Digital Ecosystems 

The aim of this research is to gain understanding of and develop methodologies for building digital ecosystems. The overall objective is to build connected, sustainable society, which is capable of cost-effectively feeding, watering, housing, educating, keeping in good health and employing its members.

The results are expected to emerge from the digital ecosystem self-organization, rather than to be achieved as explicit design goals.


According to Wikipedia, “A digital ecosystem is a distributed, adaptive, open socio-technical system with properties of self-organisation, scalability and sustainability inspired from natural ecosystems. Digital ecosystem models are informed by knowledge of natural ecosystems, especially for aspects related to competition and collaboration among diverse entities”.


Gartner’s definition is compatible, “A digital ecosystem is an interdependent group of enterprises, people and/or things that share standardized digital platforms for a mutually beneficial purpose (such as commercial gain, innovation or common interest). Digital ecosystems enable you to interact with customers, partners, adjacent industries — even your competition”.


Building of ecosystems can be best achieved by adaptive partnerships of enterprises, administrations, individuals and “things” (like in the Internet of Things).

Managing Complexity

This is a long-term project, which Professor Rzevski has directed since the year 2000. The project is financed by his commercial ventures – currently by Digital Ecosystems Ltd, London, Helsinki, Colombo, Moscow and Samara.

Codding is outsourced to (1) Samara, Russia where about 100 highly qualified software designers work under guidance of Professor Petr Skobelev from Samara Aerospace University and (2) Colombo, Sri Lanka, where 20 years ago, George has helped to establish a thriving artificial intelligence research group led by Professor Asoka Karunananda from the University of Moratuwa. 

Research Aim

Research aims to develop a practical methodology that can help organisations (businesses, as well as political and administrative organisations) to prosper under conditions of complexity.

Research Hypothesis

Complexity of our social, economic, geopolitical and technological environments is relentlessly increasing, which is manifested by:

  • Frequent unpredictable disruptive events such as:
    • unexpected changes in demand and supply
    • cancellations, delays, modifications
    • failures, fraud, electronic attacks
  • A drift into failure an occasional extreme event created by the accumulation of many small, individually insignificant causes such as:
    • repeated mistakes and omissions
    • systematic fraud

As a rule, our organisations are deterministic (rigidly structured) and are designed to work in a deterministic environment (i.e., to meet predictable demands). They cannot cope with complex environments.


It is necessary to design complexity into organisations to make them adaptive, i.e., able to:

  • Cope with unpredictable disruptive events
  • Prevent a drift into failure

Research Method

Research is conducted by a set of experiments, as follows:

  1. Design and implement large-scale complex adaptive systems for commercial clients using trial-and-error method
  2. Observe how complex adaptive systems behave during the design stage (e.g., tuning and testing) and when operating in a complex market environment
  3. From observations (rather than speculations) discover fundamental concepts, principles, theories and methods of complexity science
  4. Apply discovered principles, theories and methods to solve practical complex problems

Research Results

During the last 17 years Professor Rzevski and Professor Skobelev jointly led teams that developed many large-scale complex adaptive systems that are successfully implemented into various commercial organisations transforming them into adaptive businesses and enabling them to successfully cope with complexity of the Internet-based global market.

Some examples are listed below:

  • Real-Time Scheduling of 2000 taxis in London for Addison Lee
  • Real-Time Scheduling of 10% of the total capacity of the world seagoing tankers for Tankers International, London
  • Real-Time Scheduling of car rentals for Avis
  • Real-Time Logistics for delivering crew and cargo to the International Space Station for the Russian Space Agency
  • Real-Time Scheduling of supply chains for Coca Cola, Germany
  • Real-Time Scheduling of supply chains for LEGO, USA
  • Real-Time Scheduling of road transport for a Siberian transport operator
  • Real-Time Scheduling of trains for the mainline Moscow – St Petersburg
  • Real-Time Scheduler for production of aircrafts for a Russian manufacturer
  • Real-Time Scheduler for service engineers for Russian Post Office
  • Intelligent software capable of reading abstracts of scientific papers for an USA research lab engaged in deciphering genetic code
  • Intelligent software capable of constructing insurance policies for a UK insurer
  • Intelligent software capable of translating from Singhalese into English
  • Intelligent software for managing energy consumption in large buildings


From this experience, George and Petr have extracted concepts, principles and methods for Managing Complexity, as summarised in the book “Managing Complexity” by Rzevski and Skobelev, WIT Press, Southampton, Boston, 2014. ISBN 978-1-84564-936-4.


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