# Mobile Data Trading: Behavioral Economics Analysis and Algorithm Design

## TL;DR

This paper models mobile data trading using behavioral economics, specifically prospect theory, to better understand user behaviors and design algorithms for trading recommendations considering risk preferences and market dynamics.

## Contribution

It introduces a prospect theory-based model for mobile data trading, characterizes the non-convex optimization problem, and develops an algorithm for dynamic trading recommendations based on user risk preferences.

## Key findings

- Risk-averse users secure higher minimum profits.
- Risk-seeking users achieve higher maximum profits.
- PT users with low reference points are more willing to buy data.

## Abstract

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.