General Robustness Evaluation of Incentive Mechanism against Bounded Rationality Using Continuum-Armed Bandits
Authors: Zehong Hu, Jie Zhang, Zhao Li6070-6078
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct extensive experiments using various mechanisms to verify the advantages and practicability of our robustness evaluation framework. ... We first validate the reliability and efficiency of our framework in a hypothetical testbed where accurate results are known, and then illustrate its generality and applicability on five popular peer prediction mechanisms and the widely-adopted procurement mechanisms in crowdsourcing. ... Figure 2: (a) Accurate EQ(γ) v.s. results of CABS; (b) Average regrets comparison of different algorithms |
| Researcher Affiliation | Collaboration | 1Alibaba Group, Hangzhou, China 2School of Computer Engineering, Nanyang Technological University, Singapore |
| Pseudocode | Yes | Algorithm 1: Simulation Scheme-I Algorithm 2: Simulation Scheme-II Algorithm 3: Bounded Rationality Level Exploration |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or a link to a code repository. |
| Open Datasets | No | The paper conducts experiments on hypothetical testbeds and simulations with defined parameters (e.g., "agent s type θ follows the uniform distribution U[0.1, 10]", "workers true desired wage θ follows Be(ψ1, ψ2)") rather than using pre-existing, publicly available datasets with specific access information (links, DOIs, or formal citations). |
| Dataset Splits | No | The paper describes validating its *solver* and framework, but it does not specify train/validation/test splits for any datasets. The experiments are based on simulated scenarios with defined distributions. |
| Hardware Specification | No | The paper does not provide any details about the specific hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, specific solvers or libraries) needed to replicate the experiments. |
| Experiment Setup | Yes | For the hypothetical testbed, we consider a simple scenario where only one agent exists. Suppose agent s type θ follows the uniform distribution U[0.1, 10]. Given θ, agent s action a follows the beta distribution Be(θ, t), where t = 1 2 log(1 γ). The performance measurement g is set as g(γ) = a, and the performance robustness RP is calculated using our framework. In this case, EQ(γ) can be theoretically calculated as 1 C0 + t 9.9 log t+0.1 t+10 . Figure 2a shows the computation results of CABS when C0 = 0.5. ... we keep δ as 0.2 to ensure the reliability of the evaluation results. ... For bounded rationality models, we consider the malicious bad-mouthing agents that intentionally report 0 (Du, Huang, and others 2013). ... We assume workers true desired wage θ follows Be(ψ1, ψ2). Besides, worker s bounded rational report a = θ + (1 θ) δ, where δ Be(1, 1.5 log(γ)) and γ [0, 1] can be regarded as workers bounded rational level4. Moreover, since truthful reporting is always the dominant strategy in BFM, RI 1. ... Here, n denotes the number of participating workers, and the total budget is set as 75. |