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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fair, Individually Rational and Cheap Adjustment
Authors: Gleb Polevoy, Marcin Dziubiński
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We now design fair, individually rational and implementable adjustments to pre-Bayesian games. In this section, the planner possesses complete knowledge of the realised types. Assuming complete information in this section simplifies the definitions as follows. Consider a normal-form game N, (Ai)i N, (ui)i N , where any player i N = {1, . . . , n} has the action set Ai and her utility is ui : A R, where A = Qn i=1 Ai. Here, action and strategy coincide. An action ai of i is strictly dominant if that action is strictly preferable to i, regardless of the others actions. Formally, ui(ai, b i) > ui(a i, b i), a i Ai \ {ai} , b i A i. Slightly abusing notation, we call a A strictly dominant if ai is strictly dominant, for each i N. The notions of adjustment, implementability, fairness and individual rationality remain unchanged. We obtain the following constructive result. We consider 2 players in the body of the paper; the general case is omitted for lack of space. |
| Researcher Affiliation | Academia | Gleb Polevoy1 , Marcin Dziubi nski2 1Paderborn University 2University of Warsaw EMAIL, EMAIL |
| Pseudocode | Yes | Figure 1: The adjustment for 2 players: we subsidise the player who plays the desired profile and fine the unique deviator. |
| Open Source Code | No | The paper does not contain any statements about releasing source code or links to repositories. |
| Open Datasets | No | The paper is theoretical and does not use any datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or hyperparameters. |