Tag: borrow

In part 1, we introduced a new paradigm of economic growth; the innovation economy. In part 2, we identified information as the currency of trade for an innovation economy and we defined that currency’s relationship to knowledge and innovation.  In part 3 we demonstrated a structure for a knowledge Inventory that would enable an Innovation Economy.  In this module, we will discuss the institutions in social media that could keep an Innovation Economy, free, fair, and equitable.

In civil society, there are laws and regulations that protect our constitutional rights; these are essential institutions.

The legal system of the United States is extremely expensive, however, the expenditure is necessary to keep the society upright, productive and prevent it from falling into chaos.  Where a country’s legal system fails, so does its economy.  Entrepreneurs do not invest in places without a good legal system and where property rights are not protected. It is that important.  Investment abhors risk.

Arguably, the most important element of the Innovation Economy will be the vetting mechanism.

Fortunately, social media has the potential to serve this function; in fact in many cases it already does.  A feedback system supports Ebay ($35B Cap), community flagging supports Craigslist (40M ads/mo), peer review supports Linkedin (150M users).  These are not small numbers.  All markets must have a vetting mechanism in order to operate efficiently and if done correctly, social vetting has vast economic implications for an Innovation Economy.

First, let’s return to our financial analogy.

In the old days, the banker was the person to know if you wanted to be successful in town.  But with the emergence of the credit score, the “banker” became digitized; now a Saudi Billionaire can lend money to a young couple in Boise to buy their first home – and neither is aware of the other.  The credit score is responsible for the creation of great wealth because many more entrepreneurs could borrow money to invest in enterprise.

The credit score is statistical in nature; it isolates about 30 or so indicators of your financial activity and puts them on a bell curve relative to everyone else.  These include how much debt you have, how much your assets are worth, your income, etc.  These ratings are run through the FICO Equation and out pops your credit score.  Anyone can now predict the likelihood that you will default on your obligation.

All of the data that feed FICO are collected from public records, your employer, and the people who you borrow money from because these same organizations have a vested interest in a system of correct credit scores.

We are competing with ourselves.

It is interesting that you and I do not compete for our credit score because it is not a ranking system. On the other hand, with no credit, we are invisible and the system shuts us out.  With bad credit, the system shuts us out. We lose some freedom and privacy, but we accept these terms well because they provides us with tremendous benefit to finance a business, automobile, or a home without needing to save cash.

Now we will draw the comparable analogy from the social media.

In the old days, the hiring manager was the person to know if you wanted to get a job.  They would read your resume and compare it with “bell curve” in their experience about what has worked or not worked in their past.  This worked great in the industrial economy, but it falls far short in the innovation economy.  Innovation favors strategic combination of diverse knowledge where the Industrial economy favored identical packets of similar knowledge.

Not unlike the FICO score, the knowledge inventory is a collection of statistical variables and the social network is the reporting agencies who have a vested interest in a system of correct values.  Unlike FICO however, the variables are infinite and it responds to positive event input.
Social networks are by far among the most exciting and important new technology for an Innovation Economy.

Social networks must now evolve to become the vetting institutions for knowledge assets.

All the pieces are almost in place; now we need to develop a new type of search engine.

The Percentile Search Engine is generic term for the ability to make statistical predictions about all types and combinations of knowledge Assets in a network. Conceptually, the percentile search engine is where all of the equations that we use to analyze financial assets are now applied to knowledge assets.  The main characteristic is that the search engine returns probabilities for the entrepreneur to test scenarios.

For example; an entrepreneur may want to know if her team has enough knowledge to execute a business plan.  Perhaps the team has too much knowledge and they should try something more valuable.  Maybe the team does not have enough knowledge and they should attempt another opportunity or accumulate training.

The search engine can look into a network and identify the supply and demand of a knowledge asset. If it is unavailable or too expensive, the search engine can adjust for price, risk, or options that may emerge at a later date.

Talent will bid up to their productivity value, and brokers will bid down to their productivity value.

Competitors can scan each other’s knowledge inventory to compete, cooperate, acquire, or evade. If a key person retires, the entrepreneur would simulate the knowledge that is lost and reassign people strategically. All of these scenarios can be examines prior to spending money. They can be made during the project cycle, or after the project is completed.  Lessons learned can be used to adjust the algorithm perfecting it over time.

For example: companies such as Disney and Boeing both use Engineers, each would have proprietary algorithm of knowledge that represents their “secret sauce” of success. These recipes can be adjusted and improved to reflect and preserve the wisdom of an organization.

When the innovation economy will catches fire….

Over time, these algorithms will far more valuable then the Patents and Trade Secrets created by them – this will allow technologies to be open sourced much more profitably and shared across more industries.

In the next module, we will talk about the entrepreneurs.