Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
Dynamic prediction of communication flow using social context
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Information Systems Research
Agent-based simulation of the diffusion of warnings
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
An agent-based diffusion model with consumer and brand agents
Decision Support Systems
Expert Systems with Applications: An International Journal
Diffusion dynamics of open source software: An agent-based computational economics (ACE) approach
Decision Support Systems
An agent-based simulation model for the market diffusion of a second generation biofuel
Winter Simulation Conference
Word-of-mouth effects on social networks
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part III
On truth discovery in social sensing: a maximum likelihood estimation approach
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Adjustment of knowledge-connection structure affects the performance of knowledge transfer
Expert Systems with Applications: An International Journal
Simulating the Diffusion of Information: An Agent-Based Modeling Approach
International Journal of Agent Technologies and Systems
Maximum likelihood analysis of conflicting observations in social sensing
ACM Transactions on Sensor Networks (TOSN)
Social influence and dynamic demand for new products
Environmental Modelling & Software
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Diffusions of new products and technologies through social networks can be formalized as spreading of infectious diseases. However, while epidemiological models describe infection in terms of transmissibility, we propose a diffusion model that explicitly includes consumer decision-making affected by social influences and word-of-mouth processes. In our agent-based model consumers' probability of adoption depends on the external marketing effort and on the internal influence that each consumer perceives in his/her personal networks. Maintaining a given marketing effort and assuming its effect on the probability of adoption as linear, we can study how social processes affect diffusion dynamics and how the speed of the diffusion depends on the network structure and on consumer heterogeneity. First, we show that the speed of diffusion changes with the degree of randomness in the network. In markets with high social influence and in which consumers have a sufficiently large local network, the speed is low in regular networks, it increases in small-world networks and, contrarily to what epidemic models suggest, it becomes very low again in random networks. Second, we show that heterogeneity helps the diffusion. Ceteris paribus and varying the degree of heterogeneity in the population of agents simulation results show that the more heterogeneous the population, the faster the speed of the diffusion. These results can contribute to the development of marketing strategies for the launch and the dissemination of new products and technologies, especially in turbulent and fashionable markets.