Each of two players operates a food truck and must allocate 30 minutes between preparing burgers and preparing fries. The only item a player sells is a Combo consisting of one burger and one serving of fries. Therefore her payoff is equal to whichever is smaller: the number of fries, or the number of burgers brought on the truck. By default, one player has an absolute advantage in the production of both, but the players differ in opportunity cost.
When you allow trade, each player can propose trading burgers for fries. Because players differ in opportunity cost, there are mutually beneficial trades. When satisfied with her trades, but before the end of the round, each player must finalize production.
Trade allows an uncoupling of production and consumption. As long as there is a difference in opportunity cost, then there are trades that simultaneously benefit both players. Importantly, this holds even if one has an absolute advantage in both goods.
With total inputs fixed, the linear production model highlights the concept of opportunity cost, i.e., how many fries could I have made with the time I now devote to producing an extra burger. This allows clear distinction between having an absolute advantage (requiring less time to produce a burger) and comparative advantage (needing to give up fewer fries in order to produce a burger).
With interrelated production, consumption, and trade decisions, comparative advantage presents a complex environment. We recommend the following protocol:
The main learning objectives are supported by the default parameters. You can, however, change each type's Production Possibilities Frontier (PPF). If you do, remember that the greater the difference in opportunity cost (fries per minute/burgers per minute), the easier it will be for your students to find mutually beneficial trades. Likewise, if one type's opportunity cost is less than 1 and the other's is greater than 1, then 1 for 1 trades (which are focal) will be mutually beneficial.
For the primary learning objective, you want to show that trade allowed individuals (in at least some pairs) to consume outside of their PPF. In addition, you may want to show that this happened because individuals at least partially specialized in the task in which they have a comparative advantage.
Use the Go To menu (Figure 1) to view a different round of a multi-round game. If you used one of the Replay options, you can compare outcomes of this game with one of these linked games using the Compare button. Alternatively, you can switch to a linked game with the View button.
The results screen opens with presentation of data aggregated across all rounds. The first graph (Figure 2) shows each type's PPF and on it plots each student's final production choice across all rounds. The dot size increases in the number of observations. With trade enabled, you should see each type at least somewhat specializing in its comparative advantage. The second graph (Figure 3) shows each type's PPF and plots each student's "consumption" (i.e., the number of burgers and fries the student brings on the truck) across all rounds. In games with trade, you will see individuals consuming outside of their PPF.
Note: Because students allocate time in whole minutes, a student's outcome (accounting for trade) may only be close to, and not exactly on, the depicted 45-degree line where the number of burgers equals the number of fries.
Use the Go To menu to view round-specific data. This view defaults to the Production & Trade panel shown in Figure 4. The top summary table allows you to quickly identify which groups benefitted from trade. The top row presents the no-trade optimum: number of burgers and fries, and total payoff, for each player. Subsequent rows show the same information, after taking into account trade, for each group. From this table, you can easily see which groups found mutually beneficial trades by comparing each player's payoff to the no-trade optimum. You can graph a particular group's choices by marking that group's radio button.
Other panels in the round-specific results give the distributions of production decision and payoffs for each type across all groups. In rounds without trade, the production graphs will show how many are correctly allocating their time, and in round with trade these graphs will show specialization in production.
The robot makes offers which are sampled from the range of possible offers based on two criteria:
Additionally, the robot waits 20 seconds before making the first offer and stops trading 25 seconds before the end or when no more mutually beneficial trades are possible.
Because of the logic of the robot strategy, it will not propose any trades if the round duration is set to less than 45 seconds.