Related papers: Crowd: A Social Network Simulation Framework
Agent Based Models are very popular in a number of different areas. For example, they have been used in a range of domains ranging from modeling of tumor growth, immune systems, molecules to models of social networks, crowds and computer…
In this short note we propose using agentic swarms of virtual labs as a model of an AI Science Community. In this paradigm, each particle in the swarm represents a complete virtual laboratory instance, enabling collective scientific…
With the rise of social media, misinformation has become increasingly prevalent, fueled largely by the spread of rumors. This study explores the use of Large Language Model (LLM) agents within a novel framework to simulate and analyze the…
Recent advancements in Large Language Models offer promising capabilities to simulate complex human social interactions. We investigate whether LLM-based multi-agent simulations can reproduce core human social dynamics observed in online…
Understanding the dynamics of pedestrian crowds is an outstanding challenge crucial for designing efficient urban infrastructure and ensuring safe crowd management. To this end, both small-scale laboratory and large-scale real-world…
Mobile Crowdsensing has become main stream paradigm for researchers to collect behavioral data from citizens in large scales. This valuable data can be leveraged to create centralized repositories that can be used to train advanced…
Crowd management is a complex, challenging and crucial task. Lack of appropriate management of crowd has, in past, led to many unfortunate stampedes with significant loss of life. To increase the crowd management efficiency, we deploy…
Crowdsourcing refers to the arrangement in which contributions are solicited from a large group of unrelated people. Due to this nature, crowdsourcers (or task requesters) often face uncertainty about the workers' capabilities which, in…
Agentic AI systems are increasingly deployed not in isolation, but inside social environments populated by other agents and humans, such as in social media platforms, multi-agent LLM pipelines or autonomous robotics fleets. In these…
Crowd algorithms often assume workers are inexperienced and thus fail to adapt as workers in the crowd learn a task. These assumptions fundamentally limit the types of tasks that systems based on such algorithms can handle. This paper…
This paper discusses the implementation of a tactical network simulation tool. The tool is called Tactical Network Modeller (TNM). TNM uses some novel techniques to simplify the building of the network model using graph theory constrained…
Agent-Based Models are very useful for simulation of physical or social processes, such as the spreading of a pandemic in a city. Such models proceed by specifying the behavior of individuals (agents) and their interactions, and…
Networked environments shape how information embedded in narratives influences individual and group beliefs and behavior. This raises key questions about how group communication around narrative media impacts belief formation and how such…
The practical utility of agent-based models in decision-making relies on their capacity to accurately replicate populations while seamlessly integrating real-world data streams. Yet, the incorporation of such data poses significant…
Agent-based modeling (ABM) is a well-established paradigm for simulating complex systems via interactions between constituent entities. Machine learning (ML) refers to approaches whereby statistical algorithms 'learn' from data on their…
Recent advances in large language models (LLMs) have enabled human-like social simulations at unprecedented scale and fidelity, offering new opportunities for computational social science. A key challenge, however, is the construction of…
Traditional agent-based models (ABMs) of opinion dynamics often fail to capture the psychological heterogeneity driving online polarization due to simplistic homogeneity assumptions. This limitation obscures the critical interplay between…
Since the past few decades, human trajectory forecasting has been a field of active research owing to its numerous real-world applications: evacuation situation analysis, deployment of intelligent transport systems, traffic operations, to…
Agent-based modeling and simulation (ABMS) has been a popular approach to modeling autonomous and interacting agents in a multi-agent system. Specifically, ABMS can be applied to connected and automated vehicles (CAVs), since CAVs can be…
This study offers a new paradigm of individual-level modeling to address the grand challenge of incorporating human behavior in epidemic models. Using generative artificial intelligence in an agent-based epidemic model, each agent is…