MobLab
Guides
Aa

# All-Pay English Auction

## Game Description

In groups of any size, students participate in an R&D race (all-pay English auction). Each participant knows only her private value for the project. Participants enter the amount they wish to invest, and can increase it in response to other students' investments. When the race ends, the participant with the highest investment wins and gets her value. However, all participants have to pay for their investments and only the winner realizes the value.

This game teaches students about all-pay auctions and how they can be thought of as representing Research and Development (R&D) races. It can be used to explore other scenarios where all-pay auctions apply, such as raising money for a charity event.

### Learning Objective 1: All-Pay English Auctions

Players learn bidding strategies in an all-pay, English auction.

### Learning Objective 2: Contextual Exploration

Allows exploration of other scenarios where all-pay auctions might be useful.

## Brief Instructions

Each bidder receives a value for the unit for sale. His or her true value for the unit is drawn from a uniform distribution with default endpoints Lowest Bidder Value = \$10 and Highest Bidder Value = \$100. Each bidder draws a new value each period if Periods > 1.

Bidders can bid at any time in the auction so long as their bid improves the previous bid by the Price Increment. Regardless of if they win the unit or not, they are required to pay their last bid.

## Results

Results are presented for each period played. If you set Periods > 1, use the Go To: menu (Figure 1) to switch to a different period. Optimally, participants should bid no more than their valuation. However, without practice it is common to see bids that are greater than a player's individual valuation.

We first present a table (Figure 2), which summarizes auction behavior for each group. The columns display optimal surplus, surplus, market efficiency, and revenue. To change which group's results are displayed, use the radio buttons to the left of each group number to choose a different group.

For each group, we present a table that shows bidding behavior for the selected period (Figure 3). Bids, and their relation to a participant's valuations, are shown on the graph for the selected group. A best fit line is also displayed for reference. In addition, we include points for bids and valuations of other groups on this graph. This makes it easy to compare a specific group's bidding behavior to the overall session. Each one of these elements on the graph can be hidden by clicking on the check box in the legend.

## Robot Play

Our robot (i.e., an automated player) strategy is the following:

If the current high bid is lower than their value, the robot will increase the bid to a random number between the highest bid and a number halfway between their value and the highest bid.

#### Example

If the current bid is \$50 and the robot's value is \$70, the robot will raise the bid to a random number between \$51 and \$60.