  Motivated by the recently launched mobile data trading markets (e.g., China
Mobile Hong Kong's 2nd exChange Market), in this paper we study the mobile data
trading problem under the future data demand uncertainty. We introduce a
brokerage-based market, where sellers and buyers propose their selling and
buying quantities, respectively, to the trading platform that matches the
market supply and demand. To understand the users' realistic trading behaviors,
a prospect theory (PT) model from behavioral economics is proposed, which
includes the widely adopted expected utility theory (EUT) as a special case.
Although the PT modeling leads to a challenging non-convex optimization
problem, the optimal solution can be characterized by exploiting the unimodal
structure of the objective function. Building upon our analysis, we design an
algorithm to help estimate the user's risk preference and provide trading
recommendations dynamically, considering the latest market and usage
information. It is shown in our simulation that the risk preferences have a
significant impact on the user's decision and outcome: a risk-averse dominant
user can guarantee a higher minimum profit in the trading, while a risk-seeking
dominant user can achieve a higher maximum profit. By comparing with the EUT
benchmark, it is shown that a PT user with a low reference point is more
willing to buy mobile data. Moreover, when the probability of high future data
demand is low, a PT user is more willing to buy mobile data due to the
probability distortion comparing with an EUT user.
