Doubts in modelling the Arthur(1991) paper: Designing economic agents that act like human agent I am implementing Arthur's paper "Designing economic agents that act like human agent: A behavioral approach to bounded rationality" from 1991 in JAVA and I am having a problem understanding one part of the paper:

At one point it says: triggering actions randomly on the basis of their strengths and I don't know what this means.

The algorithm is described as follows:

1.) Actions are represented as $a = (1,2,...,A)$, these values represent the mean and all the actions have a fixed standard deviation. The mean of an action and its standard deviation help in plotting the normal distribution. The payoff of an action, which is unknown before is drawn after the action is chosen from the above described normal distribution.

2.) Ever action has a certain value of strength ($S(t)$) associated with it, the strength depends on prior beliefs (in my case it depends on prior skill set). The current sum of these strengths is C(t).

3.)At every time $t$ I calculate the probability vector $p(t) = S(t)/C(t)$

4.) Choose one action from the set according to the probability 

5.) Update the strength of that action (point 3, page 354) 

6.) Renormalized (point 4, page 354).

The problem occurs at point 4: choose an action according to the probability, what probability?

I don't know how to translate this statement into code.
Although I still don't understand what these "strengths" are all about, still, mechanically, step $4)$ is now clear.

At each point in time, there is a certain allocation of Strengths to actions, $\{S_i(t),\; i=1,2,...,A\}$. This allocation changes through time.  

The allocation is turned into a relative frequency distribution by dividing each $S_i(t)$ by their sum $C(t)$.

But then 

$$\sum_{i=1}^A \frac {S_i(t)}{C(t)} =1 $$

and (I presume), $S_i(t) \geq 0,\; \forall i$. So indeed this relative frequency distribution can be treated as a probability distribution, and each $S_i(t)/C(t)$ can be treated as a probability that characterizes action $i$ in period $t$.  

So you have to code this probability distribution, and then have the software "randomly draw" from it, as though this distribution was part of  the distributions available in a "random number generator".  

The floor is yours now.