Category Archives: Attractor patterns

Attention-Based Stigmergic Distributed Collaborative Organizations

Attention-based organizations, Attractor patterns, Attractor tokens, Biomimicry, Distributed Collaborative Organization, Incentive design patterns

In biological networks such as ant colonies, bee hives, or termites you see self organization which builds and maintains critical architecture. Human beings can also take advantage of this self organizing mechanism to build and maintain institutions. The process is called stigmergy and to take advantage of this concept fully we have to revisit the fundamental theory of “the firm” as form or organization.

An attention based view of the firm

As human beings we are guided by our attention.  It is also a fact that our attention is a scarce resource which many competing entities seek to capture. In an attention based view (ABV)  of a firm it is attention which is the most precious resource and the allocation of attention is critical to the successful management of the firm. In a traditional top down hierarchical firm managerial attention is considered to be the most precious (Tseng & Chen, 2009), and is an very scarce resource. The allocation of attention within a firm can facilitate knowledge search. Knowledge search is part of the process of producing innovation and is effective or ineffective based on how management allocates their attention.

In the top down hierarchical model of the firm you must rely on managers properly allocating their attention because their attention is scarce. The problem of attention allocation (Tseng & Chen, 2009) and attention scarcity both plague traditional top down firms. In heterarchical flat organizations this may not be true anymore and when stigmergy comes into play it opens a door to a whole new method of knowledge search.

Attention-based stigmergic Distributed Collaborative Organizations

A Distributed Collaborative Organization (DCO) is a new model of human organization which did not exist until recently. Now that technology allows for Distributed Ledgers such as what we see with the Bitcoin blockchain it opens the door to new forms of human organizations such as the Distributed Collaborative Organization. Distributed Collaborative Organizations have unique capabilities and work by utilizing a token which represents “membership” in the DCO. Because of how DCOs are set up the tokens likely do not represent securities as they would if the traditional firm were used.

Attention-based stigmergic DCOs can take advantage of swarm intelligence to direct the attention of the members of the DCO. Stigmergy can be implemented through attractor patterns/attractor tokens, and incentive design patterns, both which would direct the attention and shape the activities of the swarm through simple algorithmic rules written as smart contracts. In this instance as Larry Lessig is famously quoted as saying: “Code is Law” but in a non-hierarchical swarm the user’s attention is the most precious resource.

Because attention is the most precious resource in a swarm there should be a mechanism allowing advertisers, or others, to pay for the attention of individual members within the swarm. In this case the DCO would have to be designed in such a way that attention is treated as extremely scarce, something to be preserved by use of bots/autonomous agents (personal preference swarms?) and automation. These personal swarms or personal drone networks if you’d like to call them that would seek out knowledge and information on behalf of individuals without the possibility of distraction.

These swarms could seek out the best deals for individuals. It could collect an extremely detailed amount of information about each and every product and use algorithms to compare products.  This would allow swarms to evaluate anything from video games, to supermarket food, to stocks, to populate a list and buy, or to apply swarm intelligence to the construction of investment portfolios. All of this leads to a completely new paradigm of human organization through self organizing stigmergic institutions.

References

Grosan, C., Abraham, A., & Chis, M. (2006). Swarm intelligence in data mining (pp. 1-20). Springer Berlin Heidelberg.
Martens, D., Baesens, B., & Fawcett, T. (2011). Editorial survey: swarm intelligence for data mining. Machine Learning, 82(1), 1-42.
Ocasio, W. (1997). TOWARDS AN ATTENTION-BASED VIEW OF THE FIRM WILLIAM OCASlO. Psychology, 1, 403-404.
Tseng, C. C., & CHEN, P. C. (2009, August). SEARCH ACTIVITIES FOR INNOVATION–AN ATTENTION-BASED VIEW. In Academy of Management Proceedings (Vol. 2009, No. 1, pp. 1-6). Academy of Management.

Sharedropping as a stigmergic operation

Attractor patterns, Attractor tokens, Cryptoassets, Cryptocurrencies, Distributed autonomous communities, Incentive design patterns, Protoshares, Sharedropping, Stigmergy, Swarm intelligence, Virtual Pheromones

What is a sharedropping?

Sharedropping is a practice perfected by the Bitshares community. Stan Larimer discusses the purpose of sharedropping in the article titled Bitshares Sharedrop Theory. This quote below highlights what a sharedrop does:

It’s not about imitating Bitcoin.  It’s about attracting an affinity group.  And once you’ve motivated that group to hold onto your coin, you have eyeballs to sell.  In this case, the value of your coin is tied to the value of your group as a target for other developers’ promotional shares. This is exactly what PTS and AGS holders are: A demographic MUCH more likely to be good supporters.  These block chains are like mailing codes that let you target your shares to the people you want to reach much, much, much more precisely than using Silicon Valley mailing codes!

One of the first successful sharedrops occurred within the Bitshares community. Originally Bitshares was centralized around a company Invictus Innovations which invented the concept of Protoshares. Protoshares at the time represented nothing more than an idea. All who believed in that idea were encouraged to acquire Protoshares through either mining it with their CPUs, working for it, or buying it.

What is a stigmergic operation?

Protoshares represented the hopes and dreams of the Bitshares community symbolized as a token and the developers encouraged all participants to rally around that shared idea by formulating a social consensus. This represents a stigmergic operation and provides one of the best examples of stigmergy to date in the crypto community.

How can you conduct a stigmergic operation?

To do a stigmergic operation just follow these basic steps.

  1.  Come up with a compelling idea and share it with people who are likely to appreciate it. Be good at explaining the idea and make sure people believe it can work.
  2. Find an attractor pattern to represent the idea. This could be as simple as tokenization where anyone can mine if they believe in the idea or acquire the token somehow by buying or working for it. It can also be the joining of a mailing list, the membership on a forum,  citizenship, reputation or anything you want.
  3. Create stakeholders in the idea. This is where you conduct the sharedrop onto all who hold the token, or who are on the mailing list, or who maintained active membership on the forum or virtual citizenship group.
  4. Create a social consensus and or constitution.

Once all is in place you will have created a swarm.  The price of the attractor tokens will influence behavior of the swarm. In bees the duration of a dance is the signal but for humans price is usually the signal. The social consensus is also extremely important to follow consistently because it is the glue which holds everything together. It is trust in the distributed rule-set which holds the holonic structure together.

 

Swarm Intelligence in Honey Bees and attractor patterns in humans

Attractor patterns, Attractor tokens, Personal preference bot networks, Personal preference swarms, Swarm intelligence

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.

Incentive design patterns and stigmergic optimization

Incentive design patterns, Stigmergy, Virtual Pheromones

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 effects 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 the agents and an order would emerge as a result of stigmergic optimization allowing the swarm to solve complex problems.

References

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.

Attractor patterns and attractor tokens

Attractor patterns, Attractor tokens, Intent casting, Personal preference bot networks, Personal preference swarms, Stigmergy, Swarm intelligence, Universal Darwinism
  • 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.

References

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.