One of the things I have been thinking about in relation to conscious computation is our relationship to prediction once we have probabilistic models. I have been engaged in an archaeology of prediction. That is looking at old predictive systems to understand how we think about decision making and future events.
Geomancy is an ancient combinatorial prediction system based on making random marks generally in the dirt. You ask a question – ‘Should I buy that horse?. Make a bunch of random marks in the dirt then count whether or not you have an even or odd number of marks. You do 16 times to generate 4 sets of 4 dots (2 or 1 dot) – The mothers. From these you generate the rest of the chart (11 sets of 4 dots).
The final set of dots is the judge at that ostensibly gives you the answer to your question. Yes, buy the horse! No don’t buy the horse! Unclear! You can further interpret the answer of the ‘judge’ looking at the sets that gave rise to the dots, and to these sets that gave rise to those sets. There are elements (earth fire water air) associated to each row of dots that can further help you interpret the answer to your question.
A while ago I wrote a geomancy python script. It is super barebones. I may slap an elm front end on it and make it interactive in some way. A while ago I was using geomancy to create poems. Every day I would throw my D&D dice, ask a question, and generate an answer and then write a poem based on the answer.
I read this book (Ron Eglash ‘s African Fractals Modern Computing and Indigenous Design) thinking it might have something about geomancy, because he mentions geomancy in his ted talk. About one third of the way through he talks about African divination systems ( which in the west became Geomancy). Some of the things he mentions are: the stochastic process to generate the starter set, the possible solution set depending on the base items you use for generation, the binary or modulus 2 method of reducing a figure to 0 or 1 (or 1 or 2), and the notion of looping. After being integrated into western mysticism, Leibniz was inspired (according to the author) to develop a base 2 counting system – the predecessor to binary.
There is a discussion of this method and that of a pseudorandom number generator from shift register circuits. With a shift register the circuit also takes the mod 2 of the last two bits in the register and discards the rest.
Geomancy does appeal to the computer scientist in me because it is computational and generative. But this sort of prediction belongs to a past age, perhaps not the age of modeling. Eglash makes a difference between memetic vs model. What is the difference between a representational structure that is mimetic versus one that is modeled? Is this the difference between an image and an algorithm? Why is this important? What does it mean?