Mapping Landscape of Patterns - Vol.2

Survey Mapping the Landscape of Patterns across Domains:
LINGUISTIC AND SEMANTIC ANALYSIS OF PATTERN DEFINITIONS
IN ANSWERS TO OPEN QUESTIONS
BCSSS Research Group Systems Science and Pattern Literacy
Working Paper Version 1.0 - April 2019
Maria Lenzi - Helene Finidori
This is the second set of results from the Survey Mapping the Landscape of Patterns
across Domains initiated by the BCSSS Research Group Systems Science and Pattern
Literacy in early 2018.
Our intention here was to extract linguistic and semantic regularities from the survey
answers where respondents defined patterns in a few sentences.
We assumed that we would find frequently used general terms and associations between
terms. We therefore sought to extract couplings between linguistic and semantic
structures which could be generalized and re-used for the identification of patterns and the
development of patterns knowledge. Such „knowledge units“ would support the
development of a formal language and a diagrammatic presentation of patterns.
We applied manual analysis, tree diagrams and software tools from the open source
platform Voyant Tools, such as Cirrus, which displays word frequencies as wordclouds, as
well as tailed-Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis
(PCA), which help reduce dimensions of complexity and capture the essential structure of
the data.
We drilled down in the depth of the corpus of answers to extract regularities from varied
and ambivalent pattern definitions.
t-SNE and PCA tools supported the identification of general manifestations and attributes
of patterns, as well as associations and consistent combinations of terms. Some
persistent associations revealed mature concepts behind them.
We also observed that the application of simple software tools like Cirrus constrained the
interpretation of definitions provided in different contexts. More sophisticated linguistic and
semantic structures require more sophisticated tools and a multi-dimensional approach.
We will continue to develop a re-usable toolkit for extraction and detection of patterns.
These will include tools for text analysis to complement those presented in this report.
A sample of 140 definitions is not sufficient to make all embracing conclusions about
existing concepts and perspectives of seeing patterns. Yet it can generate some insights
and generalizations to be validated and fine-tuned with further experience and research.
Our interpretation of survey data is not final and we welcome proposals and ideas
concerning objectives, interpretations and application of tools.
Creative Commons Licence BY - NC - SA 1

SUMMARIZED OBJECTIVES
● identifying linguistic regularities in definitions of patterns
● identifying semantic regularities coupled with linguistic regularities
● identifying concepts and perspectives behind definitions
● creating „ knowledge units“ for potential re-use
● developing a toolkit for extraction and detection of patterns
APPLIED TOOLS
- Manual analysis
- Cirrus Diagram using Voyant Tools
- Tree Diagrams using Vensim Software
- t-SNE (tailed stochastic neighbor embedding) using Voyant Tools
- PCA (Principal Component Analysis) using Voyant Tools

IDENTIFICATION OF GENERAL TERMS WITH THE CIRRUS DIAGRAM
We started with eliciting the most frequently used terms in the corpus of definitions of
patterns with the help of wordcloud diagrams (Cirrus from Voyant tools).
https://voyant-tools.org/?corpus=3f5ebfd521bae2cf37bd6da440db7dcd&stopList=keyword
s-1cbc1dabfb6ec16ef24060bdea65747f&whiteList=&view=Cirrus
Cirrus diagrams helps visualize relative frequencies of usage of words, with the size of
each token in the diagram reflecting the frequency of usage.

We clustered frequently used terms around the following key dimensions of pattern
definitions:
- similarity
- dynamics
- perception
- repetition
- function
- cognition
- variety
- structure
SOME OBSERVATIONS ON CONSTRAINTS IN GENERATING CIRRUS DIAGRAMS
It is not enough just to extract the frequency of usage of terms in a corpus of answers -
there are ambiguity, asymmetries, redundancies, contextuality and multiplicity of concepts
behind definitions.
All this affect the interpretation of Cirrus Diagram results and constrain the application of
such simple tools for the identification of linguistic and semantic regularities in text.
OBSERVATION 1:
asymmetry of identified frequencies of usage
frequently used terms like „problem“, „phenomena“, „something“ and others were
repeatedly used in the same individual answers.
Example 1: „A pattern is a generalized way of describing a problem in a context, and a
solution to the problem. Patterns help people by a) creating a vocabulary to record or
communicate the problem and the solution b) putting the problem in a context, including
relationships to other patterns c) abstract the problem and the solution to help people
think about the problem space in which the pattern lives“
Example 2: A pattern is a semi-formal and mildly abstract description of the kernel of
successful solutions to problems that occur over and over again in similar contexts. They
help us understanding what works to solve whatever problem, in whatever realm. Once
we've understood this, we must adapt this pattern to our very concrete context, i.e. our
very concrete problem. That is: a pattern is a tool but we must learn how to apply it.
Example 3: An aggregate or cluster of phenomena that recurs in observable/
recognizable form, whether physical or psychosocial or spiritual. A product of synthesis of
these observed phenomena. A help to save energy in recurring situations and a
hindrance to innovation and behaviour change.
Example 4: Something in common. Something with potential. Something worth

exploring. Generation of a new boundary.
OBSERVATION 2:
linguistic regularities are applied in different contexts that change their meaning
Examples of contextuality for different tokens are shown below – it is always important to
pay attention to the context in which the terms are applied.
Paying attention to contextuality supports identification of more sophisticated linguistic
and semantic regularities.
We can see below how context changes the meaning of some terms:
„SETS“ = s.
Patterns are
- (arrangements of) s. of tilings;
- s. of relationships (among relata),
- s. of relationships (that have some form of repetition),
- s. of actions (that can be identified within different contexts),
- s. of images (that can be identified within different context),
- s. of aspects (frequently found together),
- (regular) s. of features,
- s. of elements (with that pattern)
„DIFFERENT“ = d.
Patterns
- (repeat themselves) across d.subjects
- (can be identified) within d.contexts (by virtue of their signature features),
- (meaningful interaction) of d.competences,
- (can show) d.levels of abstraction,
- (noticed repeatedly) at d.times or in d.contexts;
- (can show up) in d.forms;
- (can be realized) through d.mediums,(something)that d.objects(have in common);
- (a regular structure or process) that appears across d.fields or instantiations,
„ARRANGEMENT“= a.
Patterns are
- a. of sets (of tilings),
- a. of whatever kind (which is able to keep its configuration),
- a. (that gives identity and meaning),
- a structural a. of units (abstracted from the particular types),
- a persistent or frequent a. (of interacting elements),
- a. that has some form (of recognized regularity)

„REPEATING“= r.
Patterns are
- (evolution of information) on a r. path,
- r.abstractions (of features of a system),
- r. in time and space (or both),
- (an observed) r. of a sequence,
- (an emerging) constellation of r.events,
- (sequences of ) r. elements,
- a r. order of elements (that serve some purpose),
- a r. geometry (covering a surface),
- a r. phenomenon (that we can perceive),
- a r. structure or process (that appears across fields or instantiations)
„TIME“= t.
Patterns are/do
- (repeat) over t.,
- (dynamically stable) over t.,
- (somewhat stable for) some period of t.,
- (repeat themselves) across t.,
- (repeating) in t.,
- (takes attention) over t.,
- space and t. (interaction),
- (iterated at least two) times,
- (noticed repeatedly) at different t.,
- (a recognizable) repetition in t. (and/or space),
- (repeated) through t.,
- (recognizable connections) over t.,
- (a regular behavior) over t.

MANUAL SEMANTIC ANALYSIS WITH THE HELP OF TREE DIAGRAMS
We applied manual analysis combined with bottom up approach and tree diagrams to
identify linguistic and semantic regularities in the corpus of pattern definitions.
With a special interest for a higher order structure in definitions of patterns we payed
attention to the duality of inclusive and lateral relationships and to ambiguity which were
present in answers.
We made following observations:
OBSERVATION 1 : REPETITION
Patterns have to do with regularity and repetition
Relevance of regularity and repetition is also confirmed by semantic regularities extracted
from the t-SNE and PCA analysis.
Regularity and repetition are mentioned in connection with time, space, context and
structure.
Applied terms:
„repetitive“, „regularities“, „repeating“, „recurring“, „repeated“, „repetition“, „repeat“,
„regular“
OBSERVATION 2: HIERARCHY
Patterns can be defined as entities of a higher order
We have identified a logical hierarchy based on inclusive relationships in definitions of
patterns:
- pattern is some kind of an entity (first order)
- pattern is an order of some kinds of entities (second order)
- pattern is an order of orders of some kinds of entities (third order)
The different orders of logical hierarchy in definitions of patterns are illustrated in the
following tree diagrams which show the orders extracted from single answers.

1.Examples of the first-order definitions*: „Pattern is some kind of entity“
*we can observe, that definitions of the first-order in many cases are a hidden form of the
higher-order definitions, because such terms as „arrangement“, „relationship“ etc. already
embrace an additional order (arrangement of elements, relationship of entities etc.)

2.Examples of second-order definitions: „Pattern is an order of some kinds of entities“

3.Examples of third-order definitions: „Pattern is an order of orders of some kinds of
entities“

OBSERVATION 3: NETWORKS
patterns can be defined as networks of lateral relationships
Such terms as „arrangement“, „configuration“, „network“, „relationship“, „combination“,
„connection“ which were used to demonstrate a higher-order architecture of patterns can
also reflect lateral relationships between entities or parts - without hierarchical logical
subordination.
There are also clear definitions of patterns based on lateral relationships between entities.
Examples of definitions based on lateral relationships between entities:
Pattern(s) is/are:
● an arrangement of (whatever their kind) which is able to keep its configuration
dynamically stable over time
● an orderly dynamic that links things that may either be obviously or covertly
connected.
● something which rhymes with something else
● a perceived relationship among discrete elements, often in dynamic interaction.
● a collection of concepts or things that have some manner of connection to each
other, however vague
● semantic models defining inter-dependencies between related concepts.
OBSERVATION 4: FROM PERCEPTION TO APPLICATION
Definitions of patterns can be segmented into „conceived“ , „perceived“, „intuited“ and
„utilized“
These definitions also reflect different concepts and perspectives of patterns. Some single
answers combine multiple perspectives and allow multiple interpretations.
„Conceived“ definitions have to do with cognition and construction of meaning –
patterns are defined as a product of thinking, abstracting and generalizing:
- a pattern is a generalized way of describing a problem in a context, and a solution
to the problem
- a pattern is a way of abstracting and decomposing proven solutions to problems
- a pattern is an abstracted representation of common structures
- generic formula that be applied in multiple domains

„Perceived“ definitions present patterns as a result of observation and experience:
- an aggregate or cluster of phenomena that recurs in observable/recognizable form,
whether physical or psychosocial or spiritual
- a pattern is a systemic holistic creation. A pattern on a flower, or universe, or
design, seems always settling when observed, and balanced for all parts/parties
involved
- a pattern is a non-random, replicable set of relationships among relata, which can
be observed as phenomena and/or qualitatively experienced by participants

„Intuited“ definitions reflect intuition, sensation, emotions and creative states without
conscious reasoning - with special cases of application of metaphors:
- a complexity drop. A signal.
- a pattern starts as a simple seed that proliferates, building complexity, often adding
scales with self-similarity.
- patterns are the way in which we speak, communicate, think and create
- it has certain qualities. A flavour of influencing.
- it's linking everything together. Colour to sound to nature.
- serve as scaffolding. Patterns can emerge from creative cognition.
„Utilized“ definitions show patterns as means for practical and purposeful applications.
- a pattern is a semi-formal and mildly abstract description of the kernel of successful
solutions to problems that occur over and over again in similar contexts
- a representation of a generic response to a recurrent situation
- a piece of knowledge conceptually structured (semantic) in such a way that, either
it can be purposefuly re-used in a larger frame ...or automatically mobilised
(including triggered) when a certain condition is sensed ...
OBSERVATION 5: AMBIGUITY AND DUALITIES
Definitions of patterns are ambiguous and contain dualities and contradictions
There is some kind of a semantic ambiguity in definitions of patterns as a „way“, „may“ or
„something“ with „some“ qualities.
There are also dualities in definitions of patterns as a process and/or a structure; as
hierarchical and/or lateral connections; as bottom-up and/or top-down processes; as
natural and/or human and/or social constructs.

We identified some complementary terms or dualities, that evolve from the corpus of
definitions of patterns and are also contained in single answers:
regular random
structure process
stable dynamic
unique repeating
similar different
form flow
system network
purpose function
problem solution
energy matter
thinking acting
perception cognition
Examples of ambiguous definitions of patterns:
- Things (plural) with some coherence that can be distinguished from a background
of randomness or other things
- Pattern is a conclusion made and sustained by the mind (an observer) stating that
what they are faced with "something" --in some sense tangible and durable-- rather
than just background mess...
- Patterns guide thought and engage consciousness; serve as scaffolding.
- Patterns need not be visualizable, but many are. Patterns can be temporal, e.g.,
music and speech. Patterns are assigned meaning, or meaning is projected on
them. Patterns have no intrinsic meaning. Structuring-Processes and
Processing-Structures are a complementarity
- A pattern is a generalized way of describing a problem in a context, and a solution
to the problem.
- A pattern has two parts: an issue, which is an understanding of living activity,
human and otherwise; and a solution, which guides the physical form of the built
and natural environment.
- Patterns are a unique combination of behaviors, acts, qualities or events that
repeat themselves over space and time
-
EXPERIMENTS WITH DIMENSIONALITY REDUCTION
To complete our manual analysis we experimented with more sophisticated software tools
that support reduction of dimensionality - we applied t-SNE (tailed-Stochastic Neighbor
Embedding) and PCA (Principal Component Analysis) on the corpus of pattern definitions.
We wanted to identify linguistic and semantic regularities in the data structure and to see
whether there was a correspondence with the semantic regularities from the manual
analysis.

IDENTIFICATION OF LINGUISTIC AND SEMANTIC REGULARITIES WITH t-SNE
t-SNE - t(tailed)-Stochastic Neighbor Embedding - is a software technique that supports
visualization of high-dimensional data in a two or three-dimensional map with respect to
non-linearity.
t-SNE preserves characteristics of a data structure when it is projected from a higher
dimension to a lower dimension and helps to avoid „crowding problem“ in visualization of
proximities.
We analyzed the corpus of answers to the open question of the survey:
„How would you describe or define a pattern (in a couple of sentences)?“
792 terms were identified in the corpus of 140 answers.
Here is a link to a t-SNE setting supported by Voyant Tools:
s-44c3d8895e669029e19125fcc0735df9&limit=60&view=ScatterPlot
Our readers are welcome to experiment with different settings and to share ideas and
interpretations with us.
SETTING FOR t-SNE-ANALYSIS
number of terms - 60
perplexity – 15
iterations – 5000
dimensions - 2
Number of terms is reduced from 792 to 60 most frequently used terms.
Perplexity is a rough equivalent to the number of nearest terms (neighbors) and we have
set perplexity to the length of a „couple of sentences“ – 15 terms.
Perplexity and number of iterations are tuned to achieve a relative stability of results.
Points above zero represent the growing proximity (attraction) to other terms, and
points below zero - growing repulsion (distance) to other terms.
Sizes of circles correspond to the frequency of use of the terms.
The results of t-SNE iterations are shown below.

t-SNE setting: number of terms - 60, perplexity – 15, iterations – 5000, dimensions - 2
INTERPRETATION OF RESULTS OF t-SNE ANALYSIS
Being a stochastic tools t -SNE does not deliver an exact repetition of results – trials with
the same setting always show somehow different results – yet it is possible to identify
repeating consistent structures through multiple trials with the same setting.
t-SNE evens out semantic distances and we can follow the relative difference between
distances and the values of repulsion (below zero) and attraction (above zero).
Frequently used terms that have a shorter semantic distance to other terms in the corpus
of answers are those which appear in the attraction area above zero. They can be
characterized as general attributes of patterns.
We assumed that stand alone terms with the highest repulsion reflect general
manifestations of patterns.
Associations of terms in the repulsion area (high below zero) as well as associations of
terms in the attraction area (high above zero) can represent some kind of a bounded
system or a mature concept.
We can also observe consistent combinations of frequently used terms that could play
the role of linguistic and semantic regularities.

IDENTIFIED STRUCTURES
STRUCTURES IN REPULSION AREA BELOW ZERO
In the repulsion area below zero we can observe frequently used „stand alone“ terms that
represent how patterns are frequently named – we call them „general manifestations“ of
patterns:
something – elements - structure - form - time – set – repetition – phenomena* - way* -
sequence – relationships – dynamic - meaning - may

*terms “phenomena“ and „way“ in some t-SNE trials have high negative values on both
axes and in some trials - high negative values on one of the axes.
As a matter of fact patterns are frequently named by survey respondents
„something“, some whole of „elements“, „structure“, „sequence“, „form“, „set“, „repetition“,
„phenomena“, „relationships“, „way“ , „dynamic“ and some kind of assigned „meaning“.
The term „may“ is frequently used in definitions and reflects potentialities of patterns that
„may“ or „may not“ be something.
The term „time“ is not used as a name but is playing an essential role in definitions of
patterns in different contexts, as patterns seem to unfold over time.
In the repulsion area we can observe consistent combinations of terms that remained
without change through all the trials with the same setting:
(elements; sequence), (repetition; set), (dynamic; meaning)

STRUCTURES IN ATTRACTION AREA ABOVE ZERO
We can observe a consistent association of terms („problem“;„context“; „solution“) in
the area of a high attraction. This association represents a special perspective and a
mature concept of patterns as a tool, expressed by respondents who see pattern as a
solution to a problem in a context.
In the attraction area we can observe frequently used terms, that can be identified as
general attributes of patterns as all of them have short semantic distances and they
accompany most of definitions coupled with other terms:
Systems – Space – Similar – Different – Repeating - Common
There is also a consistent association of terms (recurring; connected; observed) that
repeats in all trials with the same setting. This association of three dimensions in
characteristics of patterns can play a role of a semantic regularity - it means, that we
should pay attention to all these aspects simultaneously when we try to detect and
describe something as a pattern.
It was interesting to compare the results of t-SNE analysis with the results of PCA
analysis, that also visualizes high dimensional data but without taking in account
non-linearity.

IDENTIFICATION OF LINGUISTIC AND SEMANTIC REGULARITIES WITH PCA
Our readers are welcome to experiment with different settings of the PCA (Principal
Components Analysis) by following the link below:
s-07c7ca5299031eed316e9296dd1fde33&limit=60&view=ScatterPlot
Both t-SNE and PCA reduce the dimensionality of the data, but PCA does it without
taking in account non-linearity.
PCA uses an orthogonal transformation to convert a set of observations of possibly
correlated values into a set of values of linearly uncorrelated variables called principal
components.
In contrast to t-SNE PCA can have a „crowding problem“.
Visualization with PCA can separate terms that nonetheless semantically belong together,
as PCA renewed iterations tend to widen distances that were already detected.
SETTING FOR PCA ANALYSIS
Number of terms – 60; Number of dimensions - 3
X axis represents principal component 1
Y axis represents principal component 2
Color intensity represents principle component 3
Size of the circles represents frequency of use.

IDENTIFIED STRUCTURES AND INTERPRETATION OF RESULTS
We can observe some clusters:
1 2 3
Structure
Repetition
Objects
Dynamic
Arrangement
Combination
Relationships
Space
Time
Recurring
Repeating
Interaction
Observed
All identified clusters demonstrate a „pattern“ in definitions of patterns:
they associate terms reflecting some kind/form of structure, dynamic and repetition.
We can try to extract some generic combinations of characteristics of a pattern:
- „dynamic repeating structures/objects“,
- „arrangement /combination of relationships in space and/or time“
- „observed repeating/recurring interaction“

We see that „problem“ and „solution“, visualized by PCA, drift apart – that can be
connected with reinforcing of some distances through iterations and concealing the actual
semantic proximity.
SOME CONCLUSIONS FROM t-SNE and PCA ANALYSIS
The results from both t-SNE and PCA analysis support some propositions, that we have
already made in the first publication of the survey analysis.
We extracted general manifestations and general attributes of patterns on the basis of the
frequency of usage and proximity values, and identified some consistent associations of
terms.
There can be many ways to interpret PCA and t-SNE visualizations - we believe, that
software tools should not guide the analysis, but should support manual analysis,
heuristics and brainstorming in the starting phase of research.
SOME CONCLUSIONS ABOUT SEMANTIC REGULARITIES
Observations we made with the help of manual analysis and application of software tools
lead us to a conclusion that there are semantic regularities in definitions of patterns - most
of these definitions always reflect some kind of a complex structure coupled with dynamics
and repetition in time. There are some basic dimensions, perspectives and general
functions assigned to patterns, that we have listed in a table below.
Identified semantic regularities are coupled with repeated application of some terms and
combinations of terms.
Formalization of these repeating structures can contribute to the development of a formal
language and will be a part a future work.
In the table below we show some findings from manual analysis and t-SNE/PCA
applications.
Semantic regularities on the basis of
manual analysis
Semantic regularities on the basis of
t-SNE / PCA analysis
TIME
● regularity
● repetition
● dynamic
STRUCTURE
● relationships between parts and a
GENERAL MANIFESTATIONS
Pattern is:
- Something
- Structure
- Form
- Time
- Set
- Repetition

whole (set, collection,
arrangement)
● emergent wholes (systems,
organizations)
● relationships between parts
(networks, interactions, links)
● relationships in time (process,
sequence)
ORDERS OF LOGIC
- pattern is some kind of an entity
(first order)
- pattern is an order of some kinds of
entities (second order)
- pattern is an order of orders of
some kinds of entities (third order)
BASIC PHYSICAL ENTITIES
● matter
● energy
● information
PROCESS
● bottom-up processes
● top-down processes
● flat processes
COMPLEXITY/VARIETY
● ambiguity of definitions
● duality of definitions
● variety of definitions
BASIC DIMENSIONS
● dimension of space
● dimension of time
● social dimension
● structural dimension
● context
● domain
● meaning
- Elements & Relationships
- Meaning
- Phenomena
- Way
GENERAL ATTRIBUTES
- Systems
- Space
- Similar
- Different
- Repeating
- Common
- Observed
- Connected
- Recurring
ASSOCIATIONS OF TERMS
- (problem; context; solution)
- (recurring; connected; observed)
COMBINATIONS OF TERMS
- (elements; sequence)
- (repetition; set)
- (features; way)
CLUSTERS OF TERMS (PCA analysis )
- Structure, Repetition, Objects,
Dynamic
- Arrangement, Combination,
Relationships, Space, Time
- Recurring, Repeating, Interaction,
Observed

PERSPECTIVES
● intuited
● perceived
● conceived
● utilized
FUNCTIONS (CYBERNETIC
PERSPECTIVE)
● function of meaning making
● function of linking things together
● function of coordination
● function of predicting
● function of explanation and
understanding
● function of problem solution
SOME INSPIRATIONS
Our pattern research is research-in-progress and interpretations we make are dynamic
and ready for change.
But already now, after having worked though multiplicity of definitions, we can see that
patterns are a kind of a homonym, which is used to define multiple things.
Pulling multiplicity of perspectives and dimensions together, patterns seem to possess a
sufficient variety to absorb complexity, embrace dualities, bridge the gaps and transcend
boundaries.
If we want to detect something as complex as a pattern, then we also need something that
embraces all the regularities we have identified, and remains dynamic and flexible –
something what we can call a „ pattern of knowledge“ about patterns.
We will continue our way following the mystery of patterns. The findings from the survey
inspire our curiosity and a future research – we are moving forwards and keeping you
informed.

Mapping Landscape of Patterns - Vol.2

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