Introducing Value Area Networks - Matched participants by shared values. By Dana Edwards. Posted on Steemit. December 14, 2018.
This concept is possible only based on the design of Tauchain presented by Ohad. In his design for Tauchain he highlights the fact that any member of the social network will be allowed to input their worldview. It has been discussed by myself previously that moral values could be an important part of Tauchain in this setting.
A Value Area Network is a concept I'm introducing which is designated to mean a kind of network where all participants are matched according to shared values. These participants in the network (economic agents, bots, machines, humans, companies, whatever) should in theory be allowed to outline as much of their current values and as long as all participants are deemed to be in alignment by the consensus algorithm of Tau then they will be considered part of unified network.
The acronym VAN can be designated to stand for Value Area Network, not to be confused by Value Added Network. Unlike a LAN (Local Area Network) which is based on physical geography, the VAN is based on "social geography". People who are closer to each other socially on the moral and "concerns and values" level would represent a sort of shared location. In social science the concept of social proximity is defined mostly in geographical terms but in the digital age with a technology like Tau in existence the idea of closeness might not have to be restricted to the geographical definition.
Closeness in terms of how close your values align to another participant in a network would represent a distinct place on a sort of map. This distinct place would be represented or quantified by a score which indicates it's potential location on a spectrum of possible locations. Of course the mathematics behind this would have to be more clearly defined in future posts but this post is to introduce the concepts for future discussion.
My concerns and reasons behind thinking up VANs is based on that fact that while social media today does a pretty good job connecting billions of people to random people it also does a horrible job connecting socially compatible people to each other. It's not good enough to connect a bunch of random people. People want to connect to people who have compatible values with themselves as their values are constantly updating over time. Tauchain in theory is the only platform which is expected to have the features to make this idea a possibility.
Values in this context could be negotiated from or derived from beliefs or worldview using Tau discussion. The values then would over time be updating as the person updates their beliefs or worldview. This would be to go the emergent route of letting Tau try to identify the values of the participant based on what the participant said in discussions (avoiding contradictions). The other would be to let the participant explicitly enter their current values and over time let Tau help them to constantly update that over time.
These are features I hope to see developed over Tau in some form some day. If I'm in the position to bring these features into development (provided AGRS works as intended) then this could be one of my contributions. The key mechanism behind this feature would be a novel matchmaking algorithm which leverages the Tau Shared Knowledge Base and reasoning capabilities. The social values map feature could be deduced via the discussions had over time or it can simply be a checkbox setting where the participant chooses by checking boxes and sliding scales.
Tauchain Update: Significant code changes in Github and discussion of progress. By Dana Edwards. Posted on Steemit. September 30, 2018.
Just several hours ago lead developer and founder of the Tauchain project Ohad Asor released his most significant code update yet. This blog post will be to discuss some of those updates and put it into context. In order to make sense of the current codebase : "Tauchain Codebase" I will also discuss a bit about the makeup of the code.
The significant breakthrough - Ohad implements the BDD
First some might be wondering what is BDD? BDD is a data structure called binary decision diagram. This data structure in my opinion is as significant to Tauchain as the "blockchain" data structure was to Bitcoin. For those who do not have a computer science degree I will elaborate on what exactly a data structure is below before discussing what a BDD is and why it is so significant.
Brief discussion on what a data structure is
In programming a data structure is a concept which represents a data organization method. For example blockchain is all about how records are stored as blocks. There are other similar data structures which represent decentralized data management and storage such as for instance the distributed hash table data structure.
A blockchain data structure looks like this for visualization:
By Matthäus Wander [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)], from Wikimedia Commons
A hash table looks like this for a visual:
By Jorge Stolfi [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0) or GFDL (http://www.gnu.org/copyleft/fdl.html)], from Wikimedia Commons
The really good programmers choose the appropriate data structure to meet the requirements of the project. BDD was chosen specifically by Ohad because it provides efficiency boosts in a key area necessary for Tauchain to function as intended. In specific we know Tauchain requires partial fixed point logic in order to have decidability in P-SPACE. We also know Tauchain requires decentralization and efficiency. Efficiency can be understood better in terms of the trade off between time and space. We do not have unlimited time or space so we must sacrifice one in order to get more of the other.
When we look at the code base we know that Ohad can optimize the code either by sacrificing space in which the executable will be bigger (but the code runs faster) or he can choose to sacrifice time in which the code is a smaller executable to save memory but might run slightly slower. This highlights the essential trade off between time and space when optimizing code but of course there is more to it because algorithms within a code base have to make similar trade offs.
Now what exactly is a BDD (binary decision diagram)?
Now that we understand the basics about efficiency and what a data structure is we can make a bit more sense of what a BDD is. In order to understand why BDD as a data structure is so important to Tauchain we have to remember that Tauchain is about logic. We can take the most basic example of Socrates:
A predicate takes an entity or entities in the domain of discourse as input while outputs are either True or False. Consider the two sentences "Socrates is a philosopher" and "Plato is a philosopher". In propositional logic, these sentences are viewed as being unrelated and might be denoted, for example, by variables such as p and q. The predicate "is a philosopher" occurs in both sentences, which have a common structure of "a is a philosopher". The variable a is instantiated as "Socrates" in the first sentence and is instantiated as "Plato" in the second sentence. While first-order logic allows for the use of predicates, such as "is a philosopher" in this example, propositional logic does not.
Based on the rules of first order logic we can have our inputs and receive our outputs. In the most basic example above we an see a bit about how logic works. To elaborate further:
Relationships between predicates can be stated using logical connectives. Consider, for example, the first-order formula "if a is a philosopher, then a is a scholar". This formula is a conditional statement with "a is a philosopher" as its hypothesis and "a is a scholar" as its conclusion. The truth of this formula depends on which object is denoted by a, and on the interpretations of the predicates "is a philosopher" and "is a scholar".
A truth table has one column for each input variable (for example, P and Q), and one final column showing all of the possible results of the logical operation that the table represents (for example, P XOR Q). Each row of the truth table contains one possible configuration of the input variables (for instance, P=true Q=false), and the result of the operation for those values. See the examples below for further clarification. Ludwig Wittgenstein is often credited with inventing the truth table in his Tractatus Logico-Philosophicus, though it appeared at least a year earlier in a paper on propositional logic by Emil Leon Post.
When we are dealing with logic we may find that a truth table helps with visualization.
Now with this knowledge we have the most basic Socrates example:
This can be represented via truth table and is called a syllogism. To solve this we simply apply a kind of reasoning called deductive reasoning. This would indicate that if All men are mortal is true and if Socrates is a man is also true then Socrates is a mortal must be true. If we were to say all men are mortal but Socrates is immortal then Socrates cannot be a man. So if Socrates is a man he must be moral or there is what we call a contradiction. Logic is all about avoiding these sorts of contradictions and in specific binary or boolean logic is to reach a conclusion which always must be one of two possible values.
If I ask you to play a game which we want to guarantee will end with either one of two possible outcomes then we have a good example of a boolean function. 1 or 0, true or false, on or off, a or b.
Some of you may be familiar with data structure we call a DAG (directed acyclic graph). For those of you who understand this concept you can visualize a BDD as being very similar to a propositional DAG.
By David Eppstein [CC0], from Wikimedia Commons
We know from DAGs that it's a finite amount of vertices, edges, etc. We may also be able to visualize topological ordering and if you remember my post on transitive closure you might also remember the visuals on how that can work:
A binary decision diagram can represent a truth table:
By The original uploader was IMeowbot at English Wikipedia. (Transferred from en.wikipedia to Commons.) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0/)], via Wikimedia Commons
And from these visuals now it should be abundantly clear how this is critical to the functioning of Tauchain. The BDD data structure allows for efficient model checking as well. To understand we have to consider the boolean satisfiability problem.
This highlights the fact that BDD can be used to create a SAT solver.
A DPLL SAT solver employs a systematic backtracking search procedure to explore the (exponentially sized) space of variable assignments looking for satisfying assignments. The basic search procedure was proposed in two seminal papers in the early 1960s (see references below) and is now commonly referred to as the Davis–Putnam–Logemann–Loveland algorithm ("DPLL" or "DLL"). Theoretically, exponential lower bounds have been proved for the DPLL family of algorithms.
Without getting overwhelmed by technical details the key points are below:
To read the code for yourself and track the progress of Tauchain development take a look at Github:
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Suggested readings to better understand the Tau ecosystem, Tau Meta Language, Tau-Chain and Agoras, and collaborate in the development of the project.
Lecturas sugeridas para entender mejor el ecosistema Tau, Tau Meta Lenguaje, Tau-Chain y Agoras, y colaborar en el desarrollo del proyecto.