A Concept

A few weeks back, I was thinking of a proposal for my Climate course at the NRI (Natural Resources Institute) here in UofM, while at the same time I was mainly working on my assignment for the statistical analysis course. So, while I was trying to write the Matlab function to do one of the problems, some notion attracted me which really sounded weird. However, it was just a raw idea that one should spend time reading all those stuff required to be known about the Social Behaviour and the Sustainable Development concepts. Anyway, cause I know I won’t do anything in this area in the future, I decided to publish my concepts for anyone who is interested. You may use it (But just please let me know). So, here we go.

Climate Change Adaptation though the Sustainable Development

A Social Behaviour Study

In the following concepts, the mean idea is to simulate the behavioral format of the societies through the course of their approach towards a sustainable development. Samples may represent individuals, social groups, or even governments. The main idea here is to provide a local as well as global setup for a better approach towards the sustainable development.


Concept 1) 25 samples of length 1000 (Figure 1c) show a better behavioral trend than those of smaller length, say length 100 or 10 (Figure 1b & 1a, respectively).

Figure 1

Concept 2) Also, if a 25 samples of 1000 observations each are viewed in the presence of two 25 samples of length 100 and 10 each, the behaviour of the less populated samples (here, of length 100 and 10) mimics the higher populated samples (i.e. here of those with length 1000). The smaller a sample size is, the more is the imitative behaviour of that sample (Figure 2).

Figure 2

Concept 3) In the presence of more populated samples, the less populated ones travel a shorter distance to reach the maximum possible status of all samples (Figure 2).

Concept 4) The highest disturbances are generally seen around the middle part of the samples (the width of samples’ distributions at their midway) (Figure 1 & 2). This is more true for more populated samples as those with less observations show a rather more uniform behaviour along the way.

The following Matlab code was used to simulate the trends by using the built-in uniform random number generator; rand.

function r = behaviour(n)

x = [1:n];

fx = (x/50)/(n/500);

plot (x,fx,'r')

hold on

for i = 1:25

plot ([1:n],sort(10*rand(1,n)));

end

end