Spicing Up Possibly Biased Algorithms with Wiener Math

June 27, 2022

Let’s assume that the model described in “The Mathematics of Human Behavior: How My New Model Can Spot Liars and Counter Disinformation” is excellent. Let’s further assume that it generates “reliable” outputs which correspond to what humanoids do in real life. A final building block is to use additional predictive analytics to process the outputs of the Wiener-esque model and pipe them into an online advertising system like Apple’s, Facebook’s, Google’s, or TikTok’s.

This sounds like a useful thought experiment.

Consider this statement from the cited article:

In this new “information-based” approach, the behavior of a person – or group of people – over time is deduced by modeling the flow of information. So, for example, it is possible to ask what will happen to an election result (the likelihood of a percentage swing) if there is “fake news” of a given magnitude and frequency in circulation. But perhaps most unexpected are the deep insights we can glean into the human decision-making process. We now understand, for instance, that one of the key traits of the Bayes updating is that every alternative, whether it is the right one or not, can strongly influence the way we behave.

These statements suggest that the outputs can be used for different use cases.

Now how will this new model affect online advertising and in a larger context how will the model allows humanoid thoughts and actions to be shaped or weaponized. My initial ideas are:

  1. Feedback signals about content which does not advance an agenda. The idea is that that “flagged” content object never is available to an online user. Is this a more effective form of filtering? I think dynamic pre-filtering is a winner for some.
  2. Filtered content can be weaponized to advance a particular line of thought. The metaphor is that a protective mother does not allow the golden child to play outside at dusk without appropriate supervision. The golden child gleams in the gloaming and learns to avoid risky behaviors unless an appropriate guardian (maybe a Musk Optimus) is shadowing the golden child.
  3. Ads can be matched against what the Amazon, Apple, Facebook, Google, and TikTok systems have identified as appropriate. The resulting ads generated by combining the proprietary methods with those described in the write up increase the close rate by a positive amount.
  4. Use cases for law enforcement exist as well.

Exciting opportunities abound. Once again, I am glad I am old. Were he alive, Norbert Wiener might share my “glad I am old” notion when confronted with applied Wiener math.

Stephen E Arnold, June 26, 2022

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