Scientific Collaboration Analytics based on Multi-Player Game Theory

Nakema Deonauth, Xiangtai Chen, Mingchu Li
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Abstract

Exploring behavioral patterns amongst scholars in Academic Social Networks (ASNs) has attracted significant attention in the field of social computing in recent years. Scholars are faced with a dilemma in which they either opt to cooperate with others or to spend their limited resources for their own individual gain. These interactions and decisions can be modeled using Game Theory. Recent research has shown that favorable longterm strategies are characterized as being reciprocal, forgiving, and flexible. Little focus has been given to the phenomenon of Upstream Reciprocity (UR), which occurs when a person who has just received help, in turn, offers help to another, in a ‘pay it forward’ manner. This paper provides a framework to model scholarly interactions of ASNs using an iterative multi-player game and investigates the occurrence of UR amongst players. The presence of UR amongst scholars is observed and shows to possess a higher publication and citation count by those who adopt it. Moreover, cascades of UR have been identified to replicate throughout the network, thereby increasing further adoption of this behavior. More importantly theoretical proof is provided that suggests that UR has the potential to become an Evolutionary Stable Strategy (ESS).
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基于多人博弈理论的科学协作分析
近年来,学者在学术社交网络(Academic Social Networks, ASNs)中的行为模式研究引起了社会计算领域的广泛关注。学者们面临着一个两难的选择:要么选择与他人合作,要么将有限的资源用于自己的个人利益。这些互动和决策可以用博弈论来建模。最近的研究表明,有利的长期策略具有互惠、宽容和灵活的特点。很少有人关注上游互惠(UR)现象,即刚刚得到帮助的人反过来以“传递”的方式向另一个人提供帮助。本文提供了一个框架,使用迭代的多玩家游戏来模拟人工神经网络的学术互动,并研究了玩家之间UR的发生。UR在学者中的存在被观察到,并且表明那些采用UR的人拥有更高的出版物和引用数。此外,已经确定UR级联在整个网络中复制,从而进一步增加了这种行为的采用。更重要的是提供了理论证据,表明UR有可能成为一种进化稳定策略(ESS)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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