Summary of Promising and Worth-to-try Future Directions For Advancing State-of-the-art Surrogates Methods Of Agent-based Models in Social and Health Computational Sciences, by Atiyah Elsheikh
Promising and worth-to-try future directions for advancing state-of-the-art surrogates methods of agent-based models in social and health computational sciences
by Atiyah Elsheikh
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Dynamical Systems (math.DS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the issue of excessive runtime and computational demand when executing agent-based models (ABMs) for large-scale simulations. The problem arises from the exponential relationship between model size, population size, and number of parameters. To mitigate this challenge, the authors propose using surrogate models, which are computationally less demanding. These methods have been employed in other fields, such as nonlinear dynamical modeling, but not extensively in ABMs within the Social Health Computational Sciences (SHCS) domain. The paper highlights the potential usefulness of these surrogate models for advancing the state-of-the-art in establishing efficient simulations for ABMs in SHCS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a problem with big computer simulations called agent-based models. These simulations are like digital societies that can be very slow and use a lot of computer power, especially when they’re really large. The authors suggest using shortcuts or tricks to make these simulations run faster without losing accuracy. They found that methods used in other fields might work for ABMs too, but haven’t been tried before. This could help make big simulations better and more useful. |
