Taming the Matthew Effect in Online Markets with Social Influence
Authors: Franco Berbeglia, Pascal Van Hentenryck
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The paper also analyzes its properties both theoretically and experimentally and shows that the protocol dramatically reduces inequalities among the best products in the market, while preserving high predictability and efficiency. The rest of the paper briefly reviews trial and offer markets, presents the new randomized segmentation protocol, and analyzes its properties both theoretically and experimentally. We now describe experimental results on the Music Lab settings (Salganik, Dodds, and Watts 2006). |
| Researcher Affiliation | Academia | Franco Berbeglia Carnegie Mellon University fberbegl@andrew.cmu.edu Pascal Van Hentenryck University of Michigan pvanhent@umich.edu |
| Pseudocode | Yes | The RSP is presented in Figure 1: It uses two key ideas to tame the Matthew effect. Figure 1: The Randomized Segmentation Protocol (RSP). |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We now describe experimental results on the Music Lab settings (Salganik, Dodds, and Watts 2006). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train, validation, and test sets. It mentions |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The qualities of the second and third best products are modified to obtain a setting in which ϵ2 = 0.005 and ϵ3 = 0.01 (the quality of the best product is q1 = 0.8). The experiments also use a standard deviation of σ = 0.1. The presentation discusses the effectiveness of the RSP to tame the Matthew effect and the cost involved in doing so. |