Technology as an Autonomous Socio-Cognitive System

Stankiewicz, Rikard

doi:10.1007/978-3-642-86467-4_2

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…'technology' encompasses applied research activities or research carried out by industries and private organizations (Grupp 1996;Schmoch 1997), it deals with the body of knowledge about techniques, and is characterized by conception and development of products with market demand for the services it diffuses (Stankiewics 1992;Grupp 1996). Modeling the behavior of science and technology requires good proxy of measurements.…”

mentioning

confidence: 99%

Modeling the behaviour of science and technology: self-propagating growth in the diffusion process

Wong

Goh

2010

Scientometrics

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Through theoretical analysis and empirical demonstration, this paper attempts to model the behavior of science and technology by investigating the self-propagating behavior of their diffusion for South Korea, Malaysia and Japan. The dynamics of the selfpropagating behavior were examined using the logistic growth function within a dynamic carrying capacity, while allowing for different effectiveness of potential influence of science and technology producers on potential adopters. Evidence suggests that the selfpropagating growth function is particularly relevant for countries with advanced science and technology, like Japan. While self-propagating growth was also found for South Korea, the diffusion process remained fairly static for Malaysia.

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mentioning

confidence: 99%

Modeling the behaviour of science and technology: self-propagating growth in the diffusion process

Wong

Goh

2010

Scientometrics

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“…Structural properties of networks as they relate to creative output pertain to integrated differentiation [16]. As a general measure of the degree of social interaction, we use density, centrality, clustering coefficient so as to determine their potential role in and relation to innovativeness.…”

Section: Metrics and Indicators For Measuring Innovation Potentialmentioning

confidence: 99%

“…Since we are interested in observing potential relations between the structure of the social network and innovation capacity of a community, two types of metrics are considered: innovation metrics and network structure metrics that pertain to integrated differentiation that is related to innovation potential [16].…”

Section: Metrics and Indicators For Measuring Innovation Potentialmentioning

confidence: 99%

InnoScape: A creative artificial ecosystem model of boundary processes in open science

Zou

Yılmaz

2011

2011 Atlanta Conference on Science and Innovation Policy

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With the increasing use of cyberinfrastructure and popularity of e-Science initiatives, science is becoming truly globalized, reducing barriers to entry and enabling formation of open and global networked innovation communities. In this study, we characterize such networks as complex adaptive communication systems that exhibit traits of self-organized creative artificial ecosystems. A simulation-based exploratory study is conducted to better understand community traits that confer increased diversity and resilience in such global participatory systems. Five types of interaction topologies are identified and simulated using agent simulation as a method of inquiry. Simulation results show that scale-free network has the highest resilience as compared to random and random group network.

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“…Structural properties of networks, as they relate to creative output, can help measure the degree of integrated differentiation (Stankiewicz 1992). As a general measure of the degree of social interaction, we use density, centrality, clustering coefficient, and percentage of strong ties so as to elucidate the relationship between structure andinnovativeness.…”

Section: Network Metricsmentioning

confidence: 99%

Exploratory simulation of collective innovative behavior in global participatory science communities

Zou

Yılmaz

2010

Proceedings of the 2010 Winter Simulation Conference

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Better understanding of how and why networks of open innovation and global participatory science communities form and evolve, and how they can be governed or influenced toward sustainable innovation and productive states are critical questions. To this end, a simulation-based exploratory study is conducted to better understand the conditions that confer increased rates of innovation in such socio-technical systems. Three types of open science communities are identified and simulated using agent simulation as a method of inquiry. Simulation results show that centrality, as a measure of degree of connectedness, exhibits positive influence for innovation output in exploratory and service communities up to a point. Also, utility-oriented communities have social network structures with low density and high centrality, suggesting high potential for innovation.

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Technology as an Autonomous Socio-Cognitive System

Cited by 19 publications

References 10 publications

Modeling the behaviour of science and technology: self-propagating growth in the diffusion process

Modeling the behaviour of science and technology: self-propagating growth in the diffusion process

InnoScape: A creative artificial ecosystem model of boundary processes in open science

Exploratory simulation of collective innovative behavior in global participatory science communities

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