Evolution of Collective Fairness in Hybrid Populations of Humans and Agents
Authors: Fernando P. Santos, Jorge M. Pacheco, Ana Paiva, Francisco C. Santos6146-6153
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Firstly, we run an online experiment to evaluate how humans react to different group decision rules. Secondly, we propose a new analytical model to shed light on how such behaviors may have evolved. Thirdly, we adapt our model to include agents with fixed behaviors. |
| Researcher Affiliation | Academia | Fernando P. Santos,1,2,4 Jorge M. Pacheco,3,4 Ana Paiva,1 Francisco C. Santos1,4 1INESC-ID and Instituto Superior T ecnico, Universidade de Lisboa, IST-Taguspark, 2744-016 Porto Salvo, Portugal 2Princeton University, Department of Ecology and Evolutionary Biology, 08544 NJ, USA 3CBMA and Departamento de Matem atica e Aplicac oes, Universidade do Minho, 4710-057 Braga, Portugal 4ATP-group, P-2744-016 Porto Salvo, Portugal |
| Pseudocode | No | The paper describes mathematical models and equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper states that 'we recruited individuals using Amazon Mechanical Turk (AMT)', which is a platform for data collection, not a public dataset. The collected data is not stated to be publicly available. |
| Dataset Splits | No | The paper describes experimental conditions ('For each combination of M = {1, N 1} with N = {5, 10} we collected 100 pairs of strategies') but does not specify dataset splits (e.g., training, validation, test sets) in the context of machine learning model training. |
| Hardware Specification | No | The paper mentions conducting experiments on 'Amazon Mechanical Turk (AMT)' for human data collection, but it does not specify any hardware used for running the analytical models or simulations (e.g., CPU, GPU, or specific server configurations). |
| Software Dependencies | No | The paper references concepts like 'Evolutionary Game Theory (EGT)' and the 'Roth-Erev algorithm' but does not list any specific software components or libraries with version numbers used for implementation. |
| Experiment Setup | Yes | For each combination of M = {1, N 1} with N = {5, 10} we collected 100 pairs of strategies (p, q). We followed the strategy method (de Melo, Marsella, and Gratch 2018)... Parameters used: h = 0.6, l = 0.1, Z = 100, µ = 0.02, N = 10. |