Francesco Catarinella, Laura Macrž

Computer Science and Simulation for Economics

Project work on

"Ponzi games."


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view/download model file: ponzi_games.nlogo


The aim of this model is to simulate how financial institutions, using the Ponzi scheme to attract new capital, can make default with a fixed number of savers. The fraudulent investing scam tha promises high rates of return at little risk collapse when the institutions haven't enough funds to pay returns giving by the difficulty to recruite new investitors.


In this model patches are the financial institutions that guarantee different returns to savers and agents walk around patches randomly. There are two kinds of patches with two shade of blue; the blue black are those that offer high returns and use "cash" of agents to pay returns while the blue light promise low returns but the capital gain derives from real assets class. There are n agents with different risk-adversion and a progressive identification number.
If the agents with a low risk-adversion, moving around the world, meet patches that guarantee high return (Ponzi) they stop and start to get returns until they don't achieve a fixed capital gain. Obteined the target they call savers with a middle risk-adversion and with a closely identification number. The simulation stop when the blue black patches haven't enough money to pay returns, giving by the fact that savers can't find new agents that want to accept the negotiation. The blue black patches fails and become red and agents can change their risk-adversion and move around the world.


The observer can choose:

-the number of agents;

-the number of Madoff patches;

-the delta risk-adversion of agents after the default;

using the sliders on the interface.

The simulation get an equilibrium when al ponzi patches become red.


If we increase the Madoff patches we can notice that a large number of financial institutions in our world makes the time to default more longer than in the case of only one Madoff patch.


The observer can change the number of agents, the delta risk-adversion and the number of Madoff, using the sliders.


Try to define patches with different level of cash.

Try to create a turtles-own that consider the grade of information.

Try to create a variable that consider the economic cycle and financial crisis.


We used the mathematical function remainder to attract agents themself; we use the turtle's variable who in this way: remainder (who - 1 + total_number_) total_number. The program always returns the number who - 1.