Friday, August 10, 2007

LEARNING in the 2.0 WORLD!


A Learning Theory for the Digital Age

December 12, 2004
George Siemens

Update (April 5, 2005): I've added a website to explore this concept at


Behaviorism, cognitivism, and constructivism are the three broad learning theories most often utilized in the creation of instructional environments. These theories, however, were developed in a time when learning was not impacted through technology. Over the last twenty years, technology has reorganized how we live, how we communicate, and how we learn. Learning needs and theories that describe learning principles and processes, should be reflective of underlying social environments. Vaill emphasizes that “learning must be a way of being – an ongoing set of attitudes and actions by individuals and groups that they employ to try to keep abreast o the surprising, novel, messy, obtrusive, recurring events…” (1996, p.42).

Learners as little as forty years ago would complete the required schooling and enter a career that would often last a lifetime. Information development was slow. The life of knowledge was measured in decades. Today, these foundational principles have been altered. Knowledge is growing exponentially. In many fields the life of knowledge is now measured in months and years. Gonzalez (2004) describes the challenges of rapidly diminishing knowledge life:

“One of the most persuasive factors is the shrinking half-life of knowledge. The “half-life of knowledge” is the time span from when knowledge is gained to when it becomes obsolete. Half of what is known today was not known 10 years ago. The amount of knowledge in the world has doubled in the past 10 years and is doubling every 18 months according to the American Society of Training and Documentation (ASTD). To combat the shrinking half-life of knowledge, organizations have been forced to develop new methods of deploying instruction.”

Some significant trends in learning:

Many learners will move into a variety of different, possibly unrelated fields over the course of their lifetime. Informal learning is a significant aspect of our learning experience. Formal education no longer comprises the majority of our learning. Learning now occurs in a variety of ways – through communities of practice, personal networks, and through completion of work-related tasks.

Learning is a continual process, lasting for a lifetime. Learning and work related activities are no longer separate. In many situations, they are the same. Technology is altering (rewiring) our brains. The tools we use define and shape our thinking.
The organization and the individual are both learning organisms. Increased attention to knowledge management highlights the need for a theory that attempts to explain the link between individual and organizational learning.

Many of the processes previously handled by learning theories (especially in cognitive information processing) can now be off-loaded to, or supported by, technology.
Know-how and know-what is being supplemented with know-where (the understanding of where to find knowledge needed).


Driscoll (2000) defines learning as “a persisting change in human performance or performance potential…[which] must come about as a result of the learner’s experience and interaction with the world” (p.11). This definition encompasses many of the attributes commonly associated with behaviorism, cognitivism, and constructivism – namely, learning as a lasting changed state (emotional, mental, physiological (i.e. skills)) brought about as a result of experiences and interactions with content or other people.

Driscoll (2000, p14-17) explores some of the complexities of defining learning. Debate centers on:

Valid sources of knowledge - Do we gain knowledge through experiences? Is it innate (present at birth)? Do we acquire it through thinking and reasoning?

Content of knowledge – Is knowledge actually knowable? Is it directly knowable through human experience?

The final consideration focuses on three epistemological traditions in relation to learning: Objectivism, Pragmatism, and Interpretivism

Objectivism (similar to behaviorism) states that reality is external and is objective, and knowledge is gained through experiences.

Pragmatism (similar to cognitivism) states that reality is interpreted, and knowledge is negotiated through experience and thinking.

Interpretivism (similar to constructivism) states that reality is internal, and knowledge is constructed.

All of these learning theories hold the notion that knowledge is an objective (or a state) that is attainable (if not already innate) through either reasoning or experiences. Behaviorism, cognitivism, and constructivism (built on the epistemological traditions) attempt to address how it is that a person learns.

Behaviorism states that learning is largely unknowable, that is, we can’t possibly understand what goes on inside a person (the “black box theory”). Gredler (2001) expresses behaviorism as being comprised of several theories that make three assumptions about learning:

Observable behaviour is more important than understanding internal activities
Behaviour should be focused on simple elements: specific stimuli and responses
Learning is about behaviour change

Cognitivism often takes a computer information processing model. Learning is viewed as a process of inputs, managed in short term memory, and coded for long-term recall. Cindy Buell details this process: “In cognitive theories, knowledge is viewed as symbolic mental constructs in the learner's mind, and the learning process is the means by which these symbolic representations are committed to memory.”

Constructivism suggests that learners create knowledge as they attempt to understand their experiences (Driscoll, 2000, p. 376). Behaviorism and cognitivism view knowledge as external to the learner and the learning process as the act of internalizing knowledge. Constructivism assumes that learners are not empty vessels to be filled with knowledge. Instead, learners are actively attempting to create meaning. Learners often select and pursue their own learning. Constructivist principles acknowledge that real-life learning is messy and complex. Classrooms which emulate the “fuzziness” of this learning will be more effective in preparing learners for life-long learning.

Limitations of Behaviorism, Cognitivism, and Constructivism

A central tenet of most learning theories is that learning occurs inside a person. Even social constructivist views, which hold that learning is a socially enacted process, promotes the principality of the individual (and her/his physical presence – i.e. brain-based) in learning. These theories do not address learning that occurs outside of people (i.e. learning that is stored and manipulated by technology). They also fail to describe how learning happens within organizations

Learning theories are concerned with the actual process of learning, not with the value of what is being learned. In a networked world, the very manner of information that we acquire is worth exploring. The need to evaluate the worthiness of learning something is a meta-skill that is applied before learning itself begins. When knowledge is subject to paucity, the process of assessing worthiness is assumed to be intrinsic to learning. When knowledge is abundant, the rapid evaluation of knowledge is important. Additional concerns arise from the rapid increase in information. In today’s environment, action is often needed without personal learning – that is, we need to act by drawing information outside of our primary knowledge. The ability to synthesize and recognize connections and patterns is a valuable skill.

Many important questions are raised when established learning theories are seen through technology. The natural attempt of theorists is to continue to revise and evolve theories as conditions change. At some point, however, the underlying conditions have altered so significantly, that further modification is no longer sensible. An entirely new approach is needed.

Some questions to explore in relation to learning theories and the impact of technology and new sciences (chaos and networks) on learning:

How are learning theories impacted when knowledge is no longer acquired in the linear manner?

What adjustments need to made with learning theories when technology performs many of the cognitive operations previously performed by learners (information storage and retrieval).
How can we continue to stay current in a rapidly evolving information ecology?

How do learning theories address moments where performance is needed in the absence of complete understanding?

What is the impact of networks and complexity theories on learning?

What is the impact of chaos as a complex pattern recognition process on learning?
With increased recognition of interconnections in differing fields of knowledge, how are systems and ecology theories perceived in light of learning tasks?

An Alternative Theory

Including technology and connection making as learning activities begins to move learning theories into a digital age. We can no longer personally experience and acquire learning that we need to act. We derive our competence from forming connections. Karen Stephenson states:

“Experience has long been considered the best teacher of knowledge. Since we cannot experience everything, other people’s experiences, and hence other people, become the surrogate for knowledge. ‘I store my knowledge in my friends’ is an axiom for collecting knowledge through collecting people (undated).”

Chaos is a new reality for knowledge workers. ScienceWeek (2004) quotes Nigel Calder's definition that chaos is “a cryptic form of order”. Chaos is the breakdown of predictability, evidenced in complicated arrangements that initially defy order. Unlike constructivism, which states that learners attempt to foster understanding by meaning making tasks, chaos states that the meaning exists – the learner's challenge is to recognize the patterns which appear to be hidden. Meaning-making and forming connections between specialized communities are important activities.

Chaos, as a science, recognizes the connection of everything to everything. Gleick (1987) states: “In weather, for example, this translates into what is only half-jokingly known as the Butterfly Effect – the notion that a butterfly stirring the air today in Peking can transform storm systems next month in New York” (p. 8). This analogy highlights a real challenge: “sensitive dependence on initial conditions” profoundly impacts what we learn and how we act based on our learning. Decision making is indicative of this. If the underlying conditions used to make decisions change, the decision itself is no longer as correct as it was at the time it was made. The ability to recognize and adjust to pattern shifts is a key learning task.

Luis Mateus Rocha (1998) defines self-organization as the “spontaneous formation of well organized structures, patterns, or behaviors, from random initial conditions.” (p.3). Learning, as a self-organizing process requires that the system (personal or organizational learning systems) “be informationally open, that is, for it to be able to classify its own interaction with an environment, it must be able to change its structure…” (p.4). Wiley and Edwards acknowledge the importance of self-organization as a learning process: “Jacobs argues that communities self-organize in a manner similar to social insects: instead of thousands of ants crossing each other’s pheromone trails and changing their behavior accordingly, thousands of humans pass each other on the sidewalk and change their behavior accordingly.”. Self-organization on a personal level is a micro-process of the larger self-organizing knowledge constructs created within corporate or institutional environments. The capacity to form connections between sources of information, and thereby create useful information patterns, is required to learn in our knowledge economy.

Networks, Small Worlds, Weak Ties

A network can simply be defined as connections between entities. Computer networks, power grids, and social networks all function on the simple principle that people, groups, systems, nodes, entities can be connected to create an integrated whole. Alterations within the network have ripple effects on the whole.

Albert-László Barabási states that “nodes always compete for connections because links represent survival in an interconnected world” (2002, p.106). This competition is largely dulled within a personal learning network, but the placing of value on certain nodes over others is a reality. Nodes that successfully acquire greater profile will be more successful at acquiring additional connections. In a learning sense, the likelihood that a concept of learning will be linked depends on how well it is currently linked. Nodes (can be fields, ideas, communities) that specialize and gain recognition for their expertise have greater chances of recognition, thus resulting in cross-pollination of learning communities.

Weak ties are links or bridges that allow short connections between information. Our small world networks are generally populated with people whose interests and knowledge are similar to ours. Finding a new job, as an example, often occurs through weak ties. This principle has great merit in the notion of serendipity, innovation, and creativity. Connections between disparate ideas and fields can create new innovations.


Connectivism is the integration of principles explored by chaos, network, and complexity and self-organization theories. Learning is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual. Learning (defined as actionable knowledge) can reside outside of ourselves (within an organization or a database), is focused on connecting specialized information sets, and the connections that enable us to learn more are more important than our current state of knowing.

Connectivism is driven by the understanding that decisions are based on rapidly altering foundations. New information is continually being acquired. The ability to draw distinctions between important and unimportant information is vital. The ability to recognize when new information alters the landscape based on decisions made yesterday is also critical.

Principles of connectivism:

Learning and knowledge rests in diversity of opinions.
Learning is a process of connecting specialized nodes or information sources.
Learning may reside in non-human appliances.
Capacity to know more is more critical than what is currently known.
Nurturing and maintaining connections is needed to facilitate continual learning.
Ability to see connections between fields, ideas, and concepts is a core skill.
Currency (accurate, up-to-date knowledge) is the intent of all connectivist learning activities.
Decision-making is itself a learning process. Choosing what to learn and the meaning of incoming information is seen through the lens of a shifting reality. While there is a right answer now, it may be wrong tomorrow due to alterations in the information climate affecting the decision.

Connectivism also addresses the challenges that many corporations face in knowledge management activities. Knowledge that resides in a database needs to be connected with the right people in the right context in order to be classified as learning. Behaviorism, cognitivism, and constructivism do not attempt to address the challenges of organizational knowledge and transference.

Information flow within an organization is an important element in organizational effectiveness. In a knowledge economy, the flow of information is the equivalent of the oil pipe in an industrial economy. Creating, preserving, and utilizing information flow should be a key organizational activity. Knowledge flow can be likened to a river that meanders through the ecology of an organization. In certain areas, the river pools and in other areas it ebbs. The health of the learning ecology of the organization depends on effective nurturing of information flow.

Social network analysis is an additional element in understanding learning models in a digital era. Art Kleiner (2002) explores Karen Stephenson’s “quantum theory of trust” which “explains not just how to recognize the collective cognitive capability of an organization, but how to cultivate and increase it”. Within social networks, hubs are well-connected people who are able to foster and maintain knowledge flow. Their interdependence results in effective knowledge flow, enabling the personal understanding of the state of activities organizationally.

The starting point of connectivism is the individual. Personal knowledge is comprised of a network, which feeds into organizations and institutions, which in turn feed back into the network, and then continue to provide learning to individual. This cycle of knowledge development (personal to network to organization) allows learners to remain current in their field through the connections they have formed.

Landauer and Dumais (1997) explore the phenomenon that “people have much more knowledge than appears to be present in the information to which they have been exposed”. They provide a connectivist focus in stating “the simple notion that some domains of knowledge contain vast numbers of weak interrelations that, if properly exploited, can greatly amplify learning by a process of inference”. The value of pattern recognition and connecting our own “small worlds of knowledge” are apparent in the exponential impact provided to our personal learning.

John Seely Brown presents an interesting notion that the internet leverages the small efforts of many with the large efforts of few. The central premise is that connections created with unusual nodes supports and intensifies existing large effort activities.

Brown provides the example of a Maricopa County Community College system project that links senior citizens with elementary school students in a mentor program. The children “listen to these “grandparents” better than they do their own parents, the mentoring really helps the teachers…the small efforts of the many- the seniors – complement the large efforts of the few – the teachers.” (2002). This amplification of learning, knowledge and understanding through the extension of a personal network is the epitome of connectivism.


The notion of connectivism has implications in all aspects of life. This paper largely focuses on its impact on learning, but the following aspects are also impacted:

Management and leadership. The management and marshalling of resources to achieve desired outcomes is a significant challenge. Realizing that complete knowledge cannot exist in the mind of one person requires a different approach to creating an overview of the situation. Diverse teams of varying viewpoints are a critical structure for completely exploring ideas. Innovation is also an additional challenge. Most of the revolutionary ideas of today at one time existed as a fringe element. An organizations ability to foster, nurture, and synthesize the impacts of varying views of information is critical to knowledge economy survival. Speed of “idea to implementation” is also improved in a systems view of learning.

Media, news, information. This trend is well under way. Mainstream media organizations are being challenged by the open, real-time, two-way information flow of blogging.

Personal knowledge management in relation to organizational knowledge management

Design of learning environments


The pipe is more important than the content within the pipe. Our ability to learn what we need for tomorrow is more important than what we know today. A real challenge for any learning theory is to actuate known knowledge at the point of application. When knowledge, however, is needed, but not known, the ability to plug into sources to meet the requirements becomes a vital skill. As knowledge continues to grow and evolve, access to what is needed is more important than what the learner currently possesses.

Connectivism presents a model of learning that acknowledges the tectonic shifts in society where learning is no longer an internal, individualistic activity. How people work and function is altered when new tools are utilized. The field of education has been slow to recognize both the impact of new learning tools and the environmental changes in what it means to learn. Connectivism provides insight into learning skills and tasks needed for learners to flourish in a digital era.


Barabási, A. L., (2002) Linked: The New Science of Networks, Cambridge, MA, Perseus Publishing.

Buell, C. (undated). Cognitivism. Retrieved December 10, 2004 from

Brown, J. S., (2002). Growing Up Digital: How the Web Changes Work, Education, and the Ways People Learn. United States Distance Learning Association. Retrieved on December 10, 2004, from

Driscoll, M. (2000). Psychology of Learning for Instruction. Needham Heights, MA, Allyn & Bacon.

Gleick, J., (1987). Chaos: The Making of a New Science. New York, NY, Penguin Books.

Gonzalez, C., (2004). The Role of Blended Learning in the World of Technology. Retrieved December 10, 2004 from

Gredler, M. E., (2005) Learning and Instruction: Theory into Practice – 5th Edition, Upper Saddle River, NJ, Pearson Education.

Kleiner, A. (2002). Karen Stephenson’s Quantum Theory of Trust. Retrieved December 10, 2004 from

Landauer, T. K., Dumais, S. T. (1997). A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of Acquisition, Induction and Representation of Knowledge. Retrieved December 10, 2004 from

Rocha, L. M. (1998). Selected Self-Organization and the Semiotics of Evolutionary Systems. Retrieved December 10, 2004 from

ScienceWeek (2004) Mathematics: Catastrophe Theory, Strange Attractors, Chaos. Retrieved December 10, 2004 from

Stephenson, K., (Internal Communication, no. 36) What Knowledge Tears Apart, Networks Make Whole. Retrieved December 10, 2004 from

Vaill, P. B., (1996). Learning as a Way of Being. San Francisco, CA, Jossey-Blass Inc.

Wiley, D. A and Edwards, E. K. (2002). Online self-organizing social systems: The decentralized future of online learning. Retrieved December 10, 2004 from

This work is licensed under a Creative Commons License

Managing the Connected Organization


If knowledge is power, what is connected knowledge?

The knowledge economy operates on the complexities of connections. All individuals, communities, systems, and other business assets are massively interconnected in an evolving economic ecosystem. In the connected economy, each network actor (individual, team, or organization) is embedded in a larger economic web that affects each participant and, in return, is influenced by that participant. In such a connected system we can no longer focus on the performance of individual actors -- we must manage connected assets.

Efforts at making sense of this new world are beginning to reveal some basic principles at work in the complex adaptive systems we call our organizations.

"There is a central difference between the old and new economies:
the old industrial economy was driven by economies of scale;
the new information economy is driven by the economics of networks..."

Information Rules
by Carl Shapiro, Hal R. Varian

Recent research on productivity and effectiveness in the knowledge economy provides insight into what works in the connected workplace. Certain patterns of connections appear around both effective individuals and successful teams when performing knowledge work. We have also discovered where to add 'missing links' that change a poor economic network into a better conduit for information, influence, and knowledge.

Improving Individual Effectiveness

Is it who you know (social capital) or what you know (human capital) that leads to success? This has been often debated with good arguments on both sides. Most managers today side with the "what you know" crowd.

In the late 1980s management researchers were starting to notice that some managers were better, than other managers, at accomplishing objectives through relationships. John Kotter, of the Harvard business school, discovered that effective general managers spend more than 80% of their time interacting with others. Other management scholars were also starting to see the importance of conversations and relationships in managerial work. Individual mastery was no longer the key -- it was human capital and social capital working together to create productivity and innovation. Ron Burt, of the University of Chicago, a leading researcher on the social capital of managers has found, through numerous studies, that certain patterns of connections that individuals build with others brings them higher pay, earlier promotions, greater influence, better ideas and overall greater career success. Burt believes that good social capital provides a much higher return on investment in human capital -- the two work together.

Arent Greve, a researcher at the Norwegian School of Economics, is also interested in the contribution of human and social capital on organizational outcomes and individual productivity. He studied project managers in a knowledge-based services company in Europe. He viewed human capital as the knowledge and skills attained by the individual over his/her career. Social capital was defined as a property of personal networks -- the ability to reach others, inside and outside the organization, for information, advice and problem-solving. He found something very interesting. As expected, better human capital and better social capital both had a positive effect on productivity, but unexpected was the effect of better social capital was noticeably stronger! Project managers with better personal networks were more productive -- they were better able to coordinate tasks and find the knowledge necessary to accomplish the goals of their projects.

Improving Team Effectiveness

Meanwhile in a high-tech firm in Silicon Valley, Morten Hansen, also of Harvard, had a similar research agenda. The key difference was that Hansen was interested in the productivity and effectiveness of teams. Hansen found that teams who could easily reach other teams and access the knowledge they needed were more successful than teams with poor network connections. Both Greve and Hansen found that the ability to reach a diverse set of others in the network through very few links was the key to success.

Hansen took his research one step further. He examined the difference between those teams that had many direct connections to other project teams and those that used both direct and indirect ties to reach the resources they needed. Hansen found that those teams that used only direct ties to seek and find information were soon overwhelmed with too many connections. The teams that used the power of the indirect tie, while at the same time limiting their direct ties, were more successful -- they did not spend as much time interacting with the network to get what they needed. A sparse radial network in which your direct ties are connected to others that you are not connected to, has been shown, by Burt and others, to provide many benefits and opportunities.

Hansen discovered one other insight that is key for knowledge management. A diverse radial network with many unique indirect ties is good for monitoring what is happening in the organization and for discovering pockets of knowledge and expertise. Yet, this type of network may not be useful for transferring knowledge. Although indirect ties help you cast a wide net and see far into the organization (and beyond it), these ties are not always efficient for transferring and utilizing knowledge once it is discovered. It depends on what type of knowledge needs to be transferred. Explicit knowledge, which can be easily codified, can be transferred indirectly through various technologies such as email, FTP, WWW or documents through interoffice mail. For example, sharing a presentation done previously for the same customer. Complex tacit knowledge knowledge requires direct interaction and sharing of experiences between two or more individuals. To transfer tacit knowledge a direct tie with the knowledge source(s) must be established. Trust and understanding must be built -- this is similar to apprenticeship. Explicit knowledge travels over computer networks, but tacit knowledge is shared and learned via human networks.

Improving Information Flow

Network ties are distributed unevenly in organizations. People that work together form networks together -- clusters emerge around established work relationships. Engineers working on Project X form a cluster, those working on Project Y form a cluster, and those working on Project Z form a cluster. Everyone knows everyone else within the local cluster, and yet only a few individuals have boundary spanning ties to other clusters. Strong, frequent, ties are usually found within clusters, while weaker, less frequent ties are found between clusters.

Clusters of concentrated connections appear throughout an organization and throughout industries. Some clusters have many ties outside the group, while other clusters have only a few. Poor connections between clusters result in very long path lengths throughout the organization. In such a network it is easy to access those in your cluster but not those in other clusters. This often results in distant clusters not knowing what information and knowledge is available elsewhere in the organization.

Often the knowledge you need is in clusters other than your own. Networks have a horizon beyond which it is difficult to see what is happening. Research by Noah Friedkin, at the University of California at Santa Barbara, has shown that this horizon of observability is usually two steps in a human network -- your direct contacts and their direct contacts. Around three steps out, things are real fuzzy -- you do not have a good idea of what is happening in that part of the network. Beyond three steps, you are blind to what is happening in the rest of the network -- except for obvious 'public' information known by everyone. So the popular idea of it being a 'small world' because we are all separated by an average of 6 degrees is misleading. Six degrees is actually a very large world -- one, two and three degrees is a small world! It is usually those separated by two degrees where the 'small world' discoveries happen -- it is here where you discover the person next to you on the plane is related to a friend from your university days.

In a network of very long path lengths between clusters, your ability to find the knowledge or information you need is very constrained. If the knowledge that you seek is not within your network horizon[1 or 2 steps], then you assume it is not available in your organization and you reinvent it, or pay for it on the outside. Exasperated with this network horizon in his organization, a former CEO of HP once lamented, "If HP only knew what HP knows".

The natural response in many organizations is to throw technology at the problem. A very poor, yet quite common, solution is to mine knowledge from employees, codify it, and store it in a knowledge database. Many large consulting firms tried this approach in the 1990s with usually poor results. They found that people were not always willing to make public their best knowledge and that codifying tacit knowledge was like trying to nail jelly to the wall.

Why not use the power of the network itself to create a solution? Improve the organizational network and then use technology to help people communicate across wide spans of the human network. At first blush, improving an organization-wide network may seem an overwhelming task. Where do we start? First, look at the networks and communities of practice/interest/knowledge that have organized around a specific topic, product, service or customer. Usually the whole organization does not have to be included in the problem space. Second, map out the network nodes and their connections (who goes to whom for expertise/knowledge/advice on X?). From this network map, you can see the various clusters and how they are connected. Figure 1 below is a network map of project teams. A line connecting two teams indicates a two-way information flow or exchange of knowledge.

Figure 1

This network of 17 project teams all work on subassemblies to a larger product. The teams are composed of mostly engineers, technicians, and project managers. All teams have less than ten members. Three clusters are evident in the network of project teams.

Before we look at how to improve the overall connectivity of the network, let's digress back to social capital. Which team has the best social capital in this network? Which team can access all of the knowledge and resources in the network quicker than the others? (Hint: this network is drawn to reveal the answer.)

Common wisdom in networks is "the more connections, the better." This is not always true. What is always true is "the better connections, the better." Better connections are those that provide you access to nodes that you currently do not have access to. Although Team F and Team Q have many connections each and have excellent local access (to the nodes near them), they have only fair access to the rest of the network. Team O has the best social capital (aka network benefits) in this network of project teams. Team O achieves this with only three direct ties -- it is connected to others who are well connected. Team O's indirect contacts bring access to information and knowledge not available locally.

The average path length in this network is 3.45 with many paths longer than the network horizon. Even in this small network there are nodes[teams] that are nearly blind to what is happening in other parts of the network.

In the summer of 1998, writing in the scientific journal Nature, a stir of excitement was generated by two mathematicians from Cornell, Steven Strogatz and Duncan Watts. While investigating small-world networks (those with many clusters), they discovered that a few randomly added crosscuts between unconnected clusters would improve[i.e. lower] a network's characteristic path length significantly. The benefits were not just local, but spread throughout the network and this improvement could be achieved with just a few added ties in the network. Very small adjustments could cause large positive changes -- a common dynamic in complex adaptive systems.

Looking back on our project team network in Figure 1, how can we improve the connectivity with just one added link? Which two nodes would you connect to bring everyone in the network closer together?

Although many combinations will increase the access of everyone to everyone else, the greatest measurable effect is when we add a crosscut between Team Q and Team F. The average path length drops a whole step! The longest path in the network is reduced from 7 steps to 4 steps. In human networks, the fewer steps in the network path, the quicker information arrives with less distortion.

Figure 2

The connection between Teams Q and F may be the optimal connection in network efficiency, but it may not be a practical connection. Both of these teams already have many ties and may not have the time and energy to support another one (remember what Hansen discovered about too many direct ties?). What is an alternative connection? If you cannot connect the highly connected nodes, how about connecting their respective network neighbors? Instead of connecting Q and F, how about connecting D and Z? This connection will not reduce the path length as much, but it is between nodes that are not overburdened with connections.

Leading Edge Management

One of the benefits of consulting with organizational network analysis is having leading edge clients. Not only are they open to new methods to improve their organizations, they usually end up teaching me quite a bit. One such client is Vancho Cirovski, Vice President of Human Resources at Cardinal Health. Vancho, an expert soccer player and coach, first noticed an interesting phenomenon on the playing field. Teams that were more integrated and communicated well amongst themselves on the field, more often than not, beat a collection of individually superior players who were not interacting well on the field. I saw a similar phenomenon on my son's soccer team. They had good players, but were divided up into several cliques which did not get along with each other -- the team as a whole underperformed consistently. It is the chemistry of the mix that matters!

Vancho saw the same effect in project teams inside organizations. He has summarized these concepts of managing connected organizations using Einstein's famous formula:

    E = MC2
    • M is the Mastery of each individual (human capital)
    • C are the Connections that join individuals into a community (social capital)
    • C is the Communication that flows through those Connections
    • E is the resulting Effectiveness of the team or organization.

Vancho further stipulates that the two Cs, communication and connections, combine to form another C: Chemistry, which leads to team or organizational effectiveness.

A common reason for the failure of many mergers and acquisitions is the failure to properly integrate the two combining organizations and their cultures. Although a formal hierarchy combining the two organizations may be in place, the right work relationships are never formed and the merging organizations remain disconnected. Ralph Polumbo, Vice President of Integration for Rubbermaid's 1998 acquisition of its European competitor, Curver, wanted to make sure the two organizations were combining effectively. He decided to map and measure the melding of information flows, work relationships and knowledge exchanges -- connections that help cultures combine. His vision was one of a boundaryless organization with no fragmentation along former constituencies. He wanted to track where integration was happening and where it was not occurring. By examining his human and social capital concurrently, he was able to visually monitor the successful integration of both organizations. An organizational merger is illustrated here.

How can managers improve the connectivity within their organization? Here are a few places to get started:

  • Look beyond the individual -- uncover their interconnections and multiple group memberships.
  • Know the difference between tacit and explicit knowledge and how it is shared and transferred.
  • Reward people for directly sharing their know-how, for including others in their knowledge-sharing networks.
  • Design computer systems that facilitate conversations and sharing of knowledge -- think communication, not storage/retrieval.
  • Help women and people of color connect to key knowledge flows and communities in the organization. This may help eliminate the glass ceiling.
  • Recruit new hires through the networks of current employees -- they will be happier, adjust quicker, and stay longer.
  • When transferring employees keep in mind their connections. Exchanging employees with a diverse network of ties can create shortcuts between departments or teams and greatly improve the overall information flow. Transferring employees from one department to another creates an overlap which enhances the transfer of information and influence between the two groups.
  • Ensure better coordination of behavior between departments or projects by adding crosscuts to minimize the path length of their information exchange networks. To reduce delays you want some redundancy in the paths -- if one is blocked then alternative communication paths are available.
  • For the HR department it is no longer sufficient to just 'hire the best'. You must hire and wire! Start new networks, help employees and teams connect --connect the unconnected!

What is connected knowledge? A competitive advantage! Your competition may duplicate the nodes in your organization, but not the pattern of connections that have emerged through sense-making, feedback and learning within your business network. And if you get Vancho's take on Einstein's formula correct, then connected knowledge is pure energy!

In the 1992 U.S. presidential race, one simple phrase refocused and re-ignited a jumbled campaign effort by Bill Clinton -- "It's the economy, stupid". Adaptive businesses see the benefits in managing connected organizations. We can adapt the old campaign slogan to reflect the new business reality -- "It's the connections, stupid!"

Software and Training in social network analysis are available from the author.

Copyright © 1999-2004, Valdis Krebs

Organizational Hierarchy

Adapting Old Structures to New Challenges


"We may not be interested in chaos, but chaos is interested in us." - Robert Cooper

When change was slow, and the future was pretty much like the present, hierarchical organizations were perfect structures for business and government. The world is no longer predictable, nor are solutions obvious. Old structures are no longer sufficient for new complex challenges.

Businesses have noticed the changes and are adapting. From GE's boundaryless organization to Toyota's amazingly flexible supply web, agility and adaptability are the mantra. Unfortunately most governments are not as quick and creative. Instead of the out-of-the-box thinking found increasingly in the business world, governments are busy shuffling boxes on the organization chart.

Figure 1 below is a typical organization chart of a generic hierarchical organization -- either business or government. Two nodes are connected by a gray link if there is a formal reporting relationship and information flow. The nodes on the bottom row represent sub-organizations, while the top two rows are individuals.

Figure 1 - Original Hierarchy

Assume the above organizational chart roughly represents the U.S. intelligence community. Node 001 is the President and nodes 007 to 016 are various intelligence agencies. Nodes 002 to 006 are the leaders of those various agencies.

Intelligence Czar

The U.S. government is currently facing a dual problem in the intelligence community:

  • improve accuracy -- WMD in Iraq?
  • improve agility -- stop terror attacks

One of the solutions being discussed is adding a new formal position to the intelligence community. This new box would be an 'intelligence czar' to which all other intelligence leaders and their agencies would report. The thinking behind this proposed solution is for there to be one aggregation point for all intelligence. Node 017 in Figure 2 represents this new position.

Figure 2 - Adding the Intelligence Czar[017] to Original Hierarchy

Connecting the Stovepipes

Another solution to integrate intelligence is to connect the various agencies to each other and start to demand and reward knowledge sharing between them. This does not require a new position. It does require the leaders of the agencies to share knowledge and information and to propogate this new culture down in their organizations where appropriate -- it requires the intelligence community to become a community! This may require new leaders who are open to connecting the stovepipes. Interconnecting the intelligence leadership is displayed by the horizontal green links in Figure 3a. [We moved nodes 003, 004, and 005 so that all green links would be visible -- their new positions on the chart have no further significance.]

Figure 3a - Horizontal Links added to the Original Hierarchy

Which solution is better? The new formal position or the interconnecting of existing positions? It depends on your goal. If you want accountability and budget responsibility then the hierarchy will work. But, if you want a smart, agile learning organization -- able to adapt to a changing enemy -- then the interconnected structure will probably perform better. The interconnected structure spans various organizations with diverse data and perspectives allowing for cross-pollination and learning.

Mathematics of Hierarchies

We apply the small-world network metrics of Watts & Strogatz to Figures 1, 2, and 3 above. One of the key metrics in the small-world model is the average path length, for individuals and for the network overall. A good score for an individual means that he/she is close to all of the others in the network -- they can reach others quickly without going through too many intermediaries. A good score for the whole network indicates that everyone can reach everyone else easily and quickly. The shorter the information paths for everyone, the quicker the information arrives and the less distorted it is when it arrives. Another benefit of multiple short paths is that most members of the network have good visibility into what is happening in other parts of the network -- a greater awareness. They have a wide network horizon which is useful for combining key pieces of distributed intelligence. In an environment where it is difficult to distinguish signal from noise, it is important to have many perspectives involved in the sense-making process.

Below are the path length metrics for each of the 3 networks above. Two metrics are of high importance. Since the President is the key destination for intelligence, his distance from the rest of the network is critical. The average path length of the whole network is important for sense-making and learning within the group.

Network President's PathOverall Path
Figure 1 - Original Hierarchy 1.67 2.88
Figure 2 - With Intelligence Czar 2.56 2.84
Figure 3a - Interconnected Agencies 1.53 2.13
Table 1 - Small World Metrics

We can see that Figure 3a is a win-win. Both the President's average path length and the group average path length are reduced. Information flows quicker, with less distortion, and President is more involved.

Figure 2 is OK for the overall group, but it increases the President's path length by almost one step. We want our President to be closer to the intelligence community, not further away! Our simple analysis shows that the Intelligence Czar option puts an extra communication burden on the President. Even if the person in this position is top notch, we are still distorting and delaying the information flows by adding this position. The centralized czar does not compute!

Figure 3b below gives us insight into why interconnecting the stovepipes is a better option. We redisplay the organization in Figure 3a by 'link patterns' and we see a totally new perspective. Figures 3a and 3b have exactly the same connections -- 3b is the emergent network view of the new organization. By adding the horizontal ties we have transformed a simple hierarchy into an interconnected group. Recent research by psychologist Patrick Laughlin of the University of Illinois shows that groups outperform even the best individuals in decision making. Intelligence information is rarely clear or complete -- a key reason for having many perpsectives and diverse experiences for cross-pollination and sense-making.

Figure 3b - Emergent Network w/Horizontal Links [Same Links as Figure 3a]

Some may ask: What about combining both solutions -- the new position and the horizontal connections? That combination does improve the average path length for the whole group, but the President's path length remains longer [as in Figure 2]. It is not a better choice than Figure 3a/3b.

It looks like new connections win out over new nodes!

The Report to the President of the United States by The Commission on the Intelligence Capabilities of the United States Regarding Weapons of Mass Destruction came to the same conclusion. Based on their findings: "In sum, today's threats are quick, quiet, and hidden" they concluded, "We need an intelligence community that is truly integrated".

In a similar finding, this RAND report explores organizational culture, integration, and the drawbacks of compartmentalizing information instead of sharing it.

Software and Training in social network analysis are available from the author.

Copyright © 2005, Valdis Krebs

1 comment:

Valdis said...

If you enjoyed the above post, you might enjoy these web pages on connectivity in modern organizations...