each other and, consequently, how beliefs spread in societies. These communities include cognitive and social psychology, sociology and social network science, biology and animal learning, evolutionary anthropology and political science, computer science, statistical physics, organization and management science. Each of these perspectives has been extremely productive and useful, but their findings were often unnoticed in other fields. Even though they often investigate similar questions, they use different methodologies and vocabularies. The workshop will enable theoretical integration of previously disconnected strands of research on social learning in different disciplines. One promising direction is the integration between cognitive psychology, organization science, network science, and statistical physics to investigate determinants and processes supporting the emergence of collective intelligence. Another promising direction is development of new artificial learning algorithms, based on insights from theories of social influence developed in social psychology. These and other directions will be discussed and discovered during the workshop.