论文标题
复杂的任务环境中的动态联盟：改变还是不改变获胜的团队？
Dynamic coalitions in complex task environments: To change or not to change a winning team?
论文作者
论文摘要
决策者通常会面临复杂的任务，这些任务无法由个人解决，但需要以联盟的形式进行协作。以前的文献认为，就随着时间的推移而对其成员进行了重组，不稳定是对绩效有害的。其他研究线（例如动态功能框架）挑战了这一观点。我们的目标是了解不稳定对解决复杂任务的联盟表现的影响。为此，我们将NK模型适应联盟人类决策的背景，并引入基于拍卖的自主联盟形成机制和人类代理人的学习机制。初步结果表明，重组创新且表现出色的团队是有益的，但这仅在某些情况下才是正确的。
Decision makers are often confronted with complex tasks which cannot be solved by an individual alone, but require collaboration in the form of a coalition. Previous literature argues that instability, in terms of the re-organization of a coalition with respect to its members over time, is detrimental to performance. Other lines of research, such as the dynamic capabilities framework, challenge this view. Our objective is to understand the effects of instability on the performance of coalitions which are formed to solve complex tasks. In order to do so, we adapt the NK-model to the context of human decision-making in coalitions, and introduce an auction-based mechanism for autonomous coalition formation and a learning mechanism for human agents. Preliminary results suggest that re-organizing innovative and well-performing teams is beneficial, but that this is true only in certain situations.
