Category Archives: Swarm intelligence

Order comes from process in digital space

BitNation, Collaborative filtering, Distributed governance, Holonic systems, Order by process, Order by protocol, Organic governance, Organic governance, Stigmergy, Swarm intelligence

The traditional models of producing order (governance)

In geo-states order tends to come from a chain of command which information flows from the bottom to the top. Orders flow from the top to the bottom. The person at the top deemed the leader, the President, is in the position to be the commander of the troops.

In traditional corporations order also tends to come from a chain of command in which knowledge flows from the bottom to the top and commands flow from the top to the bottom.

The problem with the traditional models is that in a world of increasing complexity the attention of those at the top is very scarce. Good ideas which are generated at the bottom might never flow to the top because of filters. Knowledge generated at the bottom might not reach the top because of attention scarcity. As a result those at the top increasing have to rely on expert advisers or on technologies which provide decision support.

Order from process in digital space

In cyber-states order comes from process.  The process comes from the algorithms encoded into the fabric of the cyber-state. When Larry Lessig said “Code is Law” he was revealing that process produces order in digital space and code is what represents the algorithms of digital space that govern process.

In Distributed Collaborative Organizations order also comes from process. If the DCO is built up around a blockchain then the DCO is governed by those algorithms, which encourage all participants to follow a certain set of processes which inherently produce the order we see.

This is similar to how ant colonies, bee hives, and other organic structures have order if you look closely at the distributed rule set but to the casual observer who does not study insects it might look completely chaotic. These algorithms provide the mechanisms which allow for stigmergy to shape the behavior of the swarm. All of this can be encoded into a series of smart contracts which can allow the swarm to be self governing, and to be potentially more scalable and effective at governing because of swarm intelligence which can help solve the problem of attention scarcity.

Governance by software protocol

Digital space is holonic. Every computer in digital space is a node. Every node in digital space could be called a peer for example. In human terms we could call it F2F (friend to friend) or N2N (neighbor to neighbor). If we look at Bittorrent as an example then we can see that a node can be in more than one role at a time so the node can be both a seeder and a leecher. It is the share ratio which governs the network because everyone in the network can be rewarded or punished depending on whether they meet a minimum threshold of the share ratio.

The Bittorrent example reveals that you can create order through mathematics, algorithms, ultimately making the process more important than the nodes. For this reason you do not need a leadership to create whitelists and blacklists of who can get what but instead you can have a decentralized rule set, a process which everyone knows and follows just by downloading the software itself. As a result by using the software you’re subscribing to the process and the software is only able to interact with others following the same process which produces order from adherence to software protocol.

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.

Personal preference bot networks and Ethereum’s Provenance

Distributed autonomous agents, Ethereum, Intent casting, Multi-agent systems, Personal preference bot networks, Personal software agents, Provenance, Swarm intelligence

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.

References

Ethereum London Meetup: Provenance (YouTube)

Provenance | Discover the stories of great products and their makers. (Provenance)
https://www.provenance.org/

Swarm intelligence (- Scholarpedia)
http://www.scholarpedia.org/article/Swarm_intelligence