Experimental Design Details
In our game, we consider a market with two subjects. For a total of eight treatments, we set the maximum selling price r = 12.0 for all treatments and vary the profit margin through unit wholesale cost: for the HM condition, we set c = 3.0 with a c/r ratio of 25%, and for the LM condition we set c = 9.0 with a c/r ratio of 75%.
Two demand levels are set to d_L=50 and d_H=100. How we assign different demand levels to subjects depends on whether two subjects can compete in prices. In NoPC treatments, two subjects choose order quantities only and are randomly assigned to either the low- or high-demand level. The order quantity has to be an integer from 0 to 150, and any unsold products are discarded at the end of each round. The selling price is exogenously fixed at 12.0 tokens.
In the YesPC treatments, demand is price dependent. Subjects submit both quantity and price decisions simultaneously. A subject who sets a lower price is considered more competitive in the market and therefore, he should obtain a larger market share and face the high demand while the other subject obtains the low demand. In the event where two subjects select the same price, both have an equal probability of 50% of being allocated with the high demand. The selling price chosen by subjects could range from 3.0 experimental tokens to 12.0 tokens (i.e., p ∈ {3.0,3.1,3.2, ⋯,12.0}) for the HM condition and from 9.0 tokens to 12.0 tokens (i.e., p ∈ {9.0,9.1,9.2, ⋯,12.0}) for the LM condition. Similarly, the order quantity has to be an integer from 0 to 150, and any unsold products were discarded at the end of each round.
The way we model inventory competition is through manipulating unmet demand in the market. If a participant orders too little inventory comparing to their demand, unsatisfied demand arises. The unmet demand can be reallocated to the other participant under YesDS condition whereas it goes to waste under NoDS condition.