Discovery and Diffusion of Knowledge in an Endogenous Social Network

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractWe explore the evolution of the structure and performance of a social network in a population of individuals who search for local optima in diverse and dynamic task environments. Individuals choose whether to innovate or imitate and, in the latter case, from whom to learn. The probabilities of these possible actions respond to an individual's past experiences using reinforcement learning. Among some of our more interesting findings is that a population's performance is not monotonically increasing in either the reliability of the communication network or the productivity of innovation.

Chapter 21 ACE Models of Endogenous Interactions

“…Interaction probabilities between agents i and j . Source: Chang and Harrington (2005). sis shows that this property is stronger the more groups are similar within and different from other groups.…”

“…The latter ask lower prices, experience nevertheless a higher rejection rate, maintain a lower supply/sales ratio, leading to a lower service rate, and put loyal customers towards the end of the queue. Chang and Harrington (2005): past success rate Chang and Harrington (2005) study the issue of discovery and diffusion of knowledge, and the social networks that may thus arise.…”

“…Each individual agent has an optimal, target vector that describes the optimal way to solve all his tasks. Chang and Harrington (2005) assume that individual agents, although they can not simply pick the optimal method themselves, can rank any two method vectors on their Hamming distance from their target vector (which is effectively the number of bits that is different in the two vectors). The individual target vectors may change over time following some pre-specified dynamic process.…”

“…The weight for a given agent in the population is increased each time that agent has been imitated successfully, and it decreases through some decay. Chang and Harrington (2005) focus on the properties of the emerging social networks. In much of their analysis they partition the population into a fixed number of groups to get some persistent similarity in goals, as the dynamics of the target vectors of all agents within a group follow some stochastic process (modeling turbulence in the task environment) such that they stay within certain bounds.…”

Integrating Knowledge from Network: How Explorative/Exploitative Innovations are Balanced

“…Social network as the source for organizations to access knowledge has drawn much research effort in recent years [33], and cross-level concerns have drawn attention as well, especially the intervention of every level has caused research concern [2,47]. On the other hand, the relationship between knowledge integration and innovation has caught little yet critical insight in research.…”