Guy WF Loftus in the real world is Guy Loftus, accountable, responsible and trollable, who became a Wikipedian latterly, having spent years admiring Wikipedia and making occasional small donations of money. I decided to not hide behind a spoof name to keep me honest with one foot firmly in the real world.
If I were to describe my wiki-tendencies, I am probably a WikiFairy because I love building timelines as coat-hangers for verifiable facts. A robust framework frees the writer to create abbreviated pen-portraits which are easy on the eye, with sequential narrative flows that drape comfortably over that framework. Biographies tend to accrete over time in Wikipedia, which can be messy: occasionally they need to be shaken down to get back to what is known and remove creeping speculation.
Above all, I am a geoscientist with a passion for knowledge sharing, so much so that I formed a limited company to create tools for sharing knowledge because in a world threatened by the human need to exploit finite resources, there comes a point in any commercial enterprise when sharing what you know is more advantageous to your business than concealing it.[1] Industry is becoming much more accessible to the concept, but to-date, remains resilient in denial. If free access to knowledge interests you, then read on…
My ha'p'orth (caveat lector)
My random rants & ravings, as of May 2021. (For the majority of you out there who are post-decimalisation: "ha'p'orth"[2])
Why is knowledge important?
If you define knowledge as "the unique accumulation of facts, information or skills gained through experience and learning", then it is not hard to understand that knowledge is as unique as we are. The way in which knowledge is acquired and received is entirely non-unique, prone to cognitive bias and perceptual blindness.[3] Those who would seek to horde or marketise knowledge may claim that "knowledge is power" but knowledge is powerless unless applied to specific objectives. It is pivotal to human development that some knowledge should be common to all, otherwise we would constantly be repeating the same mistakes and would cease to progress. But who decides what to share and what to conceal?
Wikipedia gets around that problem by pooling what is already in the public domain. It is up to us to acquire primary sources and discriminate between what is factual and what is anecdotal.
…knowledge is not data…
In his 1934 play ‘The Rock’ T. S. Eliot wrote: ‘Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?’.[4] The connection between information, knowledge and wisdom is well out of scope of this commentary,[5] but all enterprises require knowledge to support decision making and yet, unlike data, it is surprisingly amorphous and hard to quantify. Data are immutable, discrete and independent but knowledge is ephemeral, multi-layered and often collaborative.
Predictive knowledge
Predictive knowledge or forethought, uses knowledge of the past to predict the future. Whereas predictive knowledge is reasonably deterministic for prognosing the movement of the planets in our solar system, projecting human behaviour, for instance, calls for a much wider aperture on uncertainty.
Knowledge of the future would indeed be power but humanity doesn’t have access to that power, which never stopped us from trying. What we can do is build probabilistic models about the future based on the knowledge we have of the past. The two elements for credible forecasting, therefore, are:
1. the relevance of our knowledge
2. the robustness of our model
The model makes a prediction, which is spread across a probability density function with definable shape, which is the mathematical expression of uncertainty. Politicians, pundits or anyone wanting to influence the opinions of others, sample that curve using a single deterministic number, without informing us where in the curve that number was derived. The only honest forecasting commentary gives a range and may quote the most likely outcome based upon that range. The warning comes when anyone, even a scientist, quotes a single number for any prediction, because what we do know is that they are always wrong.
Knowledge harvesting
in prep
Perceptual Knowledge
in prep
References
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