Strategies, Credences, and Shannon Entropy: Reasoning about Strategic Uncertainty in Stochastic Environments
Authors: Wojciech Jamroga, Michał Tomasz Godziszewski, Aniello Murano
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As technical results, we compare the epistemic and information-theoretic extensions of PATL with respect to their expressiveness, succinctness, and complexity of model checking. ... In terms of technical results, we prove that (1) PATLC and PATLH have the same model checking complexity; (2) PATLC is strictly more expressive than PATLH; and conjecture that (3) PATLH is exponentially more succinct than PATLH. ... The purpose of Theorem 6 is to use Formula Size Games for proving that PATLH is exponentially more succinct than PATLC. |
| Researcher Affiliation | Academia | 1 Institute of Computer Science, Polish Academy of Sciences 2Nicolaus Copernicus University, Toru n, Poland 3University of Naples Federico II, Italy 4University of Lodz, Poland |
| Pseudocode | No | The paper primarily focuses on introducing new logical formalisms (PATLH, PATLC), proving theorems about their expressiveness, succinctness, and complexity of model checking, and illustrating their application with case studies. It does not present any structured pseudocode or algorithm blocks for implementation. |
| Open Source Code | No | The paper does not contain any statements about the release of source code for the methodology described, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper introduces theoretical logical frameworks (PATLH and PATLC) and applies them to illustrative scenarios like 'Probabilistic Analysis of Voting' and 'Signal Integrity'. While the voting scenario mentions 'opinion polls' with example probabilities, no actual dataset (publicly available or otherwise) is used for empirical evaluation, nor is any concrete access information provided for such data. |
| Dataset Splits | No | The paper is theoretical, introducing new logical formalisms and analyzing their properties. It does not conduct experiments with empirical datasets that would require specific training, validation, or test splits. |
| Hardware Specification | No | The paper is focused on theoretical developments in logic, including expressiveness, succinctness, and model checking complexity. It does not describe any experimental setup that would involve specific hardware for computation or simulation. |
| Software Dependencies | No | The paper is theoretical, focusing on logical frameworks and their properties. It discusses model checking complexity but does not provide details on specific software tools, libraries, or their version numbers that would be used for implementation or experimentation. |
| Experiment Setup | No | The paper introduces new logical formalisms and proves theoretical properties about them (expressiveness, succinctness, model checking complexity). It does not describe any empirical experiments or system implementations that would require detailing hyperparameters or training configurations. |