NetLogo User Community Models
by Corey Nyako (Submitted: 01/10/2010)
WHAT IS IT?
There are two actors in the model, shops and buyers. The shops run on rules that decide their status as open or closed. The buyers use a rule to decide when to window shop and when to buy. The main variables for the shops are 'price' and 'cost'; both are changeable on the slider. The variables for the shoppers are number of shoppers and how much window shopping they do before they buy.
HOW IT WORKS
It is possible for the shops (patches) to be in 1 of 3 states: Closed (White), Open - but has not sold any units recently (Blue), and Open - and has sold units recently (Green). When a shop is closed (White), there is a random chance that a new shop (Blue) will open in its place (changable with the 'startup_rate' slider on the interface). Shops close when their cost is more than their price. If shops do not sell any products (Blue) or have not sold any products recently (from Green to Blue), they will adjust their price in order to stay competitive. If their still have not sold any units, then they may be randomly selected to go out of business. After they have sold a unit of product, will turn green and adjust their price like the blue did, but at an independent rate. Greens will not adjust their price if the margin between price and cost is $1 (an arbitrary low number) or less in order to prevent going out of business.
Buyers window shop before they buy, meaning they compare prices of X number of shops (on average) before they decide to buy. They will then buy from the next store they run into that has a product price as low as or equal to the lowest price they found while window shopping. After completing those tasks, the shopper effectivly resets and starts the process again.
HOW TO USE IT
Price and cost of each unit of product for every shop is determined by a random number from a normal distribution. There is a slider on the interface that allows you to adjust the mean and standard deviation of the random numbers.
There is also a slider for the the average number of shops a buyer will window shop before they buy and a slider for how many buyers there are.
THINGS TO NOTICE
The "Frequency" graph shows the amount of shops in each state over time. The green shops tend to be more constant than the blue or white, suggesting that those shops tend to have the best prices. The "Cost and Price" graph shows how the average price lowers below the set average price.
THINGS TO TRY
While running the model, move the 'price_compare' and 'startup_rate' sliders and see what happens. Also, toggle the mean price and cost and see how the graph reacts. Turn off the 'time_stop' switch to let the model keep going and then play with the sliders.
EXTENDING THE MODEL
This model does not contain a cost function to determine its cost at different levels of output. This model would be better if a cost fuction was utilized in order to derive marginal cost and average cost. That would also mean a level of production must be determined also. The model also relies heavily on probabilities, perhaps a different model could utilize tick counts for some things instead.
CREDITS AND REFERENCES
Built by Corey Nyako, an undergradute student of economics at Florida State University.
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