Attractor Patterns and Attractor Tokens

Opening the Door….

But is it Art?

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 products 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 itself, not want to get rid of it, because it encourages psychological attachment to it.  For example the habit of checking email or Facebook are examples because both produce patterns that are sticky.

Screen Shot 2015-04-08 at 12.49.51 PM


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.

What are incentive design patterns?

An incentive design pattern is a configuration of attractors which indirectly or directly induce the desired behaviors. Unlike attractor patterns which attract human attention these incentive patterns can communicate signals which may indirectly motivate and coordinate the behavior of human agents but also for non-human agents in a multi-agent system. Stigmergic optimization is possible in these multi-agent systems through these incentive design patterns.

A quote from Wash and MacKie-Mason:

Humans are “smart components” in a system, but cannot be directly programmed to perform; rather, their auton-omy must be respected as a design constraint and incen-tives provided to induce desired behavior. Sometimes these incentives are properly aligned, and the humans don’t represent a vulnerability. But often, a misalignment of incentives causes a weakness in the system that can be exploited by clever attackers. Incentive-centered design tools help us understand these problems, and provide de-sign principles to alleviate them.

As an example while the attractor token might be a cryptocurrency the incentive design pattern the autonomous agent and human alike both can be incentivised by the configuration of incentives.

What is stigmergy?

Stigmergy is a process of coordination which is used by bees, ants, termites and even human beings. Ants use pheromones to lay a trace which is a sort of breadcrumb trail for other ants to follow to reach food for instance.

Humans can also utilize stigmergy in similar ways. Human beings can use virtual pheromones to lay a digital trace for the rest of the swarm. These virtual pheromones just like with the ants act as a breadcrumb trail. These virtual pheromones are the like attractors.

What is stigmergic optimization?

Stigmergic optimization is how ants find the best route to food by using pheromones to leave traces for all their peers.  At first the trace patterns appear random because the ants try all different routes to reach their goal. Optimization takes place as the most efficient path is found and the pheromone traces allow the ant swarm to learn.

In the context of a multi-agent system the agents focused on acquiring attractor tokens at first would not know the best path to take. All paths would be tried in the beginning as agents follow the trail of attractors tokens to the destination. Over time the most efficient path to the destination would be found by and an order would emerge as a result of stigmergic optimization allowing the swarm to solve complex problems.


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.

Deterding, S., Sicart, M., Nacke, L., O’Hara, K., & Dixon, D. (2011, May). Gamification. using game-design elements in non-gaming contexts. In CHI’11 Extended Abstracts on Human Factors in Computing Systems (pp. 2425-2428). ACM.
Dipple, A. C. (2015). Collaboration in Web N. 0: Stigmergy and virtual pheromones.
Heylighen, F. (2015). Stigmergy as a Universal Coordination Mechanism: components, varieties and applications. Human Stigmergy: Theoretical Developments and New Applications. Springer. Retrieved from http://pespmc1. vub. ac. be/papers/stigmergy-varieties. pdf.
Obreiter, P., & Nimis, J. (2005). A taxonomy of incentive patterns (pp. 89-100). Springer Berlin Heidelberg.
Wash, R., & MacKie-Mason, J. K. (2006, July). Incentive-Centered Design for Information Security. In HotSec.


Exploring the the Darkside of Artificial Intelligence: thoughts, ideas, rants, and research from darklight

“you think darkness is your ally. But you merely adopted the dark; I was born in it, moulded by it.”


The post Attractor Patterns and Attractor Tokens appeared first on h+ Media.

Print Friendly

Permanent link to this article: http://news.truthjuice.co.uk/index.php/attractor-patterns-and-attractor-tokens/