I am looking at this paper, and they are modeling "cohesiveness" in a group by doing the following:
First, they define a "common action" of all individuals in a group by using the average:
$\bar{x}=\sum_{i=1}^{n} \bar{x}_{i} / n$
where the xi are generated from an arbitrary distribution.
They then define an individual action as:
$x_{i}=(1-c) \bar{x}_{i}+c \bar{x}$
...for an arbitrary value of the cohesiveness parameter c.
All of this plugs into the following survivor function:
$p(\mathbf{x})=100-\beta(\mathbf{x} \cdot \mathbf{x})=100-\beta \sum_{i=1}^{n} x_{i}^{2}$
...where x is an "action" (arbitrary number), n is the "group size", and beta is a "harshness" parameter from 0 to 1.
They then plot the following, using different values of c:
But if I try to create this plot, I get the same values for every c.
For example, if I use the following values (same chosen in their plot legend):
group_size = 5;
beta = 1;
cohesive_factors = [0, 0.25, 0.50, 0.75, 1];
action_value = 5;
...and I loop through the cohesive factor values like this (JavaScript):
cohesive_factors.forEach(function(c_factor) {
const average_of_all_actions = (action_value*group_size)/group_size;
const individual_action = ((1-c_factor)*action_value) + (c_factor*average_of_all_actions);
})
I get the same individual_action in each loop, which ends up leading to the same survival probability once entered into the full equation.
What am I missing here? I am taking the same action value, but they did say this was drawn from an arbitrary distribution, so should I be setting the action_value to a random number instead of hardcoding it to 5?
I do notice that in the paper they say “individual actions that are independent come from U[−a, a].”
So if a=5 in the above plot legend, does this mean I take a random number from [-5, 5]?