Integrating Different Perspectives on Social Learning

社会学习是人类文化的基石，是重要的社会过程的基础，如信仰的传播，社会规范和集体决策。许多学科研究个体如何相互学习，经常提出类似的问题，但不知道其他领域有前途的方法和既定的结果。例如，在进化人类学、动物学习、统计物理学、社会心理学、政治学和其他领域，人们都对从众进行了研究;但在他们经常重叠的发现之间，很少有交叉引用。本次研讨会将汇集不同领域研究社会学习的领先研究人员。除了理论整合和进步，研讨会还将告知当前促进或限制社会学习的紧迫问题。有效的社会学习对人类文化很重要，但同样的过程也会导致错误信念的传播，破坏社会规范和错误的集体决策。了解促进或抑制社会学习过程的因素也具有重要的管理意义。 许多不同的研究团体调查个人如何相互学习，以及信仰如何在社会中传播。 这些社区包括认知和社会心理学，社会学和社会网络科学，生物学和动物学习，进化人类学和政治学，计算机科学，统计物理学，组织和管理科学。每一种观点都是非常有成效和有用的，但他们的发现在其他领域往往被忽视。尽管他们经常调查类似的问题，但他们使用不同的方法和词汇。该研讨会将使以前在不同学科的社会学习研究脱节链的理论整合。一个有前途的方向是整合认知心理学，组织科学，网络科学和统计物理学，研究支持集体智慧出现的决定因素和过程。另一个有前途的方向是开发新的人工学习算法，基于社会心理学中发展的社会影响理论的见解。这些和其他方向将在研讨会期间讨论和发现。

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.