Market Optimization

Capturing Value

The previous Market Simulation (MO-114 Fixed-Orthogonal Vary-Commodity Price War) demonstrated that Competitors will not be able to capture value unless their Products offer differentiating Features.

This Market Simulation illustrates the same concept. But here the Profitability of commodities increase without the Vendors making any additional effort to differentiate their Products.

The Customers in this Market Simulation are bathers spread out evenly along a hot beach. The Customers get thirsty under the sun, so 5 vendors (selling identical bottles of water) set up stands at periodic points along the beach:

  • Vendor A: at the very beginning of the beach
  • Vendor B: at a point 25% along the beach
  • Vendor C: half-way along the beach
  • Vendor D: at a point 75% along the beach
  • Vendor E: at the far end of the beach

As the sand is very hot, the Customers are reluctant to make the trip to a Vendor to buy water unless that Vendor is very close.

But the Beach ‘shrinks’ over the course of the Market Simulation. The shrinking may be due to the local city slowly building a boardwalk along the beach, or due to steadily improving sandal technology. In any event, it becomes easier and easier for Customers to reach all the Vendors (not just the nearest Vendor). The Willingness To Pay (WTP) Customers have for the water itself does not change.

The shrinking beach causes the competition between the Vendors to increase. And yet, the Market Simulation predicts that Vendors will increase their Prices even when Customers have a greater choice of Vendors.

Prices, Revenue, and Profitability continue to increase until the beach starts to get tiny. At that point the Vendors, selling commodity water, will engage in fierce Price Competition. In the end, Profitability collapses.

This Case Study provides provides a glimpse into the premium Market Optimization nodes. These premium nodes are not available in the Free Community Edition of Scientific Strategy. But a selection of problems that can be solved with the Free Community Edition nodes can be found in Case Studies and Market Simulation.

#1 Lost Value

The first step in this Market Simulation is to calculate the ‘Lost Value’ each Customer would suffer if they were to walk across the hot beach to a Vendor to buy water.

The Customer’s Willingness To Pay (WTP) for the water itself is fixed at $5.00 (though this could be easily varied by changing the ‘Value of Water’ Customer Distribution at node #8).

The ‘Lost Value’ Customers suffer in walking across the hot beach to the Vendors must be subtracted. Customers who are left with a positive Consumer Surplus after subtracting both this ‘Lost Value’ and ‘Price’ from their ‘Value of Water’ will make a purchase.

The Customer Distributions (#1) node will determine the relative location of bathers along the beach. There are 10,000 bathers evenly distributed from ‘0.00’ (located at the very beginning of the beach) to ‘1.00’ (located at the far end of the beach).

The length of the beach is set by the Tuning Loop Start (#3) node. The beach starts out with a length of 50 units and shrinks at a rate of 0.5 units each iteration. You can imagine a ‘unit’ as being approximately equal to the length of a football field so that, in the end, 10,000 bathers will be spread along a sideline.

The ‘Lost Value’ each Customer suffers is calculated as:

Absolute_Value( Customer_Location – Vendor_Location) x Beach_Length

For example, Customer C00001 is located right next to Vendor_A so would suffer 0.0 ‘Lost Value’ in walking to them. But they are located an entire beach away from Vendor_E, so would initially suffer a ‘Lost Value’ of -50.0 in reaching them.

Finally, the Product Generator (#7) node generates a Market comprising of five Products (one for each Vendor):

  • Water_A
  • Water_B
  • Water_C
  • Water_D
  • Water_E

Setup Customer



Tuning Loop




Product Array

WTP Matrix

#2 Profit Maximizing Price

Each of the 5 Vendors wish to set Profit Maximizing Prices. From an initial test Price of $2.00 per bottle of water, the Vendors will experiment with Prices while taking into account the Prices charged by the other Competitors. There is no collusion among the Vendors.

The Price War (#16) node does this by finding a steady-state equilibrium for the Prices of all Vendors.

The remaining nodes in this workflow branch are then used to collect the results data. The average Price across all Competitors is calculated, as well as the total Quantity, Revenue, and Profitability of the Market. The metrics from individual Competitors are also tracked.

Price War


#3 Shrink Beach

The Beach starts out 50 units long and ‘shrinks’ at a rate of 0.5 units. Therefore the Market Simulation runs over 100 iterations.

At first, only about 1/3 of Customers are willing to walk across the hot beach to buy water from the nearest Vendor. The Profit-Maximizing-Price set by all Vendors is around $3.00. Even the two Vendors (A and E) located at the far ends of the beach, who only get half-as-many Customers, charge $3.00.

As the beach starts to shrink, Prices remain steady while the number of purchasing Customers increases. At around iteration 70, all Customers enter the Market. From this point, all Customers are not only willing to reach their nearest Vendor, but some Customers would be willing to travel to the next two nearest Vendors.

At this point, something unexpected happens. As Competition increases, so do Prices!

Prices increase from iteration 70 to iteration 80. But eventually the beach starts to get tiny and it becomes easy for Customers to switch Vendors. The result is severe Price Competition causing rapidly decreasing Prices and Profitability.

Chart #1

Chart #2

Chart #3

Economic Theory

Location Model

Hotelling observed that it would be rational for two vendors on a beach to both be located at the center in order to split the market. But critics have noted that this law could not be applied to three or more Vendors.

Furthermore, the basic Hotelling model fails to account for strategic Pricing. D’Aspremont, Gabszewicz and Thisse (1979) showed that when firms choose both Price and Location, firms move apart to decrease Price Competition.

This Market Simulation supports the critics of the Hotelling model. Firms that sell commodity Products avoid Price Competition when located further apart. Moreover, those firms can benefit from macroeconomic factors that drive up the value of their Products even when those Products are commodities.

See also: Economic Location Models


One way to test the findings of this Market Simulation would be to look at the Prices, Sales Volume, and Profitability of firms engaged in global trade.

This Market Simulation would predict that, as globalization started to take off, and trade became easier, the Price of undifferentiated Products would first rise and then fall in an ‘inverted-U’ pattern.