A Cloaking Mechanism to Mitigate Market Manipulation
Authors: Xintong Wang, Yevgeniy Vorobeychik, Michael P. Wellman
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To study the effectiveness of cloaking, we simulate markets populated with background traders and an exploiter... Through empirical game-theoretic analysis across parametrically different environments, we evaluate surplus accrued by traders... |
| Researcher Affiliation | Academia | Xintong Wang,1 Yevgeniy Vorobeychik,2 Michael P. Wellman1 1 University of Michigan, Ann Arbor 2 Vanderbilt University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct statement or link for the open-sourcing of the methodology's code. Footnote 1 links to 'Detailed equilibrium outcomes and simulation results', which refers to data/results, not the source code. |
| Open Datasets | No | The paper describes a simulation environment where data is generated, rather than using a pre-existing, publicly available dataset. Therefore, no information on public dataset access is applicable or provided. |
| Dataset Splits | No | The paper describes agent-based simulations and parameters but does not specify explicit train/validation/test dataset splits as it generates its own data through simulation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or cloud instance types used for the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | Yes | The global fundamental time series is generated according to (1) with fundamental mean r = 105, mean reversion κ = 0.05. Each trading period lasts T = 10, 000 time steps. Background traders arrive in the market according to a Poisson distribution with a rate λa = 0.005 and the maximum number of units background traders can hold at any time is qmax = 10. Private value variance is σ2 PV = 5 106. Table 1 specifies our background trading strategy set, comprising nine versions of ZI and four of HBL. |