Mediated Cheap Talk Design
Authors: Itai Arieli, Ivan Geffner, Moshe Tennenholtz
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We characterize the set of implementable action distributions that can be obtained in equilibrium, and provide an O(n log n) algorithm (where n is the number of states) that computes the optimal equilibrium for the senders. Additionally, we show that the optimal equilibrium for the receiver can be obtained by a simple revelation mechanism. |
| Researcher Affiliation | Academia | Technion Israel Institute of Technology iarieli@technion.ac.il, ieg8@cornell.edu, moshet@technion.ac.il |
| Pseudocode | Yes | Putting everything together, our algorithm is as follows: 1. Step 1: Compute the best possible mechanism for the senders (i.e., set p(ω) = 1 for ω Ω0 and p(ω) = 0 for ω Ω1). If this mechanism gives the receiver a utility greater than or equal to β, return this configuration and terminate. 2. Step 2: Compute p0 and p1 and set the best configuration p to be the one between p0 and p1 that reports the most utility to the senders. 3. Step 3: For j = 1, 2, . . . , k, compute αj. If αj [0, 1] and pαj is better for the senders than p, set p to pαj. Return p. |
| Open Source Code | No | The paper does not provide any links to open-source code for the methodology described. It only refers to an arXiv preprint for the full version of the paper: 'The full construction can be found in the full version of the paper3. https://arxiv.org/abs/2211.14670'. |
| Open Datasets | No | The paper uses a hypothetical example with a defined state space (Ω = {ω1, ω2, ω3, ω4, ω5, ω6}) and a uniform prior for theoretical illustration. It does not use or refer to any publicly available real-world datasets for empirical evaluation or training. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments with data. Therefore, there are no training, validation, or test dataset splits mentioned. |
| Hardware Specification | No | The paper describes theoretical models, algorithms, and proofs. It does not conduct empirical experiments, and therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and describes mathematical concepts and algorithms. It does not specify any software dependencies with version numbers, as it does not involve empirical implementation or experimentation that would require them. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and characterization rather than empirical experimentation. As such, it does not provide details about an experimental setup, hyperparameters, or training settings. |