Investigation of the information dissemination mechanism in a multi-agent system in a time window

This article explores the process of information dissemination, in which each agent is represented by a continuous-time Markov chain with two states: L and M. L-state refers to the “home” while M-state refers to the “meeting place”. When the two agents remain together, they “meet” and form a connection. This means that they can exchange information, conduct commercial transactions and etc. The aim of the research is to develop an effective way to calculate the propagation time and study the dependence of the propagation process on parameters such as the number of agents, the number of uninformed agents at the end of the process and the intensity of contact. It is implied that all agents are initially in L-state and one of them necessarily has some information. A distribution model with mobile agents in a star-shaped network has been created, which can be reduced to a network with two nodes. An increase in population size has two contradictory effects that cause the propagation time to increase at first, then decrease, and, eventually, increase with asymptotic behavior similar to a harmonic sum. In this regard, the expected time required to inform an additional agent is small at first, and then increases, and the probability of informing all agents within a given period has an S-shape. Additionally, information as to how changes in the modeling parameters, such as initial and ending number of the informed agents and the intensity of contacts, affect the process is given.

1. Centola D. The spread of behavior in an online social network. Science. 2010;329(5996):1194–1197. DOI: 10.1126/science.1185231.

2. Nowzari C., Preciado V.M., Pappas G.J. Analysis and control of epidemics. IEEE Control Systems Magazine. 2016;36(1):26–46. DOI: 10.1109/MCS.2015.2495000.

3. Gorshkov A.V. Investigation of the mechanism of information dissemination in a multi-agent system based on Markov processes. Sistemy upravleniya i informatsionnye tekhnologii = Control systems and information technologies. 2022;90(4):42–48. DOI: 10.36622/VSTU.2022.90.4.009.

4. Feola G., Butt A. The diffusion of grassroots innovations for sustainability in Italy and Great Britain: An exploratory spatial data analysis. Geographical Journal. 2017;183(1):16–33. DOI: 10.1111/geoj.12153.

5. Jin M, Liu F, Zhou C. Rumor spreading: A survey. 2nd Int. Conf. on Artificial Intelligence Engrg. Appl., 2017:263–269. DOI: 10.12783/dtcse/aiea2017/14942.

6. Isella L., Stehlé J., Barrat A., Cattuto C., Pinton J.-F. What’s in a crowd? Analysis of face-to-face behavioral networks. Journal of Theoretical Biology 2011;271(1):166–180. DOI: 10.1016/j.jtbi.2010.11.033.

7. Manzo G., Gabbriellin S., Roux V., M’Mbogori F.N. Complex contagions and the diffusion of innovations: evidence from a small-N study. Journal of Archaeological Method Theory. 2018;25(4):1109–1154. DOI: 10.1007/s10816-018-9393-z.

8. Rogers E.M. Diffusion of Innovations. New York, Free Press; 1995. 518 p.

9. Simpson G., Clifton J. Testing diffusion of innovations theory with data: Financial incentives, early adopters, and distributed solar energy in Australia. Energy Research & Social Science. 2017;29:12–22. DOI: 10.1016/j.erss.2017.04.005.

10. Xiong H., Wang P., Bobashev G. Multiple peer effects in the diffusion of innovations on social networks: a simulation study. Journal of Innovation and Entrepreneurship. 2018;7(2):1–18. DOI: 10.1186/s13731-018-0082-7.

11. Sun H., Cheng R., Xiao X., Yan J., Zheng Y., Qian Y. Maximizing social influence for the awareness threshold model. Database Systems for Advanced Applications, Lecture Notes in Computer Science. 2018;10827:491–510. DOI: 10.1007/978-3-319-91452-7_32.