The effect of network topologies on the spreading of technological developments

Kocsis, Gergely; Kun, Ferenc

doi:10.1088/1742-5468/2008/10/p10014

Cited by 26 publications

(27 citation statements)

References 17 publications

(22 reference statements)

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“…Diffusion models in the economics literature (e.g., Kocsis and Kun 2008;Hohnisch et al 2008;Cantono and Silverberg 2009;Faber et al 2010) typically use simple decision rules based on cost minimization or heterogeneous reservation prices. These models frequently assume falling prices due to learning effects and tend to interpret social influence as benefits due to network externalities.…”

Section: Simple Decision Rulesmentioning

confidence: 99%

Agent-based simulation of innovation diffusion: a review

Kiesling

Günther

Stummer

et al. 2011

Cent Eur J Oper Res

422

243

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Mathematical modeling of innovation diffusion has attracted strong academic interest since the early 1960s. Traditional diffusion models have aimed at empirical generalizations and hence describe the spread of new products parsimoniously at the market level. More recently, agent-based modeling and simulation has increasingly been adopted since it operates on the individual level and, thus, can capture complex emergent phenomena highly relevant in diffusion research. Agent-based methods have been applied in this context both as intuition aids that facilitate theory-building and as tools to analyze real-world scenarios, support management decisions and obtain policy recommendations. This review addresses both streams of research. We critically examine the strengths and limitations of agent-based modeling in the context of innovation diffusion, discuss new insights agent-based models have provided, and outline promising opportunities for future research. The target audience of the paper includes both researchers in marketing interested in new findings from the agent-based modeling literature and researchers who intend to implement agent-based models for their own research endeavors. Accordingly, we also cover pivotal modeling aspects in 123 184 E. Kiesling et al. depth (concerning, e.g., consumer adoption behavior and social influence) and outline existing models in sufficient detail to provide a proper entry point for researchers new to the field.

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Section: Simple Decision Rulesmentioning

confidence: 99%

Agent-based simulation of innovation diffusion: a review

Kiesling

Günther

Stummer

et al. 2011

Cent Eur J Oper Res

422

243

View full text Add to dashboard Cite

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“…As far as we know, no study has yet analysed the influence of firms' observation network on the carbon market, neither theoretically nor empirically. However, in a broader view of network science, the influence of the observation or interaction network among agents has been a common issue in the studies of financial markets [11][12][13] and technology diffusion processes [14][15][16]. Correspondingly, the agent-based model is one of the major tools for a relevant analysis.…”

Section: Introductionmentioning

confidence: 99%

Impact of Firms’ Observation Network on the Carbon Market

Zhu

2017

Energies

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Given the important role of the carbon market in fighting against global warming, the impact of information on the efficiency of the scheme is a critical issue for both its designers and the central authority. At least two aspects of information are worthy of attention. First is the incompleteness of information, with which the firms make decisions to minimize their abatement cost and maximize their profit. Second is the mechanism of information transmission. Based on an agent-based framework we established in our previous work, we explicitly depicted the first aspect and analysed its impact on firms' decision-making and consequent market results. In this paper, we focus on the second aspect, transmission mechanism of information, which is depicted as an observation network among firms. The basis in reality is that the firms in the carbon market are usually from different industries or areas, and it is relatively easier to observe the conditions of firms from the same industry or area, corresponding to neighbours in the network. Four scenarios are considered, including no network, regular network, random network, and small-world network. We find that the existence of an observation network has a significant influence on the market results.

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“…(2) Spreading-Successful technologies spread over the system, resulting in an overall technological progress. The understanding of the technological progress of socioeconomic systems requires the analysis of the complex dynamics of several levels from the interaction of firms investing in research and doing production [5][6][7], through the emergence of new ideas captured by the network of patents [8], down to the level of individuals that use the newly developed products [9][10][11][12]. A crucial element of the diffusion of technological advancements is the spreading of information about the existence and advantages of the newly developed technology.…”

Section: Introductionmentioning

confidence: 99%

“…Diffusion of innovations also involves * feri@dtp.atomki.hu components analogous to opinion spreading and consensus formation [9][10][11][12][16][17][18][19][20][21][22][23][24][25][26][27]. As an example, telecommunication technologies can be mentioned, where the usage of a technology assumes that the communication partners are approximately at the same technological level [1,[9][10][11][12]19,20].…”

Section: Introductionmentioning

confidence: 99%

Competition of information channels in the spreading of innovations

Kocsis

Kun

2011

Phys. Rev. E

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We study the spreading of information on technological developments in socioeconomic systems where the social contacts of agents are represented by a network of connections. In the model, agents get informed about the existence and advantages of new innovations through advertising activities of producers, which are then followed by an interagent information transfer. Computer simulations revealed that varying the strength of external driving and of interagent coupling, furthermore, the topology of social contacts, the model presents a complex behavior with interesting novel features: On the macrolevel the system exhibits logistic behavior typical for the diffusion of innovations. The time evolution can be described analytically by an integral equation that captures the nucleation and growth of clusters of informed agents. On the microlevel, small clusters are found to be compact with a crossover to fractal structures with increasing size. The distribution of cluster sizes has a power-law behavior with a crossover to a higher exponent when long-range social contacts are present in the system. Based on computer simulations we construct an approximate phase diagram of the model on a regular square lattice of agents.

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The effect of network topologies on the spreading of technological developments

Cited by 26 publications

References 17 publications

Agent-based simulation of innovation diffusion: a review

Agent-based simulation of innovation diffusion: a review

Impact of Firms’ Observation Network on the Carbon Market

Competition of information channels in the spreading of innovations

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