How does conversation influence the collective’s impact on sustainability problems? With this in mind, how can we (re)design networks for better conversation?” This is a summary of ongoing research. We are asking three major questions: 1. What are specific impacts of effective conversation for a social ecological systems (SES)” (Druschke & McGreavy, 2016; Partelow, 2016)? By using sustainable aquaculture, as an example of an SES, can we find generalizable relationships between productive conversation and positive outcomes, such as resource use, options-generation, relationship-building, and accountability? 2. Using natural language processing (AI) on aquaculture-related sustainability conversations can we detect the signatures of effective conversation, and relate them to outcomes? Does a conversation signature -- recognizing dialogue and facilitation elements (Skifstad & Pugh, 2014), sequence (McCardle-Keurentjes & Rouwette, 2018), turn-taking (Buchanan & Pentland, 2007), and gender participation (Woolley et al., 2010) -- explain meaningful differences in performance – even when other explanatory variables are too costly to obtain? 3. What type of capacity-building and network design would spread effective conversation cost-effectively (e.g., self-facilitating through conflict and actions) (Silka, McGreavy, Hart, 2019)? What can we leverage from the research on cognitive bias and social norms to accelerate capacity-building and improved levels of trust (Cinner, 2018; Hoffman et al., 2015; Rand et al., 2014)?

1. Translating effective sustainability conversation
into network design
SIKM Leaders Discussion: Spapshot of an NLP journey
Katrina Pugh, Columbia University, U Maine
April 20, 2021

2. Agenda
Rationale and problem statement
Research questions
Research design
References

3. Rationale and problem statement
Aquaculture industry tensions are high
Source: “With aquaculture booming, it’s time to talk about its future in Maine,” Portland Press Herald, April 13, 2021
https://www.pressherald.com/2021/04/13/with-aquaculture-booming-its-time-to-talk-about-its-future-in-maine/
“There is something wrong when you can’t even
have a conversation in Maine without sending
people into an uproar...
There are a number of [Aquaculture lease] criteria –
navigation, competition and others... “unreasonable
interference”…undefined, ambiguous…
...We are now faced with conflict up and down the
coast.”

4. Rationale and problem statement:
Aquaculture Social Ecological System...what we think
1. Climate change/
warming waters
2. Diminished
traditional fishing,
near monoculture
3. Depts of Marine
Res, Fish & Wildlife
processes
Diverse stakeholders
(Farmers, Fishers,
Boaters, Riparian
Landowners,
Regulators,
Legislators, NGOs)
Interaction:
Meetings, Hearings
Local town halls,
Participatory Action
Research
Efficiency
Innovation
Participation
Exogenous
variables
Action
Arena
Interaction &
Outcome
Evaluative
Criteria
Action Situation:
Lease scoping mtg.
Lease Hearings
Outcome: Approval
(yes/no) for lease

5. Rationale and problem statement:
Aquaculture Social Ecological System...de facto!
1. Climate change/
warming waters
2. Diminished
traditional fishing,
near monoculture
3. Depts of Marine
Res, Fish & Wildlife
processes
4. Wealth
disparities
5. National
polarization
Diverse stakeholders
Lobbyists
Lawyers
PR agencies
Facilitators
NGO overlaps
Interaction: Meetings,
hearings
Local town halls (some
with bias, certainty,
conflict)
Participatory Action
Research
- Letters to the editors,
Soc media
Yes or no to
Aquaculture lease
approval
- Cost (of lobbying,
investigation)
- Spillover effects
- Time elapse
Exogenous
variables
Action
Arena
Interaction &
Outcome
Evaluative
Criteria?
Action situation:
Lease scoping mtg.
Lease Hearings
Publishing, Press
Outcome: Approval
(yes/no) for lease
- Community
discord, cost

6. Rationale and problem statement:
Aquaculture Social Ecological System...reframed
1. Climate change/
warming waters
2. Diminished
traditional fishing,
near monoculture
3. Depts of Marine
Res, Fish & Wildlife
processes
4. Wealth disparities
5. National
conversation
Diverse stakeholders
Lobbyists
Lawyers
PR agencies
Facilitators
NGO overlaps
Interaction: Meetings
Local town halls (some with
bias, certainty)
PAR
- Letters to editors, SM
Productive
Conversation
Yes or no to
Aquaculture lease
approval
+ Individual
conversation skills
+ Network/
community
resilience
Exogenous
variables
Action
Arena
Interaction &
Outcome
Evaluative
Criteria?
Action situation:
Lease scoping mtg.
Lease Hearings
Publishing, Press
Lease Approval (yes/no)
+ Options-Generation,
+ Relationships
+ Bias for Action

7. Rationale and problem statement:
Focus and research question
“How does conversation influence
our collective impact on
sustainability?
With this in mind, how can we
(re)design networks for better
conversation?”

8. Research Design

9. Research Design:
Significant authors
SES and
Sustainability
Science
Ostrom
Agarwal
Clark
Partelow
Cash
Johnson
Silka, Hart,
McGreavy
Dialogue, Facilitation
Schein
Weick
Isaacs
Dixon
Edmondson
Wilkinson
Danescu-Nikulesu-Mizil
See et al
Skifstad & Pugh
Complex
spreading
phenomena,
Org. learning,
knowledge NWs
Pugh & Prusak
Pentland& Malone
Watts
Centola and Macy
RARE.org
Soc Ecol. Systems Cooperation Conversation/AI Network strategy
Behavioral Insight,
Diversity, Reciprocity
Trivers
Nowak
Galinsky
Page
Thaler and Sustein
Hoffman, Rand &
Yoeli
Titus and Stasser

10. Source: Pugh, 2020, and Skifstad and Pugh, 2014.
Research Design:
What makes good conversation? (4 Discussion Disciplines)
10
Inclusion
Translation
Integrity
Courtesy
Asking clean
questions, making
statements with
data Respecting
participants,
respecting the
forum
Reaching out and
bringing in; not
“excluding” through
acronyms, terms of art,
in-language
Synthesizing,
sensemaking,
up-levelling into
actions
Derived from Dialogue practices Derived from Facilitation practices

11. Research Design:
Research process
0. Observe
Sustaina-
bility
conversa-
tions
1. Code
transcripts,
start
interviews
2. Develop
AI/ML
(NLP)
Algorithm
3. Generate
Conver-sati
on
Signatures
5. Design
Capacity
building &
Network,
test with
focus
group
4. Validate
AI/ML
Algorithm,
productize
6.
Launch
Spring-Summer 2021 Fall, 2021-Spring 2022
Fall 2020-Spring 2021
See A1: Research process detail

12. 12
Integrity
Courtesy
Inclusion
Translation
We’ve been farming under a limited permit
aquaculture [LPA] lease. This has allowed us to
experiment. Next we moved to the public input part.
That’s today’s lease scoping meeting.
We want to minimize the impact as
much as possible.
I’ll follow up with you. I want your
opinion.
To clarify: aquaculture is a multitude of different
organisms, a multitude of different activities.
Source: Aquaculture Lease scoping meeting for Farmer #4. See appendix A2 for more examples.
Research Design:
Aquaculture Examples of Four Discussion Disciplines

13. M/F Item Comment Discipline Rationale
Farmer 24 This is the point where we listen and talk and
hear what you have to say.
Inclusion Open / include
n/a 25 10 seconds Inclusion Open / include
Harbor-
master
26 I have not been on one of these [scoping
meetings]. I heard [person of authority] signed
off. I was shocked. It’s bigger than I thought.
Anti-Courtesy Word "shocked" is trigger word.
Defensive
Harbor-
master
27 I was seeing the same materials as before. Integrity Statement of fact. Assume he saw
previous lease or earlier version.
Harbor-
master
28 That lobster trap needs to go. There’s no hauling
in the town of [town name].
Integrity Statement of fact.
Farmer 29 [Relative to the experimental lease] some
changes to the species. No change to the gear.
Translation Wrapping up, recapping.
Harbor-
master
30 I’m good with everything you said. Courtesy Positivity
Research Design:
Aquaculture lease Scoping meeting excerpt showing identity issues, aquaculture context
complexity
Injects doubt. Could
have derailed the
meeting here
(common pattern)
Sides with
farmer and
refers to reg
policy or lack of
reg policy
(common fisher
pattern)
Farmer is not
defensive
(previously: “we
want to be a
good neighbor”)
“Pivotal juncture”
opened inquiry
(40 more moves)

14. Research Design:
Six lease scoping meeting transcripts from Oct ‘20-March ‘21 (approximately 550 moves)
Integrity
dominates, with
Courtesy and
Integrity-Q next
Female % of
utterances
approx, 20%*
Contribute fewer
“anti” comments
*Except with female farmer leading scoping meeting. .

15. Research Design:
Percent of moves, by discussion discipline, by aquaculture lease scoping meeting
Relationship-Building, Intent to act
Relationship-Building
Options Generation, Relationship Building
Options Generation, Intent to Act
Intent to act
Relationship-Building, Intent to act

16. Research Design:
Six lease scoping meeting transcripts from Oct ‘20-March ‘21 (approximately 550 moves)
Options
Generation
Relationship-
building Intent to Act
Integrity (+) (+) (+)
Integrity-Q +
Courtesy +
Inclusion +
Translation (Weak)
Anti (courtesy,
integrity, inclusion)
(-)
Outcomes observed
Above-avg.
discussion
discipline

17. Research Design:
AI/ML Natural Language Processing: Using Cornell’s Convokit
Parsing
language
snippets
(pre-processi
ng and
feature
abstraction)
Ingesting
conversa-ti
on
transcripts
Cluster
analysis/m
atching to
four
discussion
disciplines
Testing on
“virgin”
transcripts,
generate
conversation
assessment
utility

18. Research Design:
AI/ML Natural Language Processing: Using Cornell’s Convokit
2. Separate
common
combination of
words that
have direction
(e.g.,
before-after,
parent-child)
3. Collect
those
directed word
combinations
that are
similar
4. Cluster
those motifs
together as
rhetorical
intentions
(questions,
answers,
provocations)
5. Identify
PromptTypes
’ Clusters
that most
correspond
to the four
discussion
disciplines.
6. Validate on
virgin
transcripts and
prepare for
statistical
analysis
1. Ingest
text from
transcripts
(raw
training
data)
Clean text
up
“Corpus”
compiled.
“Stop
words”
removed.
“TextClea
ner”
applied
“TextToArc
produces
ArcTypes,”
which are like
vectors -
directional
relationships
between words
“PhrasingMotifs”
are frequently-
occurring sets of
dependency-
parsed arcs
Use analyses,
1. graphs
2. rankings Propttypes
closest to the middle
(centroid) for that
cluster. Doing
stepwise, e.g., first
assign Integrity.
“PromptTypes” are
vector
representation
based on how they
are responded to --
as well as types of
rhetorical intentions
TF*IDF and SVD
Parsing language snippets (pre-processing and
feature abstraction)
Ingesting text Analysis/Matching to
discussion disciplines
Explanation
ConvoKit
Transformation

19. Research Design:
TF*IDF Vectorizer Matrix: From Phrasing motifs to Prompt Types
Phrasing
Motif 1
Phrasing
Motif 2
Phrasing
Motif 3
... Phrasing
Motif Z
Utteranc
e 1
1 0 1 ... 3
Utteranc
e 2
0 2 1 ... 0
Utteranc
e N
1 5 0 ... 7
Totals for each Phrasing Motif, for each
utterance, is then fed into SVD (Single Value
Decomposition) Matrix
https://scikit-learn.org/stable/modules/gener
ated/sklearn.decomposition.TruncatedSVD.h
tml
Overview: https://www.onely.com/blog/what-is-tf-idf/
SciKit-Learn Approach:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text
.TfidfVectorizer.html
For a Phasing Motif t in Utterance d, the weight Wt,d of
term t in document d is given by:
Wt,d = TFt,d log (N/DFt)
Where:
● TFt,d is the number of occurrences of t in Utterance
d.
● DFt is the number of Utterances containing the term
t.
● N is the total number of utterances in the corpus.
For SEO one would want a LOW TF*IDF. (Phrasing Motif
is rare). But for us, we would want a HIGH TF*IDF
(Phrasing Motif is not rare).
This is the frequency that this phrasing
motif occurs in the utterance (row).
The column Total X is the total
number of the phrasing Motif in the
CorpuS, with N utterances.
Total 1 Total 2 Total 3 Total ... Total Z

20. “TextToArc produces
ArcTypes,” which are
like vectors -
directional
relationships between
words in a sentence
Research Design:
Using Convokit
2. Separate common
combination of words
that have direction
(e.g., before-after,
parent-child)
3. Collect those
directed word
combinations that
are similar
3a: Politeness
strategies
4. Cluster those motifs
together as rhetorical
intentions (questions,
answers, provocations)
5. Identify
PromptTypes’
Clusters that most
correspond to the
four discussion
disciplines.
6. Validate on virgin
transcripts and prepare
for statistical analysis
1. Ingest text from
transcripts (raw
training data)
Clean text up.
“Corpus”
compiled. “Stop
words”
removed.
“TextCleaner”
applied
“PhrasingMotifs” are
frequently- occurring
sets of dependency-
parsed arcs
Use analyses,
1. graphs
2. rankings of prompttypes
closest to the middle
(centroid) for that cluster. May
do stepwise, to first assign
Integrity.
“PromptTypes” are vector
representation based on how
they are responded to -- as well
as types of rhetorical intentions
(clusters 0-7)
Parsing language snippets (pre-processing and feature abstraction)
Ingesting text Analysis/Matching to discussion disciplines
Explanation
ConvoKit
Transformation
'There is a guy
who whips
through there
in a Boston
whaler. I’m
pretty sure he
cut up my line
once.'
['a_* boston_* guy_* guy_a guy_whips in_*
in_whaler is_* is_guy is_there there>* there_*
through_* through_there whaler_* whaler_a
whaler_boston whips_* whips_in whips_through
whips_who who_*', "'m_* 'm_i 'm_sure cut_*
cut_he cut_line cut_once cut_up he_* i>* i_* line_*
line_my my_* once_* pretty_* sure_* sure_cut
sure_pretty up_*"]
['',
"'ll_* have_*__have_i__i>*__i_*
i>*__i_*",
'my_* the_*',
"'ll_* in_* the_*",
'a_* have_*__have_i__i>*__i_*
i>*__i_*']
[0.29997174
0.95394809]
(PromptTypes
Coordinates)
Example

21. Prompt Designation Response Designation
1
Motifs as input
field, utterances
may be
represented as
both prompt and
response. Pretty
noisy, some
trends: courtesy
mapping close
to prompt
centroid 4.

22. Research Design:
Influences on conversation outcomes (bold are observable in our data
as explanatory variables)
1. Conversation moves (discussion disciplines and
anti-disciplines)
2. Conversation moves’ sequence (e.g., time and type of
climax)
3. Gender (pertains to social sensitivity, and could be
captured by the “Courtesy” moves. Could also be related
to bias) (Woolley et al, 2010)
4. Region/Geography of lease (relates to wealth,
development, gentrification, environmental quality
5. Type of lease (e.g., duration up to 20 years, species,
equipment, relates to property rights)
6. Type of aquaculture tech (e.g., oyster, mussel rings)
7. Relationship history/costly signals (Grattan et al, 2007 on
“heritage relationships. May be captured by the
Region/Geography variable.)
8. Physical movement (Body language, pitch, cadence) (Pentland,
et al 2007)
9. History of the region (number of recent lease applications or
wins) (Ostrom, Partelow)
10. History of the conflict (nature of previous interactions across
the community, especially public or litigious actions) (ditto)
11. Regulation (governance systems) (ditto)
Likely to be available in the
conversations and publicly
available sources
More difficult to acquire with
current resources, and more
difficult to translate into
resources for sustainability
networks

23. Research Design:
Statistical analysis: What impact do the conversation attributes have
on outcomes, and is it statistically significant?
Explanatory variables Dependent variable: Outcomes
● X1 Signature (categorical variable determined by clustering: e.g.,
○ Sig 1 could be “above average inclusion, above average
integrity-Q,”
○ Sig 2 could be “below average inclusion, below average
integrity Q, above average translation”)
● X2 Sequence (categorical variable: climax type, such as early,
followed by loading) [Could also be part of X1]
● X3 Gender proportion
● X4 Region (proxy for costly signals, envtal qual, gentrification)
● X5 Type of Lease
1. Options generation
(innovation)
2. Intent to act (closure)
3. Relationship building
(Can be either positive or
negative)

24. Research Design:
Intervention: Accelerate the learning curve for productive conversation
1. Capacity-Building
● Training in Four Discussion
Disciplines (NLP research for
targeted impacts)
● Spreading boundary objects, like
pocket cards
● Collaborating with existing teams
(e.g., Maine Sea Grant,
Aquaculture in Shared Waters
training, DMR training)
2. Network Weaving
● Designing networks as “cradle for
conversation.”
● Incorporate behavioral models, social
norming
● Leveraging existing networks (e.g.,
FocusMaine, Maine Aquaculture
Association)
● Propose random controlled trials

25. Research Design:
Knowledge Network Effectiveness Framework (read right to left)
25
What are the impacts?
1. Learning / innovation
(tangible products)
2. Horizontal
Coordination (scale,
buying power)
3. Member Support
(trusted advice)
4. Translation/ Local
Adaptation
(sense-making)
What behaviors do we see?
•People identify with the
network out loud
•People share contacts,
knowledge even before
being asked
•People speak up, even
across hierarchy or political
divides
What dynamics play out?
• “if I contribute routinely, I
will, in turn, get value from
the network”
• “If I make mistakes, I will
learn in a community of
learners.”
• “If members identify with
the network, they
reciprocate.”
• “If leaders role-model
vulnerability, members will
take appropriate risks.”
What levers do we pull?
8 Design Dimensions:
Strategic
1.Leaders’ shared theory of change
2.Objectives/Outcomes/ Purpose
3. Expert-Learner balance
(psychological safety)
4. Inclusion/Participation
Structural
5. Operating model
6. Convening structures & infrastructures
7. Facilitation and social norm
development
Tactical
8. Measurement, feedback and incentives
Increasing leverage
KNEF on a page 210202
“Products,
negotia-tio
ns”
“Relation-
ships,
trust”
Source: Pugh & Prusak (2013) “Designing
Effective Knowledge Networks,” MIT Sloan
Management REview

26. Research Design:
Bringing it all together: Conceptual Model
Conflict in
sustainability
interactions
Outside
facilitation
Investment in
Conversation
Capacity
building
Depen-
dency on
outside
facilitation
+
-
-
-
+
-
+
B
B
Current: “Conflict is masked,
conversation skills atrophy”

27. investment in
Conversation
Capacity
Research Design:
Bringing it all together: Conceptual Model
Conflict in
sustainability
interactions
Outside
facilitation
Investment in
Conversation
Capacity
building
Depen-
dency on
outside
facilitation
Conflict in
sustainability
interactions
Desirability of
engagement
Credibility of
conversation
model
+
-
-
-
+
-
+
-
-
+
+
R
B
B
Current: “Conflict is masked,
conversation skills atrophy”
Goal: “Networks and practice support
productive conversation”

28. investment in
Conversation
Capacity
Research Design:
Bringing it all together: Conceptual Model
Conflict in
sustainability
interactions
Outside
facilitation
Investment in
Conversation
Capacity
building
Depen-
dency on
outside
facilitation
Conflict in
sustainability
interactions
Desirability of
engagement
Credibility of
conversation
model
Network
convening
Empirical
evidence of
conversation
driving impact
Data science
and network
design
programs
+
-
-
-
+
-
+
-
-
+
+
+
+
+
+
R
B
B
Current: “Conflict is masked,
conversation skills atrophy”
Goal: “Networks and practice support
productive conversation”

29. Out beyond ideas of wrongdoing and rightdoing,
there is a field. I’ll meet you there.
When the soul lies down in that grass,
the world is too full to talk about.
Ideas, language, even the phrase ‘each other’
doesn’t make any sense.
Jalal ad-Din Mohammad Rumi
13th Century Persian scholar, theologian, poet
Words of hope...

30. Recognitions
Thank you to my Doctoral Committee, research team, and mentors!
● Teresa Johnson, PhD., UMaine, Doctoral Advisor
● Nancy Dixon, PhD., Columbia University, committee member
● Mohamad Musavi, PhD., UMaine, committee member
● Linda Silka, PhD. UMaine, committee member
● Erez Yoeli, PhD., MIT, committee member
● Emily Currie ‘22, research team
● Peter van Walsum, PhD., mentor
● David Hart, PhD., mentor
● Keith Evans, PhD., mentor
● Larry Prusak, Columbia University, mentor
● Christine Beitl and the whole ANT 560 Research Methods Class

31. Appendix

32. 0. Sustainability
conversation
1.
Manual
coding
2. AI/ML
Algorithm
development
3. Signature
generation
5. Designs for
Capacity building &
Network
Aquaculture
stakeholder in
conversation
Research
team
performing
linguistic
analysis
Research
team
performing
statistical
analysis
Supplemental
data (e.g.,
BERT libraries)
Programmer,
using NLP
libraries
0a. Lease
scoping or
hearing
0b. Record.
0c. Transcribe
Parse into
utterances
1a. Classify for
4DDs, gender,
sequence,
outcome.
1b. Perform basic
statistics, pivot
tables to find
patterns
1c. Generate
infographics.
2a. Parse new conversation and
supplement. Gen. training data (HITL)
2b. Gen. and test algorithms for 4DDs,
gender, impacts
2c. Acquire more
conversation data as 4DD
examples
3a. Do statistical analysis
(e.g., regress composition
of moves, sequence of
moves, gender, etc. on
outcome.) draft
signatures
4. AI/ML Algorithm
Validation,
productization
4d. Revise algorithm to detect
4DDs, gender, sequence, and,
signature on new data
4e. Create new libraries wishlist
4b. Record.
4c. Transcribe
Parse into
utterances
5a. Draft,
improvements
to networks to
include skillful
conversation
5b. Focus
groups
1c. Interviews
to determine
outcomes
6. Launch
6a.
Develop
training,
toolkits
6c S/H
Training
4a. Lease
scoping or
hearing
Research
Process
ECE 598 Research Process and Roles
Spring-Summer 2021 Fall, 2021 -Spring 2022
Fall 2020-Spring 2021

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