All posts by darklight

Virtual mutual aid societies can supplement and eventually replace national welfare services

Distributed autonomous communities

Virtual mutual aid societies can be formed by any group of people who have access to the Internet. To achieve this we must achieve a state of affairs where Internet access is perceived as a human right rather than a privilege.  Virtual mutual aid societies could be fully programmable, based around decentralized digital reputation, decentralized trust, and distributed authority.

Virtual mutual aid societies may start out on social networking sites such as Facebook where individuals can join Facebook groups. Virtual mutual aid societies may also start out in Second Life, in gaming universes, or anywhere that a group of people can meet. The criteria for entry can act as a programmable filter and the ability to vote may also be built in.

Virtual mutual aid societies can provide for their members by offering membership rewards, membership dividends, or if these virtual mutual aid societies are self governing then citizen’s dividends, discounts, etc. Decentralized reputation will allow people to know far more people than the limits of Dunbar’s number and due to the fact that it is programmable the logic of the mutual aid society can be continuously improved and updated.

I will have more posts to offer insight on the subject of virtual mutual aid societies in the future.

 

In the United States the Democrats are attached to old ideas. One of these old ideas is the Robin Hood protocol. The Robin Hood protocol requires that voters elect Democrats and in exchange for party loyalty the Democrats will tax the rich and give to the poor. Since a lot of or perhaps the majority of voters are poor and since a majority of Democrats are also probably poor this amounts to a kind of mass bribe or in less provocative terms a method of buying votes. At the same time this creates a “lifeline” dependency on the Democratic party and it’s politicians while giving the politicians the power to cut that “lifeline” dependency off if they have a change of mood.

This state of affairs may have worked in the past because the threat of revolution, civil unrest, organized crime, all but required that the government placate the have nots through social programs aka “hand outs”. I will state for sake of clarity that I have no issue with “hand outs” because corporations receive these “hand outs” as well in the form of government contracts. The problem arises from the fact that due to technological advances leading to “total transparency” or “total surveillance” there eventually will no longer be a fear of revolution, unrest, organized crime, etc. Once this delicate balance of power is broken the Robin Hood protocol which encompassed the strategy of the Democrats for decades going back to FDR will also be broken. The social contract breaks down under a state of total surveillance & total transparency and after the fear of social unrest is gone is there any reason for politicians to continue with the social programs aka “hand outs” to keep the majority of the population placated?

I believe we cannot predict the outcome or future political events. I also don’t believe that people who are in a position to do something today should put their fate and their future into the hands of political authority, religious authority, Jesus, aliens, or anyone else. I take the opinion that we make our fate and no entity from up above will come down to save those down below.

How did communities do social programs prior to there being a government? Prior to nationalist governments providing social programs there were mutual aid societies/benefit societies which provided the social welfare services. These mutual aid societies/benefit societies were based on the value of community service and the Freemasons are an example of this. To get accepted into a mutual aid society a person had to be of good character and other members had to vouch for their character.

Fast foward to 2015 and we now have the blockchain which allows for programmable distributed autonomous societies. These new global societies could take on similar functions that mutual aid societies took on during times when there were no government social programs. By building these sorts of distributed autonomous societies now we can reduce the risk of chaos and social upheaval in the future. By building these sorts of societies now we might be able to prevent a rise in organized crime, terrorism, resource wars, which could come about if we stay on the current course.

One of the first implementations of a programmable distributed autonomous society will be the digital autonomous virtual state (DAVS) project. More information will be available about this in future postings. This post and future postings on the subject can be seen as a historical documentation of my thoughts and intentions going forward.

References

Beito, David. ‘From Mutual Aid To Welfare State: How Fraternal Societies Fought Poverty And Taught Character’. The Heritage Foundation. N.p., 2000. Web. 1 Mar. 2015.

Scandlen, Greg, and Greg Scandlen. ‘Mutual Aid Societies: It’S Amazing What People Can Do Together’. The Federalist. N.p., 2015. Web. 1 Mar. 2015.

Wikipedia,. ‘Mutual Aid (Organization Theory)’. N.p., 2015. Web. 1 Mar. 2015.

 

 

Distributed Interactive Genetic Algorithms / Distributed Evolutionary Computation

Genetic algorithms

Distributed Interactive Genetic Algorithms are similar to Interactive Genetic Algorithms with additional features such as lack of a single point of failure, redundancy and potentially anonymous scalable human participation in the process.

Traditionally the Interactive Genetic Algorithm relies on the computer to be in the “innovator” role. The human being typically is in the “selector” role. Often the Evolutionary Computation  is done by a single human sitting in front of a computer who must evaluate the quality of the candidate solutions proposed by the innovator which in this case would be the computer.

An example of a typical Interactive Genetic Algorithm would be the human/computer interaction which generates through Evolutionary Computation the camouflage patterns on military outfits. Additional examples would be to use the Evolutionary Computation process to generate efficient designs for unmanned drones or computer chips.

One of the potential bottlenecks in using Interactive Genetic Algorithms is due to the human who may suffer from fatigue because the human has to ultimately judge the aesthetic quality of each candidate solution.

Distributed Interactive Genetic Algorithms / Distributed Evolutionary Computation would be similar to taking both the humans, the computer processing and putting them both in the cloud. In this descriptive example we could say that Ethereum has the ability to handle the distributed computation necessary and at the same time the humans could be given monetary incentive to participate in the selection process. In that example you would have the makings of a Distributed Interactive Genetic Algorithm running on the Ethereum platform. An example use case could be a distributed content based media retrieval system as has been already accomplished centralized using an Interactive Genetic Algorithm (Patel, Meshram, 2012).

References

Reynolds, C. (2010, July). Using interactive evolution to discover camouflage patterns. In ACM SIGGRAPH 2010 Posters (p. 113). ACM.
Patel, B. V., & Meshram, B. B. (2012). Content based video retrieval systems. arXiv preprint arXiv:1205.1641.

Distributed Human-based Genetic Algorithms

Adaptive systems, Genetic algorithms

What we know is that human based genetic algorithms can utilize the Darwinian process of “natural selection” to evolve a candidate solution. Human based genetic algorithms utilize the human as the selector and innovator. The data itself represents the sequence.

The sequence is a data pattern. The human beings contribute data patterns such as the”unique content” which makes the human beings the innovators. The human beings also are the selectors because they determine which content is “fit” or “unfit”.  This can be managed by “like and dislike” or “upvote and downvote”.

An example of this would be an Ask site where human beings can ask questions and where answers selected up.  Additional examples would include sites like Digg, Reddit, and Stumbleupon. The problem with these kinds of human based genetic algorithms is that there are centralized entities which means the website can be shut down. Distributed Human-based Genetic Algorithms would be not have a single point of failure and can be run on decentralized autonomous platforms such as Ethereum, NXT and any similar platform which allows for scriptability.

References

Okano, J., Hamano, K., Ohnishi, K., & Koppen, M. (2014, October). Particular fine-grained parallel GA for simulation study of distributed human-based GA. InSystems, Man and Cybernetics (SMC), 2014 IEEE International Conference on(pp. 3508-3513). IEEE.

Distributed sousveillance to counter institutional corruption

Heterarchy

Distributed sousveillance to counter the hierarchy of surveillance.

Since if there is no hierarchy the power is distributed and no one can benefit more than any other from data collection and moral hazard is reduced.

What that means is the best option is to make the sensors as small as possible, as ubiquitous as possible, as democratically accessible as possible, so that secrecy does not exist for anyone.

Because secrecy at the top allows for corruption and collusion just as much as secrecy at the bottom.

How would can I trust the  justice system or the jury system when I know that each individual within them could be in secret communication or have secrets on each other to coerce and collude with each other? They could determine I’m guilty before the trial even starts, they could determine that I’m going to prison before I’m arrested.

Then we have the private prison industry which can give incentives to encourage collusion and corruption. So law enforcement would become like a gang, and their offspring might have stocks in private prisons and other family financial incentives. Doesn’t that give the judge, the police, any anyone else who owns these stocks the incentive to get people locked up?

The first use of distributed sousveillence should be to investigate corruption in the justice system and in law enforcement. If we have sousveillance we can do that because they can’t hide just like we can’t, but if we only have surveillance then how do we police the police?

I’m for collecting as much data as possible. No one has any secrets, and anyone can be caught with their hand in the cookie jar. No one is above the law? But since everyone is corruptible no one should have secrets, especially if they are in authority  (I distinguish a difference between having secrets and having private or sensitive information).

Judges for example are going to want their secrets but how do we know the judges and prosecutors aren’t friends? As a result we cannot trust either of them. How do we know the police, judges, prosecutors, defence attorney, the news media, aren’t all in a big underground gang working together to selectively and subjectively take certain people off the streets and ignore others?

We don’t know that. So we should assume they collude if the capability of collusion exists in the design of the system. Since the system is designed to support collusion and corruption, I expect it to be corrupt until we redesign it to make corrupting it more difficult and then show that the redesign results in less corruption.

Since there is absolute no initiative to redesign the obviously corrupt institutions, institutional corruption shall persist until the system eventually collapses as has happened throughout history.

References

 Associated Press. (2013, November 13). Supporters of 14-year-old SC boy executed in 1944 for killing 2 girls want a new trial. Fox News. Retrieved January, 2014, from http://www.foxnews.com/us/2013/11/09/supporters-14-year-old-sc-boy-executed-in-144-for-killing-2-girls-want-new/

 

Heterarchical systems vs hierarchical systems

Heterarchy, Hierarchy

Unnecessary hierarchy and artificial scarcity are a major source of problems in the world.

 

In hierarchy there you’re either a boss or a worker, a master or a slave.

 

In heterarchy you’re a master and slave at once, and as much or as little of each as you choose to be.

 

In heterarchy you’re a client and a server at the same time, like Bittorrent if you’ve ever used it. You download and upload simultaneously to increase the utility of the distributed heterarchical network,

 

In some situations hierarchy may be necessary and when it is not coercive in nature then an individual can choose how much and what form they require for survival. In a world of scarcity hierarchy is necessary to determine who can access or use the limited resources. The digital world is not a world of scarcity, and artificial scarcity produced by copyright, software patents and ownership of ideas enforces an unnecessary hierarchy.

Governments support this unnecessary hierarchy in a desperate attempt to protect the jobs of an entitled few. As we face a future of technological unemployment, intelligent machines, complete with distributed governance, we have to develop new systems to encourage innovation to replace copyright which is broken completely.

 

The origins of any new idea are unknown and can never be proven definitively

 

If Alice owns a patent it is not proof that Alice created the invention or that she owns the idea. The idea could have been stolen from Bob or anyone else and given to Alice. For this reason the patent system is broken and cannot serve the purpose of rewarding innovation when there is no way in the world to know the true source of an innovation. Ideas have no owners.

 

Autopoietic DACs

Autopoietic computing, Autopoietic DACs

Autopoietic DACs

(Defined as a self creating/self constructing decentralized autonomous corporation which is seeded by an instruction set, watered by crowd funding, and provided sunlight through community participation).

To understand how DACs are created we can remember the metaphor of seed, water, sunlight (SWS).

  1. Every DAC starts as an instruction set which is just an algorithm which determines how it will behave and what it can grow into. The instruction set is akin to the DNA of the DAC.
  2. Every DAC instruction set which survives peer review is then given water (crowd funding).
  3. Every DAC which receives water (crowd funding), will also need human attention/participation (sunlight).

An autopoietic DAC is a DAC which is self creating, self designing, and self improving. This kind of DAC is almost like an artificial lifeform because it is given a set of instructions to act as its DNA, those instructions allow the DAC to evolve over time. The instruction set is just an algorithm, and a DAC can be designed in a way so that sunlight (human participation) is incentivized when it produces a more intelligent DAC.

 

Finding an algorithm to build an autopoietic DAC is not going to be easy. Proof of Stake allows for voting (human participation) on bounties. Proof of Commitment/Contribution is an algorithm which rewards human participation in DACs by measuring the level of commitment and crediting that for payout. A decentralized bounty exchange can be used by DACs to submit bounties to human participants in the form of Ask/Bid with the bounty token being a unique digital token similar to cryptocurrencies, highly divisible, and exchangeable between humans before an expiration date. The human who submits the token ultimately receives the credit for Proof of Commitment if the decentralized bounty exchange algorithm is used.

 

An additional method would be for the autopoietic DAC to license it’s construction out to the builder or getwork DAC. That DAC uses Proof of Stake voting to allow the community of shareholders to set a priority level for every bounty via a rating, list descriptions of jobs they believe are necessary, and the Proof of Commitment/Contribution algorithm treats human labor as if it is mining. So difficulty rises as more humans are doing the same task which means the payout adjusts downward, while difficulty decreases when fewer humans want to do a particular task which makes the payout adjust upward. The more unpopular the task the greater the payout becomes which allows for a smooth and predictable voter generated bounty distribution system to pay for the construction of any DAC.

 

Simplified as a metaphor and acronym: seed, water, sunlight (SWS).