In part 2, we discussed a framework to consider the physical inputs required for an individual to exist in a cultural environment. Here, we will observe the interactions between individual units within larger systems and develop a framework that examines the units of currency exchanged which enable an individual to alter his / her perception of reality.

In short, I seek to elaborate upon how time serves as the universal currency of all and how allocations of time serve as the means by which individuals can accumulate data points that serve as leverage to influence reality.

The Implicate and Explicate Orders

In 1980, David Bohm developed two frameworks for understanding different aspects of reality: the implicate and explicate orders. The explicate order consists of an unfolded organization of abstractions that humans can easily perceive such as sight, sound, and touch. The implicate order represents the folded order – which represents how one person perceives the “unfolded” order. An example of which is a car crash:

A large number of spectators witness the explicate order (car crash) from a multitude of angles; each will perceive the situation on the basis of their proximity to the crash and degrees of focus devoted to other activities at hand (eg: prior to the car crash, Person A is drinking coffee with Person B around the corner from where the car crash occurs (they eventually hear, but do not see the car crash); Person C is sitting on a bench observing the cars passing by on the street which the car crash occurs; Person D just stepped out of his / her building which is situated on the street which the car crash occurs for a cigarette break); when the accident occurs, each person absorbs the details of the incident dependent upon the aforementioned – each person will walk away from the incident with a “folded” perception of the event – resulting in a multitude of implicate perceptions of the car crash.

Technological innovation has given rise to the means by which we form our expectations of reality. The printing press enabled non-physical transfers of knowledge to exist; the telephone and telegraph decreased the latency associated with transmitting information. The television and more recently, the internet, has enabled the rapid transmission of data to large audiences. Our perceptions of reality are formed when we digest different notions of the explicate order which in turn shape how we perceive reality on an implicate basis.

As a result, this innovation has significantly affected how information is sent and received, which are formed through the following:

• Centralized Networks
• Decentralized Networks
• Distributed Networks

Centralized Networks

Radio, tv, and newspapers represent centralized networks: individuals contribute thoughts and ideas to a central mediator who in turn decides what is / is not published to its audience.

Diagram: Example of Centralized Network, The Financial Times Newspaper

In earlier times, the printing press itself acted as a centralized network: what was / was not published was determined at the jurisdiction of the owner of the press.

Decentralized Networks

Decentralized networks act as 2-sided networks by which contributors simultaneously act as audiences on centralized platforms. Mediation is limited to the degree by which moderators of a community choose to censor its users (eg, disallowing hate speech or the dissemination of adult content). Examples include forums and message boards.

Diagram: Example of Decentralized Network, Seeking Alpha

Distributed Networks

Distributed networks operate in the absence of mediation. Each node within a network is connected to virtually any other node and information flows in an unlimited number of directions. The structure by which we can understand distributed networks are personal blogs: individuals exist on a standalone basis, unencumbered by mediators or content platforms.

Diagram: Example of Distributed Network, Blog Network

Information Networks

In the past, geographical or socioeconomic positions played important roles in accessing information. Much of this was ruled by accessibility to educational institutions, cost of books, cost of technology (radio, tv, computers), and cost / access to transportation (availability of public transportation or cost of motor vehicles). Temporality presented an additional confound: operational hours set by institutions restricted the times by which individuals could access information (eg; a library’s hours of operation, when a tutor is available, or when a professor decides to host office hours). Today, the internet has enabled the reduction of many of these barriers and confounds, contributing to a greater degree of information democratization.

Technology largely defines the rates by which we can update our orientations to problems in the real world. Problems remain unsolved to the extent by which an individual lacks the necessary knowledge base to understand the relationship between inputs and drivers within a system and how varying inputs affect how units in a system interact with one another and affect processes and outcomes.

Reality, here, is defined as identifying the primary data points which define a class or idea which (generally) are confirmed by a consensus of (a select) majority.

Consider the following example of the Fourth of July for many Americans:

Americans associate the Fourth of July with BBQs, fireworks, and red white and blue. We encounter the explicate order of this event in America through socialization (growing up and attending bbq’s, firework shows, etc.) and internalize these events which effect our expectations of what the date should entail on an implicate basis. The explicate order is strengthened by implicate expectations: I / (we) expect to see fireworks on the fourth of July on an implicate basis; we contribute to the explicate reality by photographing these events, etc, further molding the construction of the reality and influencing expectations of others, and ultimately, the explicate order.

The fourth of July is but a point in time in space: Americans associate this class with a multitude of instances that define that class [1]. The fourth of July is a symbol which triggers associations for many Americans, associations not necessarily manifested in non-Americans with respect to the fourth of July. As such, red white and blue, bbq’s, and fireworks exist as a reality of the fourth of July for Americans, but not so much so for other subsets of people.

In the context of Bohm’s aforementioned framework, exposure to data points associated with a class symbol form the basis of an individual’s conception of reality: the more data points one individual accumulates with respect to an idea, subject, or experience, the greater leverage he/she harbors regarding any interaction with the the idea, subject, or experience simply because this individual harbors an understanding of how units and processes interact with one another to define a class of ideas.

Developing the Foundation for An Idea or Class of Ideas

Thus far, we have noted that a conception of reality is defined by how strongly a multitude of data points meet an expectation for what characterizes, or defines, a class symbol. Here, we break down how these relationships interact with one another.

Consider for a moment the data points which define the idea of Graph Theory, as a concept:

Diagram: A Visualization of the Wikipedia Entry for Graph Theory

 

To understand the class symbol: Graph Theory, it is necessary to define a subset of data points associated with the topic:

• The definition of Graph Theory
• Applications of Graph Theory
• Its History
• Graph Drawing
• Graph-Theoretic Data Structures
• Problems in Graph Theory
• Areas Tangentially Related to Graph Theory

The subset of data points associated with Graph Theory each embody their own subsets of information:

Problems in Graph Theory:

• Graph coloring
• Subsumption and Unification
• Route Problems
• Network Flow
• Visibility Problems
• Covering Problems
• Decomposition Problems
• Graph Classes
• Enumeration
• Subgraphs, Induced Subgraphs, and Minors
• Graph Coloring

Also Related (to Graph Theory, as represented by the subtopic “See also” from the diagram above):

• Related Areas of Mathematics
• Generalizations
• Prominent Graph Theorists
• Algorithms

In order to engage in an information exchange associated with Graph Theory, it is necessary to understand the aforementioned subtopics to conceptualize the context surrounding the class symbol, generally.

Consider the list below in relation to the diagram above on the basis of 3 levels:

• Graph Theory serves as the Class Symbol (Level 1)
• The subset of data points above serve as Instances of Graph Theory (Level 2)
• The subset of data points associated with each instance is a Manifestation of Graph Theory (Level 3)

From broadly identifying Graph Theory as a class (Level 1) to assembling the components associated with it – its subtopics – or, instances and manifestations, the accumulation of each successive data point represents a unit of currency by which actors can trade ideas associated with Graph Theory ranging from:

• Teaching others about graph theory
• Collaborating with one another on the topic of how to tackle problems associated with graph theory
• Challenging commonly held beliefs, or assumptions, associated with the topic

Conceptualizing Digital Knowledge Networks

By now, we have developed the basis by which we can conceptualize how subsets of information serve as units of information that embody class symbols that have the potential to exist as units of exchange between actors in a system.

The primary distinction between the current age and all other points in time derives from an altercation by which information is sent and received. A larger set of data is available for any individual (who has access to the internet) to explore and accumulate. Actors, in the absence of mediators and institutions, have the ability to absorb, synthesize, and share information, contributing to virtually any discourse hosted via the internet.

Here, we can observe the construction of distributed networks and, more specifically, examine the distinction between young and mature knowledge networks.

Design

I’d quickly assembled a database and script parsing xml of four websites; two of my friends (Chris Chang and Kingston Hon) as well as two more mature website (Paul Graham and Aaron Schwartz). These diagrams serve as visualizations by which we can observe the information that they have shared with the internet: potential units of currency enabling each to connect and transact with any other node on the internet network.

Infantile Knowledge Networks

Below represent the site structures for kingstonhon.wordpress.com and chrisckchang.com

Diagrams: Visualizations of Infantile Blog Structures

In accordance with each site structure, both are laid out quite simply: domain.com/article. Each site pushes articles every few months, and have been accessible for less than 2 years each.

Mature Knowledge Networks

Next, we examine mature knowledge networks, those of Paul Graham and Aaron Schwartz. Each have been online for a greater number of years and are comprised of a greater number of posts.

Diagram: Visualization of Mature Blog Structure, paulgraham.com

Unlike Chris and Kingston’s blog structures, Paul Graham organizes his website on the basis of deeper categorization:

• Essays
• H&P
• Books
• YC
• School
• Arc
• Lisp
• etc.

Above, we examine but one subset of his blog: paulgraham.com/articles. Here, we can easily observe that he has pushed a significantly greater number of posts to the internet than Chris and Kingston – a higher volume of currency to transact and transmit ideas.

Diagram: Visualization of Mature Blog Structure, aaronsw.com

Aaron Shwartz arranges his blog through three sub topics:

Aaronsw.com/

• Quote Blog
• Web Blog
• Technical articles

Here, we can observe the frequency by which Aaron Schwartz pushes information to the internet; from greatest frequency to least: Web Log Posts, Technical Articles, Quote Pages.

A Micro Analysis on Allocation of Time

The website visualizations above represent the digital constructions of time invested in by each individual. Each individual manifests a synthesis of ideas which they share to participate in a cultural environment.

In an effort to further articulate the relationship between time and knowledge networks, I embarked on a project by which I logged each point of information I absorbed for a period of two months. The following project demonstrates how an individual can allocate his / her consumption of time to accumulate data points associated with classes of ideas which enable him / her to transact these points of information with others within a cultural environment.

Breadth and Depth: Input Volume by Subject Type

Diagram: Time Series Analysis of Volume of Information Logged Within My Personal Database Over a 2 Month Period

For the select period, we can track the interest by which I pursued a broad array of subjects and topics ranging from macroeconomic themes, exercise, specific equities, and specific investment themes (beef, consumer retail). Notably, this module enables me to observe which themes I explored with great depth in comparison to others which sparked my interest yet failed to follow up on.

This module enables me to accurately track where my attention is allocated over periods of time.

 

Applied Databasing: Sourcing Recommendations and Building Efficient Recall Methods

At the end of April, 2015, I’d requested help from my network of friends, shooting off the following email:

Friends – I’m working on a little project and would appreciate your help.

Could you send me a list of your favorite items within the following categories? Don’t need to list for all 3 (but can if you want). 1 is more than enough.

• Books
• Movies
• Music (Artists)

I’ll send over the output once completed so you can see where your contribution stands. Your help is much appreciated.

I yielded the following results:

The above graph represents the entire field of results I harvested from my query. The purpose of the exercise; however, was to construct my database such that I could effectively recall information on the basis of the following criteria:

• Popularity (frequency)
• Alphabetical order
• Date Logged

I’d successfully assembled my database to handle the aforementioned criteria. The results of the survey were as follows:

Diagram: Most Popular Artists

Diagram: Most Popular Movies

Diagram: Most Popular Books

As we can see above, while my data is not statistically representative of the tastes and preferences of my generation (which would be impossible to deduce from a < 30 person population sample), the exercise did challenge me to design my database with an application in mind.

Reflection: Allocation of Time Over Two Month Period (captured in database)

At the end of my two month period, the top subjects in my database comprised of the following topics:

Diagram: Snapshot of Composition of Top Datapoints as of June 2015

The above graph serves as a brief overview of where most of my time was directed at a specific point in time. However, it is necessary to observe my database from a higher perspective as other modes of exploration don’t require high volumes of data.

Diagram: Overview of Topics Captured in Database as of June 2015 and Sample User Interface

Diagram: Graphical Representation of Topics Captured in Database as of June 2015

At the conclusion of the two month observation period, I’d focused most of my time following macroeconomic events while paying special heed to individual equities. At this point in time, I’d developed a thesis on low oil prices and constructed valuation analyses on companies hit hard by the oil pricing environment. I also studied macroeconomic events to understand its effects on consumer spending (notably, Harley Davidson (HOG)) as well as the effects of high cattle prices as a result of the California drought on tertiary industries (poultry, namely: Sanderson Farms (SAFM)). During this time, I developed significant positions in the aforementioned equities. Additionally, I diligently followed my exercise routine (logging the distances I’d run and times I’d run those distances in), as well as embarking on an additional extracurricular project within a project: sourcing movie, music, and book recommendations from my network of friends and redesigning my personal database to efficiently sort and recall these points of information.

Additional points of interest over the two month period include:

• Nutrition
• Aggregating Notable Finance Blogs to Bookmark
• A study on Capital Allocation Methods

Information Transmission and Transaction: Leveraging Digital Networks to Manifest the Currency of Time

The study above represents an observation of how time is spent and over what range of topics information is accumulated. In my view, I like to consider that individuals exist as nodes in space (Level 1; recall from illustration of Graph Theory above) that allocate their time investing in a breadth of subjects / hobbies / ideas (Level 2) within their cultural environments by which they develop depths of information (Level 3) which enables them to transact these points of data with one another to teach / collaborate / challenge one another.

The world today looks like a series of nodes in space by which individuals can harvest data points from a virtually limitless web of associations. This generation is defined by high degrees of access to information. Individuals readily and ably share information while associations with institutions or proximity to physical social networks play less a role in impeding an individual from accumulating relevant data points associated with specific topics.

These network graphs serve as reminders of our investments in time: with what breadth and what degree of depth is information being harvested? Is time being optimized to address physical inputs which address individual resource requirements? To what facets of cultural environments are individuals learning from or contributing to?

Affecting Reality

As such, individuals with the greatest depth of information associated with a specific topic or class of ideas has the greatest propensity to navigate and shape a reality. Understanding the inputs that comprise of the whole class, individuals with a high degree of understanding of the inputs which control units within a system and how these units interact with one another in simultaneous processes has the greatest ability to shape prototypes of ideas. The enfoldment of the explicate order (data points harvested from the unfolded abstraction of reality, also known as the implicate order) enable an individual to affect systems to the highest degree.

Up Next

So far, we’ve studied:

• Structures of Physical Systems
• Structures of Cultural Environments
• Framework of Time as Currency for Transacting

Next, we’ll discuss the motivations associated with why people choose to accumulate different units of currency as well as the neurological and psychological processes which affect these systems.

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[1] The class / instance framework is borrowed from Douglas Hofstadter and his Prototype Principle through which he notes: specific events have a vividness which imprints them so strongly on the memory that they can later be used as models for other events which are like them in some way. Thus in each specific event, there is the germ of a whole class of similar events […] (through which) unconsciously, (people) rely on a host of presuppositions about (events) […] that trigger relations with other class symbols. 

See page 352 of Hofstadtr’s Godel, Escher, Bach for detail