Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
An Ambiguity Aversion Model for Decision Making under Ambiguity
Authors: Wenjun Ma, Xudong Luo, Yuncheng Jiang
AAAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 17,586 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,920 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Some insightful properties of our model and the validating on two famous paradoxes show that our model indeed is a better alternative for decision making under ambiguity. and Finally, we will valid our model by solving two famous paradoxes. |
| Researcher Affiliation | Academia | 1 School of Computer Science, South China Normal University, Guangzhou, China. 2 Institute of Logic and Cognition, Department of Philosophy, Sun Yat-sen University, Guangzhou, China. |
| Pseudocode | No | The paper defines concepts and provides mathematical formulas but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making its source code available or providing a link to a code repository. |
| Open Datasets | No | The paper validates its model using well-known conceptual paradoxes (Ellsberg and Machina) which are scenarios rather than traditional datasets with explicit access information. |
| Dataset Splits | No | The paper does not describe dataset splits (e.g., training, validation, test) as it focuses on theoretical model validation against conceptual paradoxes rather than empirical data evaluation. |
| Hardware Specification | No | The paper does not mention any specific hardware specifications used for experiments or computations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical model development and mathematical analysis of paradoxes, and therefore does not include details on experimental setup, hyperparameters, or training configurations. |