Designing filtering, collaboration, thinking, and learning tools for the next 200 years

information overload

Over the last century human communication technology has improved exponentially in terms of reach, cost, and bandwidth. Today communication

  • has zero marginal cost,
  • is available on demand,
  • allows nearly anyone to reach out to any possible audience,
  • and is delivered via synchronous and asynchronous channels.

Whilst these improvements in communication have transformed our economic and social systems, they have also put into sharp focus the limits of the ability of human cultures adapt to new contexts, the limits of human language, and related limits of our ways of collaboration, thinking, and learning.

Our ability to shunt data around the planet has greatly improved, but our cognitive limits prevent us from better understanding the full implications of all our actions. The aggregate human technological capability to process information and make decisions is hitting the following constraints:

1. Inputs – The age of big junk data

The data flows that are available as inputs are of highly variable quality. Each transformation of measurements from the natural environment by humans and human created technologies into derived information introduces an element of interpretation and corresponding assumptions, many of which are implicit and not available for critical analysis.

Most of the information that humans are confronted with has already passed through several stages of transformation and is unlikely to be free of cultural bias. The more human societies rely on automation, the more derivative information “products” are in circulation as potential inputs for further decision making. Such culturally biased information artefacts are socio-technological constructions that increasingly insulate human perceptions and thought processes from events in the natural world and the planetary ecosystem.

To peel back some of the cultural bias and in order not to become overwhelmed by the flood of available data streams we need powerful and individually configurable information filtering tools.

2. Systems – Lack of systemic and intentional trust

Increasingly humans rely on ultra-large-scale software systems and data repositories that are operated by large corporations with a profit motive or by large government departments. The centralisation of information management services leads to systemic risks related to data security, data corruption, and misuse of data for purposes beyond agreed intent.

Data breaches, abuses of power by data custodians, and misuse of data have become regular occurrences, leading to a reinforcing feedback loop between low levels of trust in software systems and data repositories and low levels of trust in the organisations that operate such systems (corporations and big government).

To increase the level of systemic and intentional trust requires a new approach to the operation of software systems and data repositories that is capable of addressing the systemic risks. Replacing centralised data management with a distributed system and data architecture is an obvious step to improve overall resilience. A distributed approach to data management allows operational responsibilities to be put into the hands of more trustworthy organisations and people, such as local governments, local employee owned businesses, and individual citizens.

The need to shift towards trustworthy distributed data management highlights a need for a new breed of open source collaboration tools that are not entangled with the interests of corporations or the interests of big government.

Current so-called collaboration tools and social platforms are depressingly trivial. The market provides hundreds of simplistic services for sharing and working on informal information artefacts yet very little in terms of powerful tools for:

  1. Higher-level knowledge work
  2. Developing and validating shared understanding via structured conceptual models

At a recent panel discussion of well-known AI researchers and entrepreneurs David Chalmers suggested the objective of constructing human-like AGIs with a view of addressing the questions of human understandability, consciousness, and human / AGI collaboration.

3. Scale – Billions of viewpoints

Zero marginal cost communication has not only lead to organisations and individuals producing and sharing much more information, it has also multiplied the number of people and institutions that are accessible to an individual by many orders of magnitudes. Human cognitive abilities have evolved for interactions with up to at most a few hundred people (and more or less human like software agents).

Increasingly economic thinking based on scarcity makes no sense and is counter-productive. Manual labour and human professional expertise and domain knowledge are increasingly replaced by advanced automation. At the same time the automated systems we rely on are becoming less and less understandable by humans and by other software systems – resulting in new sets of questions and systemic risks.

In order to make sense of the world, and to incrementally identify the 100 to 200 people and systems that are best equipped to collaborate with us, we need new thinking tools.

We cannot simply apply the tools and models of past eras to the present situation. The changes sweeping the Earth right now are literally planetary in scale and so filled with complexity that few among us even have a semblance of knowing what is actually going on. This makes it very difficult to navigate the troubled waters of the 21st Century.
Joe Brewer

The ability to construct elaborate social delusions is a uniquely human capability. The illusion of human superiority is the product of an anthropocentric viewpoint. Humans are roughly as much of control of the planet as a disease causing pathogen is in control of its host. The only difference is that there is only one host planet and there is  no further host to exploit that could allow the pathogen to replicate its “clever” strategy.

One of the most powerful forces in history is human stupidity. But another powerful force is human wisdom. We have both. … There’s a close correlation between nationalism and climate change denialism. Nationalists are focused on their most immediate loyalties and commitments, to their people, to their country. Why can’t you be loyal to humankind as a whole?
– Yuval Harari

4. Outputs – Rapidly growing bodies of knowledge

Alongside all the big junk data sensor networks and scientists are continuously adding to growing bodies of valuable knowledge. Scientific data, related software tools, and related knowledge often still resides in arcane domain-specific or organisation-specific silos.

Whilst the internet has revolutionised international and inter-disciplinary collaboration, there is still significant room for further work to simplify the process of searching for specific knowledge and the process of sharing research results in a form that is at the same time easily understandable by humans and available for processing by software tools.

There is a need for new learning tools that enable humans and software agents to ingest and validate new knowledge.

Biologist Ernst Mayr has argued that, judging by the empirical record regarding species success, it is clearly better to be stupid than to be smart. Species with no brains or very small brains tend to survive for much longer periods and are more resilient than “smarter” species with larger brains. Noam Chomsky recently reminded his audience of Ernst Mayr’s insights as part of a lecture on the human reaction to climate change. The brain-oriented definition of intelligence used by humans seems to be unsuitable for assessing species survival – this should be food for thought for anyone who has high hopes for the current approaches to developing AGI systems. Perhaps much higher levels of intelligence in terms of survival value can be found in the genome and in the operating models of biological cells.

A few researchers have started to work on reverse-engineering human learning and cognitive development and, in parallel, engineering more human-like machine learning systems:

Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?

At a deeper level, how can a learner know that a given domain of entities and concepts should be represented by using a tree at all, as opposed to a low-dimensional space or some other form? Or, in causal learning, how do people come to correct framework theories such as knowledge of abstract disease and symptom classes of variables with causal links from diseases to symptoms?

The acquisition of abstract knowledge or new inductive constraints is primarily the province of cognitive development. For instance, children learning words initially assume a flat, mutually exclusive division of objects into nameable clusters; only later do they discover that categories should be organized into tree-structured hierarchies. Such discoveries are also pivotal in scientific progress: Mendeleev launched modern chemistry with his proposal of a periodic structure for the elements. Linnaeus famously proposed that relationships between biological species are best explained by a tree structure, rather than a simpler linear order or some other form.

Such structural insights have long been viewed by psychologists and philosophers of science as deeply mysterious in their mechanisms, more magical than computational. Conventional algorithms for unsupervised structure discovery in statistics and machine learning— hierarchical clustering, principal components analysis, multidimensional scaling, clique detection— assume a single fixed form of structure. Unlike human children or scientists, they cannot learn multiple forms of structure or discover new forms in novel data.

The biggest remaining obstacle is to understand how structured symbolic knowledge can be represented in neural circuits. Connectionist models sidestep these challenges by denying that brains actually encode such rich knowledge, but this runs counter to the strong consensus in cognitive science and artificial intelligence that symbols and structures are essential for thought. Uncovering their neural basis is arguably the greatest computational challenge in cognitive neuroscience more generally—our modern mind-body problem.

Biologists are also starting to look into the role of genetics in brain wiring, as animals and humans have innate behaviours whose features are consistent across generations, suggesting that some synaptic connections are genetically predetermined. This avenue of research underscores that neurodiversity is part of the evolutionary diversity of human brains and minds.

So what? – How can new tools make a difference?

The focus of the next CIIC workshop on 3 June is on human scale computing and the design of assistive technologies for humans and machines to improve filtering of information streams, collaboration, critical thinking, and learning.

This theme allows us to build on the results from the CIIC workshop in March, which focused on support for neurodiversity, individual behavioural patterns within typical work environments, and collaboration across organisational boundaries.

One set of possible concrete objectives for assistive technology is the following:

  1. Create and maintain exactly one [computing] system (repository of activated knowledge), not zero, and not two or more
  2. Teach the system what you value, what you believe to be facts, and the models that you use to assess new inputs – nothing more, and nothing less
  3. Collaborate with your system to interact with the world as often or as infrequently as it suits your cognitive lens and communication style
  4. Manage cognitive load in a timely manner, ideally before you become overwhelmed 
  5. Adopt a human scale definition of organisation

A technology that provides such functionality not only acts as a mirror of your conscious knowledge and understanding, but your interaction patterns with the system also reflect your specific learning style and your cognitive preferences.

Human scale computing represents an opportunity not only to improve communication between humans and software systems, but also to improve communication and trusted collaboration between humans with different kinds of minds and cultural backgrounds.

Urgent need for acceptance of neurodiversity

This month was supposed to be autism acceptance month. A few days ago I learned about an aspie suicide that underscores the urgency of the need for cultural change.

Will H. Moore is dead because of society’s intolerance of neurodiversity and unwillingness to acknowledge all the culturally constructed social delusions that surround us. When reading some of the other posts on his blog it becomes clear that he was a very astute observer of human society, a very compassionate human being, and a political scientist and educator with a genuine desire to teach others how to think critically and think for themselves.

I am afraid that many more will have to die before society changes and is ready to replace the pathology paradigm with full recognition of the value of neurodiversity – not one month per year but every day. In the meantime the least we can do is to offer mutual support to each other and not submit to and perpetuate the pathology paradigm.

Dates and times


CIIC-off Melbourne

The “CIIC-off” event in Melbourne last night at the Knowledge Management Leadership Forum at RMIT was very enjoyable.


The themes of neurodiversity and creativity (slides – including an appendix with pointers to neurodivergent perspectives and literature) led participants to ask a whole number of interesting questions that are worthwhile exploring in-depth at the inaugural CIIC Melbourne workshop at RMIT on 18 March 2017, which will be facilitated by Helen Palmer.



All CIIC participants are encouraged to submit interdisciplinary problem statements for further discussion with the CIIC community on the registration page for the full-day unconference workshop on 18 March 2017.


In addition to the questions generated at the CIIC-off in Melbourne, many of the challenges that have been submitted and discussed in depth at CIIC in Auckland over the last two years also warrant discussion in the context in Melbourne and Australia.

The scope of CIIC is intentionally focused on topics that defy categorisation and simplistic approaches to solution development. Participants are invited to bring along controversial perspectives to focus on challenges that go beyond the established framework of research in industry, government and academia.

Many thanks again to Arthur Shelley from KMLF for organising a fantastic CIIC-off!

If you have further questions and suggestions for the CIIC unconference series in Melbourne, please contact Helen Palmer from Questo.

Human scale computing


At one of the upcoming CIIC workshops I would like encourage further exploration of the notion of human scale computing. We briefly touched on this topic in the last workshop as part of the discussions around semantic search.

One of the 26 MODA + MODE principles is:

Adapt the cognitive load generated by technology to human cognitive limits

This principle 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. Human scale computing can be understood as the elaboration of the role of cognitive characteristics of humans within ergonomics.

In an increasingly software and data intensive human world the objective of human scale computing is to improve communication and collaboration:

  1. between humans,
  2. between humans and software systems,
  3. and between software systems.

This objective is another way of stating the goal of developing a language that is better than all human languages reliant on linear syntax.


Current economic and social problems go deeper than challenges related to flows of money and physical resources. For at least two decades now people have been happy to engineer and construct opaque software intensive systems, neglecting the role of understandability for humans, and by implication, creating webs of dependencies that no one understands and that can’t easily be analysed in terms of potential risks.

The risks associated with opaque systems are not limited to classical software systems and also extend to artifically “intelligent” (AI) systems that make use of machine learning techniques.  AI systems, the way they are currently designed, further grow the web of dependencies, complete with plenty of naive/simplistic assumptions about human nature(s) and economics baked in.

In terms of potential risks the web of dependencies in the world of software intensive networked systems (which includes program code and data – the distinction between program code and data is meaningless, yet software engineers often completely forget about the dependency web between data elements) is comparable to nuclear power. The data/software incidents to date are roughly comparable to a level of up to the Three Mile Island accident (large scale data security breaches have become a regular occurrence; increasingly cars, buses, and airplanes require regular rebooting to avoid potentially fatal accidents; significant disruptions of complex multi-level supply chains can easily impact millions of people; etc.).

It will take at least a couple of Chernobyl scale and Fukushima scale impacts for human societies to consider a change in approach. By that point isolation of negative externalities and disentanglement from the web of software and data dependencies will have become the hardest problem that humans have ever faced – and that problem will not be isolated in abstract cyberspace but will create and compound problems and impacts in the physical world on a daily basis.

With these concerns in mind I propose the following draft definition of human scale computing:


Human scale computing refers to a new communication system that enables you as a human individual to:

  1. Create and maintain exactly one [computing] system (repository of activated knowledge), not zero, and not two or more
    1. Instances of the system should be able to replicate themselves
    2. All instances of the system must synchronise their models at least once a day
  2. Teach the system what you value, what you believe to be facts, and the models that you use to assess new inputs – nothing more, and nothing less
    1. All the concepts in your language
    2. All your models of the world
    3. Your value system and motivations
    4. Your heuristics for inductive, deductive, and abductive reasoning
    5. All the data instances that you rely on to justify your models (this translates directly into a configuration of a machine learning tool that is understandable by you and by your computing system)
    6. To write and draw fluently in your language
    7. To trust those agents who you trust, no one else, and no one less
    8. What you believe to be translations between your language and the languages of the agents you trust
    9. Which models and data instances you have shared with which agent
    10. Your heuristics for trust building and destruction
  3. Collaborate with your system to interact with the world as often or as infrequently as it suits your cognitive lens and communication style
    1. Synchronise models with your system
    2. Spend time to validate semantic equivalences (shared understanding) with other agents
    3. Refine your models based on the learnings resulting from collaboration with other agents
    4. Update the scope of applicability your models as needed, some models may become obsolete over time
  4. Manage cognitive load in a timely manner, ideally before you become overwhelmed
    1. Analyse the sources of potential overload and teach your system to identify the triggers
    2. Reduce the level of interaction with other agents to a comfortable level
    3. Create algorithms that allow selected cognitive processing to be automated (delegated to your system)
    4. Delegate cognitive processing beyond your understanding to trusted agents with relevant domain knowledge and spare capacity
    5. As needed, share further models and data instances with your trusted agents to facilitate effective delegation
    6. Signal spare cognitive capacity to your trusted agents and respect the load of your trusted agents
  5. Adopt a human scale definition of organisation
    1. An organisation consists of a set of collaborative relationships between trusted agents – nothing more, and nothing less
    2. An agent within an organisation values all its/her/his relationships with other agents within the organisation
    3. The sum of relationships between an external agent and the agents within an organisation are valued by the first agent and by at least one of the agents within the organisation

The resulting system not only acts as a mirror of your conscious knowledge and understanding, but your interaction patterns with the system also reflect your specific learning style and your cognitive preferences.

Need for further research and exploration

Our current technologies and communication tools hardly meet any of the human scale computing criteria to a satisfactory level. Despite all the hype about exponential technological change – which must not be confused with progress, software assisted communication and collaboration is still in its infancy.

Making human scale computing a reality requires significant further work. It represents an opportunity for tapping into the unique cognitive abilities and challenges of neurodivergent individuals, who often find themselves on fringe of society. A quote from Tony Attwood, 
author of The Complete Guide to Asperger’s Syndrome, provides a suitable call for action:

There’s work to do this century – diseases to cure, environments to save, freedoms to preserve. Fortunately, there are people with minds capable of the challenge, with the ability to focus and persevere. They possess perspectives and talents unique enough to solve the biggest of problems, or enhance the most challenging projects. They are Aspies.

Neurodivergent people learn and play differently. In particular aspies and autists only have a limited if any interest in competitive social “games”. They communicate and enjoy themselves by sharing information and knowledge, and not by negotiating social status. Neurodivergent individuals:

  • 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

Collaboration can take many forms. Human scale computing represents an opportunity not only to improve communication between humans and software systems, but also to improve communication and trusted collaboration between humans with different kinds of minds and cultural backgrounds.

Submissions that deserve attention

The following is a sample of some of the problems statements that have been submitted by CIIC participants. To join the discussion, and to tap into the CIIC community to address these and other wicked problems that you deeply care about, register for the 6th CIIC unconference on 3 December 2016.


I’m passionate about information overload management. Making information interactive would be my dream. I’ve already worked out methods to perform word sense disambiguation (WSD) using neural networks. This solves problems in information retrieval (IR) by making information more relevant in targeted searching. I am now investigating how to better present / store information to help people “wade” through it.
– Peter de Vocht


In the domains of health informatics and clinical decision support systems there is a lack of quality assessment of extracted knowledge for clinical decision making. There are two questions:

  1. Can clinical decision support systems (CDSS) cope with rare or unusually presenting diagnoses?
  2. How to make sure that the knowledge provided by CDSS is reliable?

The knowledge used in clinical decision support systems must be both up to date and relevant for the cases that are being presented to it. However, finding the latest accurate clinical knowledge to support clinical decision making is difficult, because knowledge is changing rapidly, and it might be located in many different repositories in different formats. Additionally, the range of knowledge required in a particular case may be very wide, especially when dealing with multiple co-morbidities.
– Seyedjamal Zolhavarieh


New Zealand’s contribution to a sustainable world. The stakes are high in NZ. Many of our exports are associated with our clean green image. Yet our clean performance data shows we’re behind much of Europe, and falling behind. The demand for energy efficient living just isn’t there, especially in transport and housing. Rather than relying on policies, how can industries change this? How can we be on the path to a green sustainable future on an international level?
– Ivan Li

The future of human agency

cartoon from

Insights about human behaviour are being ignored by policy makers and entrepreneurs

Gaining a comprehensive understanding of human behaviour is not possible from within any single discipline. Not only is each discipline focused on specific aspects of human behaviour, but the different disciplines that examine human behaviour rest on mutually contradictory assumptions about human nature. This excellent talk by Herbert Gintis outlines the limitations of established disciplines and provides the motivation for an interdisciplinary approach.

Those who cling the most to the use of specific models of human behaviour tend to be the ones who actually don’t have any understanding of the limitations of the models they are using. Especially when a model is non-trivial, most people confuse being able to “use” a model with understanding the model, all the underlying assumptions, and the limitations. Economists and psychologists work with implicit assumptions all the time without without worrying much about it.

Whilst insisting on sharp boundaries between disciplines is unhelpful and leads to weak models, the established boundaries are not entirely arbitrary. This is easily seen when visualising the scope of the various disciplines in terms of spacial and temporal scale. Psychology for example focuses on the behaviour of individuals across a human lifetime, sociology focuses on the behaviour of groups at various levels of scale over the course of recent history and anthropology focuses on both individual and group behaviour over the entire course of human evolution.


The assumptions that are baked into current schools of economic thought, which influence government policies and corporate decision making – and therefore the quality of our daily lives, extend at best over the last few centuries of human history, and in many cases are at odds with the evidence and assumptions made by other disciplines.

It is therefore not surprising that up to 80% of people globally are disengaged at work, and that important insights about human behaviour, which only become apparent when studying human history and human evolution over the last 2 million years, are ignored  by policy makers and business decision makers.

We live in a time of exponential changes in communication technology. Just a few decades ago humans only needed to learn one of two languages and perhaps the jargon of a particular profession to be equipped for a successful life. Today thousands of new apps (little languages) become available every month, far more than anyone can ever learn to use, appreciate, and trust. More and more people are realising that quantity does not equal quality when it comes to digital technologies.

The disciplines of design and engineering play an increasingly important role in a world where communication between people and all forms of economic activity are by default being mediated via digital technologies.

To understand the full implications of the new technologies that we are churning out every month, is it enough for designers to be familiar with the latest in pop-psychology and for engineers to be familiar with the latest economic fads and monetisation models?


What if some important considerations about human nature have fallen between the cracks, and if the rate of technological change has outpaced the rate at which human cultures can evolve? Being able to design, build, and use technology does not equate to understanding all the implications.

In the meantime deeply flawed economic assumptions continue to be baked into new technologies.

The very fact that flawed assumptions about human behaviour are being perpetuated is an indicator that the feedback loop of cultural learning is not working well enough.

The limits of human agency

A single human brain can only process a finite amount of cognitive load. Perhaps we have become a bit too eager to offload cognitive load to our digital “helpers”, and along the way, perhaps we are also unconsciously unlearning or failing to exercise and improve our critical thinking skills.

The following trends are appearing on the horizon:

  1. A shift away from centralised digital services towards individualised intelligent digital exoskeletons that are programmable according to our personal value systems, and which complement our capabilities and compensate for our human weaknesses.
  2. A shift away from brittle centralised data architectures towards end-to-end encrypted decentralised personal knowledge and data repositories.
  3. A shift away from big corporations towards much more agile and adaptive employee owned institutions that are democratically ruled.
  4. A shift away from big government towards decentralised and increasingly automated governance at various levels of scale, based on open source software and real-time democratic feedback loops.

The shift away from centralised intelligent digital services is still in its infancy, but it is the inevitable consequence of the shift towards decentralised data architectures and the slowly growing appreciation of the risks of opaque artificial intelligences that are being fed a culturally biased and pre-filtered diet of information.

The need to shift away from centralised data architectures grows with every major security breach or misuse of trust by the operators of popular digital services. The first steps can already be seen in the growth of end-to-end encrypted personal data stores.

The shift away from big corporations is reflected in employee engagement metrics, in the growth of employee owned businesses, and in the growth of the not for profit sector.

The desire to shift away from big government is visible in the recent election outcomes in the US and the UK, but even more so in the growing economic relevance of specific cities and regions, and the corresponding loss in relevance of nation states. There is a lot to be said for the Swiss model of governance, where the 26 cantons enjoy a lot of autonomy, and where the federation does not even need a head of state.

Earlier this month at the HINZ conference on health informatics in Auckland I learned that the data from one credit card is worth USD 1 on the black market and that a basic healthcare data record is worth USD 50 on the black market. The latter number in combination with the recent data breach at the Red Cross Blood Service in Australia illustrates that centralised data architectures are way past their best used by date. The healthcare sector alongside the finance sector  is plagued by a lack of trust and collaboration across organisational boundaries. Establishing trust and improving collaboration in a context of brittle and unreliable systems is not an easy matter.

The future of software intensive systems over the next two decades can not be understood through the lens of popular digital services such as Google, Facebook, and Amazon. Smaller providers of digital services and start-ups are well advised to stay clear of advertising based and consumption oriented business models that reflect a set of increasingly outdated cultural values.

Much of the foundational technological work today is being performed by the open source software communities that develop and maintain the components underneath the digital candy wrapper services from Google, Facebook, etc.

Although many open source software projects have been co-opted by corporations, the tacit knowledge associated with open source software increasingly lives in brains that reside outside the sphere of corporate influence.

The trend towards zero marginal cost means that proprietary digital candy wrappers become less and less relevant, as alternative non-proprietary services unencumbered by corporate interests can be offered at close to zero cost.

Individualised intelligent digital exoskeletons hold a huge potential for re-establishing human agency on a new platform – without needing to sacrifice the benefits of automation. Autists for example might program their digital exoskeletons to act as powerful social filters that handle some or even most of the interactions with other humans.

Once we appreciate the value of technologies that compensate for human weaknesses more than technologies that exploit human weaknesses, we are well underway towards the cultural transformation that W. Edwards Deming envisioned several decades ago.

Register for the next CIIC unconference on 3 December to look at innovation and collaboration through a multitude of neurodiverse lenses, and apply your creativity to wicked problems that are not solvable by “monetising data”.

Neurodiversity – The Core of Creativity at CIIC on 3 December 2016


Typical humans are highly programmable. The capacity for cultural transmission of beliefs and behaviours is perhaps the most significant distinction between humans and other animals. Humans are so good at subconscious imitation and copying from each other that refraining from imitation requires conscious effort, and that books such as “Immunity to change – How to overcome and unlock the potential in yourself and your organisation” have to be written to help people to become aware of some of the negative effects of the human copying instinct.

No serious discussion on the topic of innovation, learning, and collaboration can afford to ignore the relevance of neurodiversity, and in particular the role of the autistic spectrum.

The neurodiversity movement asserts that neurological differences should be recognized and respected as a social category on a par with gender, ethnicity, sexual orientation, or disability status. It frames autism, bipolar disorder and other neurotypes as a natural human variation rather than a pathology or disorder, and its advocates believe them to be authentic forms of human diversity and valuable sources of human talents and innovative potential.

People on the autistic spectrum learn and play differently. Autists communicate and enjoy themselves by sharing information and knowledge, and not by negotiating social status. Over the last two decades it has become increasingly clear that autistic cognitive lenses are apparently an essential element in all human societies, especially in the context of innovation and in terms of reducing spurious complexity in human culture.

It is time to liberate autism from the pathology paradigm. This can only be achieved if autists take ownership of the definition of autism and share their experience of human cultures through an autistic lens from a first hand perspective.

Neurodiversity is the diversity of human brains and minds – the infinite variation in neurocognitive functioning within our species. Neurodiversity is a biological fact. In the realm of conventional academic literature (e.g., peer-reviewed journals and books from mainstream academic presses) the discourse on autism is dominated by the voices of non-autistic writers whose work is based in the pathology paradigm. Autistic voices and narratives that pose critical challenges to this dominant discourse, and to the host of beliefs and practices around autism that are rooted in the pathology paradigm, are systematically marginalized in this literature – excluded, silenced, disingenuously misinterpreted, or condescendingly dismissed. – Nick Walker

I have gained extensive experience simply by living with fairly severe autism for my lifetime. Difference can be wonderful, and autism shouldn’t be tampered with, or altered. Autistic people shouldn’t be changed. – Jasmine Lee O’Neill

Google – who I spend 90% of my advertising budget with – are partnering with the controversial organisation Autism Speaks on the ‘Ten Thousands Genome Program (AUT10K)’ project to help find a possible cure for autism. I’ve concluded that this is a system where there really is no place for ethics. – Lydia Andal

jorn4Jorn Bettin, a managing partner of  S23M and an advocate of autistic collaboration, will present a keynote talk at the CIIC unconference on 3 December 2016 on the relationship between neurodiversity and creativity, the impact of widespread discrimination against people with autistic traits in the workplace, and on the need for radical autistic activism.

Topics proposed for discussion

Register and join the CIIC community of innovators for the 6th conference in open space, and combine interdisciplinary collaboration with neurodiverse cognitive lenses to address challenges such as:
  • economic progress in NZ
  • sustainable supply chains
  • effective healthcare
  • blending human interaction and automated  processes
  • energy efficient transport and housing
  • the limits of financialised economics
  • development of embryonic industries

The list of submitted  CIIC problem statements is growing.

The Future of Zero Marginal Cost at CIIC on 3 September 2016


Zero marginal cost explains why zero-waste value cycles are of extreme importance. A related book titled The Zero Marginal Cost Society by Jeremy Rifkin makes the case that vertically integrated industries have no future, and that decentralised networks and collaborative niche construction define the new organisational and economic structure.

Technology evolves faster than most people realise. Vast amounts of software are open source. Software vendors increasingly provide only a thin candy wrapper around open source technology. The wrapper is becoming a distraction, and often contains no value whatsoever. Software and electronics are commoditised, and it is increasingly impossible to make a big margin on technological innovation.

Dr. Pete Rive, one of the founding members of the Colab Industry Advisory group at AUT university, will present a keynote talk at the CIIC unconference on 3 September 2016 about the implications of zero marginal cost on the future of human societies.


Topics proposed for discussion

Join the CIIC community of innovators for the 5th conference in open space, and use interdisciplinary collaboration and systems thinking to address challenges such as:
  • economic progress in NZ
  • sustainable supply chains
  • effective healthcare
  • blending human interaction and automated  processes
  • energy efficient transport and housing
  • the limits of financialised economics
  • development of embryonic industries

The list of submitted  CIIC problem statements is growing. We are in particular looking forward to progressing the discussion of concrete challenges in the healthcare sector.

The last CIIC event on 4 March 2016

Participants started with the notion of People + Purpose = Performance to frame a discussion of core challenges within the healthcare sector in New Zealand.


  • Design and development of tools for effective self-care: With increasing numbers of people dealing with health issues that don’t go away, e.g. diabetes, we need to find innovative ways to help them keep as healthy and independent as possible. How do we engage people in self-care that is effective, and what are the tools that we can use to keep them engaged? Monitoring and education tools are good, but surely there are other tools we can develop to solve this problem of people needing supervision, coaching and clinical guidance from doctors and nurses who are already overworked and only accessible for short consultations.
  • How do we mobilise and align NZ’s policy, research, healthcare and commercial capabilities to deliver world-leading health outcomes, generate substantial economic returns, and attract, develop and retain talent?

Results and insights

Topics covered in earlier CIIC events

economic value networks


  • Is there a place for barter? It seems likely that money, and hence financial systems, arose from bartering.  How did this happen, how did bartering arise in the first place, and what does it tell us about the modern world/what can we use it for?
  • How do we need to redefine economic progress?
  • What is value? How do value systems influence the process of creating and maintaining trust?
  • Which values can be said to be universal across most cultures? What specific values are conducive to innovation and long-term collaboration?

Results and insights

Open Space

CIIC is an entirely participant driven open space event.

  • Whoever comes are the right people
  • Whatever happens is the only thing that could have
  • When it starts is the right time
  • When it’s over it’s over

open space technology