Mobile data trading: A behavioral economics perspective

Abstract

Motivated by the recently launched 2CM data trading platform of China Mobile Hong Kong, we study the optimal user mobile data trading problem under the future demand uncertainty. We consider a brokerage-based market, where sellers and buyers propose their selling and buying prices and quantities to the trading platform, respectively. The platform acts as a broker, which facilitates the trade by matching the supply and demand. To understand users' realistic trading behaviors, we use prospect theory (PT) from behavioral economics in the modeling, which leads to a challenging non-convex optimization problem. Nevertheless, we are able to determine the unique optimal solution in closed-form, by utilizing the unimodal structure of the objective function. When comparing with the benchmark expected utility theory (EUT), we show that a PT user with a low reference point is more willing to buy mobile data. Moreover, when the probability of high demand is low, comparing with an EUT user, a PT user is more willing to buy mobile data due to the probability distortion.

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