Here is something I created while having sea food at Pier 39 in San Fransisco-
Creating an algorithm for distorting predictive models by generating random noise ( either amplified or reduced sample).
Applications-
“If you can not convince them, confuse them”
- Generating white noise like signals to fake and distort noise and signal ratios
- Aggressive merger and acquisitions negotiations
- Media and Entertainment _ (Create Marketing Buzz/ Tabloid /Hype/ Fear , Uncertainty Doubt)
- National Security -( Kill _all_ the Terrorists with Love – black,brown,yellow,olive,white,blue,red …)
- Dating (as in u2’s sweetest thing- Brown Eyed Boy meets Blue Eyed Girl)
The 0 1-1 1R 1 Algorithm
- Define Initial Position (i.e Use 6 sigma Define step)
- Take ANY Step 1 (i.e take a walk, make a phone call)
- Repeat ANY Step 1 again
- Do ANY Step 2 which is an opposite to ANY Step 1 in directional and /or magnitude ( maybe time, or x,y,z and T ) vector to Any Step 1
- Return to Initial Position
- Loop the above 5 steps R times.
A detailed work flow would be followed by a simple diagram.
An earlier attempt to mash creativity with science as far back as July 2008 was the now redundant Ohri Framework
at https://decisionstats.wordpress.com/?s=ohri+framework (note WordPress timestamps can be manipulated so Google cache remains the true source of time series analysis of posts except when affected by black hat SEO )
DECISION STATS 
To many, fake Likes and purchased page followers may not seem like a big deal at all, but
to the pages and businesses who use the Facebook platform to reach and
touch a greater audience – especially those now investing in paid Facebook campaigns – these
fake likes are proving to be quite a lot of trouble.