AGENT-BASED MODEL : Collective dynamics of decision-making for Smart-City with multi stakeholder engagement.

My last study consisted in profiling stakeholders engaged in decision-making for smart-city from graph-based analysis of representative social networks. In “SOCIAL NETWORK ANALYSIS : Classifying Citizen Engagement in Smart-City with Graph-Based Machine Learning.” I spotted that the popularity indicators are not directly correlated to the activity on the social networks. It suggests that the social reward expected by a user from the other users do not follow a systematic rule, and some external factors might affect the social popularity.

In the following Agent-Based Model I am integrating data from all previous studies of my research : survey, opinion mining and social network analysis, to represent six categories of stakeholders owning different levels of the same attributes in network behaviours. The six categories of stakeholders are Public sector, Corporate companies, Startups, Academics, Civil society and Medias.

In the following computer simulation, the six representatives of these categories will try to influence the opinion of a population of undecided nodes (see Fig. 1). The Stakeholder Engagement model observes how the ones can take an advantage on others, and how it potentially affects the public opinion. It has been coded in python and is available on my github.

=> To mention this article: “AGENT-BASED MODEL : Collective dynamics of decision-making for Smart-City with…