The INNAXIS Institute focuses its resources on the core elements of Complexity Science, namely General Systems Theory, Mathematical Modelling, Cybernetics & Computational Science, Non-Equilibrium Thermodynamics, Dynamical Systems & Chaos Theory.
Complexity Science is an ubiquitous discipline and its applications cover an extraordinary variety of fields. Among other issues, INNAXIS works actively on transport systems and traffic management, macroeconomic evolution and financial markets, innovation and learning processes, threats and opportunities of globalization.
In order to do that, as a substantial part of its approach to research, INNAXIS openly partners with many groups and individuals with relevant activity in specific areas of knowledge.
Our research activity is structured in three major areas:
Complex Autonomous Systems & Artificial Intelligence
From software agents to unmanned vehicles,, capabilities are being developed to design, build and operate intelligent artificial systems which can be used for a variety of purposes. In practical terms, they can be used in different areas of application to replace, extend or even outreach human capabilities in many tasks, especially those labeled as the 3 Ds (dull, dirty or dangerous).
On the other hand, for research purposes, complex agents are widely used for scientific simulation of multi-layered, multi-agent systems, which in turn provides deep insights into the nature of human intelligence and behaviour.
This area of research provides many of the building blocks for the other disciplines of Complexity Science.
Social Networks and Collective Learning
The extraordinary increase in connectedness between individuals and institutions all over the world is among the most relevant phenomena of the last decades, and is giving birth to new social forms, where common interests and attitudes, instead of blood relationship, are the foundations for a new type of kinship.
As tribes, families, cities and states did in the past, these forms will impact strongly on social interactions and therefore on the evolution of societies. Putting these phenomena at work for collective learning is a research challenge in itself.
While it is a generally accepted vision that networks know more than the sum of their nodes, many questions and research issues are still pending, with a huge potential for practical applications, for instance in education and training methods.
Governance of Complex Social Systems
Both everyday life and long-term evolution of societies depend more on more on social systems: as a few examples, we work for corporations, we use energy and transport systems, we are aware of the evolution of financial markets, we vote for and sometimes participate in political institutions. Much has been said about the optimal governance of such social systems, often with more dogmatic bias than scientific foundation.
Complexity Science has still a long way to run to produce breakthrough knowledge in this particularly complex field, but time has already come to work in practical applications of science to the problem of governance, i.e. the definition and setting of the collective, multidimensional goals of a complex social system, and the development of best practices, both technical and cultural, to achieve those goals.
Tackling this problem requires new approaches in the field of foresight: the future is unknown, but we need to get clear which futures are possible, which are not viable and which factors will make reality take one way or another, in order to build tools and institutions to ensure that the real future looks as much as possible like our dreams.
