Study goals
To develop an agent-based simulation platform that integrates informational,
social, and individual variables, enabling analysis of how these dimensions shape group
decision-making processes and outcomes in strategic contexts.
Relevance / originality
The product addresses gaps in traditional decision models that
treat variables separately. Its novelty lies in integrating three critical dimensions
(information, social structure, and individual traits) into a single interactive and
configurable environment.
Methodology / approach
The software was implemented in Python with a Streamlit
interface, adopting agent-based modeling. Its modular architecture enables configuration
of agents, social networks, and informational scenarios, while advanced analytical
algorithms (PageRank, HITS) assess influence patterns and collective outcomes.
Main results
The platform produces reproducible simulations with metrics on individual
and collective accuracy, consensus, robustness, and equity. Results demonstrate how
different attribute combinations influence strategic decisions, providing a safe
environment to test scenarios of collective deliberation.
Theoretical / methodological contributions
he solution advances methodology by
overcoming fragmented approaches, offering an integrated model for understanding
group decision-making. It contributes theoretically by articulating causal mechanisms
explaining how information, structure, and cognition interact in shaping collective strategic
outcomes.
Social / management contributions
The product supports managers, consultants, and
policymakers by enabling the simulation of strategic alternatives and anticipating the
effects of social and informational configurations. It promotes more effective, transparent,
and aligned collective decisions, strengthening governance and coordination practices in
complex organizations.