Themes
The COSNet wiki is now available for all COSNet members to collaboratively define the extended COSNet theme descriptions. Access to the specific wiki page is available by clicking on the specific sub heading below.
1. Irreversibility and Emergence in Nonequilibrium Systems
Most systems in nature are in non-equilibrium states and undergo irreversible transformations, for which a description through classical thermostatics and equilibrium statistical mechanics is impossible. Such irreversible processes (for example heat and mass flow in response to temperature and concentration gradients) are describable theoretically only through non-equilibrium statistical mechanics and/or irreversible thermodynamics, subjects which have been well developed in mainstream physics, but which have yet to find their full potential in analysis of complex systems. Of central importance is the entropy production, which according to the second law can never be negative, and which exhibits an extremal nature under certain circumstances. This enables a system, however complex, to be analysed by a single all-encompassing variational principle. Empirical evidence suggests that this approach may provide a useful general explanation of the observed behaviour of a wide range of complex systems, from photosynthesis to climate patterns. If predictions of the behaviour of such complex systems could be made in this way, the practical consequences and theoretical implications would be immense. Irreversibility is also an essential component in the phenomenon of emergence, another being nonlinearity. Questions related to the reasons behind emergence, and the detection of organisation and of emergence are important in a wide range of fields, including evolutionary dynamics, artificial life, neuroscience, phase separation, self assembly and turbulence.
2. Turbulence and Coherent Structures, Control and Computation
A comprehensive understanding of fluid turbulence remains one of the grand challenges in science and engineering. This is especially so for non-Newtonian fluids, plasmas, and reactive flows; not only because of their growing economic importance in Australia and world-wide, but because of their intrinsic, highly nonlinear feedbacks. Fundamental problems related to onset, growth, energy spectra, multi-scaling, and structuring of turbulence are actively researched across disciplines such as engineering and environmental fluid dynamics, space and astrophysics, and plasma physics. In mixing and heat and mass transfer applications turbulent transport has long been the big issue, and research in these areas is driven by a need to model, predict, and control accurately both the onset of turbulence and the energy distribution in fully developed turbulence. Dynamical systems theory and high-performance computational techniques have an essential role in this endeavour. Because of its breadth of application the study of turbulent fluid and plasma flows is a truly multi-disciplinary venture in which advances in one area synergise developments in other areas.
3. Dynamics and statistics of multi-scale systems
Complex systems often evolve and interact on scales spanning several orders of magnitude in space and in time. In recent years, multifractal scaling analysis has proven useful in uncovering scaling properties in a diverse range of temporal and spatial data sets, the statistics of such systems being non-Gaussian. Scaling laws are also a common feature in nonlinear complex systems that evolve self-organised structures — self organisation driven by power law scaling is called Self-Organised Criticality (SOC), the paradigmatic example being the sandpile. Applications of multi-scale analysis range from financial data analysis to diffusion in biological media, earthquake prediction and turbulence in fluids and plasmas, including space physics applications.
4. Network Theory
Networks are everywhere: neurons, individuals, societies, economies, and environments are all interconnected by intricate and dynamically changing networks. Likewise, vast networks of interacting cells and genes control growth and development. Patterns of network connections determine many features of complex systems. To deal with large networks requires an understanding of how global organisation and behaviour emerges from local interactions. End users need tools to help them plan and influence the behaviours that emerge from networks. An improved understanding of networks and how to manage them would benefit many areas of activity, including economics and commerce, advanced computing, environmental management, biotechnology, medicine and government.
5. Cellular, Automata, Agent-Based Modelling and Simulation
Since the release of Conway’s Game of Life, cellular automata have been used as models in many areas of the physical and spatial sciences, biology, mathematics and computer science, as well as in the social sciences. They are a very useful modelling platform, as cells on a grid that switch on or off according to states of neighbouring cells can represent a host of dynamic phenomena – individuals, attitudes or actions, for example. A cellular automaton (CA) models any world in which space can be represented as a uniform grid, time advances by steps, and the “laws” of that world are represented by a uniform set of rules which compute each cell’s state from its own previous state and those of its nearby neighbours.
Once we wish to develop automata that are somewhat more complex in their internal processing and consequently in their behaviour, we enter a different world known as agent-based modelling or simulation (or multi-agent systems). Such automata are conventionally called agents, and there are several growing streams of thought on how the agents should or can be designed, built and used. While there is no generally agreed definition of what an agent is, the term usually implies an autonomous, intelligent entity that may interact or communicate with other autonomous, intelligent entities. As with CA, there are rules governing interactive behaviour and the agents “operate” in or on an environment of some sort.
The agents emanating from the literature on (distributed) artificial intelligence often correspond to self-contained software and/or hardware (e.g. robots) that control their own actions based on their perceptions of their operating environments. Multiple agents are designed to work together to achieve a desired goal. Typically, their goal is pre-specified and they are engineered and controlled to achieve it with very little tolerance for error. Although purposive, systems in which human agents interact with ecological systems, for example, display open-ended outcomes. Here the collective behaviour is unknown in advance, but emerges during the simulation. Some emergent outcomes may be unexpected and undesirable. Artificial life has been the source of inspiration for this open-ended kind of simulation.
In agent-based simulation, rules governing agents’ behaviours can range from simple “IF—THEN” clauses to quite sophisticated machine learning algorithms (such as genetic algorithms) that allow agents to modify and improve their behaviour during the simulation. Data mining is often used to ensure that agents behave in ways that realistically depict how individual decisions are made in that system. Parameters of the model are set to represent a situation of interest and the model is run for several hundreds of iterations, until a preferred solution is found. Where possible, simulation models are calibrated against historical data to ensure that the model is accurately replicating the behaviour of the real system.
Agent-based simulations can provide valuable information about the dynamics of the real world(s) that they emulate. Complex systems scientists see them as more useful than equations-based methods in a world of multiple possible futures, partly because they are built synthetically “from the bottom up”. As a wide variety of agents interact within a social simulation, for example, results show how their collective behaviours govern the performance of the entire system – for instance, the emergence of a successful product, a congested area of traffic, or a polluted water catchment. This is of great benefit to stakeholders, because they can see a role for themselves (as agents) in the simulation itself, as well as an opportunity to learn from the simulated outcomes. Thus an area of the agent-based modelling field that is growing rapidly is known as companion modelling – the use of participatory agent-based simulation to improve our understanding of human interactions with an environment (e.g. natural resource management).
Agent-based simulations are also powerful tools for “What if” scenario analysis. As certain agents’ characteristics or behavioural rules change, the impact of the change can be seen in the model’s collective output. It is these adaptive learning features that sometimes give agent-based simulation an edge over more traditional modelling and optimisation methods. Perhaps most important of all, the computer can generate outcomes or strategies that a scientist or stakeholder might never have imagined.