A Decomposition of Forecast Error in Prediction Markets
Authors: Miro Dudik, Sebastien Lahaie, Ryan M. Rogers, Jennifer Wortman Vaughan
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate via numerical simulations that these bounds are tight. Our results yield new insights into the question of how to set the market s liquidity parameter and into the forecasting benefits of enforcing coherent prices across securities. (...) 7 Numerical Experiments We evaluate the tightness of our theoretical bounds via numerical simulation. We consider a complete market over K = 5 securities and simulate N = 10 traders with risk aversion coefficients equal to 1. (...) In Fig. 1 (center) we plot the bias µ (b; C) µ as a function of b for both LMSR and IND. We compare this with the theoretical approximation µ (b; C) µ b( a/N) HT ( µ) C ( µ) from Theorem 5.6. |
| Researcher Affiliation | Collaboration | Miroslav Dudík Microsoft Research, New York, NY mdudik@microsoft.com Sébastien Lahaie Google, New York, NY slahaie@google.com Ryan Rogers University of Pennsylvania, Philadelphia, PA rrogers386@gmail.com Jennifer Wortman Vaughan Microsoft Research, New York, NY jenn@microsoft.com |
| Pseudocode | No | The paper presents theoretical models, equations, and mathematical derivations, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about open-sourcing code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper states, 'We consider a complete market over K = 5 securities and simulate N = 10 traders with risk aversion coefficients equal to 1.' This indicates a simulation study rather than the use of a publicly available dataset. No public dataset source or access information is provided. |
| Dataset Splits | No | The paper describes a simulation setup rather than using an existing dataset with defined splits. There is no mention of training, validation, or test splits for data, or cross-validation. |
| Hardware Specification | No | The paper mentions that 'simulations are tractable' but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper describes the mathematical models and simulation setup but does not specify any software dependencies (e.g., programming languages, libraries, or solvers) with version numbers. |
| Experiment Setup | Yes | We consider a complete market over K = 5 securities and simulate N = 10 traders with risk aversion coefficients equal to 1. (...) We fix the ground-truth natural parameter θtrue and independently sample the belief θi of each trader from Normal(θtrue, σ2IK), with σ = 5. We consider a single-peaked ground truth distribution with θtrue 1 = log(1 ν(K 1)) and θtrue k = log ν for k = 1, with ν = 0.02. Trading is simulated according to the all-security dynamics (ASD) as described at the start of Section 6. |