the network economy - a digital primer

Eduardo Mace - January 2016 - rev.1.09

THE NETWORK ECONOMY

In the new economy as more people are connected to a network, greater is the

value of the network. They now connect primarily through social media networks

where the vast majority of connections happen between consumers and, with

increasingly frequency, organizations. These online social networks have

changed forever human relations to the world and with brands. The opportunity

for business is to make this networked space their operations center so that their

brands can engage consumers at scale. People and communities networks are

no longer a passive audience as in broadcast; they are active agents, critics with

interactive relationships, who want their intelligence respected. They want to

belong to something greater than themselves and can only be monetized

according to their desire.

Organizations are finding it difficult to understand and get results at scale from

online social networks. In my experience, businesses cannot pin down exactly

what happens in networks, because they are still geared to simple cause and

effect principles of the past. The reality of the new economy is not linear

anymore and its network effects are a challenge to visualize or control. An

example of this is what happens with some blog articles that though scarcely

shared, have a huge amount of readers. I can think of a recent example of an

article about Guanabara Bay in Rio and the Olympic Games, from a small Spanish

newspaper, which was shared directly from the source very few times in twitter,

facebook and linkedin, but had more than 18 million readers in 55 minutes. In a

monitoring analysis, we were able to verify that two influencers who linked early

to the published article generated the network effects.


Metcalfe's Law

In 1981 Robert Metcalfe, inventor of Ethernet, proposed that the number of

connections in a digital network is roughly the square of the number of

participants connected to it. Metcalfe's law, as it was named in 1993, was

used along with Moore's law by new digital economy businesses, and still reigns

supreme in their premises to this day. Metcalfe launched with this law the

understanding of the so-called “network effects”, which in the 1990’s influenced

sociologists, physicists and virologists to start a new branch of academia:

Network Science.

The online social networks have great benefits by being on the internet, where

the cost of adding a node (person or machine) and connections (relationships)

can become marginal. If you look at the physical world, these same networks are

contained by the effects of the "efficiency of Paretto" – a principle whereby for

someone to win, another has to lose – that the digital world seems to minimize.


From 1996 onwards, with scientists like Barabási, Dorogovtsev, Mendes and

others, networks science made several important discoveries: a) power law

distributions of scale-free networks explained by preferential attachment - thus

generating a mathematical model explaining the formation of long tail

distributions present in many industries, b) the phenomenon of Small World

networks and the importance of weak ties - read the next article, and c) how

social network architectures with their hidden structures determine node activity

and network performance. These and many other findings make up a robust

multidisciplinary body in the sciences and help scientists cope with network

complexities, allowing computer sciences to build social big data tools to

measure, monitor and analyze social networks.

Weak Ties

Back when Rolodexes were popular, there was a general feeling that networks

formed almost at random. People knew each other by chance connection,

exchanged contacts and maybe an important relationship would entail. The term

"networking" was almost synonymous with luck. Even though this might be a

good personal strategy, it is a very limited way of thinking about networks.

The network economy affects billions of people worldwide and is responsible in

part for the current robustness of growth in the US. The drive of the human

network economy is interpersonal connections based on affinities – the German

writer Goethe was the first to study marriage as a connection of affinities. Most

recently in 1954, the Russian mathematician, Rapoport and in 1973, the

American sociologist, Granovetter found that these networks are formed by

people and groups connected to each other by three types of bonds: strong,

weak and absent.


“More novel information flows to individuals through weak than through strong

ties. Because our close friends tend to move in the same circles that we do, the

information they receive overlaps considerably with what we already know.

Acquaintances, by contrast, know people that we do not and, thus, receive more

novel information. This outcome arises in part because our acquaintances are

typically less similar to us than close friends, and in part because they spend less

time with us. Moving in different circles from ours, they connect us to a wider

world. They may therefore be better sources when we need to go beyond what

our own group knows, as in finding a new job or obtaining a scarce service. This

is so even though close friends may be more interested than acquaintances in

helping us; social structure can dominate motivation. This is one aspect of what I

have called “the strength of weak ties.” (Granovetter, 1973, 1983)

Human social networks are driven by small cohesive communities that are

connected to others by weak ties. In this sense, it only takes a bit of

interconnection between these groups to have a Small World network, an effect

popularized by the challenge named after the American actor Kevin Bacon,

where in a few connections you can reach anyone in the planet - or the network.


Small World networks are very powerful because they are resilient, resistant to

attacks, transport information easily and filter out what is most important.

Essentially networks with this feature reduce distances and make local

connections scale globally. Networks of this type are organic, form naturally and

the best way to nurture them is not to inhibit them. A recent study on

competitiveness between companies in California found that a law that forbade

'non-compete' agreements improved exponentially the amount and quality of

innovations.

Network Analysis

With these researches and the discovery of Scale-free networks in 1998,

scientists concluded that networks have internal structures that define important

performance characteristics of the network itself. However it is only more

recently that the world began to understand about the relationship between

specific activities and network architectures - especially after the research on the

networks involved in the 9/11 attacks. Since then it was clear that terrorist

networks – like health, communication, and all the natural networks - have

temporal and hidden structures or architectures that determine how they

behave and perform. Several research groups in the US later proved that social

networks can be quantified, analyzed and managed.


Above we have the six network architectures of twitter, each map representing a

major organization with different outcomes for their network. Each of the six

architectures has a typical node behavior and performs functions in a specific

way. The formation (or transformation) of these architectures carry a lot of value

for business relationships and can be adjusted over time for a certain purpose.

Therefore, if we understand how a network is formed, how the network activity

unfolds and know how the network architecture performs - and in what period it

manifests itself - we can do experiments and draw strategies to improve the

performance of this social network. That is why it is now possible to manage

social networks scientifically.

Networked ecosystems

“In the past, the primary role of managers was to increase efficiency. By

motivating and monitoring employees, honing the firm’s capital structure and

negotiating firmly with customers and suppliers, corporate executives could


reduce costs across the value chain and achieve sustainable competitive

advantage”.

However today there are no more isolated verticals or industries, but rather

widely connected ecosystems with few global borders, as in the digital world. The

fact that many do not take into account this change cannot blind us to the fact

that these ecosystems already exist, are becoming digital and gain the

momentum of dominance through their networks. As in the chart below, for each

country we have a rate of inter-connectivity and maturity of ecosystems in their

use of content monetization, sharing, and network effects. (WEF 2015).

The dominance of digital networks as a driving force in economics - from the

physical directed connections networks with closed groups - to open shared

networks, with interactive connections changes everything. In digital

communities, the individuals become more relevant through their collectives.

The dynamics of ecosystems that use the digital social networking model support


the connection of individuals to new communities, and between communities,

generating a frenzy of weak ties in Small World networks. All this is very effective

not only for communication, but for any recurring service, for intellectual services

and to share limited resources with network effects as do Uber and KickStarter.

Traditional organizations to compete in this new environment need to reach out

to the tools of network science, build Small World networks (Watts e Strogatz,

1998), accumulate Social Capital (Ferragina, 2010) and position themselves as

Brokers between communities, in what Professor Ronald Burt calls Structural

Holes (Burt, 1995 and 2004).

Structural Holes

In the concept of a Structural Hole, a Broker is the agent making the

interconnection of communities (clusters) that without it would have absent ties

among themselves. People who connect clusters in social structures are more

susceptible to generate innovations, have more influence, greater access to new

markets and are more likely to take advantage of trade between clusters. As part

of the business intelligence 3.0 stack, social network analysis (SNA) reveals the

power of each network, the sub-groups (or communities) and the individuals

within it.

Networks can be analyzed, monitored and influenced, as the large digital

conglomerates and VCs are already doing. Hence, their positioning as the current

owners of key ecosystems in mobile and the web, with the likes of AirBnb and

Houzz - to name only two - owning their respective markets through brokerage

of structural holes and carefully constructing relationship networks.

Both structuring and leveraging the opportunity between difficult to find market

offerings directly to consumers. A social monitoring and social network analysis

of any group will yield a great many insights with threats and opportunities in


relation to possible network actions, and should be part of any intelligence stack.

Internal Networks

The same techniques apply to internal modeling of organizations. The American

Government, after the attacks on the twin towers, had to improve internal

efficiency, marksmanship (Iraq has weapons of mass destruction?) and improve

speed (stop terrorist threats). How to accomplish this in the current hierarchical

structure? How can there be more cohesion without centralization? The result

generated by an organizational network analysis revealed that of the three

possible models the better and more agile was the Small World network from a

central decision-making group. See table below for results – the shorter the

distance (betweeness) the better, because it means that the information from

any point of the network comes faster to decision makers:


Network strategy

(3 models) Distance to President

Distance

to decision makers

Original Hierarchical 1.67 2.88

Intelligence Czar 2.56 2.84

Interconnected Agencies 1.53 2.13

The new reality, of networks, presents us with the fact that competitive

advantage is no longer only about the sum of all efficiencies, but especially, the

resultant of all connections. It is in the collective intelligence of the networks that

current organizations using network science find ways to eliminate systemic

risks, cut entropies, gain reach, increase speed and knowledge. It is through the

strategic use of networks that organizations can build lasting mutually profitable

relationships in the digital space.

January, 2016

by Eduardo Mace (@edumace)

CEO 18moons and Managing Partner BRIAN Start-up Studio

the network economy - a digital primer

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