Market for Lemons Guide

May 2024 · 6 minute read
Market for Lemons Guide

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MobLab’s Market for Lemons game is our implementation of a classic lemons market (Akerlof, 19701). Students are paired with one other student for this game: one as the role of buyer and one as the role of seller. The seller knows the quality of her car and makes a take-it-or-leave-it offer to a buyer, who knows only the range of possible car qualities. To facilitate learning, the buyer learns the value of the car at the end of each period, regardless of whether or not she chooses to purchase.

Learning Objective 1: Asymmetric Information

This game introduces students to a classic market with asymmetric information: one side has payoff-relevant information (car quality) that the other side wishes it had.

Learning Objective 2: Adverse Selection

Markets where one side has information that is not observable to the other side of the market can lead to adverse selection where only the worst types are transacted (low-quality cars in this case).

This is a two-sided lemon market game. To view our single-player game, check out our "Lemon Market Buyer" game in the library!

Once in their buyer/seller pairs, the student who is the seller makes a take-it-or-leave-it offer to the buyer for the car. If the buyer chooses not to purchase at the seller’s asking price, both the buyer and seller earn zero. If the buyer purchases the car at the asking price, the buyer's payoff is buyer valuation minus the price. The seller's payoff is price minus seller valuation. The buyer's valuation is equal to the seller’s valuation * Beta (%) (equal to 150 by default). Setting this greater than 100 means that transacting the car is efficient.

The difference between buyer and seller valuations (Beta (%)) is not reported on student screens. This is something you may choose to announce to your students before the game begins.

The range of seller car valuations extends from Low Value to High Value. The default range of 500 to 5000 creates an environment where buyer losses can be significant, which expedites learning that the buyer should avoid high-priced cars. This learning conclusion may come after a couple of iterations of the game.

Note: If you set Periods greater than 1, pairings remain fixed across periods. Adverse selection will be most pronounced when pairings change between periods. To change pairings, set Periods equal to 1 and choose Repeat after a game ends. With Fixed Role, a player stays in the same role but is randomly assigned with a new partner. With Rotate Role, a player switches roles and is randomly assigned a new partner. With Random, both a player’s role and next partner are randomly assigned.

The game offers a Full Information treatment in which buyers know the value of the car. This treatment allows you to compare the asymmetric market and the full information market to show how the asymmetric information for the buyer produces the unraveling of the market.

Repeat interaction may mitigate adverse selection, as a seller can increase the likelihood of future sales by dealing fairly with a buyer today. If you set Periods greater than 1, each buyer and seller pair interacts repeatedly, with car quality randomly chosen each period. For greatest effect, make sure students know that pairings are fixed, but do not tell students the number of periods.


For each game, the results screen will show one table and one graph per period. If you have played a multi-period game, use the Go To dropdown menu to switch between periods. If you have replayed a game, click the results button in the last of the string of replayed games. You can then use the View dropdown menu to easily switch between games.

The results table (Figure 2) presents a summary, for each period and the overall average, of the percent of cars sold and the percent of buyers who lost money in the market.

The graph (Figure 3) displays each car and its asking price. An observation is plotted where the seller’s value of the car (horizontal axis) intersects the seller’s asking price (vertical axis). Accepted offers are depicted by a blue triangle and rejected offers by a red square. The graph also includes two reference lines. The dashed yellow line depicts the seller’s reservation value (i.e., the 45-degree line where asking price equals seller valuation). The dashed green line depicts buyer valuation (seller valuation * Beta (%)). To interpret this graph:

Our robot (i.e., an automated player) strategies for each role are the following:

1. Akerlof, George A. (1970). “The Market for “Lemons”: Quality Uncertainty and the Market Mechanism.” Quarterly Journal of Economics, 84(3): 488–500. tiled icons

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