Synergistic Small Worlds that Drive Technological Sophistication

Advanced economies exhibit a high degree of sophistication in the creation of various products. While critical to such sophistication, the nature and underlying structure of the interactions taking place inside production processes remain opaque when studying large systems such as industries or entire economies. Using partial information decomposition, we quantify the nature of these interactions, allowing us to infer how much innovation stems form specific input interactions and how they are structured. These estimates yield a novel picture of the nuanced interactions underpinning technological sophistication. By analyzing networks of synergistic interactions, we find that more sophisticated industries tend to exhibit highly modular small-world topologies; with the tertiary sector as its central connective core. Countries and industries that have a well-established connective core and specialized modules exhibit higher economic complexity and output efficiency. Similar modular networks have been found to be responsible for maintaining a balance between integration and segregation of information in the human brain, suggesting a universal principle underlying the organization of sophisticated production processes

Causality and emergence in complex systems: Perspectives from information theory

Information theory is becoming increasingly useful in the field of complex systems. In part because of the greater availability of data from various real-world systems but especially because of its ability to capture complex interdependencies in systems with many interacting parts. This thesis explores many such applications of information theory on a wide range of systems to understand two central themes in complexity sciences: \textit{Causality} and \textit{Emergence}. Inspired by Judea Pearl's ladder of causation, the type of analysis conducted in order to achieve the goals of this thesis are divided into three main parts, \textit{Associations}, \textit{Interventions} and \textit{Modelling}. These are the three probes used in this thesis to study causality in complex systems in this thesis. The first part deals with passively observing local, network-level and higher-order differences in the statistical associations in the brain activity that lead to an altered resting-state perception in schizophrenia. Here we find two interesting differences, firstly an increase in directed functional connectivity from frontal to the posterior brain regions and abnormal higher-order information processing in the parietal region of the brain. In the second part, we study the improvisational state of mind in musicians by studying their brain activity. We also explore the impact of this intervention on the audience that witnesses this improvisation in a concert-experiment setting. The findings here relate closely to the entropic brain hypothesis and yield useful and objective measures of engagement among the audience. Finally, we explore models of perception, opinion dynamics and evolution in the later part of this thesis that uses mathematical modelling to explore emergent phenomena like psychosis, polarization and species-environment interactions. These models enable us to highlight the role of aberrant Bayesian priors in schizophrenia and psychedelics, echo chambers in polarization and higher-level organization of species in ecosystems.Open Acces