The process of buying and selling textbooks is a challenge that most Penn students face every semester, and it has yet to be conquered in a successful way. Oftentimes students spend exorbitant amounts on textbooks at the beginning of each semester, and fail to resell them after use--whether that is because the bookstore will not buy them back, or the students are simply lazy and/or cannot connect with the right buyers. Between the expense, the weight, and the space they take up, having used textbooks around is truly a burden. Existing platforms that enable students to buy and sell textbooks are inconvenient and inefficient, as they require significant effort and often do not result in a transaction--let alone clear the market.
Thus, Bookworm aims to become the most convenient and efficient solution on the market for all textbook problems. In terms of front-end, Bookworm is a mobile app with an easy-to-use interface and features such as a barcode scanner that speed up the process of uploading books. The back-end of the app employs an algorithm that corresponds to a double auction economic model designed to optimally match buyers and sellers of a given textbook by finding a competitive equilibrium and clearing the market.
By taking care of the matching process for users, Bookworm eliminates the burden of having to wait for a buyer or seller with the complementary supply or demand, respectively. The algorithm also maximizes the number of transactions possible given the price ranges requested by the users, providing users with the greatest probability of success in solving their textbook problems. With each iteration of the algorithm for a given book, in addition to falling within each user’s requested ranges, the price is consistent for every buyer-seller pair, making the process fair for anyone involved. Both the convenience factor as well as the matching algorithm give Bookworm a competitive edge over existing platforms because they improve user experience in making textbook transactions.
Fall semester our focus was on developing both this economic model and corresponding algorithm, as well as the fundamental features of the app. The algorithm was tested with data collected from a survey and proved to be efficient in that it cleared 90% of the market, and of the buyers and sellers who were not paired to make a trade, not one of them requested a price that a rational counterpart would accept. We plan to beta test the app in January as students look to sell their books from fall semester and buy their books for spring semester, and we will improve the app accordingly based on feedback regarding user experience.
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