Category Archives: Personal preference bot networks

Swarm Intelligence in Honey Bees and attractor patterns in humans

Attractor patterns

The honey bees dances which act as the “attractor patterns”. These dance patterns signal the “best site”. Swarm intelligence is what we are seeing with bees but we also see it with humans. With humans we could substitute the “dance pattern” with something as simple as a data feed (which often is just a rising price trend). The data feed pattern represents an attractor pattern which can attract a swarm.

Human beings are looking for “value”. Swarms of human beings interested in value will look for certain attractor patterns that signal the kind of “value” they are looking for. Personal preference swarms bring AI into the mix by allowing non-human software agents to augment the human swarm capability.

Attractor patterns and attractor tokens

  • A data sequence is a pattern.
  • Patterns are everywhere.
  • Some patterns are more attractive than others.
  • Attractor patterns are attractors of human attention.

What makes a pattern aesthetically pleasing?

Aesthetically pleasing patterns evolve from a process of natural selection.  The same process which is at work in various forms of genetic algorithms whether human based genetic algorithms (HBGAs) or interactive genetic algorithm (IGAs) the aesthetic quality is determined by the selector which in these examples must be human.

Measuring pattern attractiveness

An obvious way to measure the attractiveness of various different candidate patterns is to use the process of selection. For example in a market based approach to selection the patterns could be product designs. How do we determine whether or not the product is successful or a failure? By the popularity of the product, and how often it is used.

  1. Example: If the product pattern is a game then you could track how often the game is played to determine how attractive the game is.
  2. Example: If the product pattern is a song then you could track how often the song is listened to in order to determine how attractive it is.
  3. Example: If the product pattern is a website then you can see how often the website is visited and for how long visitors stay in order to determine how attractive it is.

Attractor patterns are sticky

In order for an attractor to be sticky a person has to not want to stop paying attention to it, not want to get rid of it, because it encourages psychological attachment to itself.  For example the habit of checking email or Facebook are examples because both product patterns are sticky.

Measuring stickiness of a pattern

A pattern is sticky if people continue to pay attention to a particular pattern as a habit. This could be because the pattern fulfils some psychological need or it could be because the pattern meets a critical utility.

A token is only effective as an attractor if a lot of people want it. A lot of people will only want it if it’s exchangeable for something a lot of people want. If it’s exchangeable for something that a lot of people want a  whole lot of then it’s going to be extremely effective as an attractor token but it is still just only an attractor token.

The purpose of attractor tokens is to stimulate stigmergy

The purpose of attractor tokens is to attract the swarm of attention. These attractor tokens and attractor patterns in general facilitate the process of stigmergy. Stigmergy is what actually coordinates and directs the swarm allowing for swarm intelligence to emerge.


Chang, J. F., & Shi, P. (2011). Using investment satisfaction capability index based particle swarm optimization to construct a stock portfolio. Information Sciences, 181(14), 2989-2999.

Miller, P. (2007). Swarm theory. National Geographic, 212(1), 1-17.

More on PPBNs now referred to as “personal preference swarms”

How should personal preference bot nets (PPBNs) be reframed?

It has been brought to my attention that the phrase “personal preference bot networks”(PPBNs) may be problematic because it evokes a bad frame in the minds of certain individuals. An alternative phrase for “personal preference bot network” (PPBNs) which acquires the same meaning while maintaining the mass appeal would be “personal preference swarms” (PPSs).

You who relays the message may decide the best frames for your audience

So it is at the discretion of those who relay these concepts to choose between “personal preference bot network” and “personal preference swarm” depending on who their audience is. You are also free to “remix” because “remix-ability is good”. If you can find a better way to express these concepts to your audience then please do so in your own words as long as there is accuracy in getting it across.

The message should be remixed and the most fit frames selected for each audience

The process of using a human based genetic algorithm applies here. The data sequence (core concepts and algorithms) must be consistent and unchanged. The innovators of new frames are whomever understands the core concepts and algorithms well enough to remix and repackage them without losing their meaning. The audience is the selector of effective frames and depending on who that audience is there may be different frames and packages which appeal to your audience.

Know your audience

To know your audience requires a feedback loop. You test a frame, you let them like or dislike any word or phrase in your article. If the data supports that the frame is catchy in a good way then continue to promote the frame. Just like evolution the most fit frames emerge from selection not from top down design. Maybe a good way to generate quality frames would be to have a prediction market and a way to track the success rate or failure rate of certain frames in certain demographics.

I encourage all understand the concepts in this blog to remix and share the concepts to the best of their understanding from the perspective they have for the audiences that follow them. May the best frames thrive.

Personal preference bot networks and Ethereum’s Provenance

What are Personal Preference Bot Networks?

PPBN’s are personal software agent networks owned by individuals. PPBNs work by allowing the individual to delegate tasks to their personal swarm of bots which can trade on their behalf. This can include shopping for example where Alice uses intention casting to set forth her bot(s) in accordance with her intention to find the best deals for a acquiring list of items.

These PPBN’s in theory should be able to integrate and interact with DApps, DACs, DAO’s, virtual states, or even traditional centralized entities.

What is Provenance?

Provenance solves a particular part of this problem by revealing exactly how the products are made. If Provenance has an open API which allows for easy integration with bots then bots could scour the Internet for products which meet the fitness criteria of the swarm of bots. Those bots would then purchase the most fit and avoid purchasing the least fit.

What problem does this solve?

It solves the problem of attention scarcity and adverse selection by utilizing automated transparency. Human shoppers typically are not rational and also do not pay attention to details. A human being for example may not have the attention or time to read every ingredient for every food product they buy to make sure it doesn’t contain anything unethical or harmful. As a result many humans eat products which contain substances they are unaware of.

Fitness criteria (swarm preferences) and swarm intelligence

When PPBN’s (Personal Preference Bot Networks) converge then purchase patterns can favor certain “fitness criteria” which we would call swarm preferences. So the personal preference bots allow for intention casting as well as an automated multi-agent system of supply and demand. Provenance allows for the necessary transparency so that the intelligent swarm can know whether or not what is being supplied meets “fitness criteria” aka swarm preferences.


Ethereum London Meetup: Provenance (YouTube)

Provenance | Discover the stories of great products and their makers. (Provenance)

Swarm intelligence (- Scholarpedia)

Personal preference bot nets and the quantification of intention

Personal preference bot nets

“Personal preference agents” are software autonomous agents (most commonly known as bots) that act on behalf of the individual. So if you for example tell your “personal preference agents” your needs such as a shopping list, the “personal preference agents” would automatically pursue tasks using AI to fetch whatever is on the list.

In the shopping example this would mean you would not have to spend time shopping and you would not be susceptible to vicious subliminal ads. It would save scarce time and attention for the human being by delegating AI to useful tasks.

Intent casting 101

Traditional literature would call the “personal preference agents” a conditional preference network or in the generic sense a software agent network. Doc Searls calls it intent casting in his video on the subject. The main idea of Doc Searls’s intent casting is to create an intention economy. Personal preference bots would be a means of bringing the decentralized “intention economy” to virtual citizens.

The “personal preference agent” would be a particular kind of software agent which can accept preferences by the person and using AI seek to meet those preferences by connecting to various different external networks, blockchains, the web, etc.

The idea is each virtual citizen should have the capability to utilize personal preference bots / “personal preference agents”. These bots would then interact with multiple blockchains, multiple API or network interfaces so that for example the bot owned by Alice could contact the bot owned by Bob over any open protocol in automated fashion to relay an encrypted message, conduct a trade/transaction, or coordinate as a swarm (a sort of collective transaction). This would give each virtual citizen the swarm intelligence capability and empower virtual citizens.


Searls, D. (2012). The customer as a God. The Wall Street Journal, 1-4.
Searls, D. (2013). Eof: Android for independence. Linux Journal, 2013(227), 9.