Monthly Archives: April 2015

Blockchain independent services are needed

Blockchain agnosticism, Blockchain independent assets, Blockchain independent platforms, Blockchain independent services, Internet of Blockchains

In order to maximize the utility and interoperability of blockchain technology as a whole it is important for service providers to design their services to be blockchain agnostic from the beginning. The reason being is that the current Bitcoin infrastructure is a lot like the nation states where you have a sort of lock-in. Vender-lock in is not good for the long term evolution of blockchain technology even if it might temporarily benefit the Bitcoin network effect.

Smart wallets

Smart wallets are designed to be blockchain agnostic. Instead of the user having to trade between different kinds of coins or download multiple wallets there is one unified wallet which can allow the user to send and receive any kind of cryptocurrency or cryptoasset. This blockchain agnostic approach can limit code duplication and technical debt.

Holy transactions: is one of the first smart wallets. Holy transactions in addition to being a smart wallet is also an exchange. The flaw with holytransactions is it’s a centralized service which is not the recommended approach for long term security.

Moonstone.io is a currently in development smart wallet and in addition it will provide a truly decentralized exchange. The backend of Moonstone.io is the Bitshares blockchain which allows for the trading of BitAssets such as bitBTC, bitUSD, bitLTC, bitGLD (bitGold) and more. Moonstone.io intends to allow for trading 1:1 between bitBTC, bitUSD and other BitAssets and in addition it will in some cases even allow physical delivery such as with BitGLD.

Smart blockchains

Smart blockchains are blockchains which are reconfigurable and which can communicate with other blockchains. It is important that an “Internet of Blockchains” form if blockchain technology is going to evolve effectively. In order for this to happen blockchains must be able to communicate with other blockchains and this can happen through atomic cross chain transactions (ACCTs).

Bitshares currently is intending to be one of the first smart blockchains. The core developers are focusing on adaptability through reconfigurability and atomic cross chain transactions are already coded waiting to be enabled.

What are atomic cross chain transactions (ACCTs)?

One of the most important aspects to allow blockchain technology to scale up is to have multiple communicating chains. These communicating blockchains would form an “Internet of Blockchains” (IOBs). Examples of this would be BlockNet and SuperNet. In the end all blockchains must develop the ability to link up to each other so that transactions can flow between blockchains allowing for example a transaction to be initiated on the Bitshares blockchain and filled on the Ethereum blockchain or vice versa.  The communication between chains would make each blockchain a node in a network of blockchains and this is why blockchain agnosticism is a prudent approach for current and future developers.

 

References

Kruchten, P., Nord, R. L., & Ozkaya, I. (2012). Technical debt: from metaphor to theory and practice. IEEE Software, (6), 18-21.

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.

 

Holonic systems

Holonic systems, Organic governance

What is a holon?

A holon is both a whole and a part. An example would be that an individual human is composed of cells which are composed of atoms. The individual human is considered to be a whole human but that individual human is also a part of the human society making the individual part of a bigger whole. So the human being would be a good example of a holon but so would a node in a complex adaptive system.

What are holonic systems?

If we remember take bees as an example this time instead of humans then the holonic system would be the bee hive. The bee hive would be a representation of the societal governance structure of the bees that belong to the hive. The bees would be holons because they would be part of the hive due to the fact that they follow the rules of the hive. They cooperate between each other but are all subordinates to the rules of the hive which represents the whole. The bee hive represents a holonic system, as do ant colonies, swarms of birds, schools of fish.

The difference between pathological hierarchies and holonic hierarchies

In a pathological hierarchy perhaps one individual or one node in the network assumes the role of the “whole”. This individual might consider him or herself to be the ruler, the number 1, the head authority, who must coerce and control everyone below them. This structure typically takes the shape of a rigid pyramid where the decision making and thinking typically comes from the head authority, with limited room for individuality below.

Holacracy embeds a generative mix of autonomy and cooperation in a flexible fabric of holonic design constitutional rules. It constitutes a new operating system for organizations that regulate the individual/
group dynamics to eliminate on one side the possibility of capture via power games, and on the other side, the inherent chaos characteristic of “leaderless,” decentralized organizations.

 

In a holonic system of governance there is a system for generating rules to provide order. So for instance in the case of virtual governance the nodes on the network can all run the same software, the same configurations, and each changing of the configurations of each node in the network the rules for the network can change in collaborative fashion. Roles and rules are set collectively and collaboratively.

Every participant in a holacracy is a sensor for what is going on, and each plays a role in identifying the tensions in a timely way while taking active steps to resolve them. Effectiveness and resistance to
capture are achieved by enhancing the power of collective decision locally via procedures such as: “After taking Individual Action, a Part-ner should tell any affected Role about it, and, on their request, initi-
ate actions to resolve any Tension created by the Individual Action or refrain from taking this Individual Action again in the future.”

Reference

Ulieru, M. Organic Governance Through the Logic of Holonic Systems.

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.

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

Evolutionary artwork, Evolutionary framing, Intent casting, Memetics, Personal preference bot networks, 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

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