From artificially intelligent systems towards real thinking tools and human scale models that improve both human and machine learning 2018 06 20

Collaboration for Life
From artificially intelligent systems towards  
Real Thinking Tools and Human Scale Models  
that improve both human and machine learning
Jorn Bettin
Auckland AI and Machine Learning Meetup, June 2018

What is learning?
to learn : Gain or acquire knowledge of or skill in something by study, experience,
or being taught. Commit to memory. Become aware of something by
information or from observation.
Learning takes place at different levels of spatial and temporal scale:
• Individual bacteria, species of bacteria
• Individual plants, species of plants,
• Individual animals including humans, species of animals
• Social groups of animals from one species  
(in particular social insects and humans)
• Mixed groups of living creatures  
(from microbiomes to ecosystems and the planet)
• Individual software agents created by humans
• Social groups of software agents created by humans
• Mixed groups of human and software agents
expressed genes
genome
genetic diversity
chemical gradients
neural networks
cultural knowledge
experimental data
models
competency networks

Individual contributions to the creation of learning systems
kin  
group
ecosystem
planet
individual  
agent
resources, knowledge, offspring
maintenance, 
resources
physical forces,  
physical resources
changes in physical, chemical,  
and biological processes
resources and learning opportunities
resources, knowledge, mates
social group
maintenance, 
resources
resources, knowledge
maintenance,  
resources
resources,  
knowledge
changes in  
ecosystem function
interactions
& flows

Human contributions to the creation of learning systems
category 
of agent
techno-social 
ecosystem
planet
human or 
software  
agent
resources, knowledge, evolution
maintenance, 
resources
physical forces,  
physical resources
changes in physical, chemical,  
and biological processes
resources, knowledge, collaborators
organisation
maintenance, 
resources
resources, knowledge
maintenance,  
resources
resources,  
knowledge
changes in  
system function
value
creation
activities
interactions
& flows

Evolution of learning

Imitation, understanding, creativity
Useful working definitions:
• Imitation : Ability to replicate behaviour.  
Probabilistic reasoning, resulting in know how. (billions of years ago)
• Basic understanding : Ability to answer why questions.  
Causal reasoning, resulting in know why. (180,000 years ago)
• Creativity : Ability to ask new questions.  
Counterfactual semantic reasoning,  
pushing the boundaries of understanding. (50,000 years ago)
cultural 
evolution
genetic
evolution

2 million years of gene culture co-evolution

The atoms of the “language” of thought
Human mental models have been around for much longer than human language.
Here is a synopsis of the thinking tools that predate human language:
• Shared attention (awareness that another animal is looking at the same thing)
• Pointing (based on having limbs)
• Mental representations ( = models) of the things we interact with
• Categories ( = mental models of groups of similar things)
• Containment and connectors (spatial relationships between things = graphs)
• Operations (mental models of patterns of movements and changes over time)
• Simulations (to predict events and arrive at decisions)

Achieving shared understanding
Prior to spoken or written language communication via shared attention and pointing was the
was way of establishing shared understanding, and such shared understanding related to very
down-to-earth representations. Pre-language humans communicated within a highly local
context in space and time. The things being referenced were “close at hand”. It was reasonable
for people to assume that others understood what they referred to. The risks for
misunderstandings were limited.
Spoken language entered our world as a serialisation format for communicating simple
references to things within our local context. We started to reference abstract things,
references to references, and experiences that occurred many years ago. The number of
misunderstandings in communication grew exponentially. Since people could not visit the past
of other people, this lead humans down the path of extensive social delusion, where they
started to assume that they understood each other much better than they actually did. The
seeds for storytelling had been sowed. The first human hive minds emerged.
Written language made things worse in terms of the scope of social delusion. People had
opportunities to “read” large volumes of information out of context in space and time. People
started importing many thousands of references to very unfamiliar abstractions into their
mental models on top of their first hand experiences. The human tendency to believe in the
validity of our imagination after hearing or reading a story allowed storytelling and belief systems
to rise to new heights. A few people started scratching their heads about weird human
behaviours and the beliefs that underpinned the observed behaviours.

Exponential change in communication
- 1,800,000 years: Cumulative cultural transmission, teaching, imitation, experimentation
- 200,000 years : Spoken human languages — local communication of tacit knowledge
- 5,400 years : Written human languages — communication across time, explicit knowledge
- 600 years : Printing press — 1-to-many communication across space, scale
- 180 years : Electrical telegraph & telephone — global peer-to-peer communication, on demand
- 15 years : Internet — global 1-to-many communication, zero marginal cost, dirt cheap
- Now : Internet of things – machine-to-machine communication, new technologies every month 
apps
time
2 million years of gene culture co-evolution

The case for science and reason as forces 
for steady and measurable human progress
Steven Pinker
• The Better Angels of Our Nature: Why Violence Has Declined, 2011
• Enlightenment Now, 2018

And yet: Dysfunctional feedback loops
family
capitalised busynesslocal environment
government
local community
planet
individual
demands
busyness &  
consumption
lack of attention
capitalised bank
demands
busyness &  
consumption
does not meet
human needs
impaired ecosystem
functions
climate
change
lack of attention
waste
sells need
for busyness
demands
attention
legitimises
generates the debt 
that fuels busyness
waste
lack of attention
does not meet
human needs
stressed
primary externalities
secondary externalities

Gamification of society
• Paul Babiak and Robert Hare, Snakes in
suits: When psychopaths go to work, 2006
• Susan Long, The perverse organisation
and its deadly sins, 2008
• Susan Long, Socioanalytic methods –
Discovering the hidden in organisations
and social systems, 2013
• The Milgram experiment https://
www.youtube.com/watch?v=8g1MJeHYlE0
• The Stanford prison experiment https://
www.youtube.com/watch?v=sZwfNs1pqG0
• The Asch conformity experiment https://
www.youtube.com/watch?v=TYIh4MkcfJA
psychopathic traits are common in the upper echelons of the corporate world, with a prevalence of between 3% and 21%
Nathan Brooks, http://www.psychology.org.au/news/media_releases/13September2016/Brooks, 2016
neurotypical
copying
social games
innovation
psychopathic
lack of empathy
weak
neurological traits
derived/aggregate behaviours
selfish intent
collaborative intent

Bullshit jobs make up a big part of the Big Data “economy”
Working Definition: a bullshit job is a form of paid
employment that is so completely pointless,
unnecessary, or pernicious that even the employee
cannot justify its existence even though, as part of
the conditions of employment, the employee feels
obliged to pretend that this is not the case.
Those who work bullshit jobs are often surrounded by
honor and prestige; they are respected as
professionals, well paid, and treated as high achievers
—as the sort of people who can be justly proud of what
they do. Yet secretly they are aware that they have
achieved nothing; they feel they have done nothing to
earn the consumer toys with which they fill their lives;
they feel it’s all based on a lie—as, indeed, it is.
David Graeber, Bullshit Jobs: A Theory, 2018
80%
BS
40%
BS

Popularity = The economy of “likes” (Douglas Rushkoff)
https://youtu.be/6_n1Dro0Uec
narcissistic
behaviour
cultural
rituals
neurotypical
copying
social games
hierarchies
psychopathic
lack of empathy
neurological traits
selfish intent
collaborative intent
You gonna travel
anywhere this summer?

The future of management
A good assessment of the speed of progress over the last 50 years by a colleague in the UK:  
Old Is the New New https://youtu.be/AbgsfeGvg3E

Technology
All human artefacts are technology. But beware of anybody who uses this term.  
Like “maturity” and “reality” and “progress”, the word “technology”  
has an agenda for your behaviour: usually what is being referred to as  
“technology” is something that somebody wants you to submit to.
“Technology” often implicitly refers to something you are expected to turn over to
“the guys who understand it.” This is actually almost always a political move.
Somebody wants you to give certain things to them to design and decide.  
Perhaps you should, but perhaps not. 
 
– Ted Nelson, 
Pioneer of information technology,  
philosopher, and sociologist.  
He coined the terms hypertext  
and hypermedia in 1963.

We have perverted the definition of intelligent behaviour 
ability to deceive others = “intelligent behaviour”
George Soros developed the theory of reflexivity based on the ideas of Karl Popper. Reflexivity
posited that market values are often driven by the fallible ideas of participants, not only by
the economic fundamentals of the situation. Reflexive feedback loops are created where ideas
influence events and events influence ideas. Soros further argued that this leads to markets
having procyclical "virtuous or vicious" cycles of boom and bust, in contrast to the
equilibrium predictions of more standard neoclassical economics." 
 
http://www.tandfonline.com/doi/abs/10.1080/1350178X.2013.859415
ideas
events psychopathic
lack of empathy
(social games)

The resulting spurious cultural complexity is also known as guard labour
Guard labor is wage labor and other activities that are said to maintain (hence "guard") a capitalist system. Things that
are generally characterised as guard labor include: management, guards, military personnel, and prisoners.  
 
Guard labor is noteworthy because it captures expenditures based on mistrust and does not produce future value.
Guard labour is an increasingly common form of busyness, the term was coined by  
Arjun Jayadev and Samuel Bowles https://en.wikipedia.org/wiki/Guard_labor
fear busyness

Planet Xerox
https://drive.google.com/ﬁle/d/0B6ocDmkEP1-8MFE1TzRSN09UWHM/view

Intelligent behaviour : finding and operating a niche in the living world
dead alive
sick at “work” chores
the arts and other autistic pursuits
collaborative play and learning
sports
How about a better definition of intelligence?

Human scale learning

Collaboration for Life © 2016 S23M
Creating a learning organisation / system
• The SECI model is a useful conceptual tool for
organising and structuring new service / product
development, and for extending the concept of
continuous improvement into the realm of digital
business and knowledge-intensive processes
• Takeuchi, Nonaka, The New Product Development
Game, https://hbr.org/1986/01/the-new-new-product-
development-game , 1986
• Nonaka, Toyama, Hirata, Managing Flow: A Process
Theory of the Knowledge-Based Firm, Palgrave
Macmillan, 2008
Japan
1980

Human scale cognition and sense making
We argue that conceptual blending is responsible for the origins of
language, art, religion, science, and other singular human feats, and that it
is indispensable for basic everyday thought as it is for artistic and scientific
abilities…
People pretend, imitate, lie, fantasise, deceive, delude, consider
alternatives, simulate, make models, and pose hypotheses. Our
species has an extraordinary ability to operate mentally on the unreal
…
Because linguistic expressions prompt for meanings rather than represent
meanings, linguistic systems do not have to be, and in fact cannot be,
analogues of conceptual systems. Prompting for meaning construction is a
job they can do; representing meanings is not…
Vital relations, which include Cause-Effect, Change, Time, Identity,
Intentionality, Representation, and Part-Whole, not only apply across
mental spaces, but also define essential topology within mental spaces.
One of the overarching goals of compression through conceptual
blending is to achieve “human scale” in the blended space…
2002
A brilliant book on the cognitive foundations of human scale conceptual semantic modelling

Conceptual blending is at the core of creativity
Diverse knowledge is necessary to solve various problems in the world and
to create value in the future, and overcome challenges that go beyond the
framework of research in industry, government and academia.
 
[from the mission of the Honda Research Institute]

Creativity = Having a “less well functioning mental bureaucrat” (*)
Neurodivergent people:
• Adhere to idiosyncratic moral value
systems rather than social norms
• Are okay with exploring ideas that upset
the “social order”
• Spend much more time experimenting
and implementing ideas that others
would consider crazy or a waste of time
• Have untypical life goals: new forms of
understanding, making a positive impact,
translating ideas into artistic expression
Autists in particular tend to:
• Easily suffer from sensory and social
overload
• Have unusually developed pattern
recognition abilities
• Have an unusual ability to persevere
(*) Jeffrey Baumgartner
autistic
perseverance
autistic
hypersensitivity
autistic pattern
recognition
neurodivergent
creativity
autistic
authenticity
individual
autistic rituals
invention
innovation
neurological traits

The Two Traits of the Best Problem-Solving Teams:
Cognitive Diversity and Psychological Safety
HBR
autistic 
collaboration
absence
of
neurodiversity

Machine learning

Imitation, basic understanding, creativity
Most animals, as well as present-day learning machines,
are on the first rung, learn to imitate via associative /
probabilistic reasoning.
Tool users, such as early humans, are on the second rung if
they act by scenario planning / causal reasoning and not
merely by imitation. They use experiments to gain a basic
understanding of the effects of interventions.
Counterfactual learners, on the top rung, can imagine and
understand worlds that do not exist and infer reasons for
observed phenomena via blending of semantic / vital
relationships.
Pearl, Judea; Mackenzie, Dana. The Book of Why: The New
Science of Cause and Effect

The state of machine learning
The state of the art in artificial intelligence today is merely a souped-up
version of what machines could already do a generation ago: find hidden
regularities in a large set of data.
Deep learning works for certain tasks. But it is the antithesis of
transparency. …
Yes, we forgive our meager understanding of how human brains work, but
we can still communicate with other humans, learn from them, instruct
them, and motivate them in our own native language of cause and effect.
We can do that because our brains work the same way.
If our robots will all be as opaque as AlphaGo, we will not be able to
hold a meaningful conversation with them, and that would be quite
unfortunate. …
Transparency enables effective communication.
Pearl, Judea; Mackenzie, Dana. The Book of Why: The New Science of
Cause and Effect

Neglecting human scale
We have used linear syntax programming languages to cobble together many thousands of software
systems by trial and error that no one understands anymore.
… and now we are in the process of cobbling together machine learning systems by trial and error
that produce correlation maps that are devoid of explanatory power.
On the implications:
1. Are you a model builder or a storyteller? 
https://jornbettin.com/2017/08/22/are-you-a-model-builder-or-a-story-teller/
2. Designing filtering, collaboration, thinking, and learning tools for the next 200 years 
https://ciic.s23m.com/2017/04/25/designing-filtering-collaboration-thinking-and-learning-tools-
for-the-next-200-years/
3. The antidote to misuse of mathematics and junk data 
https://jornbettin.com/2015/04/
4. Ending the curse of software maintenance 
https://the-software-artefact.blogspot.com/2011/05/ending-curse-of-software-maintenance.html
5. Fatal software errors 
https://jornbettin.com/2011/04/23/fatal-software-errors/

Human scale machine learning
We have to equip machines with a model of the environment. If a machine does not
have a model of reality, you cannot expect the machine to behave intelligently in that reality.  
 
The first step, one that will take place in maybe 10 years, is that conceptual models
of reality will be programmed by humans. The next step will be that machines will
postulate such models on their own and will verify and refine them based on
empirical evidence.
That is what happened to science; we started with a geocentric model, with circles and
epicycles, and ended up with a heliocentric model with its ellipses.
https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-
effect-20180515/
https://www.quantamagazine.org/the-math-of-causation-puzzle-20180530/

The objective of human scale computing
… is to improve filtering, collaboration,
thinking, and learning:
1. between humans,
2. between humans and software systems,
3. and between software systems.
This will require equipping artificial human-
scale agents with our individual motivations /
value systems.
We will need to think about hard and soft wired
cognitive lenses and about hard wired and soft
wired values and norms.

Human scale representations of knowledge

Modern mathematics is human scale
At all times throughout human history a few people would have realised that human
language has severe limitations in terms of ambiguity and precision.
Given the limitations of human languages, it is perhaps not entirely surprising that
modern foundations of mathematics take us back to core concepts that pre-date
human language – to the atoms of the language of thought:
• Model theory expresses the biological foundations of human mental models in
a formal symbol system.
• Denotational semantics is based on the simple observation that we can
abstract human understandable symbol systems into corresponding machine
readable symbol systems.
• Category theory is a thinking tool for articulating large scale patterns and
establishing semantic equivalences between different domains, it does not
involve any concrete symbol systems. We perform such semantic calculations
in our minds all the time, mostly subconsciously.

Abstract math is useful? What is category theory?
Applied category theory: https://forum.azimuthproject.org/discussion/2105/lecture-28-chapter-2-ignoring-externalities/p1
See also AI is harder than you think: https://www.nytimes.com/2018/05/18/opinion/artificial-intelligence-challenges.html

Tapping into the visual processing capacity  
of the human brain
The brain’s capacity for processing visual data is
around 20 times higher than the brain’s capacity for
processing audio data.
Even with simple technologies such as whiteboards and
markers it is possible to design and use highly expressive
and unambiguous visual languages that are much easier
for humans to parse and understand than information in a
linear format (audio or text).
MODA+MODE therefore makes extensive use of visual
languages and provides guidance for developing
further domain specific visual languages.

play, learn, observe, question, imagine
a language for
describing
value creation
a language for
describing 
motivations
a language for
describing
interactions
The human lens
to make sense of the world and the natural environment from a human perspective, 
to evolve our value systems, and to structure and optimise human activities
Thinking Tools for Interdisciplinary Research, Design, and Engineering 
https://coininco.files.wordpress.com/2017/08/moda-and-mode-lenses-and-principles.pdf
system
lens
semantic
lens
logistic
lens

design / engineering
transportation /
communication
quality / maintenance
energy / food 
production
culture
value creation
human artefactshuman symbols
critical self-reﬂection
nature
human societies
motivations
resourcesevents
agents
interactions
The human lens defines 
categories that are invariant across cultures, space, and time
learn
play
observe
question
imagine
system
lens
semantic
lens
logistic
lens

Human scale computing can be understood as the
elaboration of the role of cognitive characteristics of
humans within ergonomics.
Systems, models and technologies are only
understandable as long as they do not generate
cognitive loads that exceed human cognitive
limits.
This observation can only be put to good use if human
cognitive limits become a primary concern in the
design of human institutions and technologies, in much
the same way that human scale physical dimensions
and characteristics have shaped the discipline of
ergonomics.
Developing visual languages and interaction styles  
that are better than English or any other linear language

Designing tools for the next 200 years
It is time for creating a visual language and interaction style that is better than English or any
other linear language:
1. for validating and representing knowledge
• in a way that is intuitive and easily understandable for humans
• in a way that is easy for processing by software tools
2. for validating and representing knowledge flows
• between individual agents/teams/organisations/communities
• in a way that facilities collaborative validation of knowledge and beliefs
• as a substrate for interdisciplinary innovation and the creation of context specific variants
3. for filtering, validating, and representing economic flows
• supports domain specific accounting of all kinds of knowledge flows
• using Culture, Engineering, Maintenance, Energy, and Transportation as the basic sectors
for modelling economic value cycles
• with explicit tools that assist with the detection of deception
1. https://ciic.s23m.com/2017/04/25/designing-filtering-collaboration-thinking-and-learning-tools-for-the-next-200-years/
2. https://ciic.s23m.com/2017/08/30/addiction-and-story-withdrawal/
3. http://s23m.com/about/index.html – Contact jorn.bettin@s23m.com for related PhD research opportunities

Language is a very powerful thinking tool
Language frames people's thoughts and emotional response.  
Time to start consistently talking about concepts that can improve our lives:
1. Niche construction and symbiosis rather than competition  
– to create organisations and products that are fit for purpose and valued by
the wider community
2. Company rather than business – to focus on the people and things we care
about rather than what is simply keeping us busy
3. Values rather than value – to avoid continuously discounting what is priceless
4. Physical waste rather than wealth – to focus us on the metrics that do matter
5. Human scale and progress rather than large scale and growth – to create
structures and systems that are understandable and relatable
6. Competency networks rather than leadership – to get things done and
distribute decision making to where the knowledge resides
7. Coordination rather than management  
– to address all the stuff that can increasingly be automated, management is
often the biggest obstacle to automation
8. Creativity and divergent thinking rather than best practices  
– when facing the need to innovate and improve

Human scale thinking tools

Model Oriented Domain Analysis and Engineering
formalising
agent and
perspective-
based
modular
models
connecting
formal models
between agents
updating of
individual mental
models and
related examples
interactive
sharing
of knowledge
MODA + MODE uses formal
conceptual models to
represent explicit knowledge
in support of the SECI model
(socialisation, externalisation,
combination, internalisation)

Viewpoints and perspectives

Validation via instantiation (or “concretisation”)
Golf
ABC 123
instantiate
abstract
Observation: We need less speculation and
much more validation via instantiation !
instantiate

6 Questions
Investigating decision making processes that occur when applying knowledge:
• When and how often does a decision require revision? – Events and frequency
• Who arrives at the decision? – Agents
• Why is the decision made? – Purpose (which agents benefit?)
• Where (or in which information artefact) is the decision made? – Location
• What are the possible choices? – Limits of understanding
• How is the decision made? – Heuristics
to surface tacit knowledge about systems

MODA + MODE backbone principles for  
creating learning organisations and understandable systems
26 principles that provide  
a meta paradigm to avoid  
getting entrapped in a paradigm
MODA + MODE thinking tools for interdisciplinary research, design, and engineering:  
https://coininco.files.wordpress.com/2017/08/moda-and-mode-lenses-and-principles.pdf

Human scale learning organisations

The fundamental axiom of values for the knowledge age
A metric either
measures a physical quantity,  
and can serve as a measure of constraints that a group of people cares about 
XOR
is an abstract non-scalable approximation  
of values that a small group of people cares about

Improvements and innovation
All effective approaches for continuous improvement (such as
Kaizen, Toyota Production System, Waigaya, …) and innovation
(Open Space, collaborative design, …) share one common principle.
In order to successfully identify and implement opportunities for improvement and innovation  
the belief in the existence and relevance of social hierarchies must be suspended
Why is this the case? What does this tell us about society?

The effect of hierarchical structures on innovation
1. Any form of hierarchy indicates a dampened
feedback loop.
2. Power is the privilege of not needing to learn.
3. A hierarchical organisation is the anti-
thesis of a learning organisation.

An advice process 
replaces power with learning

Competency networks
Definition: A competency network is the graph of experience-based pair-
wise trustworthiness ratings in relation to various domains between the
members of a group.
Trustworthiness ratings are tied to specific pairs of individuals; they are not
directly transferable and they can not easily be aggregated. This limitation
probably was one of the key reasons for the small size of pre-historic hunter-
gatherer societies.
The notion of competency networks is inspired by the correlation between
software system structures and the communication patterns between human
software developers observed by Mel Conway in 1967:
Any organization that designs a system (defined broadly) will produce a
design whose structure is a copy of the organization's communication
structure. http://melconway.com/Home/Conways_Law.html

Prosocial core design principles @ S23M
 
 
Tailored Core Design Principles:
1. Trusted relationships within the group and strong understanding of purpose
2. Fair distribution of costs and benefits
3. Fair and inclusive decision-making
4. Fast and empathetic conflict resolution
5. Authority to self-govern
6. Appropriate relations with other groups
7. Tracking agreed upon behaviours
8. Graduated responses to transgressions  
to prevent a person or a subgroup from  
gaining power over others
A working advice process
minimises the need for tracking
Fair and inclusive distribution
of resources minimises the
need for coercion
Supports an open and inclusive
neurodiverse & creative team
Applying evolutionary science to coordinate action, avoid disruptive
behaviours among group members, and cultivate appropriate
relationships with other groups in a multi-group ecosystem  
(the work of Elinor Ostrom, Michael Cox and David Sloan Wilson)

Thinking in systems, designs, patterns
the team the tools
We distil knowledge and enable knowledge to flow to all
the places where it can be put to good use for the world

The MODA + MODE approach has a fractal characteristic that enables
it to operate at all levels of scale, with explicit support for feedback
loops between different levels of scale:
• Development of collaboration platforms that improve the resilience
and performance of economic ecosystems.
• Development of technology platforms that harness deep domain
expertise to streamline the development of new products.
• Improvements in quality, reliability, and productivity of specific
teams or technological systems.
• Integrating the knowledge of multiple domain experts in a cross-
disciplinary context to co-create innovative solution designs.
• Translating tacit knowledge into explicit knowledge that does not
decay over time.
Typical use cases in industry, academia, and government

Organisational learning = Cultural evolution
Evolution is triggered by:
1. Formation of a new group
2. Merging of two or more groups of
3. Establishing patterns of regular interactions between two or more groups
4. Splitting a group into two or more groups
5. A new person joining a group
6. A person leaving a group
Future software systems and machine learning systems can evolve in a similar way
– iff they are modular at human scale
See also https://ciic.s23m.com/2018/06/13/creating-human-scale-learning-organisations/

Coordination across levels of scale
Definitions of living systems and cultural constructions from large to small:
1. Planet – Biodiversity and level of resilience of the planetary ecosystem
2. Government and companies – Coordination of human activities and
state of the environment at a macro / regional level
3. Local community – Coordination of human activities and resource use at
a local level
4. Local environment – State of the environment at a local level
5. Family – Coordination of human activities and resource use between kin
6. Individual – Individual activities and resource use

Individual contributions to the creation of learning systems
family
local environment
planet
individualmental support
maintain in 
a healthy state
diversity => resilience
monitor and 
start to understand
economic coordination
local community
maintain in 
a healthy state
attention
attention
attention
we know what to
do and measure
value
creation
activities

Thank you!
Jorn Bettin 
jorn.bettin@s23m.com
Nothing beats capturing the knowledge flow
of leading domain experts to co-create
organisations & systems that are
understandable by future generations of
humans & software tools.

Appendix

The MODA + MODE backbone – principles 1 to 8
# MODA + MODE principle
motivations motivations motivations motivations motivations
critical self-
reﬂection
human
societies
human symbols
human
artefacts
nature
1
Understand that minorities and outsiders are well positioned
for uncovering attempts of deception
addressing
corruption
honesty
2
Give minorities and outsiders access to private means of
communication
development of
new theories
equality
3 Operate transparent governance access to evidence trust
4
Adapt the cognitive load generated by technology to human
cognitive limits
understandability
ease of
communication,
happiness
simplicity usability
5
Recognise neurological differences as authentic and valuable
sources of innovative potential
discovery of
externalities
resilience,
happiness
resilience
6
Value metrics from the physical and biological world more than
human opinions
minimise human
bias
minimise cultural
bias
7 Value local perspectives more than widely-held popular beliefs learning
collaboration
between groups,
happiness
8
Value the strength of shared beliefs and corresponding
evidence more than the number of shared beliefs
trust, happiness trust, happiness

critical self-
reflection
human societies human symbols human artefacts nature
9 Use information quality logic to minimise ambiguity
shared understanding,
precision
quantification of
knowledge
10 Use probabilistic reasoning to acknowledge uncertainty
honesty, risk
assessment
quantification of risk quality management
11 Conduct commonality and variability analysis
collaboration,
simplicity, agility
simplicity usability
12 Formalise the results of commonality and variability analysis
resilience
sharing, automation automation
13
Develop visual domain specific languages to describe familiar
domains in unambiguous terms
precision
simplicity,
understandability
quality of design,
manufacturing, recycling
14
Understand that all information is dependent on perspective and
viewpoint
diversity usability, fitness for purpose
15 Understand that a multitude of perspectives generates new insights learning, resilience innovation
16
Validate shared understanding by sharing of models and
corresponding instances
evidence
quality of design,
17 Understand that power gradients stand in the way of transformation
courage,
transformation
reduction of externalities
18 Aim for optimal conflict in a supportive and trusting team environment agility, learning
quality of design,
19
Use agile experiments when venturing into unfamiliar domains to
learn from mistakes
experiments, learning
quality of design,
20
Conduct an adequate number of experiments in different contexts to
minimise risk before global application of major changes
caution minimisation of externalities

critical self-
reﬂection
human
societies
human
symbols
human artefacts nature
21
Understand that collaboration occurs to the extent
that there is shared understanding
shared
expectations
design of value cycles evolution of ecosystems
22
Recognise paradoxes and disagreements as the
essence of continuous improvement
evolution
continuous improvement of
design, manufacturing,
recycling
evolution
23
Practice everyday improvement, everybody
improvement, everywhere improvement
continuous
parallel
evolution
continuous improvement of
design, manufacturing,
recycling
continuous parallel
evolution
24 Engage in niche construction
diversity,
resilience,
happiness
resilience in design,
biodiversity, resilience
25 Use feedback loops to create learning systems
learning
systems
speed of innovation
codes, cell chemistry,
recursion, neural networks
26
Use modular decentralised design to promote
reuse without compromising resilience
simplicity,
resilience
resilience in design,
cells, organs, organisms,
species, ecosystems
A culture may have further bones, but one or more 
missing vertebrae severely compromise capability

play, learn, observe, question, imagine
Scientists, Engineers, Entrepreneurs, Artists & Mathematicians
CIIC brings together academic researchers and practitioners every 3 months  
to tackle wicked problems that don’t have an obvious solution.
Challenges that Go Beyond the
Established Framework of Research in
Industry, Government and Academia
Conference on Interdisciplinary
Innovation and Collaboration
https://ciic.s23m.com/

CIIC workshop results (https://ciic.s23m.com/expected-results/):
• March 2018 – Topics at the intersection of agriculture and healthcare
• December 2017 – Design of interaction patterns for knowledge validation and trust building
• September 2017 – Interaction and collaboration of humans and intelligent machines
• June 2017 – Human scale computing
• March 2017 – Neurodiversity
• December 2016 – Making information interactive
• September 2016 – The potential and limits of clinical decision support systems
• March 2016 – Design and development of tools for effective self-care
• December 2015 – Is there a place for barter?
• September 2015 – How do we need to redefine economic progress? What is value?
• June 2015 – Growing New Zealand’s contribution to a sustainable world

• Allows knowledge to flourish in the open creative spaces between disciplines and organisational silos
• Complements the typical yearly cycle of domain-specific conferences
• Has a quarterly cycle and feedback loop between teams that supports  
on-going and longer-term collaborations more effectively than yearly events
• Captures knowledge flows and transdisciplinary insights in reusable semantic models and patterns
• Currently runs in Auckland at AUT and in Melbourne at RMIT,  
and the CIIC community is available to assist with replicating the concept in other locations
• Can help coordinate collaboration between locations via the CIIC Web site and related online tools
• Invites further co-sponsors from industry, academia, and government
• Is an antidote to bureaucratic limitations; large organisation are now inquiring about establishing
regular in-house CIIC style events
• Provides a safe environment for neurodivergent / autistic people to connect and collaborate
CIIC invites communities and economic ecosystems  
to sharpen their collaborative edge by embracing open innovation.
https://ciic.s23m.com/about/

AUT – Auckland University of Technology
MODA + MODE is being integrated into the  
curriculum on entrepreneurial strategies, creative technologies,  
and methodologies for trans-disciplinary research and collaboration

From artificially intelligent systems towards real thinking tools and human scale models that improve both human and machine learning 2018 06 20

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