Equilibrium Behavior in Competing Dynamic Matching Markets
Authors: Zhuoshu Li, Neal Gupta, Sanmay Das, John P. Dickerson
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper provides the first analysis of equilibrium behavior in dynamic competing matching market systems first from the points of view of individual participants when market policies are fixed, and then from the points of view of markets when agents are stochastic. When compared to single markets running social-welfare-maximizing matching policies, losses in overall social welfare in competitive systems manifest due to both market fragmentation and the use of non-optimal matching policies. We quantify such losses and provide policy recommendations to help alleviate them in fielded systems. We investigate equilibrium behavior via simulation of two markets in MODEL II in Section 5. Figure 1a shows an example of the results when the fraction of random agents is φ = 0.4. Figure 1b shows overall social welfare in the Competing system with a single Greedy market and a single Patient market under different settings of θ when the fraction of random agents φ = 0.4. Finally Figure 1c shows the range of separating and pooling equilibria as a function of φ, the proportion of random agents. |
| Researcher Affiliation | Academia | Zhuoshu Li1, Neal Gupta2, Sanmay Das1 and John P. Dickerson2 1 Washington University in St. Louis 2 University of Maryland |
| Pseudocode | No | The paper describes matching algorithms (Greedy, Patient) conceptually and through equations, but it does not include any formal pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any links to open-source code or state that code is available in supplementary materials or upon request. |
| Open Datasets | No | The paper mentions using 'Monte Carlo simulations' and 'simulated the long-term utilities', indicating that data was generated through simulation rather than using an existing public dataset. No information on publicly available datasets or access links is provided. |
| Dataset Splits | No | The paper describes using 'Monte Carlo simulations' and 'bootstrap samples' but does not specify traditional train/validation/test splits with percentages or sample counts for model training and evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the simulations or experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | Yes | We simulated the long-term utilities for two markets M1 and M2 with Patient(α1) and Patient(α2) matching policies, respectively, for T = 250 periods, and 100 trials. We estimated the best response functions BR1 and BR2 for markets M1 and M2, respectively by simulating two markets with overlaps γ1 {0.1, 0.2, . . . , 0.9} over a grid of patience parameters α1, α2. We took X = 2500 bootstrap samples of size n = 50. ... k = 100 in our simulations. ... for p = 0.02, δ = 0.05, Ts = 2, Tl = 3. |