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Relational Entity Network synthesis
Tommi Viljamaa 18.8.2017 version 2

Relational entity network (REN) : synthesis of theories conserning human cognition, consciousness,
understanding and potentialities in motion dynamics
This essay will have 4 parts. In part 1, I go through the theories underlying the relational entity network
(REN) synthesis. In part 2 I will go trough the mathematical equations needed to understand these
theories. In part 3 I will bring together these theories and lay down the logical and theoretical
foundations of REN. In part 4 I will try to create equations and algorythms formed in the synthesis
At the end there is list of references (books and wikipedia articles) that I have used in this synthesis.
Part 1 - What theories are needed to understand human understanding as relational entity network
This essays main goal is to understand deeply and widely the act of human natural language
understanding. I have found 8 theories that when brought together might help in this goal. First is
Passive Frame Theory of Consciousness. Second is state context property formalism (SCOP formalism).
Third is theory of potentialities in movement dynamics. Fourth is the theory of Epistemologically
different worlds. 5th is relational framework theory. sixth is theory of pattern recognition. Seventh is
theory of pattern compression. 8th is theory of imagination. I will now go trough the basic assumptions
in these theories, and in part 2 I continue to lay down the logic .
Passive frame theory of Consciousness
Passive frame theory is itself a synthesis of several theories, so now in here it will be a synthesis within
synthesis. It is not directly aimed to understand natural language understanding, but action and lower
level processes like nausea urges or percepts. However the framework can be applied to understanding
how concepts are formed and how from this understanding emerges.
The main point of Passive frame theory is that consciousness has evolved to guide voluntary action
selection in paraller responses to skeleto motor output system (PRISM). Passive frame of consciousness
is contrasted with unconscious processses which create the frame. Entities appearing in the frame of
consciousness do not know about each other (encapsulation). The content in the frame can only affect
other content in the frame trough unconscious processes. However, the frame is seen as unitary,
creating the sense of one self and agency. The starting point guided by the EASE principle starts at
olfactory consciousness. I see that even olfactory perceptions affect the concept formation.
From the perspective of REN, passive frame of consciousness gives a stage for the entities to appear to
be in relation with each other, forming networks. The basic substance is entity, not a neuron or
quantum particle or wave. These are entities themselves.

State context property formalism (SCOP)
SCOP formalism aims to give a formal description of concepts. In SCOP, entities haves states, contexts
and properties which create the possibility to “observe” the entity (there is no real observer, as the
observer/object distinction is not even wrong. Both are constructed from the relational entity network
in which the contextual properties can be counted by SCOP)
Theory of Potentialities
Jan Treur proposes a novel explanation for movement of an entity. Action property potentialities makes
possible the movement to happen from one state to next state. This solution to age old problem gives a
possible explanation and predictability for how relational entity network moves.
Epistemologically different worlds

EDW Perspective shows that many of our times great scientific problems are in fact pseudo problems
and cant be solved in the “unicorn” world framework. The main point is that the “world” does not exist.
I propose that entity networks give rise to EDWS. World is an EDW, but not very useful one since it
gives rise to many contradictions. So, instead of trying to model world, universe or environment, which
would require agents observers or mind(which are not even wrong frameworks), I try to model
understanding in general formed by these relational entity networks.
Relational Frame Theory
This psychological theory is about human language. It sees that we come to understand and form
concepts trough relating different properties of objects as well as objects with each other, thus creating
these relational frames. Here I try to give a formal treatment for these relational frames, constructing
them from relational entity networks.
Pattern recognition
Core idea in this synthesis is that we as humans create our (natural language) understanding trough
preceiving patterns in our environment and in ourselves. Our language can create infinitely complex
patterns and this easily leads to explosion in meaning if we can’t define our context useful for our
current situation. I believe that the best approach to create meta-understanding algorithms and
equations is to use Bayesian approach to formalize our understanding created trough pattern
recognition. I will publish these equations in part 4. In humans, we cut our perceptions in to bits or
qbits into features and try to match these to templates in our long term memory.

Data compression
We as humans have to compress the data from our senses and memories to match our current context
created by our body, cognitions and environment. Also here Bayesian probability algoritms will be
implemented to create best possible context by cutting down the data to guide our action towards more
optimal moves of mind and body creating as effective context as possible.

Theory of imagination
We as humans see relational frames in our environment, body and mind (cognitions) to create our
understanding to guide our action to best possible out come, forming a relational entity network
creating our unitary experience of I in passive frame of consciousness. These networked relational
entities are recognised as patterns and are then compressed in lossy manner to form Epistemologically
different worlds (EDWS) thus creating hyperverse. We can imagine these networks in our mind and
basically hyperverse is infinite, but we narrow down the context by trial and error so that only useful
EDWs appear in our passive frame of consciousness. This frame is recreated at each time step t to time
step t to time step t’’ and so forth. Here I present losely how these networks are formed as meta level
visual understanding, and in part 4 I present the Bayesian algorithms and SCOP formalism
potentialities to create meta understanding of the cycle of pattern recognition and data compression
(state property context creation and matching. This pictures below shows how the network is created at
each time step and then narrowed down to match our situation we are currently in.

Relational Entity network. Each time step t is implied in each Potentiality (P) and Realizer (r) and this pair forms the next time step t'
of Actualization/Realization pair A(t')(r') and these timesteps continue to infinity (t'') (t n)

Relational Entity Network (REN) is formed trough Dynamic Disequilibirum

Part 2 - Equations for SCOP and Potentialities and Bayetsian inference in pattern recognition and data

SCOP formalism

Special states and Contexts in SCOP formalism


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