Communication and negotiation to improve Agent-Based Models

Abstract

[Abstract]: Agent-based models (ABM) play a fundamental role in studying and modeling complex real-world systems, primarily relying on reactive agents. Despite their simplicity, the interactions between agents and their environment enable the simulation of diverse systems, contributing to their widespread adoption, particularly in the social sciences. Similarly, though distinct in purpose, multi-agent systems (MAS) are designed to tackle complex, diverse, and distributed problems by leveraging communication, negotiation, and coordination capabilities. Both types of approaches have been used successfully in numerous areas; the power of ABM lies in thousands of interacting agents, while MAS usually employs a smaller number of agents with more capabilities. Including MAS agents’ capabilities in ABM agents allows the generation of more realistic simulations that aid in the study of the modeled systems. In this paper, we present a generic ABM model whose agents possess more capabilities, such as comm unication and negotiation, allowing this enhanced ABM to address more complex modeling problems. To exemplify the usefulness of this enhanced ABM, we propose to use it as a sandbox-tool to test “case-if” scenarios in a model that studies the evolution of a society’s opinion on a given subject, specifically in this example, the implantation of superblocks in the city of Vitoria-Gasteiz (Spain)