From Tribes to Digital Networks

Fig. – (Organizational Complexity trough History) Four forms behind the Organization and Evolution of all societies (David Ronfeldt TIMN). Each form also seems to be triggered by major societal changes in communications and language. Oral speech enabled tribes (T), the written word enabled institutions (I), the printed word fostered regional and global markets (M), and the electric (digital) word is empowering worldwide networks (N). [in David Ronfeldt, “Tribes, Institutions, Markets, Networks: A framework about Societal Evolution“, RAND Corporation, Document Number: P-7967, (1996). PDF link]

[…] Organizational complexity is defined as the amount of differentiation that exists within different elements constituting the organization. This is often operationalized as the number of different professional specializations that exist within the organization. For example, a school would be considered a less complex organization than a hospital, since a hospital requires a large diversity of professional specialties in order to function. Organizational complexity can also be observed via differentiation in structure, authority and locus of control, and attributes of personnel, products, and technologies. Contingency theory states that an organization structures itself and behaves in a particular manner as an attempt to fit with its environment. Thus organizations are more or less complex as a reaction to environmental complexity. An organization’s environment may be complex because it is turbulent, hostile, diverse, technologically complex, or restrictive. An organization may also be complex as a result of the complexity of its underlying technological core. For example, a nuclear power plant is likely to have a more complex organization than a standard power plant because the underlying technology is more difficult to understand and control. There are numerous consequences of environmental and organizational complexity. Organizational members, faced with overwhelming and/or complex decisions, omit, tolerate errors, queue, filter, abstract, use multiple channels, escape, and chunk in order to deal effectively with the complexity. At an organizational level, an organizational will respond to complexity by building barriers around its technical core; by smoothing input and output transactions; by planning and predicting; by segmenting itself and/or becoming decentralized; and by adopting rules.
Complexity science offers a broader view of organizational complexity – it maintains that all organizations are relatively complex, and that such complexity arises that complex behavior is not necessarily the result of complex action on the behalf of a single individual’s effort; rather, complex behavior of the whole can be the result of loosely coupled organizational members behaving in simple ways, acting on local information. Complexity science posits that most organizational behavior is the result of numerous events occurring over extended periods of time, rather than the result of some smaller number of critical incidents. […] in Dooley, K. (2002), “Organizational Complexity,” International Encyclopedia of Business and Management, M. Warner (ed.), London: Thompson Learning, p. 5013-5022. (PDF link)

The Internet has given us a glimpse of the power of networks. We are just beginning to realize how we can use networks as our primary form of living and working. David Ronfeldt has developed the TIMN framework to explain this – Tribal (T); Institutional (I); Markets (M); Networks (N). The TIMN framework shows how we have evolved as a civilization. It has not been a clean progression from one organizing mode to the next but rather each new form built upon and changed the previous mode. He sees the network form not as a modifier of previous forms, but a form in itself that can address issues that the three other forms could not address. This point is very important when it comes to things like implementing social business (a network mode) within corporations (institutional + market modes). Real network models (e.g. wirearchy) are new modes, not modifications of the old ones.

Another key point of this framework is that Tribes exist within Institutions, Markets and Networks. We never lose our affinity for community groups or family, but each mode brings new factors that influence our previous modes. For example, tribalism is alive and well in online social networks. It’s just not the same tribalism of several hundred years ago. Each transition also has its hazards. For instance, while tribal societies may result in nepotism, networked societies can lead to deception. Ronfeldt states that the initial tribal form informs the other modes and can have a profound influence as they evolve:

Balanced combination is apparently imperative: Each form (and its realm) builds on its predecessor(s). In the progression from T through T+I+M+N, the rise of a new form depends on the successes (and failures) achieved through the earlier forms. For a society to progress optimally through the addition of new forms, no single form should be allowed to dominate any other, and none should be suppressed or eliminated. A society’s potential to function well at a given stage, and to evolve to a higher level of complexity, depends on its ability to integrate these inherently contradictory forms into a well-functioning whole. A society can constrain its prospects for evolutionary growth by elevating a single form to primacy — as appears to be a tendency at times in market-mad America. [in David Ronfeldt, “Tribes, Institutions, Markets, Networks: A framework about Societal Evolution“, RAND Corporation, Document Number: P-7967, (1996). PDF link]

Finally, on these areas (far behind the strict topic of organizational topology and complex networks), let me add two books. One his from José Fonseca, a friend researcher I first met in 2001, during a joint interview for the Portuguese Idéias & Negócios Magazine, for his 5th anniversary (old link) embracing innovation in Portugal. His book entitled “Complexity & Innovation in Organizations” (above) was published in December that year, 2001 by Routledge. The other one is more recent and from Ralph Stacey, “Complexity and Organizational Reality: Uncertainty and the Need to Rethink Management After the Collapse of Investment Capitalism” (below), Routledge, 2010. Even if, Ralph as many other past seminal books on this topic. Both, have worked together at the Hertfordshire University.

Nature’s cover (today)

Fig. – (today’s NATURE Journal cover, Vol. 473 N. 7346 May 12 2011) Control theory can be used to steer engineered and natural systems towards a desired state, but a framework to control complex self-organized systems is lacking. Can such networks be controlled? Albert-László Barabási and colleagues tackle this question and arrive at precise mathematical answers that amount to ‘yes, up to a point’. They develop analytical tools to study the controllability of an arbitrary complex directed network using both model and real systems, ranging from regulatory, neural and metabolic pathways in living organisms to food webs, cell-phone movements and social interactions. They identify the minimum set of driver nodes whose time-dependent control can guide the system’s entire dynamics. Surprisingly, these are not usually located at the network hubs. On the cover, part of the cactus structure, a subset of nodes that have a key role in the control of real networks, with nodes in blue and drivers in red, visualized by Mauro Martino.

What follows are excerpts from the Northeastern University press realise (here) deliver today:

(May 12, 2011) […] Northeastern University researchers are offering a fascinating glimpse into how greater control of complex systems, such as cellular networks and social media, can be achieved by merging the tools of network science and control theory. Albert-László Barabási and Yang-Yu Liu coauthored a paper on the research findings, featured as the cover story in the May 12 issue of the journal Nature. Barabási, a world-renowned network scientist, is a distinguished professor in the Departments of Physics and Biology and the College of Computer and Information Science, and is the founding director of Northeastern’s Center for Complex Network Research. Liu is a postdoctoral research associate in Barabasi’s lab.

The researchers said this approach can lead to major strides in understanding complex engineering and biological systems. For example, controlling the neural and metabolic pathways in living organisms could lead to health-care breakthroughs in drug discovery and disease treatments. “Most large complex networks have been created for some practical purpose: metabolic networks to process the food we eat, the Internet to transfer information, organizational networks to achieve the goals of an organization,” said Barabási. “The tools developed in this paper offer the possibility to better understand how to control these systems. This could potentially generate more efficient metabolic pathways, with applications in developing cures to metabolic diseases, to offering new insights into the design of better organizations.”

Barabási and Liu collaborated with MIT researcher Jean-Jacques Slotine on the paper. The researchers note that control theory already offers mathematical tools for steering engineered and natural systems — such as synchronized manufacturing processes, cars, robots and electrical circuits — toward a desired state. However, they said a framework is lacking to take charge of complex, self-organized systems — such as cellular and social networks. To meet this challenge, they combined the principles of control theory with their innovative network science research to develop an algorithm that can assess the driver nodes, or connection points, within a particular complex network. By doing so, they can determine how many nodes are necessary to control in order to gain control of the system.

The trio was interested in discovering the minimum number of driver nodes needed to control a complex network. They found that denser networks with more connections — such as online social networks — were easier to control than cellular networks. They also found that sparse networks, like many biological and communication networks, are the hardest to control. Liu said this work represents a fundamental contribution to both control theory and network science research. “This work was not possible 10 years ago, because at that time we didn’t know how to categorize these complex networks. We didn’t have the data,” Liu said. “But today, we have the data available for empirical studies on many large-scale networks.” […]

Sir Karl Popper

[…] … I do not really believe that we shall succeed in creating life artificially; but after having reached the moon and landed a spaceship or two on Mars, I realize that this disbelief of mine means very little. But computers are totally different from brains, whose function is not primarily to compute but to guide and balance an organism and help it to stay alive. It is for this reason that the first step of nature toward an intelligent mind was the creation of life, and I think that should we artificially create an intelligent mind, we would have to follow the same path. […], Karl Popper – Popper, K. R. and Eccles, J. C. (1983), The Self and its Brain: An Argument for Interactionism, Routledge & Kegan Paul plc, London.