Complex Systems, despite their names, are composed by simple components. Complexity arises from the local interactions. One of the core questions for engineering and exploiting the extraordinary properties of complex systems is how to define and use simple local rules to generate higher levels of organizations. But we must answer also the questions if this “emergence” higher levels are stable or instable, temporary or permanent and so on.

Complexity emergence is a fascinating subject of study in mathematics, physics, biology, social sciences…but not only, it is also very often considered when studying future networks as complex systems of communication, processing and networking resources.

Interestingly, this paper elaborates a view of emergence of life by analyzing the mathematical properties of autocatalytic sets (collections of molecules which catalyze each other’s reactions, helping to bring each other into existence).

Simple example of autocatalysis

Autocatalytic sets have a complex structure of their own: imagine a system of multiple loops and chains, loops within loops, mutual cross-feed relationships connecting them, inhibitory connections, preferential reactions given different substrate concentrations…like an ecosystem! Paper argues that “ self-sustaining, functionally closed structures can arise at a higher level (an autocatalytic set of autocatalytic sets), i.e., true emergence“.

What makes the approach so interesting is that the mathematics does not depend on the nature of chemistry, i.e. it is substrate independent. So the building blocks in an autocatalytic set need not be molecules at all but any units that can manipulate other units. These units can be complex entities in themselves.

Also economy is essentially the process of transforming raw materials into products that themselves facilitate further transformation of raw materials and so on. So they argue that “Perhaps we can also view the economy as an (emergent) autocatalytic set, exhibiting some sort of functional closure“.

Could it be that this theory of autocatalytic sets can provide a new mathematical approach for modeling future networks ecosystems ?

Human imprinting on the Earth as seen from above

Big Data can be exploited in diffrent ways for different purposes. An example is the work of anthropologist Felix Pharand-Deschenes who has used the information to map the human connection systems (from roads to airplanes to pipelines to the internet) in the Anthropocene (the era of humans domination on the Earth). The Cartography of the Anthropocene is an effort to illustrate the many different ways that global humanity connects and is interdependent. Apart from its initial educational purposes, this information retrieved from multiple sources, provides an interesting perspective for further speculations both in academic and business environments.
Big Data are like underearth treasures: they are there waiting to be dug and exploited by those who have the knowledge and vision to take advantage of them. Here is the new gold rush!

Coupled Oscillators (represented on a torus)

Synchronization is some kind of ubiquitous phenomenon in Nature. Coupled oscillators are everywhere. An amazing example is the synchronous flashing of swarms of fireflies. Many examples of biological, physical and chemical systems can be modelled of as a large ensemble of weakly interacting oscillators.

Scientist Arthur Winfree pioneered a method to investigate this phenomenon: he looked at the behaviour of a large collection of interacting limit-cycle oscillators, in an attempt to model collective synchronization in large groups, indeed observing the flashing fireflies. His model was based on the following major assumptions: 1) that the oscillators are nearly identical, and 2) that the coupling among oscillators is small. Moreover, the effect on each oscillator’s phase is determined by the combined state of all of the oscillators (known as a mean-field approximation). In other words, in his model, the rate of change of the phase of an oscillator is determined by a combination of its natural frequency and the collective state of all of the oscillators combined.

Kuramoto (in 1975) progressed the work by assuming that each oscillator is affected every other oscillator. This kind of interaction is called global coupling. He further assumed that the interactions were equally weighted and depended only sinusoidally on the phase difference. Adopting this model, Kuramoto was not only able to prove that there will be a phase transition to synchronization with his model, but also to find a direct equation that gives the critical coupling strength necessary for synchronization.

Today, Winfree and Kuramoto models and the mean-field analysis are important instruments for studying dynamics on complex networks.

An example where applicability of Kuramoto model provided interesting conclusions is the paper of F. Varela “The brainweb: phase synchronization and large-scale integration”: paper argued that the emergence of unified cognitive moments in brain as a result of the synchronization of functionally specialized regions.

As another example, I’ve recently read this paper where they have applied Kuramoto phase oscillator model on the Facebook Oklahoma network. Amazingly, mean field simulations of the Facebook network have shown a very good agreement with theory! After comparison with other networks, this study argued that the mean field theory applies very well for networks in which low-degree nodes connect preferentially to high-degree nodes.

I’m wondering whether this avenue of studies may enable us to offer insight into networks behaviours: for example think about finding community structures dynamics (synchronization) in social networks.

We’ve been talking in the previous posts about future networks as complex dynamical network of networks run by multiple owners having diverse biz and operational objectives. If we start modelling future networks like that, then immediately we have to face one of the most challenging “Unsolved Problems in Mathematical Systems and Control Theory”.

Please have a look at this book (it’s downloadable):

http://www.asiaing.com/unsolved-problems-in-mathematical-systems-and-control-theory.html

The problem is “Decentralized control with communication between controllers” (4.4)

Consider a network of controllers, each of which has partial observations of the system. The controllers can exchange information on their partial observations, state estimates, or input values, but there are constraints on the communication channels between them. The problem is how enabling this network of controllers with a proper communication protocol, allowing the integration of information directly into the controllers’ logics, in order to reach a common control objective.

This is a very complicated problem which is analogous to human communication in groups, firms, and organizations. A simpler formulation, suggested by the author, is “what information of a controller is so essential in regard to the control purpose that it has to be communicated to other controllers?”. The information to be communicated has to be dealt with at a global level; the information which does not need to be communicated can be treated at the local level.

Which way to approach this challenging problem? I wonder whether Nature can inspire us.

I’ve concluded my last post (“Can Quantum Mechanics contribute to Social Sciences ?”) with a question: can we early detect tipping points at which sudden regime transition of a complex system’s behaviour may happen ?

Predicting such critical points before they are reached is extremely difficult, but it might have a huge impact in several fields, from medicine to business, from meteorology to social networking, from new materials to control of future networks.

Please have a look at this paper “Early-warning signals for critical transitions”

http://www.nature.com/nature/journal/v461/n7260/abs/nature08227.html

Paper is suggesting the analysis of generic early-warning signals indicating, for a wide class of complex systems, the approaching of a critical threshold, where small forces can cause major changes in the state. Examples of such transitions might include the collapse of over-harvested ecosystems, climatic changes, or stocks markets dynamics.

For example one symptom is the critical slowing down: when the system approaches a critical transition, it becomes increasingly slow in recovering from small perturbations (which is translated mathematically into an increase in the autocorrelation and variance of the fluctuations).

Another signal that can be seen in the vicinity of a catastrophic transition point is flickering. Stochastically, the system moves back and forth between the basins of attraction of two alternative attractors (bistable region).

Spatial patterns is a third example: an ecosystem may show a predictable sequence of self-organized spatial patterns as it approaches a critical transition (e.g. a semi-arid vegetation to increasing dryness of the climate).

Still many challenges have to be overcome: for example, how collecting meaningful data, filtering techniques to increase the sensitivity of indicators, preventing false positives and others.

In any case progresses in detecting early-warning signals might be valuable in several fields. I wonder if this theory can also be used in controlling stability of future networks and related business ecosystems.

The World Trade Web (WTW) is an economic network representing international trade activities: in other words it is a sort of a weighted network whose nodes correspond to countries with edge weights reflecting the value of imports and/or exports between countries.

Recently, a group of mathematicians at Dartmouth College in New Hampshire have recreated the WTW from between 1870 and 2006 and then simulated it under different stress conditions.

Actually, once a sensible model is defined, network simulations could shed light on the interdependence of the network nodes and components and the implications for robustness to sudden component or node failures. So, mathematicians at Dartmouth College have shown that the WTW evolved over time through a main trend moving towards “robust yet fragile” configurations (robust under random attacks but fragile under targeted attack). Said configurations are highly correlated with the connectance of the network. Moreover, simulations showed that rapid transitions appeared in the structure of the network in the 1960s and 1970s, where the measures of robustness rapidly increased (due to coming globalization) before resuming a declining trend (as indicated by connectance and trade imbalances). On one side higher connectance provides benefits, on the other side it provides shorter paths for impacts to propagate through a network.

Mathematical studies like these are fascinating and can play a role in the evaluation (and prediction) of the trends of economic markets, particularly with regard to stability.

What about applying this mathematics to make an extinction simulation of the Telecom market ? (extinction analysis is a technique used in the analysis of ecosystems, for the purposes of investigating the robustness of a network)

The paper is available at:

http://arxiv.org/PS_cache/arxiv/pdf/1104/1104.4380v1.pdf

Enterprise Interoperability (EI) is a term introduced to describe a field of activity aiming at improving the way with which enterprises internally operate, interoperate with each other, and with other organisations. Information and Communications Technologies (ICT) represent an important enabler of EI.

Today, EI is becoming a strategic feature for building the future business fabric of all innovation ecosystems. As such, interoperability is no longer just about basic interconnectivity and interoperability at the level of technology, or just about information exchange between two entities but it is becoming closely related with the dynamics nature of future business needs, at the level of both the single enterprise and the ecosystems of enterprises. As a matter of fact there are several technology-based solutions that claim to support interoperability for enterprises, with several commercial solutions. Nevertheless innovative approaches are needed to make EI combining both technology and business to catalyze and sustain new models and value networks. Enterprises, both big and small, should be able to do their business seamlessly, to adapt dynamically to changes in the environment and to exploit new emerging opportunities rapidly. This can be achieved by harnessing the full potential of ICT services, which should become an invisible part of business ecosystems.

Brain, like an ecosystem, is highly complex, non-linear, and self-organizing: it is the most beautiful and (probably) effective architecture invented by Nature. Understanding its functioning is one of the grand challenges of Science: many methods have been applied to analyze and study brain structure and functions. Interest is clearly motivated by the fact that the some general principles of brain functioning seem to govern also other complex networks, including social, biological and communications networks.

As such, I believe it would be interesting investigating also the possibility to exploit some of the principles governing brain for enterprise self-organization and eventually to enable EI.

As a matter of fact, ICT for Enterprise is already beginning to borrow some inspiration from biological models: model of an organisation can be seen as a complex ecosystem, rather than a rigid hierarchical structure, disconnected from its environment.

The use of bio-mimetic interaction and communication primitives and algorithms, neutral networks, genetic computation are becoming more and more popular. Moreover, we see that networks are evolving towards pervasive environments of link, processing and storage resources and services. It is also known that virtualization is enabling multiple logical networks to co-exist on top of a same physical infrastructure: on one side this will offer new business opportunities but on the other hand it will increase management tasks. Indeed, understanding the brain would mean taking inspiration for designing future network from the most efficient networking and processing paradigms in Nature; for example autonomics, self-organization, are being studied, developed and assessed. Think about open worldwide infrastructures creating a new fabric for EI based on these principles.

On the other hand, enterprise management modelling towards an efficient, self-regulating, self-organising, self-evolving framework is still in its infancy. It should be noted that this approach is very critical for enterprise sustainability in a highly dynamic and unpredictable socio-economic environment.

Adopting this metaphor, Enterprise should be capable of monitoring itself in relation to its internal functions and to the external environment; assessing its performance against its predictions and requirements. Real-time efficient feedback mechanisms should improve performance and productivity by self-optimising its functions and self-correcting its (internal and external) actions. All of this constantly built on a shared knowledge.

Let’s consider for example “Theory of mind”: this theory concerns the human capability to explain and predict other people’s behaviour by attributing to them certain mental states (see my previous post). Actually the effectiveness of an animal’s relationships with its key coalition/competitor partners is a function in part of its ability to integrate them into its mental social world (so it is a cognitive problem) and the time it can afford to invest in grouping with these individuals (which is an ecological problem).

Another interesting example is the application of the free energy principle to brain functioning (see my previous post). The free-energy principle says that any self-organizing system that is at equilibrium with its environment must minimize its free energy. Brain would be like an inference machine that actively predicts and explains its sensations; furthermore it continuously tries to optimize probabilistic representations of what caused its sensory input. Then it is easy to show that value is inversely proportional to surprise, in the sense that the probability of a phenotype being in a particular state increases with the value of that state. Acting to optimize value and perception are two aspects of the same principle; namely the minimization of a quantity (free energy) that bounds the probability of sensory input, given a particular agent or phenotype.

Indeed, as Roberto suggested in a previous comment, it would be fascinating designing Enterprises’ genomes and let the interplay of their phenotypes in the ever growing complexity of the EI determine their success in terms of biz and therefore their overall evolution.

Internet has shrunk the world, letting million of people to walk in the same square, the one the like most at that particular time. And tools are letting people build things together once they have gathered in that square. It is not just a shrinking in space, it is also a warping in time. One can be at that particular “square of interest” at a time that is different from the one another person will be there but the tools (the data set being created) creates a framework where contributions pile up.

The one who can find a way to harvest the wealth of crowd-sourcing is in business. This is what seems to happen with Pachube, www.pachube.com.

This start up is leveraging the exponential growth of sensors and the fact that most of them are being deployed by individuals independently one another. By providing people tools to post their sensors’ data they enable the creation of a massive data base, worldwide.

Taking the pulse to the world, through crowd sourced sensors

There are several attractive propositions to stimulate people to post their information. One can choose to upload the data generated by his sensors and to view those data from anywhere using a web browser on his phone, his laptop or whatever. The connection is provided via Pachube Data Store. If one would rather keep these data private he can activate a subscription where for a monthly fee he still have the possibility to see the data but it will only be him that can view them. Actually, Pachube provides for several different subscription schemes, at different prices, each one with specific services and features attached.

Many people are likely to be contented in viewing their data and letting others viewing them too (as shown in the above map). This creates a wealth that can be exploited and Pachube is set to do that.

This approach is very interesting: Internet and tools can create an attraction point that in turns generate value. Leveraging on that value requires the control of data flow and access, a control point. In turns, this requires a data centre (a virtual one in the cloud would do too). What is amazing is that an idea can be turned into a business proposition with very little up front investment as the CAPEX required (and it may be massive over the years) can grow as the business and the related revenues grow. In a way Internet makes it possible to shift money from CAPEX to OPEX and related OPEX to revenue thus dramatically decreasing the investment risk. And that is not all. By decreasing the upfront CAPEX the number of potential bidders grows exponentially and this is fueling innovation from all over the world and basically killing the upper hand big companies used to have.

Of course there are also several other factors in the game. But most of them are aligned in the direction of stimulating a diffuse innovation by small players.

Network effect refers to the effect when the value of a product or service increases as the number of users grow. When network effects are present, they are among the most important reasons for consumers to pick up one product or service over another. For instance, when it comes to online shopping website, eBay is clearly the one that stands out among thousands of other similar website.

According to the research of Dr. Gallaugher from Boston College, the value derived from network effects comes from three sources: exchange, staying power, and complementary benefits.

•         Exchange

A network becomes more valuable because its users can potentially communicate with more people. Every product or service subject to network effects fosters some kind of exchange. For instance, an MSN account will not be so interesting if only one person has it on this planet. A mobile phone will simply severely decrease its value of communication if very few people own it. For firms leveraging technology, this might include anything you can represent in the ones and zeros of a data stream, such as movies, music, money, video games, and computer programs.

•         Staying Power

Networks with greater numbers of users suggest a stronger staying power. The staying power, or long-term viability of a product or service, is particularly important for consumers of technology products. The concept of staying power (and the fear of being stranded in an unsupported product or service) is directly related to switching costs, and switching costs can strengthen the value of network effects as a strategic asset. And the elite Boston Consulting Group is really talking about a firm’s switching costs when it refers to how well a company can create customers who are “barnacles” (that are tightly anchored to the firm) and not “butterflies” (that flutter away to rivals).

•         Complementary Benefits

Complementary benefits are those products or services that add additional value to the network. These might include ‘how-to’ books, software add-ons, even labor. Products and services that encourage others to offer complementary goods are sometimes called platforms. Allowing other firms to contribute to your platform can be a brilliant strategy, because those firms will spend their time and money to enhance your offerings.

I was thinking of a title to introduce the topic of today, and I was reminded of the ironic phrase of the baseball player Yogi Berra. It’s certainly a challenge to imagine what’s to happen down the road, but there are those who try. Every day a number of journalists, marketers, researchers and analysts is working for foretelling us what is to come, seeking to describe the outlook of the future through predictions about economic and social trends that affect us all. Their daily job consists in aggregating and organizing past and present happenings as a network of data linked to the topic, in order to predict the future scenario of anything. The predictive analysis, this is the name of such activity, refers to a set of techniques which belongs to disciplines such as statistics and game theory, through which you can better manage past and current data, to arrive at plausible estimates of future events. In particular, this technique is employed to give future observations and predict the trend of development of particular entities in different areas such as economy, finance, marketing, society, insurance, etc.
Now users have their toy for open source intelligence analysis. It’s a browser-based temporal analytics tool developed by a team of computer scientists, statisticians and linguists for the analysis of large amounts of time-based data from around the web: people, markets, locations and whatever. Recorded Future, a data analytics company headquartered out of Boston, is the interesting start-up that received, a few months ago, investments from Google Ventures for developing this project. Yes, there was still something unknowable for Google, the future. Just over a year ago, in fact, Google was far away from predicting it. However the Big G claimed to predict the present through Google Trends, which as you know provides, together with Google Insights for Search, a daily insight into what the Google users are searching for, by showing the relative volume of search traffic in Google for any search query. An understanding of web search queries offers interesting ramifications for advertisers, marketers, economists, scholars, and anyone else interested in knowing more about his object of study. Obviously some search queries and categories have trends that are quite seasonal, with repeated patterns. See the search trends for “gift” during the Christmas time. Many other search trends, however, are quite irregular and hard to predict. See the search trends for “Facebook”. In the 2009 landmark study Predicting the Presents with Google Trends, Hal Varian, Google’s chief economist, and Hyunyoung Choi describe the forecasting models that learn basic seasonality and general trend, showing how aggregated search trends of Google Categories can be used as extra indicators and effectively leverage several US econometrics prediction models. In the paper they use the frequency of certain search terms to forecast retail, automotive and home sales, as well as travel behavior. We are talking about a gain ranging from a few percent to 18 percent in the “Motor Vehicles and Parts” case.
A small digression, back to Recorded Future. How this engine works is very simple. By continually scanning, through sampling methods and data mining algorithms, thousands of web sources (blogs, reviews, government sites, etc.) for the nature and frequency of references to a certain occurrence, the system computes what they call a “momentum value” for each entity in the database. This means to extract information from text including events, entities and the time when they occur, measuring momentum for each item in the index, as well as sentiment.
All these aspects could be useful for example for the web manager to identify recurring patterns in the accesses or requests for certain pages, delivering useful information about their future trends and getting answers in real time. But also for counterterrorism analysis, and this is already happening, since the system extracts terrorism related events from the public web, as well as info from structured sources like the Institute for the Study of Violent Groups.