Math 1.0 can be considered to be a special case of Math 2.0, where certain aspects of reality are ignored for the purpose of making things black and white and therefore easier to manipulate and compute.

Math 1.0 is helpful in specific circumstances like simple counting and manipulation of number, adding, subtracting, multiplying, dividing of pure number, and for making approximations, and is also useful in statistical manipulations where it is valid to manipulate data away from its context. Math 1.0 thinking successfully delivered a rocket to the moon but has failed to deliver insight into most chronic problems affecting humanity today. Math 1.0 thinking is part of the problem!

Math 1.0 is not valid in the domain of measurement nor when ‘counting’ is actually for the purpose of measuring ‘things’. And yet we use Math 1.0 with measurement all the time!

Using Math 1.0 as the ‘logic vehicle’ for interpreting changes in measurement data is a major reason why we have witnessed so many decisions by leaders and politicians in the last few decades that have turned out to be wasteful and that have exacerbated rather than solved ‘problems’. This happens when the Math we learn at school (Math 1.0) is applied into the world of measurements. And a science based around this maths re-inforces it as a science of reduction and ‘ism’ (“ism” happens when a discipline comes to believe its working model of the ‘world’ as true rather than ‘useful in defined situations’). So the belief (as true) in the mechanistic universe and the use of Math 1.0 as a sturdy, reliable and incontrovertible companion has led the traditional Newtonian scientist up the proverbial garden path and is still being led there daily. Multi-billion pound projects based on the assumptions of a reductionist science leading absolutely nowhere, whereas situations that could be drastically improved based on a science thinking in terms of systems and Math 2.0 are not being allocated the same research money.

To fully appreciate the meaning and consequences of data measurements, a good understanding of Math 2.0 and its application is required. If we care to look, we will find that the scientific, political and business literature is littered with examples where statisticians (who we would think would know better) have fallen into the trap of applying the thinking of Math 1.0  to the situations described best by Math 2.0, thereby giving us misleading ‘expert’ information and advice

This venn diagram shows the relationship between Math 1.0 and Math 2.0:

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