Understanding the mechanism underlying the mobile telecommunications technologies (MTTs) diffusion in a country is crucial for telecom planners to know how to accelerate their diffusion by designing appropriate scenarios. Considering the technology diffusion as a bottom-up process, this study is aimed at exploring this mechanism, drawing on insights from diffusion of innovation theory and social network theory. Accordingly, an agent-based model is proposed to investigate how MTTs are diffused in Iran over time. The results of this study show, (1) social network of Iranian society seems more similar to a Watts–Strogatz small-world network than a Barabási–Albert preferential attachment network, where the clustering coefficient is high and average path length is low, (2) compared to the compatibility parameter, the advertisement parameter not only is less influential on diffusion of a targeted MTT (i.e., 4G) but also is not necessary for it, and (3) scenarios having the least number of steps and turning points are more appropriate for continuous diffusion of 4G. The proposed study is empirically validated against real-world data ranging from 7/1/2017 to 12/31/2017. We believe it provides telecom planners insights regarding MTTs diffusion mechanism in a social complex structure and the how of scenario designing for increasing their diffusion.
Nowadays, we are surrounded by a large number of complex phenomena such as virus epidemic, rumor spreading, social norms formation, emergence of new technologies, rise of new economic trends and disruption of traditional businesses. To deal with such phenomena, social scientists often apply reductionism approach where they reduce such phenomena to some lower-lever variables and model the relationships among them through a scheme of equations (e.g. Partial differential equations and ordinary differential equations). This reductionism approach which is often called equation based modeling (EBM) has some fundamental weaknesses in dealing with real world complex systems, for example in modeling how a housing bubble arises from a housing market, the whole market is reduced into some factors
It has been proposed that climate adaptation research can benefit from an evolutionary approach. But related empirical research is lacking. We advance the evolutionary study of climate adaptation with two case studies from contemporary United States agriculture. First, we define ‘cultural adaptation to climate change’ as a mechanistic process of population-level cultural change. We argue this definition enables rigorous comparisons, yields testable hypotheses from mathematical theory, and distinguishes adaptive change, non-adaptive change, and desirable policy outcomes. Next, we develop an operational approach to identify ‘cultural adaptation to climate change’ based on established empirical criteria. We apply this approach to USDA data on crop choices and the use of cover crops between 2008 and 2021. We find evidence that crop choices are adapting to local trends in two separate climate variables in some regions of the US. But evidence suggests that cover cropping may be adapting more to economic incentives than climatic conditions. Further research is needed to characterize the process of cultural adaptation, particularly the routes and mechanisms of cultural transmission. Furthermore, climate adaptation policy could benefit from research on factors that differentiate regions exhibiting adaptive trends in crop choice from those that do not.
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