移动数据交易：行为经济学分析与算法设计
Mobile Data Trading: Behavioral Economics Analysis and Algorithm Design
摘要：
受最近推出的移动数据交易市场（例如，中国移动香港第二交易所）为例，研究未来数据需求不确定性下的移动数据交易问题。我们引入了一个基于经纪人的市场，卖方和买方分别向匹配市场供求的交易平台提出他们的买卖数量。为了理解用户的现实交易行为，提出了一个行为经济学中的前景理论模型，其中包括广泛采用的期望效用理论作为特例。虽然PT建模导致了一个具有挑战性的非凸优化问题，最优解的特点是利用单峰结构的目标函数。基于我们的分析，我们设计了一种算法来帮助估计用户的风险偏好，并考虑最新的市场和使用信息，动态地提供交易建议。模拟结果表明，风险偏好对用户的决策和结果有显著影响：风险厌恶型主导用户可以保证较高的最小利润，而风险寻求型主导用户可以获得较高的最大利润。通过与EUT基准的比较，表明具有低参考点的PT用户更愿意购买移动数据。此外，当高未来数据需求的概率较低时，由于与EUT用户相比的概率失真，PT用户更愿意购买移动数据。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
