Emergence and Exploitation of Collective Intelligence of Groups

This dissertation deals with the emergence and exploitation of the collective intelligence of human groups. The first part of the work (chapter 2) aims to review the mechanisms beyond the swarming behaviors in natural systems, focusing on their properties, potentialities, and limitations, as well as providing the state of the art in the developing field of swarm robotics. In chapter 3, some of the most known biologically inspired optimization algorithms, are introduced, highlighting their variants, merits and drawbacks. In chapter 4, the author introduces a new decision-making model (DMM), firstly proposed by Carbone and Giannoccaro (Carbone & Giannoccaro, 2015) for solving complex combinatorial problems, showing a detailed analysis of its features and potentialities. In Chapter 5 an application of the DMM to the simulation of a management problem, easily adaptable to the simulation of any kind of social decision-making problems, is reported. In chapter 6 the author introduces a novel optimization algorithm belonging to the class of swarm intelligence optimization methods. The proposed algorithm, referred as Human Group Optimization algorithm (HGO), is developed within the previously mentioned DMM (Carbone & Giannoccaro, 2015) and emulates the collective decision making process of human groups. To test the ability of the HGO algorithm, we compare its performance with those of the Simulated Annealing (SA), and Genetic Algorithm (GA) in solving NP-complete problems, consisting in finding the optimum on a fitness landscape, the latter generated within the Kauffman NK model of complexity. Chapter 8 contains all the mathematical tools and the basic notions, necessary to a complete understanding of the models and procedures mentioned in the work.