Criticality triggers the emergence of collective intelligence in groups

Abstract

A spinlike model mimicking human behavior in groups is employed to investigate the dynamics of the decision-making process. Within the model, the temporal evolution of the state of systems is governed by a time-continuous Markov chain. The transition rates of the resulting master equation are defined in terms of the change of interaction energy between the neighboring agents (change of the level of conflict) and the change of a locally defined agent fitness. Three control parameters can be identified: (i) the social interaction strength βJ measured in units of social temperature, (ii) the level of confidence β^{'} that each individual has on his own expertise, and (iii) the level of knowledge p that identifies the expertise of each member. Based on these three parameters, the phase diagrams of the system show that a critical transition front exists where a sharp and concurrent change in fitness and consensus takes place. We show that at the critical front, the information leakage from the fitness landscape to the agents is maximized. This event triggers the emergence of the collective intelligence of the group, and in the end it leads to a dramatic improvement in the decision-making performance of the group. The effect of size M of the system is also investigated, showing that, depending on the value of the control parameters, increasing M may be either beneficial or detrimental.

Team Resilience in Complex and Turbulent Environments: The Effect of Size and Density of Social Interactions

“…The team decision-making process is modelled referring to the model first developed by Carbone and Giannoccaro [24] and then by De Vincenzo et al [25]. Here, the team is engaged in solving a combinatorial decision-making problem, consisting in identifying the combination of multiple and interdependent decisions, yielding to the highest payoff for the organization.…”

“…The higher K, the more complex the landscape is. Solving a NK Kaufmann combinatorial problem, that is, finding the optimum of the NK landscape, is classified for K > 2 as a NPcomplete problem [68] (for details about the landscape generation see [25,28,29,[37][38][39][40][41]69] …”

“…To accomplish our research aim, we use simulation as research methodology and adopt the model developed first by Carbone and Giannoccaro [24] and then by De Vincenzo et al [25], which reproduces how individuals collectively make decisions in complex but static environments. Several agent models can be found in the literature aimed at reproducing the decision-making process in groups [19-21, 26, 27].…”

“…This theory argues that individuals make changes to their feelings, behaviors, and decisions, as a result of the interaction with the others [44]. Therefore, this model applies very well to teams, where individual's decision is affected by the opinion of neighbors or interacting members [24,25,[44][45][46][49][50][51].…”

Inteligencia colectiva en el aula. Un paradigma cooperativo - [Collective Intelligence in the classroom. A cooperative paradigm]

“…Esta idea se ha aplicado diversas áreas en el contexto educativo, como en medicina para mejorar los diagnósticos (Kämmer, Hautz, Herzog, Kunina-Habenicht, & Kurvers, 2017), para la toma de decisiones (De Vincenzo, Giannoccaro, Carbone, & Grigolini, 2017), por tanto, la inteligencia colectiva ya ha sido trabajada en la escuela.…”