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..
Cooperation and Learning Dynamics under Wealth Inequality and Diversity in Individual Risk
Authors: Ramona Merhej, Fernando P. Santos, Francisco S. Melo, Francisco C. Santos
JAIR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We draw our conclusions based on social simulations with populations of independent reinforcement learners with diverse levels of risk and wealth. |
| Researcher Affiliation | Academia | Ramona Merhej EMAIL INESC-ID and Instituto Superior T ecnico, Lisbon, Portugal ISIR, CNRS, Sorbonne University, Paris, France Fernando P. Santos EMAIL Informatics Institute University of Amsterdam, The Netherlands Francisco S. Melo EMAIL Francisco C. Santos EMAIL INESC-ID and Instituto Superior T ecnico Universidade de Lisboa, Portugal |
| Pseudocode | Yes | Algorithm 1: Roth-Erev RL algorithm in an adaptive population with asynchronous updates of propensities. Algorithm 2: Sampling with assortment bias. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper describes conducting "social simulations" and defines the game parameters and agent learning dynamics. It does not utilize or provide access to any pre-existing public datasets, as the data is generated through the simulations themselves. |
| Dataset Splits | No | The paper describes simulation parameters and training steps (e.g., "2.5 x 10^5 learning steps", "averaged over 5 independent runs") but does not mention dataset splits in the traditional sense of dividing a pre-existing dataset into training, validation, and test sets. |
| Hardware Specification | No | The paper mentions "computer simulations" but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run these experiments. |
| Software Dependencies | No | The paper discusses various algorithms (e.g., Roth-Erev Algorithm, Q-learning) but does not specify any software libraries, frameworks, or their version numbers used for implementing the simulations. |
| Experiment Setup | Yes | In all settings, we consider a population of Z = 200 individuals. The average wealth in the population is set to b = 1 yielding W = Z. A contribution represents 10% of an agent s wealth, i.e., c = 0.1. We set the target to be achievable if at least M = N/2 agents in the group contribute, i.e., t = Ncb/2. If the threshold target is not achieved, agents lose an additional 70% of their remaining wealth, i.e., p = 0.7. We test varying risk values r {0.1, 0.3, 0.5, 0.7, 0.9}, varying group sizes N {2, 4, 6, 8, 10, 20} and varying risk perception diversity factors ฮด {0.1, 0.2, 0.3, 0.4, 0.5}. We sample qi,0(A) from a normal distribution N(ยต = 10, ฯ = 1). The forgetting parameter is set to ฯ = 0.001. In all sections, the evaluation proceeds by allowing the agents to train for a total of 2.5 10^5 learning steps, while imposing a minimum number of K = 3 10^4 learning steps for every agent. The values reported in the three criteria correspond to the values observed at the end of the training period, averaged over 5 independent runs. |