Alessandro Dovis, Filippo Maggi

**Computer Science, Simulation and
Economics**

Project work on

"**Equilibrium on nine separated
markets with different levels of price flexibility and market
opennes**"

The applet requires Java 1.4.1 or higher to run. It will not run on Windows 95 or Mac OS 8 or 9. Mac users must have OS X 10.2.6 or higher and use a browser that supports Java 1.4 applets (Safari works, IE does not). On other operating systems, you may obtain the latest Java plugin from Sun's Java site.

created with NetLogo

view/download model file: isoleweb.nlogo

`WHAT IS IT?
-----------
We present a very simple model that tries to describe the dynamic
behaviour of the prices of an undifferentiated good among
different markets under different strategies implemented by the
consumers. We assume that in each market operates a
representative producer and that there are 4 categories of
consumers (constrained, pseudo-rational, random-ones, inverted)
that follow different behavioural rules.
HOW IT WORKS
------------
The price in each market is set by a simple price-rule: P = F + Q*0.25
where F is typical of the market and Q is total demand for the
market. The procedure follow in this way:
(i) we generate the 9 markets, the 9 representative producers and
the consumers
(ii)the consumers are casually placed
(iii)following the price rule the representative producers set
the initial prices in the 9 markets
Then in every period:
(iv)the consumers decide if and where to move according to their
rules:
the "constrained" don't have the possibility to move
the "random-ones" move randomly with different
probabilities according to the ratio P/average P registered in
the former period: if P/average P <0.9 pr-green, if 0.9 < P/average P> 1.1 pr-yellow, if P/average P
> 1.1 pr-red
the "pseudo-rational" move to the market where the
price was minimum in the former period with different
probabilities according to the ratio P/average P: if P/average P <0.9 pr-green, if 0.9 < P/average P>
1.1 pr-yellow, if P/average P > 1.1 pr-red
the "inverted" move to the market where the price was
maximum in the previous period
(v)after the movement of the consumers the producers set the
prices in the 9 markets
HOW TO USE IT
-------------
With the "setup" button we create the world in which
the agents interact and the initial value of the variables (i-iii).
With the button "go" it starts the simulation.
The button "step-once" simulate just one period.
Using the sliders before the Setup one can set the value of the
parameters number-random-ones, number-pseudo-rational, inverted?
(the number of the consumers pseudo-rational, random-ones and
inverted) and indirectly the number of constrained (100 - number-pseudo-rational
- number-casual); while pr-green, pr-red, pr-yellow can be change
every time during the simulation.
There are 3 graphics: "local-price" represent the
dynamic of the price in a single market (the one characterized by
pxcor=-1, pycor=1), "max-min" plot the max, min price
in each period and their difference, "pay-off" plot the
average pay-off of each category of consumers (which are also
displayed in the 4 monitors).
THINGS TO NOTICE
----------------
The key variables to look at in the simulation are the difference
between the max and min price (stability of the markets) and the
pay-offs of the categories of consumers (in order to analyze the
efficiency of their strategies).
The patches (markets) are green if P/average P <0.9, yellow if 0.9 < P/average P> 1.1, and red if P/average
P > 1.1
THINGS TO TRY
-------------
The value of pr-green, pr-red, pr-yellow can be interpreted as a
measure of the flexibility of the prices: low values means that
the prices are updated after that few consumers moved.
The number of "constrained" can be interpreted as a
measure of the opennes of the economy.
The presence of the inverted consumer helps to understand if the
behaviour of the pseudo-rationl agents is really rational.
EXTENDING THE MODEL
-------------------
The biggest limit of this model is the absence of really rational
agents. Representative producers and every category of consumers
have a fixed behavioural rule. An interesting extension could be
implemented introducing agents able to learn and modify their
behaviour while the model is running (allowing the consumers to
coordinate their actions).
Moreover, the model could be extended assuming that the
representative firms have to set the price at the beginning of
the period forecasting the demand they will face.
`