Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Coopetition Against an Amazon
Authors: Ronen Gradwohl, Moshe Tennenholtz
JAIR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper analyzes cooperative data-sharing between competitors vying to predict a consumer s tastes. We design optimal data-sharing schemes both for when they compete only with each other, and for when they additionally compete with an Amazon a company with more, better data. We show that simple schemes threshold rules that probabilistically induce either full data-sharing between competitors, or the full transfer of data from one competitor to another are either optimal or approximately optimal, depending on properties of the information structure. We also provide conditions under which firms share more data when they face stronger outside competition, and describe situations in which this conclusion is reversed. The paper primarily focuses on theoretical models, mathematical proofs, theorems, and lemmas (e.g., "Theorem 1", "Lemma 2", "Claim 1"). |
| Researcher Affiliation | Academia | Ronen Gradwohl EMAIL Ariel University Ariel 40700, Israel, Moshe Tennenholtz EMAIL The Technion Israel Institute of Technology Haifa 3200003, Israel. Both authors are affiliated with academic institutions. |
| Pseudocode | Yes | The paper includes multiple algorithm blocks, clearly structured with `procedure`, `if`, `then`, `else`, and `return` statements, such as "Mediator 1 Mediator for P = {1, 2} with jointly complete information 1: procedure Mv no A(V, W) V = P1(ω) and W = P2(ω) for realized ω Ω 3: i arg maxk {1,2} vk 4: j 3 i 5: if vi 1/2 then return (gω, gω) Full data-sharing 7: gj s j(ω) 8: Choose γ [0, 1] uniformly at random. 9: if γ < 2 2vi αj 10: return (gω, gω) 11: else if i = 1 then return (gω, gj) 12: else return (gj, gω)" (and similar for Mediator 2, 3, 4, 5, and 6). |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to any code repositories for the methodologies described. |
| Open Datasets | No | The paper describes theoretical models and scenarios for data-sharing but does not use or refer to any specific publicly available datasets for experimental evaluation. The examples given are illustrative and abstract. |
| Dataset Splits | No | The paper is theoretical and does not perform empirical experiments requiring dataset splits. No information on training, validation, or test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve running experiments on specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper describes theoretical models and algorithms but does not mention any specific software implementations or list software dependencies with version numbers. |
| Experiment Setup | No | The paper is a theoretical work and does not describe any empirical experimental setup, hyperparameters, or system-level training settings. |