Networks of People

Today we’ll talk more about networks of people. 

Networks of people grow around the purposes of the entities that hire the people – no big surprise there.  Common type are quite familiar – governmental/regulatory agencies, educational institutions, and companies.  Of course there are also churches, charitable organizations, and social organization like Masons and commerce chambers, and on another tangent you find political parties, action groups, sports leagues, and hobby or special-interest groups for anything you can imagine.  One really good organization, with a network of teams, and which gets lots of scrutiny, is the US military; they are REALLY motivated to be good at adapting and remaining capable.  Note that each person typically is part of multiple networks, and the interconnections between networks is important to how organization effectively work. 
Each network is also a system, with its unique purpose, resources, and goals, and like all systems they attempt to maintain some sense of equilibrium and to adapt to their environment.  For many networks of people – teams – learning and adapting are important capabilities, and yet it’s often not easy.  Good teams are generally good at adapting and learning, and a team can gain new capabilities in several ways:

-          Add new skills to the team by adding new people

-          Add new skills by forming new connections to other teams

-          Add new skills by improving skills new members

-          Change the environment so adaptation isn’t needed after all

As part of the learning/changing process, teams often do trials, and Ries would advocate for purposeful, modest experiments with clear intentions, solid analysis of results, and further adaptation.

So where is this going?  That’s the next point:  our economies are an amalgamation of lots of teams, and the capability of any country to be competitive on the world stage is a function of the effectiveness of its companies and other institutions.   The competitiveness of the products largely determines income opportunities for the country, and Hidalgo’s key insight is that the embedded complexity of the products impacts the ability of a country to make them.

Way back at the beginning of this series we talked about products embodying knowledge, and enabling the leverage of the skill of their builders by their users.  For all but the simplest products the embodied knowledge is the output of a team, or teams of teams, and not just individuals.  The level of embodied knowledge is thus a function of the skill of the team that builds it, and the ability of a country to build products is limited to the sorts of skills they have available.  This means that the long-standing tenet of economics, that manufacturing of products moves to the area of lowest cost, is not accurate, or at least is not complete:  manufacturing will only go to locations where the necessary skills are available, else the local cost must also reflect the cost of gaining the skills, and the time to do so.  If we revisit Metcalfe’s law, a large network of resources will have more capability and value than a small network, and I’ll add a corollary that a network of better resources will offer still more value, and a second one that a network that grows in scope and in strength of resources will expand in value exponentially.

What follows is that complex economies must have complex, differentiated skills, and associated teams of workers/scientists/regulators/etc.  Simple economies can only make simple products, and this goes a long way toward explaining the “curse of oil” and other “resource curses” that have plagued third-world countries over the centuries.  This pattern is not merely notional, as Hidalgo performed quite extensive regression analysis of global exports and national GDP at the granularity of common product codes, and obtained solid statistical results. 

Hidalgo then went further, projecting which nations should have GDP upside based on the capability of their economies, and which are already maxing out their potential.  In the years since, others have followed these projections and the natural evolution of economies, and the theory seems to hold.

Next round we’ll chat about how we might leverage these ideas, and move from talking about the past to talking about the future.