Collective Knowledge Systems: An Experimental Platform for Aggregation, Prediction, and Emergent Intelligence. Research Project Seeking Collaboration and Development

This work presents an experimental research framework designed to investigate the emergence of collective knowledge through independent aggregation of human judgments. This project proposes the design and future implementation of an experimental platform aimed at studying how collective knowledge emerges from the aggregation of independent individual judgments. Building upon classical observations such as the estimation experiments analyzed by Francis Galton, where the average of a large group demonstrated remarkable accuracy, this research seeks to investigate the conditions under which distributed human responses converge toward reliable outcomes. The proposed system integrates magnitude estimation tasks, predictive questions with verifiable outcomes, and multiple-choice problems under conditions of limited individual knowledge. Participants provide responses independently, without exposure to others’ answers. The central focus is the dynamic analysis of the aggregation process, including convergence, stabilization, and potential deviation as the number of participants increases. Particular attention is given to the relationship between collective error and dispersion, as well as to the influence of response range and task structure. This project is currently in an exploratory stage and aims to generate empirical evidence on how collective knowledge is constructed, under what conditions it achieves accuracy, and what its operational limits are.