Statistical Issues in Agent-Based Models | AISC

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Published on ● Video Link: https://www.youtube.com/watch?v=lVn2rC25kRI


Duration: 47:58
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For slides and more information on the paper, visit https://ai.science/e/statistical-issues-in-agent-based-models--WXi513bzgyWzjLjU2LYt

Speaker: Professor David Banks; Host: Nour Fahmy

Motivation:
Agent-based models (ABMs) are computational models used to
simulate the actions and interactions of agents within a system.
Usually, each agent has a relatively simple set of rules for how it
responds to its environment and to other agents. These models are used
to gain insight into the emergent behavior of complex systems with many
agents, in which the emergent behavior depends upon the micro-level
behavior of the individuals. ABMs are widely used in many fields, and
this talk reviews some of those applications. However, as relatively
little work has been done on statistical theory for such models, this
talk also points out some of those gaps and recent strategies to address
them



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