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Understanding Complexity: How to Answer the Big Questions |
| Paper ID: |
300 |
Last updated: 31/01/2012 09:08:31
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Where: Regional |
When: 3-10yrs |
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| Keywords: |
systems, emergence, complex systems, non-linear, social science |
Summary  |
| The value of complexity theory lies in its potential to enrich our understanding of the relationships between overall outcomes and individual decisions. [1] As we become more aware of the interconnected nature of the world, we become more cautious about ascribing simplistic cause and effect relationships to events. For this we need technical approaches that allow us to understand emergent phenomena, in other words complex patterns arising out of many simple interactions. Complexity theory has established itself as a scientific field offering insight into these phenomena and has been applied to a broad range of areas. Increasing computing power facilitates ever more precise modelling and simulation of complex systems, and increases our understanding of their behaviour. |
Discussion |
Scientific method often leads us down the path of reducing systems into their component parts in order to study them. However, the limitation of this approach is that a better understanding of the properties of individual system elements would not necessarily translate into a better understanding of the properties of the system as a whole.
Complexity theory offers a solution to this problem by considering systems as complex. A complex system is a system with interconnected parts that as a whole exhibits properties that are not obvious from the properties of its constituent parts. This characteristic of a system is called emergence and it is central to the study of complex systems. [2] [3] [4] A practical illustration of emergence is the World Wide Web. It did not have an initial blueprint but rather evolved as people connected computers together and began to interact, employing strategems and rules of thumb, making errors but also discoveries. [1] [5]
Complexity theory also regards complex systems as non-linear. This means that causes and effects are not proportional to each other. An example is a system that is hardly responsive to interventions, or which suddenly changes regimes when a critical point is reached. [6] However, non-linearity does not imply that effects of policy interventions could not be evaluated, but rather it suggests that the measurement of system’s responses should allow for non-linearities and interactions. [1] [5]
When a system of interconnected elements displays adaptive behaviour, or possesses the capacity to change and learn from experience, it is defined as a Complex Adaptive System (CAS), a term coined by the Santa Fe Institute, the cradle of complexity research and the current leader. [7] Within a CAS, control (insofar as it exists) is highly decentralised, and the future of a system or its behaviour is difficult to predict. Examples of CAS include the immune system, termite colonies, the financial markets, air transportation, air traffic control, [8] supply systems [9] and almost all group-based human interactions. [10] [11]
The study of complex systems requires new approaches and tools. One is agent-based modelling (ABM). Instead of looking at and trying to model causality, ABM relies on simulating a system composed of 'behavioural entities' – such as cells, individuals, firms. The simulation specifies the rules of behaviour of these entities, as well as the rules of interaction among them. Then, using a computer model, it is possible to explore the consequences of the specified individual-level rules on the behaviour of the population as a whole. [3] [12] Recent examples of ABM applications are biomedical engineering studies of tissue patterns [13] and economic studies of electricity markets. [14]
Complex systems research in all areas of science and technology is increasing; and so is its application to complex problems. If that growth continues, the policy implications of better understanding and successfully modelling complex systems could be immense.
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Implications |
Complexity science seeks to understand complex systems, including human systems. The principles of complexity theory may become increasingly influential in policy design and organisational change, including areas such as education and health care. [11] [15] [16] [17] [18]
Reductionist management techniques that seek to understand problems by breaking them into constituent parts may be challenged by better understanding how the parts of the system that generates the problems relate to each other. This approach may help reduce unintended or adverse consequences associated with organisational change. [15]
Traditional notions of control and leadership may be reassessed. Complexity theory suggests that problems are often best addressed by those closest to the issue – where interconnections are clearest and the trade-offs between different strategies can be best understood and negotiated. [11] [16] This may argue for decentralisation of power and decision-making, spreading responsibility across all involved in a complex environment or problem.
In economics and business, ‘Complexity Economics’ [1] [19] attempts to understand collective behaviour in economic contexts such as financial markets, by relaxing traditional assumptions of rational choice and perfect markets. It recognises the importance of beliefs and allows us to formally model sub-optimal behaviour. [20] For example, the stock market can be modelled as a CAS where individual actors (equity traders and investors) with different beliefs about the future compete and cooperate through exchanging and reacting to information. Booms and crashes in markets may be a common feature of environments in which evolving behaviours interact to determine individual financial decisions. [1] Existing classical models are considered inadequate to capture the range of human responses to these extreme circumstances, illustrated by banking sector’s failure to anticipate the 2007 financial crisis. [21]
Eric Beinhocker [19] describes the economy as a CAS that operates according to the evolutionary logic of 'differentiate, select and amplify'. Beinhocker uses an evolutionary framework to show why companies fail to sustain competitiveness, pointing at the lack of adaptability as one possible reason for failure. Hence, the adoption of business strategies involving evolutionary adaptability and flexibility may be the means of continued corporate survival under uncertainty. [22] Complexity approaches may enhance organisational resilience [23] and describe innovative business strategies. [24]
Better understanding of emergent systems will lead to the rethinking of design principles in many domains other than natural systems. Wikipedia, eBay [25] and open-source projects are specific examples. They demonstrate new structures for production, new methods of exchange and new processes for creation of value [2] that are adding up to an 'emergent' framework for economic life. Many companies are beginning to use emergence as a design principle for marketing. This involves viewing customers as dynamic 'swarms' and networks. [14] Organizational designs based on emergent leadership and dynamic information sharing may prove superior to classical hierachies.
Microsimulation modelling in complex social systems is another area of active research and development. It aims to inform social and economic policy on the direct and indirect, i.e. system-wide, effects of intended policy interventions. [26]
In the area of security and defence, the application of complexity science to problems in the areas of security, intelligence and nuclear proliferation research [27] [28] is likely to increase, as well as its use in modelling and understanding complex systems such as terrorist networks. [29] In an uncertain world of new threats, strategic approaches that employ complexity principles may become increasingly important. [30] Complexity methods may be used to assess opportunities, risks, contingencies and low probability, high impact ‘black swan’ events. [31] Complexity principles combined with scenario planning techniques could generate new approaches to conflict resolution. [32] Network-centric warfare presents a challenge to traditional command and control structures and lends itself to developing new concepts of operations on the basis of complexity concepts such as adaptation, emergence, etc. [33]
The issues posed by complexity are central to the future development of all areas of knowledge. Boundary and cross-disciplinary research is becoming increasingly prevalent [7] [34] [8] as expertise in one area is commonly insufficient in addressing the complexity of the problem at hand. Cross-disciplinary cooperation is likely to have a substantial impact on the way we create, understand and apply knowledge. In areas of technology, such as robotics and nanotechnology, cooperation between scientists is already resulting in the building of complex autonomous systems. Further understanding the functioning of the human brain with the help of biology, biotechnology and artificial intelligence will enable the engineering of smart biotech systems, such as advanced prosthetics sufficiently complex to have emergent properties of their own, [35] and also will contribute to better social models and hence significantly improved policy tools.
The global ecosystem, regarded as a complex adaptive system, is another example which requires a broad range of expertise and application of an inter-disciplinary approach to be understood and managed. Complex systems science may be pivotal in achieving an environmentally sustainable solution to economic development by drawing on the interactions of a broad range of scientific disciplines and integrating their emergent interactions in a single comprehensive model.
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Early indicators |
Establishment of research centres on complexity all over the globe.
Growth in application of complex science approaches and methods to various types of problems.
Increase in inter-disciplinary research.
Successful applications of complexity theory for modelling financial markets, urban growth and traffic.
Advances in science and technology allowing for increasingly complex and larger-scale simulation.
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Drivers & Inhibitors |
Drivers:
Moore's Law.
Advances in computing.
Moves toward interdisciplinarity in scientific research.
New understanding of emergence from biology, IT and other disciplines.
Inhibitors:
Many complex systems are still not well understood at the fundamental level. For example, the function of most genes and proteins in the human body is still unknown, and the pathways along which they interact are only beginning to be mapped out.
There is a discrepancy between our ability to gather data and our ability to interpret it.
Limitations in our ability to understand human behaviour sufficiently well to capture it in software for use in models.
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Parallels & Precedents |
The concept of calculus developed by Leibniz and Newton in the 17th century.
General acceptance of computers as an aid to business and life in general, 1980s to date.
Paradigm-shifts such as the Big Bang and evolution by natural selection in 19th and 20th centuries.
Evolution of systems engineering and systems analysis in the 1950s and 1960s.
Development of the Black-Scholes equation and the Capital Asset Pricing Model.
The Gaia hypothesis by James Lovelock in the 1960s.
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| The contents of this paper were provided by the Outsights-Ipsos MORI Partnership. Any views expressed are independent of government and do not constitute government policy. |
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