Fair and Truthful Giveaway Lotteries
Authors: Tal Arbiv, Yonatan Aumann4785-4792
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the question of how best to design such lotteries. We first establish the desired properties of such lotteries, in terms of fairness and efficiency, and define the appropriate notions of strategy proofness (providing that agents cannot gain by misrepresenting the true groups, e.g. joining or splitting groups). We establish inter-relationships between the different properties, proving properties that cannot be fulfilled simultaneously (e.g. leximin optimality and strong group stratagy proofness). Our main contribution is a polynomial mechanism for the problem... |
| Researcher Affiliation | Academia | Tal Arbiv,1,2 Yonatan Aumann1 1Bar Ilan University 2The College of Management Academic Studies talarbiv7@gmail.com, aumann@cs.biu.ac.il |
| Pseudocode | Yes | Algorithm 1: Iterative Probability Maximization (IPMAX) Input: families F1, . . . , Fn, ordered in decreasing size; c Output: Distribution D 1: H ; ˆpˆpˆp H () (the empty list) 2: for i = 1 to n do 3: Solve LP(H, ˆp H). Let D be the optimal assignment and ˆp the optimal value 4: H H {i}; ˆp(Fi) ˆp 5: end for 6: return D |
| Open Source Code | No | The paper does not provide any concrete access information (link or explicit statement of release) for open-source code related to the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets, therefore it does not mention dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies with datasets, therefore it does not mention validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and proofs, thus it does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical algorithms and their complexity but does not list any specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe computational experiments, therefore it does not include details on experimental setup such as hyperparameters or training configurations. |