Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.09313 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916480004653056 |
|---|---|
| author | Costa, Carlos J. Aparicio, Joao Tiago Aparicio, Manuela |
| author_facet | Costa, Carlos J. Aparicio, Joao Tiago Aparicio, Manuela |
| contents | The widespread adoption of generative artificial intelligence (AI) has fundamentally transformed technological landscapes and societal structures in recent years. Our objective is to identify the primary methodologies that may be used to help predict the economic and social impacts of generative AI adoption. Through a comprehensive literature review, we uncover a range of methodologies poised to assess the multifaceted impacts of this technological revolution. We explore Agent-Based Simulation (ABS), Econometric Models, Input-Output Analysis, Reinforcement Learning (RL) for Decision-Making Agents, Surveys and Interviews, Scenario Analysis, Policy Analysis, and the Delphi Method. Our findings have allowed us to identify these approaches' main strengths and weaknesses and their adequacy in coping with uncertainty, robustness, and resource requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09313 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Socio-Economic Consequences of Generative AI: A Review of Methodological Approaches Costa, Carlos J. Aparicio, Joao Tiago Aparicio, Manuela Computers and Society The widespread adoption of generative artificial intelligence (AI) has fundamentally transformed technological landscapes and societal structures in recent years. Our objective is to identify the primary methodologies that may be used to help predict the economic and social impacts of generative AI adoption. Through a comprehensive literature review, we uncover a range of methodologies poised to assess the multifaceted impacts of this technological revolution. We explore Agent-Based Simulation (ABS), Econometric Models, Input-Output Analysis, Reinforcement Learning (RL) for Decision-Making Agents, Surveys and Interviews, Scenario Analysis, Policy Analysis, and the Delphi Method. Our findings have allowed us to identify these approaches' main strengths and weaknesses and their adequacy in coping with uncertainty, robustness, and resource requirements. |
| title | Socio-Economic Consequences of Generative AI: A Review of Methodological Approaches |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2411.09313 |