How do perception biases affect modal choice simulation?
A survey about perceptions of mobility, to inform an agent-based simulator of modal choice
February 18, 2025
https://arxiv.org/pdf/2502.12058This paper explores how people choose their transportation (car, bike, bus, walking) and builds a simulation to model these choices. It considers practical factors like cost and time, but also psychological factors like habits and biases.
For LLM-based multi-agent systems, this research offers insights into:
- Agent Modeling: The described agent model, with priorities, biases, and habits, could inform the design of more realistic and human-like agents in multi-agent simulations. LLMs could be employed to generate diverse agent profiles based on the described criteria.
- Simulation Design: The NetLogo simulation demonstrates a method for simulating complex social systems with relatively simple agent rules. This could be adapted to LLM-based systems, where the LLMs could handle more nuanced decision-making processes within the agents.
- Bias Integration: The paper’s emphasis on biases and habits underscores the importance of incorporating these factors into agent behavior. LLMs themselves can exhibit biases, and this research provides a framework for explicitly modeling and mitigating such biases within multi-agent systems.
- Habit Formation and Change: The simulation’s handling of habit formation and disruption provides a valuable model for how LLMs might learn and adapt their behavior within a multi-agent context. This could also be used to explore how to influence agent behavior and drive system-level change.