OpenSource For You
Exploring Software: Modelling the Shortage of Change Using Netlogo
Last month we explored Netlogo, the multi-agent programming environment which simulates natural and social phenomena. In this article, the author skilfully uses an existing real-world situation, namely, the shortage of change due to demonetisation, to set
We have all recently experienced the shortage of cash or change. Since my expectations of the cash availability situation post demonetisation did not turn out to be correct, I wanted to see if a simple model could be created in Netlogo, to model the shortage of change.
The simple model I chose was as follows:
Our world consists of shops, shoppers, coins and notes. Each note is equal to note-value coins.
There are a fixed number of shops, N_shops, at fixed locations.
Each shop starts with starting-change coins.
There are a fixed number of shoppers, N_shoppers, who move a step on each tick.
Each person starts with no coins but an infinite number of notes.
If a person lands at a shop, (s)he makes a purchase of a random value between 0 and note-value excluding the end points.
Each person pays with coins if (s)he has enough and does not hold onto them.
Each shop returns the change if it has enough, even if the sale is for a small amount.
If neither the shop nor the shopper has an adequate number of coins, the sale fails.
You can vary the values of N_shops, N_shoppers, starting-change and note-value to study the rate of failed sales.
The interface screen is shown in Figure 1. This includes the values you can set for the number of shops and the number of shoppers in this world. You also configure the amount of change each shop has and the number of coins that add up to the value of a note.
You need to define ‘turtles’ called shops and shoppers, and various variables for the model. The global variables keep track of the total value of sales, the number of sales and the number of sales that failed because there was no change.
breed [shops shop] breed [shoppers shopper] shops-own [shop-coins] shoppers-own [coins] globals [sale no-change count-sales]
The set-up code sets up the shops at non-overlapping locations. Each shop is a square. The shoppers are created at random locations and shaped as dots.
to setup clear-all set sale 0 setup-shops setup-shoppers reset-ticks end
ask n-of N_shops patches [sprout-shops 1 [ set shape "square" set colour grey set pcolor yellow set shop-coins starting-change set no-change 0 set count-sales 0
to setup-shoppers create-shoppers N_shoppers [ setxy random-xcor random-ycor set color blue set shape "dot" set coins 0
The modelling code runs for 500 ticks. Each shopper is asked to move forward a step and check if there is any shop in that location. If there is a shop, a sale is attempted. The value of each sale is selected at random by a number that ranges between 1 and the note value. If a shopper has enough coins, (s)he pays with the coins. If (s)he does not have enough coins, (s)he pays with a note and the shop returns the change if it has enough coins. The sale fails if that is not possible.
to go if ticks >= 500 [ stop ] ask shops [ set color grey ] ask shoppers [ forward 1 if any? shops-here [
] tick end
to make-sale let value max (list 1 random note-value) ifelse coins >= value
[ set coins coins - value ask shops-here
[ set shop-coins shop-coins + value set color green
] set count-sales count-sales + 1 set sale sale + value
[ ask shops-here
[ ifelse shop-coins >= (note-value - value)
[ set shop-coins shop-coins - (note-value - value) ask myself [ set coins coins + (note-value - value)] set sale sale + value set count-sales count-sales + 1 set color cyan
[ set no-change no-change + 1
set color red
During the running of the model, the interface shows the failure rate and the revenue per sale attempted.
Running the model
The objective is to code. But you can verify that some of our intuitive perceptions are reasonable. You may choose the number of coins in a note to be 20 (modelled on ` 2000/` 100). Let’s suppose there are 100 shops and 500 shoppers, and the shops start out with change of a 100 coins each. We expect that the failure rate will be low. It stabilises at about 5 per cent with an average earning per visit of nine coins. Now let’s suppose that the starting change of each shop is 20 coins. How badly would the sales be affected? In our model, the failure rate reaches over 60 per cent and the average earnings per visit drop to about 3.5 coins.
You can explore and play around with the model.
The keyword ‘ask’ is the critical construct which allows you to code for a specific breed of turtles or patches (the location/coordinates in our world).
The ‘trickiest’ code is the creation of N shops located randomly. In this case, you ask any N patches to ‘sprout’ a shop (ask n of N patches ….).
This is a very useful feature if you need to have only a random subset of turtles to do something.
The key takeaway is that surprisingly little code is needed to start exploring complex phenomena and understand the statistical implications of even simple interactions.
By: Dr Anil Seth The author has earned the right to do what interests him. You can find him online at http://sethanil.com and http://sethanil. blogspot.com, and reach him via email at firstname.lastname@example.org.