One of the fundamental strengths of natural systems is their adaptive and self-organising capabilities. These characteristics allow natural systems to respond to changing environmental conditions. Recent times have seen an increasing interest in applying concepts from the study of adaptive systems to examine business and economic phenomena.

The key components of adaptive systems in business and economics are a population of agents or entities which are capable of learning, and co-evolving to create their own future. Unlike classical micro-economic models, the concept of equilibrium has little meaning in adaptive systems, as the interactions of the agents results in continual movement between system states. Adaptive systems typically exhibit collective intelligence and emergent behaviour.

The Natural Computing Research and Applications Group in collaboration with Professor Ernesto Costa (Universidade de Coimbra), Arlindo Silva, Tiago Ferra de Sousa (both of Instituto Politecnico de Castelo Branco) and Professor Robin Matthews (Kingston University), has a number of on-going research projects in this domain. Research is currently focussed on two primary themes, organisational adaptation and product invention. An important output from this research has been the creation of novel software tools which allow us to address important research questions in both areas in new ways.

Organisational Adaptation
Organisational strategists inhabit a dynamic environment and must continually adapt their organisation's strategy in an attempt to ensure organisational survival. A particular feature of organisational learning is that it is an amalgam of both social and individual learning, but there is also resistance to change (organisational inertia). We have developed a simulation model (Orgswarm) of the process of organisational adaptation which allows the examination of the roles of social learning, past organisational experience and inertia, in facilitating successful organisational adaption.

OrgSwarm Simulator

Product Invention
Insight into the process of invention is important for several reasons. Without a robust model of invention, the ability of managers to create organisations which encourage inventive practices is constrained, and policy-makers risk making sub-optimal decisions regarding how best to encourage invention in society in order to promote long-term economic growth. We have developed an agent-based simulation model (InventSim) of the process of product invention which allows the examination of the role of various search heuristics of inventors on the societal rate of inventive progress.

InventSim