SLC Catalyst: Learning in Exchange Systems: Human and Machine Interactions

商品、服务和信息的交换在日常生活中无处不在。特别是，社会和工作场所中的个人之间的互动以交换为中心。交换系统起源古老，在不同的人类文化中普遍存在，并且与生物和生态过程一起是自我排序复杂系统的例子。今年将举办两个研讨会，重点是组织、市场和社会的学习和交流。 通过交换过程理解和扩展思维能力，可以对个人和组织如何分配其稀缺资源产生巨大影响，并可以更好地利用其资源来扩大机会。 这些研讨会将汇集正在开发人工学习新的跨学科算法和方法的研究人员，以及那些试图理解学习方面的研究人员，因为他们将沿着神经科学应用于市场设计和协调（认知和经济学）与个人和非个人的交换有关。工作坊将研究软件代理的设计和使用，数据可视化和机器学习，以扩大交换系统的范围。 特别是，全球化的到来和分布式通信系统的扩散使得交换机构的设计成为经济的一个重要方面。信息的传递和呈现方式可以帮助分布式学习系统。此外，机构规则提供的激励可能会影响分布式网络中提供的信息的质量，以及网络中决策的强化学习。如果没有一个能够及时收集和分发信息的交换机构，大脑的稀缺资源将被用来寻找贸易伙伴和有关价值权衡的信息。市场是心智的延伸，这一观点现在可以在专业化和学习分工中得到补充。随着交换发生的环境变得更加复杂和分布式，使用计算机算法来帮助人类将他们的学习集中在特定的大回报/小成本领域可以促进交换。 这就提出了一个问题，即软件代理何时负责一个事务。软件代理可以强制执行事务吗？ 软件代理能否相互协调，以找到以某种方式表示给人类同行的解决方案？

The exchange of goods, services and information is pervasive in everyday life. In particular, interactions among individuals in society and workplaces are centered on exchange. Exchange systems are ancient in origin, universal across disparate human cultures, and-along with biological and ecological processes-are examples of self ordering complex systems. Two workshops will be held this year that will focus on learning and exchange in organizations, markets and society in general. Understanding and extending the capacity of the mind through exchange processes can have a tremendous impact on how individuals and organizations allocate their scarce resources and to can better utilize their resources to expand opportunities. These workshops will bringing together researchers who are developing new interdisciplinary algorithms and methodologies for artificial learning and those who are trying to understand aspects of learning as they apply to market design and coordination along with the neuroscience (cognitive and economics) associated with personal and impersonal exchange.The workshops will investigate the design and use of software agents, data visualization and machine learning to expand the scope of exchange systems. IN particular, the advent of globalization and the proliferation of distributed communication systems have made the design of exchange institutions a vital aspect of the economy. The transfer of information and the way it is presented can assist in distributed learning systems. In addition, the incentives provided by an institution's rules can affect the quality of the information provided in a distributed network and reinforcement learning from the decisions made in the network. Without an exchange institution that aggregates information and distributes it in a timely matter, the scarce resources of the brain would be used to try and find trading partners and information concerning value trade-offs. This idea of the market as an extension of mind can now be supplemented with a unique function in the specialization and division of learning. As the environments in which exchange occurs become more complex and distributed, the use of computer algorithms to assist humans in focusing their learning to specific large reward/small cost areas can facilitate exchange. These raise question concerning when a software agent responsible for a transaction. Can a software agent enforce a transaction? Can software agents coordinate among each other to find solutions that are somehow represented to their human counterparts?