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created with NetLogo
view/download model file: banks_companies.nlogo
The aim of the model is to simulate a simple economic market in which are present two kinds of agents: “Banks” (patches) which grant funds anyone with a different level of risk-aversion and “Company” (turtles) that want to finance themselves and receive funds from banks according to their Public-Rating.
This model takes inspiration to the introduction of the principles of “Basilea II”.
Banks are represented by the patches; they assume different colour based on their risk-aversion, floating between 0 and 1: if their risk-aversion is low (< 0,4) they’re yellow, if their risk aversion is high they are green. The number of aggressive banks on the market (yellow ones) depends on the binary variable called “Bank-probability”; if the variable is “False” there are less yellow banks and more careful ones. The variable connected to the patches and called “Transaction Done” explains if the funds are granted or not and can only assumes value of 1 or 0. When the number of patches is equal to 0, the simulation stops.
Companies are represented by turtles with the shape of a person; they assume different colour, size, breed, initial-capital and loan’s amount based on their dimension: if their dimension is lower than 1 (Small Company) they assume colour orange, size 1, breed “Small”, initial-capital floating between 0 and 300 and loan’s amount demanded floating between 0 and 3; if their dimension is upper or equal to 1 (Medium Company) they assume colour brown, size 1,5, breed “Medium”, initial-capital floating between 0 and 10*300 and loan’s amount demanded floating between 0 and 10*3 ; if their dimension is upper or equal to 2 (Large Company) they assume colour red, size 1,8, breed “Large”, initial-capital floating between 0 and 100*300 and loan’s amount demanded floating between 0 and 100*3 . The number of small companies active on the market depends on the binary variable called “Company-probability”; if the variable is “False” the number of small companies is the same of the number of medium and large ones, else the number of small companies is greater than the number of medium and large ones. The turtles have also different variables connected to themselves: “Real – Rating” which shows the effective reputation of the company, assumes value round 1 and which is known only by the company itself; “Public - Rating” which points out the reputation of the company, assumes value round 1 and which is known by the whole market; “Cdefault” that indicates the company’s probability of becoming insolvent.
Turtles move on the market at any clock with random direction.
We introduce a rule to associate the probability of bankruptcy to a company ( “Cdefault” ) based on the sum between “Real – rating” and “Public – Rating”. Everytime a bank accords a loan to a company, “Cdefault” increases of 0,0001, if “Cdefault” becomes upper or equal to 1 the turtles die and exit from the market.
Another rule introduced in the model connects the probability of default and the sum between granted loan and initial capital of a company to the capacity of creating (hatch) a new turtle; basing on this rule, a company is able to create a new company with the same or smaller dimension.
The “Setup” button create turtles (Companies) and patches (Banks) on the market.
Through the slider “Company” it’s possible to modify the number of company interacting on the market.
The switches “Limit-speed” and “Show-loans” allow us respectively to fix or not a limit-speed for the movement of the turtles round 0,6 and to put in evidence or not the number of loans granted.
It’s also possible to added or not informations on the market using the switch “Information”, if the switch is on the market is more efficient.
The switches “Bank-probability” and “Company-probability” allow us respectively to increase the number of aggressive banks and the number of small companies active on the market.
The displays on the left of the screen show respectively: “RFC” the total amount of the Reproduceble Fixed Capital; “GDP” the total amount of PIL (= RFC * 4); “Loan” the sum of the funds financed by the banks; “Small” the number of the Small Companies active on the market; “Medium” the number of the Medium Companies active on the market; “Large” the number of the Large Companies active on the market; “Live turtles” the number of companies surviving.
There are three graphics that show some interesting connections between the variables.
“RFC and GDP” points out the total amount of RFC and GDP.
“Transaction Done” puts in evidence the number of the positive transactions (Granted loans-blue) and the negative ones (Refused loans-black).
“Dimension and Loan” shows the total sum of loans given respectively to Small firms (orange), Medium firms (brown) and Large ones (red).
The switch named “Information” allow us to observe market’s efficiency in two different situation: when the switch is on, the companies’s public rating known by the banks is equal to the real rating and the market is perfectly efficient ; when the switch is off, the companies’s public rating known by the banks is upper than the effective real rating and the market is less efficient with an increasing bankruptcy’s risk.
It’s decidedly interesting modify market conditions while agents are interacting, this action allow us to compare two situations in the same graphics.
The slider called “Company” fluctuates from a minimum of 1 to a maximum of 200, it’s interesting to change this value to simulate different scenarios.
To improve the model we could introduce a database where each bank could collect informations about companies with whom it has had relationships in the past.
On the web it’s possibile to find other NetLogo models similar:
“Asymmetric information in the market for lemons”
“Lemons and fine cars”