Customer Sharing in Economic Networks with Costs
Authors: Bin Li, Dong Hao, Dengji Zhao, Tao Zhou
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we investigate the above customer sharing problem under an economic network setting in the view of mechanism design. Taking into consideration the transaction costs, we propose two mechanisms which can be used to incentivize the sellers to become mediators and diffuse other sellers sale information. The first mechanism is an extension of the recently proposed information diffusion mechanism (IDM) [Li et al., 2017]. We show that if the economic network forms a tree structure, the extended mechanism is individually rational (IR), incentive compatible (IC), budget balanced (BB) and efficient. Nevertheless, the extended mechanism fails to work in general graphs. Therefore, we further develop a novel mechanism called customer sharing mechanism (CSM). We prove that CSM is IR, IC, BB and efficient in general cases and the revenue generated by CSM is always higher than that given by the Vickrey auction. |
| Researcher Affiliation | Academia | Bin Li1, Dong Hao1, Dengji Zhao2 and Tao Zhou1 1University of Electronic Science and Technology of China, Chengdu, China 2 Shanghai Tech University, Shanghai, China |
| Pseudocode | Yes | Information Diffusion Mechanism with Transaction Costs (IDM-TC) Given a feasible type report profile t , allocate the commodity to buyer m = arg maxi NSWi and choose LCCm as the trading chain (i.e., efficient allocation with random tie-breaking). Denote the ordered set {1, 2, , m 1, m} by Cm. The payment policy for each agent is given as:... |
| Open Source Code | No | The paper does not provide any specific links to source code repositories, nor does it explicitly state that the code for the described methodology is publicly released or available in supplementary materials. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific dataset, public or otherwise, for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments that would involve training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications for running experiments are provided. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, as it is primarily theoretical and does not describe an implementation requiring them. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with specific hyperparameters, training configurations, or system-level settings. |