Name
Abstract | ||
|---|---|---|
Human decision-making is influenced by social, psychological, neurological, emotional, normative, and learning factors, as well as individual traits like age and education level. Social/cognitive computational models that incorporate these factors are increasingly used to study how humans make decisions. A result is that agent models, within agent-based modeling (ABM), are becoming more heavyweight, i.e., are more computationally demanding, making scalability and at-scale simulations all the more difficult to achieve. To address these challenges, we have developed an ABM simulation framework that addresses data-intensive simulation at-scale. We describe system requirements and design, and demonstrate at-scale simulation by modeling 3 million users (each as an individual agent), 13 million repositories, and 239 million user-repository interactions on GitHub. Simulations predict user interactions with GitHub repositories, which, to our knowledge, are the first simulations of this kind. Our simulations demonstrate a three-order of magnitude increase in the number of cognitive agents simultaneously interacting. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5555/3306127.3331884 | adaptive agents and multi-agents systems |
Keywords | Field | DocType |
agent-based simulation,simulation framework,distributed simulation | Computer science,Matrix (mathematics),Normative,Computational model,Artificial intelligence,Cognition,System requirements,Machine learning,Scalability,Distributed computing | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Parantapa Bhattacharya | 1 | 140 | 9.82 |
| Saliya Ekanayake | 2 | 90 | 9.34 |
| Chris J. Kuhlman | 3 | 216 | 25.03 |
| Christian Lebiere | 4 | 1152 | 253.98 |
| Don Morrison | 5 | 1 | 0.71 |
| Samarth Swarup | 6 | 213 | 28.37 |
| Mandy Wilson | 7 | 1 | 1.73 |
| Mark G. Orr | 8 | 0 | 0.34 |