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In which we identify patterns based on what we have learned thus far and build a model we can use in the future.

The chapter will examine what we have observed about information revolutions thus far in a more formal manner. Then we will think slowly about how those things might be seen in future information revolutions so as to, “call our shots”.

The patterns we have already seen in the information revolutions we have examined are:

Timing – Each has been shorter and shorter – timing: hunting and gathering to agriculture – 5-6 thousand years; agriculture to writing – 2 – 3 thousand of years; the Fall of Rome – 700 years; liberation of information to the press – 400 years (all times are approximate).

Unit Size – Each has seen a change in the size of the unit that people see as relevant to their everyday lives – unit size: hunting and gathering band – 20 people; kinship (agriculture pre writing) – 100 people; kingships to empires (writing) – thousands; fall of Rome – manor, village, small Episcopal cities.

Limits – Each has shown that there are limits to how big a system can grow with a given information technology – limits: hunter/gatherers through agriculture before writing were limited by peoples’ memories; writing allowed groups to grow as large as the Roman Empire before it reached its information limit.

Moving forward to more modern information revolutions, we can achieve a finer focus than we have thus far, because more written records are available. Reasoning slowly suggests that we should see:

Information Access – That where there is more access to information there will be more innovation. For example, assuming that 1% of any given population is innovative, and assuming access to information increases information access and then in a population where 10% of the population has information access will be relatively disadvantaged compared with a population where 50% of the population has information access.

Competition – Where there is competition those with better information access will always be advantaged over those who have less information access. This will be less important in contexts where there is no competition.

Synchronisity – Since information technology or information change impacts all sectors of an economy similarly we can contrast how innovation in standard technology differs from innovation in information technology. For example if a new weaving technique is invented, first it improves the production of cloth, then the manufacture of clothing, then the sale of soap to clean the clothing. On the other hand, if double entry bookkeeping is introduced it brings savings in transaction costs to weavers, tailors and soap makers at the same time. This synchronisity should result in noticeable periods of economic downturn, perhaps accompanied by other evidence of instability.

Success is Conservative – Success makes people and institutions conservative, “Why interfere with what works?” so new innovation should come from the non-elite. For example, we saw during the fall of Rome that people did not perceive that wealth could be gained from improvements in production. Wealth was obtained through war and taxation. They had all the ingredients to invent capitalism except the perception Therefore, it could be said that they were addicted to war as a generator of wealth.

Phased Behavior – Putting the above together we can reason that there should be observable phases of development.  Thus, for the most part, the elites of any group will have first access to the benefits of any new information technology they will innovate to make current practices more efficient and streamlined. It will allow for a growth in the unit size. This will be roughly similar in all contexts where the elite have information access.

Phased Behavior, Information Access, and Limits – In contexts where other non-elite groups benefit from the information technology there will be innovation in what they do and the same pattern will be repeated.  If there are many new groups, or new technological improvements (as we will see in the electric and computer revolutions) there will be many phases as new innovations cause growth, run their course and those innovators become conservative in turn. Each of these will be ‘punctuated’ with economic downturns.

Impact of Fear – When these downturns are closely spaced – because information revolutions are becoming faster and faster – it stand to reason that there will be a sense of dislocation from changes in perception and from actual economic hardship during the downturns, this, in turn, may lead to  repressive reaction due to fear. Where that reaction negatively impacts the adoption and development of the information technology economic development will slow, and in competitive contexts, lead to economic ruin.

We should bear in mind the following questions as we move forward to more modern information revolutions:

  1. All things being equal do we see greater growth where there is more information access?  Where the context is competitive are contexts with high information access better off (do they grow more) than those with low information access?
  2. Do unit sizes increase because of new information technology? Are businesses, and/or government sizes increased in a positive information revolution or decreased in a negative information revolution? Do people identify with a larger social, economic, or political group?
  3. Do we see synchronisity – similar kinds of growth across contexts where information access is similar?
  4. Can we identify ways in which success is conservative – people do what has worked? and/or,
    its obverse do we see innovation fueled growth arising from the “out group” (people who are not members of the existing elite) who have information access, which out strips the growth from the old elite?
  5. Can we identify the effect of growth limits? For example, do we see social and economic instability as a result of rapid, innovation fueled, growth that overshoots the unseen limit and results in a sudden downturn?
  6. Does this lead to fear and repressive reaction?
  7. Is there phased behavior? Is it associated with elite and non-elite information access or is it associated with improvements in information technology?

In the chapters that follow we will be able to add to these patterns and elaborate and refine those we have identified here.