Ethics, Prosperity, and Society: Moral Evaluation Using Virtue Ethics and Utilitarianism
Authors: Aditya Hegde, Vibhav Agarwal, Shrisha Rao
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our simulations show that unethical agents make short term gains but are less prosperous in the long run. We find that in societies with positivity bias, unethical agents have high incentive to become ethical. The opposite is true of societies with negativity bias. We also evaluate the ethicality of existing strategies and compare them with those of virtue agents. 4 Experiments and Results Agents are placed on a toroidal grid on arbitrary cells such that no two agents are on the same cell. We configure the parameters with the default values mentioned in Tables 1 and 2 unless explicitly specified otherwise. Each simulation consists of 50 agents for a given value of the ϵ parameter. We discuss our findings from simulations in the following subsections. While we present plots for a single set of simulations, the results have been verified across different parameters and random seeds to ensure robustness. |
| Researcher Affiliation | Academia | Aditya Hegde , Vibhav Agarwal and Shrisha Rao International Institute of Information Technology Bangalore, Bangalore, India {aditya.shridhar, vibhav.agarwal}@iiitb.org, shrao@ieee.org |
| Pseudocode | No | The paper describes its models and interactions using natural language and mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology, nor does it state that code will be made available in supplementary materials or via a specific repository link. |
| Open Datasets | No | The paper describes simulations with agents and initialized parameters, but it does not use or provide access information for a publicly available or open dataset. It states: 'Every agent A0 s resource is an integer denoted by r A0 and is initialized with the same value for all agents at the start of the simulation.' |
| Dataset Splits | No | The paper describes simulation experiments but does not provide specific dataset split information (like percentages or counts for training, validation, or test sets), as it does not use a traditional dataset. |
| Hardware Specification | No | The paper describes simulations but does not provide any specific hardware details (such as exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper describes a modeling framework but does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We configure the parameters with the default values mentioned in Tables 1 and 2 unless explicitly specified otherwise. Each simulation consists of 50 agents for a given value of the ϵ parameter. ... Table 1: Summary of model parameters ... Table 2: Summary of agent parameters. |