The New Sciences of Networks & Complexity: A Short Introduction

3.2.2 Some Properties
The promise of complexity science for policy applications is, at its core, the hope that science can help anticipate and understand the key patterns in complex systems that involve or concern humans, thus enabling wiser decisions about policy interventions.

Some important characteristics of complex systems are:

  • Adaptability: independent constituents interact changing their behaviors in reaction to those of others, and adapting to a changing environment;
  • Emergence: novel pattern that arises at the system level not predicted by fundamental properties of the system’s constituents;
  • Self-organization: a system that operates through many mutually adapting constituents where no entity designs it or directly controls it;
  • Attractors: some complex systems spontaneously and consistently revert to recognizable dynamic states known as attractors. While they might theoretically be capable of exhibiting a huge variety of states, in fact they mostly exhibit the constrained attractor states;
  • Self-organized Criticality: a complex system may possess a self-organizing attractor state that has an inherent potential for abrupt transitions of a wide range of intensities. For a system that is in a self-organized critical state, the magnitude of the next transition is unpredictable, but the long-term probability distribution of event magnitudes is a regularly known distribution (a “power law”);
  • Chaos: chaotic behavior is characterized by extreme sensitivity to initial conditions;
  • Non-linearity: non-linear relationships require sophisticated algorithms, and are sometimes probabilistic in nature. Small changes might have large effects, large changes could have little or no effects;
  • Phase Transitions: system behavior changes suddenly and dramatically (and, often, irreversibly) because a “tipping point”, or phase transition point, is reached. Phase transitions are common in nature: boiling and freezing of liquids, the onset of superconductivity in some materials when their temperature decreases beyond a fixed value;
  • Power Laws: probabilistic distribution characterized by a slowly decreasing function (log-log), different from the ‘familiar’ bell-shaped curve.

3.2.3 Tools and Techniques for Complexity Science
Some of the most important complexity tools being used in public policy domains at this time are:

  • Agent-based or Multi-agent Models: in computerized, agent-based simulations, a synthetic virtual “world” is populated by artificial agents who could be individuals, families, organizations, etc. The agents interact adaptively with each other and also change with the overall conditions in the environment;
  • Network Analyses: a common feature of many complex systems is that they are best represented by networks, which have defined structural features and follow specific dynamic laws. Scientists seek to identify configurations that are especially stable (or particularly fragile); some network patterns have been identified as predictors of catastrophic failures in real-life networks: electricity-distribution or communication infrastructures.

Additional complexity-related techniques deserve a special mention, although their use is not unique to complexity science: Data Mining, Scenario Modeling, Sensitivity Analysis, Dynamical Systems Modeling.

3.3 Possible Applications in the Public Policy Domain

Several examples of application domains have been explored, e.g.: epidemiology & contagion; traffic, identification of terrorist associations. Of more general interest is climate change, in particular the social and human aspects – connection between economy, finance, energy, industry, agriculture and the natural world. These new degrees of sophistication can only be achieved using complexity science.

Complexity science techniques can be useful in identifying dangerous tipping points in the human-earth system, which can occur independently of purely geophysical transitions. Perhaps, the most likely disruption of this type involves the management of water resources. Drought and water stresses occur regularly across large sections of Europe and the developing world. There are indications that a tipping point may be near, leading to massive long-term water shortages.

3.4 A Recent Topic: Economic Complexity19
The recently published The Atlas of Economic Complexity and the Index (ECI) defined in that publication have largely inspired what follows.

Gross Domestic Product (GDP) is the most used indicator to measure the level of economic activity and its evolution in time in terms of economic growth. GDP per capita is used to express the average wealth of the population of a country. However, GDP falls short when it comes to evaluating the well-being of a society.

Many attempts have been undertaken to improve or find better indices to express real progress in well being. In the frame of the Science of Complexity, an interesting approach has been proposed, rather recently, with the creation of the Economic Complexity Indicator (ECI), which focuses on the structure of the economy of a country and enables the diagnosis of its further development or progress, essentially based on the amount of knowledge available in a society for producing goods and services.

In a way ECI shows substantial progress in the evaluation of the economy of a country compared to what the GDP does. The many attempts for elaborating a ‘new’ economic system cannot oversee this innovative approach in using new sciences such as Networks and Complexity.

3.4.1 What is Economic Complexity?20
The complexity of an economy is related to the multiplicity of useful knowledge embedded in it. For a complex society to exist, and to sustain itself, people who know about design, marketing, finance, technology, human resource management, operations and trade laws, must be able to interact and combine their knowledge to make products. These same products cannot be made in societies that are missing parts of this capability set. Economic com­plexity, therefore, is expressed in the composition of a country’s productive output and reflects the structures that emerge to hold and to combine knowledge.

Knowledge can only be accumulated, transferred and preserved if it is embedded in networks of individuals and organizations that put this knowledge into productive use. Knowledge that is not used, at least as used in this economic context, however, is also not transferred, and will disappear once the individuals and organizations that have it retire or die.

Complex economies are those that can weave vast quantities of relevant knowledge together, across large networks of people, to generate a diverse mix of knowledge-intensive products. Simpler economies, in contrast, have a narrow base of productive knowledge and produce fewer and simpler products, which require smaller webs of interaction. Because individuals are limited in what they know, the only way societies can expand their knowledge base is by facilitating the interaction of individuals in increasingly complex webs of organizations and markets. Increased economic complexity is necessary for a society to be able to hold and use a larger amount of productive knowledge, and we can measure it from the mix of products that countries are able to make.

3.4.2 The Economic Complexity Index (ECI) & the Product Complexity Index (PCI)
First, the amount of embedded knowledge that a country has, is expressed in its productive diversity, or the number of distinct products that it makes. Second, products that demand large volumes of knowledge are feasible only in the few places where all the requisite knowledge is available. We define ubiquity as the number of countries that make a product. Using this terminology, we can observe that complex products – those that are based on much knowledge – are less ubiquitous. The ubiquity of a product, therefore, reveals information about the volume of knowledge that is required for its production. Hence, the amount of knowledge that a country has is expressed in the diversity and ubiquity of the products that it makes.

Economic Complexity Index (ECI) refers to countries. The corresponding measure for products gives us the Product Complexity Index (PCI). The mathematical approach exploits the combination of these indices as well as the diversity and ubiquity to create measures that approximate the amount of productive knowledge held in each of these countries.

In short, economic complexity matters because it helps explain differences in the level of income of countries, and more importantly, because it predicts future economic growth. Economic complexity may not be simple to accomplish, but the countries that do achieve it, tend to reap important rewards.

4. Complexity Science: New ways of Thinking for Policymakers (see OECD Report)21
The suggested new ways of thinking focus their attention on dynamic connections and evolution, not just on designing and building fixed institutions, laws, regulations and other traditional policy instruments:

  • Predictability: the science of complex systems focuses on identifying and analyzing trends and probabilities, rather than seeking to predict specific events. It will be challenging, though necessary, for policymakers and scientists alike to move beyond strict determinism if they wish to effectively engage in decision making under conditions of uncertainty and complexity.
  • Control: control is generally made possible by identifying cause-and-effect chains and then manipulating the causes. But cause and effect in complex systems are distributed, intermingled and not directly controllable. Complexity science offers many insights into finding and exploiting desirable attractors; identifying and avoiding dangerous tipping points; and recognizing when a system is in a critical self-organizing state.
  • Explanation: analyses done using complexity science methods, insights about the underlying mechanisms that lead to complex behavior are revealed. Although deterministic quantitative prediction is not generally achieved, the elucidation of the reasons for complex behavior is often more important for comprehending what might otherwise be puzzling real-world events.
  • Changing the Mindset: understanding the basic ideas of complexity of the world together with unpredictability. One should not forget that Albert Einstein has warned: “Not everything that counts can be counted, and not everything that can be counted counts”.

19. Ricardo Hausmann et al., The Atlas of Economic Complexity- Mapping Paths to Prosperity (Cambridge: MIT Press, 2011)
20. Guido Caldarelli et al., Ranking and Clustering Countries and their Products; A Network Analysis, PLoS ONE 7, no. 10 (2012): e47278
21. “Report on: Applications of Complexity Science for Public Policy”

Pages: 1 2 3