Monthly Archives: April 2015

Safe to Fail Probes

Cynefin Framework, Emergent engineering, Safe to Fail Probes, Safe-to-fail experiments

The freedom to fail

In video games learning takes place because the players are not afraid to die. As a result of this freedom to fail a lot of experimentation takes place and over time learning takes place. Hackers tend to think in a similar fashion and the art of “fuzzing” is similar in that it attempts to make the system fail by trying to push the boundaries of the application design. In essence these are both “brute force” solution discovery methods where essentially trial and error are used in a systematic way. It may take many failures to achieve a success which is why there has to be a freedom to fail.

What are safe to fail probes?

The safe to fail probe is an experimental technique which can be applied in complex adaptive systems to discover new knowledge. Dave Snowden pioneered safe to fail probes and one of the key points is that each experiment should be different. It’s important to not have 20 out of 100 people all trying the same experiment.

Blockchain based distributed autonomous communities are not inherently chaotic

Despite how things may seem in the media the blockchain based decentralized community is not inherently chaotic. It is a complex adaptive system where order exists but it is too complicated to deal with using the top down approach. It is inflexible to think of blockchain communities as corporations because typically a corporation is a top down hierarchy, has public leadership roles, and is fairly predictable in how it operates with changes taking place rather slowly. The decentralized communities do not operate like corporations but at the same time are not “anarchy” in the sense that it’s a lawless “wild west”.  The law of the decentralized community is encoded into the software that each individual must run.

To think of each blockchain and or DApp as an experiment is to allow for the idea that a blockchain can be like a safe to fail probe.  The only way to discover what is or isn’t possible in a completely new environment is to probe the environment through multiple simultaneous experiments. So just as distributed sousveillance can allow a blockchain to probe an environment to create collective intelligence a similar approach can be used to decentralize knowledge generation through safe to fail probes.

Blockchains are distributed sensor networks

This will become clear as prediction markets utilizing distributed oracles show that the consensus (Schelling point/focual point) of truth can be identified. Methods such as using prediction markets, powered by zero knowledge proofs, oracles, connected to a blockchain are quite powerful. This means perspective is valuable for determining the truth of any event and the blockchain can combine globally distributed perspectives of an event or situation in a quantifiable way. Additionally this has implications for distributed autonomous security systems as well which at this time have not been fully explored or exploited.

A problem with blockchain maximalism (one blockchain to rule them all)

One of the major problems with blockchain maximalism is that while it does promote the network effect of the winning blockchain it doesn’t actually contribute to the process which led to the innovation of the blockchain itself. In an environment where there is a diversity of blockchains where many attempts are being made to innovate or to find solutions to previously unsolved problems then you have a way to experimentally probe the space which I’ll call the “search space” to find the optimal solution to a problem.  It allows more developers (and more minds in general) to attack the same problem with similar economic incentives to the incentives of the early Bitcoin adopters had.

Viewed as a complex adaptive system the evolutionary process is artificially slowed if we stop experimenting. If we stop experimenting then it will take longer for us to learn the optimal algorithms, ways of doing things, and methods for self management/self regulation of this new space.

A problem with funding too many versions of the same experiment

One of the problems in the current blockchain tech community is that VCs and entrepreneurs seem to think very much in orthodoxy. A lot of money is flowing into duplications of the same experiments while some of the truly unique experiments don’t receive funding. As a result there are dozens of exchanges, wallets, and similar services which don’t actually innovate much except in marketing yet which are being flooded with cash. A likely result is a lot of these duplicates will fail, will be consolidated, and while this is good in some ways it is not good in all ways. Companies which truly bring innovative new ideas to the space should be given a chance to monetize them and build out but at the same time a lot of businesses are being built just to make money rather than to improve on how something is being done.

References

Snowden, D. (2010). Safe-fail probes. www. cognitive-edge. com/method. php.

 

Snowden, D. J., & Boone, M. E. (2007). A leader’s framework for decision making. harvard business review, 85(11), 68.

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.

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.