Archive for the ‘Networks’ Category
In complex environments, weak hierarchies and strong networks are the best organizing principle. One good example of complexity that we can try to fathom is nature itself. Networks thrive in nature.
We are just beginning to realize how we can use networks as our primary form of living and working. 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. 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.
The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples.
This paper seeks to further substantiate and appreciate the importance of West Churchman’s pragmatic philosophy, and to propose the development of what we call the participatory and rhizomatic systems approach. The aim of rhizomatics is to create a deterritoriazation of current social ﬁelds and to make sense of the creation of the rhizomatic networks and ethics for the marginalized group in practice. This paper takes the contributions of Gilles Deleuze and Felix Guattari’s notion of rhizome on ethical reasoning and incorporates them into a test. It examines how ethics for the marginalized group can assist in appreciating and developing ethical management of any systemic intervention. The paper looks into what ethics for the marginalized group is and how it is achieved in the context of rhizomatic networks.
Our ideas about creativity, and particularly the most important kind–what Richard Ogle calls “breakthrough creativity“–are governed by a long-standing and deep-seated myth: “the mind inside the head.” From ancient times, philosphers of mind have held that important ideas and insights come from the individual brains of geniuses with awesome rational powers, whose minds seem to function on a higher plane than those of normal folk. In recent years, however, as advances in cognitive science and network science have highlighted the importance of the external world, the social, cultural, and economic context in which ideas are generated, a classic paradigm shift has occurred. Mihaly Csikszentmihalyi has posited the idea of the “extended mind,” radically suggesting that the source of creativity lies not inside of our heads and brains, but outside them, in the connections between people and ideas. There has also been a concurrent, growing recognition of the role that imagination and intuition play in scientific breakthroughs, where in earlier times it was thought that superior rational thinking and logic were responsible for such advances. In The Mind Out There, Richard Ogle describes this paradigm shift and crystallizes its nature and implications for the first time. He argues that developments in the study of cognitive science, network science, and complexity, now allow us to see and understand how breakthrough ideas happen in a much clearer way, offering the beginnings of “a new science of ideas“. The key to this science resides in what the author calls “idea-spaces,” a set of nodes in a network of people (and their ideas) that cohere and take on a distinctive set of characteristics and dynamics leading to the generation of breakthrough ideas.
For interaction, the challenge is engagement. Widening the circle of involvement means expanding who gets to participate. It is about inviting and including relevant, new and different voices. The measure is built on the social graph: how many of your friends know each other?
The network design principles successful organizations follow are:
- shortening the distance between two randomly picked files/nodes/people.
- getting more people who you personally know to know each other.
The perspective of network science views knowledge as socially created and socially re-created not as stuff of the mind that can be shared and stored by individuals. Knowing is a process of relating. From the network-based, relational perspective knowing is viewed as an ongoing and, never-ending process of making meaning in communication.
The potential of social media cannot be realized without a very different epistemological grounding, a relational perspective. Independently existing people and things then become viewed as co-constructed in coordinated networked action. Accordingly, the role of management is different, opening up new possibilities: power in networks is about “power to” or “power with”, and not “power over”.
Teams seem to be the answer to many important questions. Here I will try to open some of the questions that has lead to this answer and why we are asking them. Let’s start with two working definitions by Peter Senge:
- A team is a group of people that have the resources to define, maintain and develop the set of useful solutions to the challenges of the times
- Organizations are filled with teams within teams.
If this definition of a team is correct many of our understandings of organizations is becoming outdated. We usually think of an organization as a relatively closed system. This has become outdated because we have outsourced many tasks and insourced specialists to help us with all the things we do not know our selves.
Transition cannot be achieved from the top down. It will require central and local government, businesses, communities and individuals to develop their own understandings of sustainability and social justice and to debate and negotiate with each other about the way forward.
At the moment, however, there is no easy way to get this kind of debate to happen. Our social fabric is fragmented, and opportunities for debate are few and far between. There is little space for groups to deliberate about complex, pressing issues and even less space for them to share their views with each other. The internet is at best a partial solution: there is no substitute for face to face conversations.
Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges).
Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy.
Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network.