The role of nutrition in our lives

For the CIIC workshop on 3 March we are encouraging submissions on topics that are related to both the healthcare sector and the agriculture sector such as nutrition.


Industrial farming and industrial food production led to a significant shift in diets. More recently though, diets have started to be influenced by an increasing awareness of  unsustainable farming practices and the negative health effects of consuming highly processed foods.

Greenhouse gas emissions created by dairy have doubled since 1990. Cows and sheep are the source of nearly all agricultural greenhouse gas emissions in New Zealand, which amounts to almost half of the total green house emissions of the entire economy. Current trends do not lead us anywhere close to a carbon neutral or carbon negative economy, and similar observations can be made regarding agriculture in Australia.

NZ_greenhouse_gases_by_sector.svg.pngDiets are changing, and perhaps not in ways anticipated by the dominant forces in the local agricultural sector. In New Zealand for example, one in 10 people now follow a vegetarian diet, a 27 per cent increase in just five years.


At the same time, according to government health statistics, less than half of all New Zealand adults eat enough vegetables and fruit, and thirty-one percent of adults are obese. Statistics from the EU include similar findings, such as one in three 11-year-olds being overweight or obese.

The economic significance of agriculture and healthcare services

In total, agricultural production including forestry and derived food products constitute 54% of New Zealand’s exports (29 billion NZ$), the entire economy is critically dependent on agriculture exports. In comparison the annual New Zealand government healthcare budget is around 16.8 billion NZ$, but this number obviously does not include additional individual expenses related to healthcare.

In terms of economic significance, overall annual healthcare costs and productivity losses resulting from poor health do not trail far behind the value of NZ agriculture exports – if at all.

Beyond greenhouse gas emissions New Zealand’s reliance on agriculture product exports has led to monocultures and to water quality problems in rivers and lakes caused by intensive dairy farming, which in turn pose human health risks that can no longer be ignored.

Influences on the future of healthcare

  1. Individual behaviour (including nutrition) and associated cultural factors
  2. Individual genetics
  3. Individual microbiomes
  4. Proactive approaches towards maintaining physical and mental health
  5. Sensors that provide near real time health data
  6. Nanotechnology and nano-devices
  7. Further levels of automation powered by real time data feeds
  8. Shift away from medicines to individualised diets and mass customised foods

Influences on the future of agriculture

  1. Shifting to carbon neutral or carbon negative production
  2. Measures to eliminate pollution of rivers and lakes
  3. Global demand for clean food production techniques
  4. Large sensor networks that provide near real time data
  5. Further levels of automation powered by real time data feeds and robotics
  6. Production techniques that minimise the land use footprint such as urban agriculture and vertical farming
  7. Increasingly automated logistics and supply chains that connect producers and consumers
  8. Local demand for foods tuned to individualised dietary needs

Cleaning up the agriculture sector

For New Zealand becoming carbon neutral or negative is just a much a matter of transforming the agriculture sector – by shifting away from traditional milk and meat production, as it is a matter of moving away from the use of fossil fuels.

Realistically a complete transformation of the sector will take at least a decade. The need for transformation must be understood by the participants in the sector and beyond, so that an alternative approach to land use and agriculture exports can be developed.

Proactive maintenance of physical and mental health

Moving towards a proactive approach to health requires a reframing of our understanding of healthcare:

  1. Each individual, family, and community has a big active role to play. This is only realistic if the education system prepares individuals for their role in maintaining their health and the health of their family and community.
  2. Genetics research will be a prerequisite to better understanding individual health risks and to providing individuals with guidance related to health promoting behaviours and diets.
  3. Assistive technologies will play a critical role in helping individuals adhere to behaviours and diets that they have chosen to adopt to optimise their health.
  4. Appropriate regulation needs to be designed and enacted to empower individuals, families, and communities to make informed choices about physical and mental health promoting measures, and to prevent coercive measures and treatments that may conflict with individual or cultural values.
  5. Individuals need to be given control over who has access to their health data, to empower individuals to selectively share health information with people and institutions they trust.
  6. Appropriate regulation needs to be designed and enacted to limit the monetisation of health data in order to avoid commercial interests from dominating the discussion of desirable healthcare outcomes.
  7. Healthy food needs to be made available to everyone, not just to those who can afford it.

There is significant overlap in the technologies that are available to power innovation in both healthcare and agriculture. Supporting and funding innovation in healthcare, agriculture, knowledge engineering techniques, and automation technologies has the potential to create a virtuous cycle.


It is important to recognise that research and development is needed not only on technologies and service delivery mechanisms, but also on cultural factors and the social changes that can either drive or hinder transformative improvements to large sectors of our economy.

Share the challenges and opportunities that you see in relation to healthcare and agriculture at the upcoming CIIC workshop on 3 March at AUT in Auckland and at RMIT in Melbourne!

Dates and times


Trust vs capital

Recently I came across an article that boldly claimed in crude language that truly non-hierarchical organisations do not exist and never will exist. Given the success of several non-hierarchical organisations that I am aware of, given that I am part of one such organisation, and especially given the text was penned by an author representing an organisation in the sharing economy, I was more than a bit disappointed.


There is no shortage of articles claiming the world is inherently hierarchical, ranging from simplistic opinion pieces to scientific papers from various disciplines.

Networks vs hierarchy

To avoid pointless arguments about labels and semantics, I am going to stick to the following dictionary definitions.

  • hierarchy : (a) a system or organisation in which people or groups are ranked one above the other according to status or authority (b) an arrangement or classification of things according to relative importance or inclusiveness
  • authority : the power to give orders, make decisions, enforce obedience, and influence others
  • network : a group or system of interconnected people or things

There are two key differences between hierarchies and networks as defined above:

  1. The connections in a hierarchy form a directed tree, whereas the connections in a network may form any kind of graph
  2. A hierarchy always depends on a ranking/importance metric, whereas a network only depends on the formal definition of a graph

In other words, hierarchies always have a human social and political aspect shaped by human perception and culture, whereas networks need not.

Whilst undoubtedly hierarchies have been part of the social furniture throughout written human history so far, the following observations are worthwhile considering before elevating hierarchy to a fundamental law of human nature, biology, or perhaps even physics:

  1. The growing body of knowledge about pre-historic human societies points towards highly egalitarian forms of organisation, and to strong social norms against any individual attempts to gain power over others
  2. The digitally networked world has fundamentally altered human patterns of interaction, and has created information flows that do not adhere to any ranking/importance metrics – even if there are some who actively attempt to reverse this emergent behaviour within digital networks
  3. By definition, a hierarchy is a social construct, and like all social constructs, it only functions to the extent that members of a group believe in its relevance and have a shared understanding of the specific rankings within the hierarchy
  4. All human observations of the biological world are biased by our human perspective, including current cultural baggage relating to hierarchical forms of organisation
  5. Referring to spatial containment in the physical world as a hierarchical form of organisation is a reflection of human [grandiosity] bias rather than a reflection of human dominance over the universe

The real challenges with hierarchical forms of organisation result from cultural inertia and from the extreme level to which humans are culturally programmable.

We do not yet have enough evidence to know to what extent it is possible to transform large hierarchically organised social groups into non-hierarchical networks, but there is no shortage of examples of groups that have emerged over the last few decades that operate on a set of principles that do not include a social ranking/importance metric.

It is conceivable that a shift towards non-hierarchical forms of organisation is a generational cultural shift that is only accessible to digital natives and those who are not deeply embedded in traditional pre-digital cultures.

Learning vs power


A hierarchical organisation is the antithesis of a learning organisation

This observation is backed up by evidence from thousands of organisations that strive to improve or establish a culture of innovation. All effective approaches for continuous improvement (such as Kaizen, Toyota Production System, Waigaya, …) and innovation (Open Space, collaborative design, …) share one common principle:

The belief in the existence and relevance of social hierarchies must be suspended

This is no accident. By definition, hierarchies confer power on specific groups and individuals, with immediate effects on the ability of a group to learn and adapt to a changing environment. Any form of hierarchy or power indicates dampened feedback loops. Power can be understood as the privilege of not needing to learn.

Competence vs authority

Looking under the hood of any hierarchical organisation and analysing communication and collaboration patterns reveals three social structures:

  1. The official hierarchy as specified in an organisation chart or similar artefact, which defines the scope of various “authorities” within the organisation.
  2. The unofficial hierarchy, which reflects the actual coercive power structure, which inevitably emerges within all hierarchical structures, and serves as the career climbing ladder within the hierarchical structure.
  3. The competency network within the organisation, which is the union of all the multi-dimensional domain-specific competency rankings that individuals allocate to the other members within the group. Whilst this network includes social rankings, each individual independently allocates competency rankings to other group members, leading to a multi-dimensional network rather than a tree based on a unidimensional ranking.
    In concrete terms, within a given organisation I may have a preferred contact for ERP software problems, and may know of another couple of less preferred contacts for ERP problems, and my colleague may have a different preferred ERP problem solver. And of course I would engage with my preferred ERP expert primarily in relation to ERP issues and not in relation to problems related to new product development.

The competency network is the only social structure that directly supports the purpose of an organisation. Whilst some parts of the competency network may bear similarities with the official and unofficial hierarchies, many parts will diverge significantly from the official structure.

As another concrete example, in my local community, I may have a preferred dentist and may know a few less preferred dentists, and my neighbour may have a different preferred dentist. Of course I would not engage my preferred dentist to solve plumbing problems or transportation problems – all competency ratings are domain specific.

The existence of competency networks represents an inconvenient truth for all authorities, it contradicts the simplistic claim that a lack of hierarchy leads to chaos and dysfunction. However, removal of an established hierarchy does not automatically result in a well-oiled competency network. Cultural inertia can keep fear, mistrust, and in-group competition alive, and easily leads to the emergence of new oppressive hierarchical structures.

As far as I can tell, all those who claim that hierarchical organisation is an inevitable result of [human] nature confuse unofficial hierarchies with competency networks. Only the former roughly correspond to trees. The latter tend to be much more complex graphs that are not governed by any simple one-dimensional ranking. It can be argued that in terms of resilience and adaptiveness, unofficial hierarchies are as least as counter-productive as official hierarchies.

All healthy and resilient communities have a well-functioning competency network. If a healthy community also claims to have a hierarchical structure, the hierarchy tends not to be associated with significant decision making power.

Agency vs disengagement

One simplistic argument sometimes advanced to “prove” the universality of hierarchy refers to the function of animals, citing the brain as the dominant locus of control or the subordination of fingers to the hand and arm, etc. What all these arguments ignore is that agency and control is the result of interactions between billions of cells, not emanating from any top level cell, and that modularity in terms of organs or spatial containment at other levels of scale does not conform to the definition of hierarchy.

Hierarchical structures compromise the learning ability of a group by inducing fear and reducing the level of agency perceived by the individuals within the group. What may be perceived as a well-functioning organisation by the authorities within a hierarchy is characterised by cultural inertia and increasing levels of disengagement amongst those who do not benefit from the hierarchical arrangement of power.

Hierarchical groups are easily out-competed by non-hierarchical groups. Whilst the latter benefit from the wisdom of crowds (a group of collaborating and independently thinking agents) the former suffer from the stupidity of crowds (a mix of disengaged cynics and a small group of ego-centric authorities preoccupied with in-group competition).

Trust vs capital

A good way to understand competency networks is via the notion of trustworthiness and the nurturing and maintenance of trusted relationships. Trust is a meta-belief that allows propagation and installation of beliefs in a network of agents.

Trust between two agents develops through an ongoing process of maintaining shared understanding, and it correlates with the intensity and duration of maintaining shared understanding.

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 invention of money was a more or less “successful” attempt to decouple trustworthiness ratings from specific pairs of individuals. Throughout history money has been used as a proxy for trust. The relationship between money and trustworthiness has always been on very shaky ground, regularly leading to (a) wars – the option that showcases collective human stupidity, and (b) debt jubilees – the option that showcases some level of human self-understanding and empathy.

Modern capital, even in its digitised form, including cryptocurrencies, still suffers from the flawed assumption that somehow it is possible to divorce trustworthiness ratings from specific pairs of individuals, and to aggregate and liquidate such ratings. Yes the social delusion seems to work, but in a world of exponentially accelerating transactions, only over increasingly short time horizons.

The real opportunity for human society lies not in the invention of ever smarter forms of liquid capital and in-group competition, but in the recognition of human cognitive limits, and in the recognition of the extreme value that resides in competency networks.

The age of digital networks for the first time gives us the opportunity to construct cognitive assistants that help us to nurture and maintain globally distributed human scale (= small) competency networks – networks of mutual trust.

Humans knew how to build and maintain mutual trust many hundreds of thousands of years ago, and our brains are still designed to operate on mutual trust. It is time to tap into this potential and to combine it with the potential of zero-marginal cost global communication and collaboration.

Distributed collaborative competency networks

We are only beginning to rediscover the potential of competency networks, but I believe there is more than enough evidence on the table to dismiss the myth of hierarchical organisation as a law of nature.


Describing how to bootstrap competency networks on top of a dysfunctional economic platform, and describing concrete operating principles for such networks goes beyond this short article.

Related questions and experiences can be shared and explored in depth at the upcoming CIIC workshop on 2 December at AUT in Auckland and at RMIT in Melbourne. Join us!

… and in case you can’t join us in December but have relevant contributions, by all means, please comment below. Or join us at the subsequent CIIC, which is never more than three months away.

Dates and times


The language of thought is underrated

… and the language of human speech is overrated.

spoken language

Given that human children learn to use spoken language to attach labels to mental representations very early on, and given that much of human communication is based on spoken and written language, it is tempting to perceive human language as our main thinking and reasoning tool.

The more we learn about the reasoning abilities of non-human animals, the more doubt is cast on the position of human language as the ultimate thinking tool.

Many philosophers have used language to write about their mental models of the world, and yet, when we read their explanations, to what extent can we be confident that the mental models that we reconstruct from a written text resemble something similar to what the writer had in mind?

Argumentative writers and speakers of human languages easily forget is that every mind is different, has made unique experiences, and therefore creates unique mental models of the world. Arguing about which mental models are “better” or “correct” has entertainment value for some, but it distracts from the fact that mental models can’t be debated – the debates are always about partial mental models that have been serialised into human language and that are interpreted out of context, without the substrate of all the unique experiences that gave rise to the shared models.

The atoms of the “language” of thought

thinking.pngHuman mental models have been around for much longer than human language. To understand the core mechanisms of human reasoning and thinking, and to appreciate the dangerous limitations of human language, we need to step back in time and look at how language evolved from a biological perspective.

Here is a synopsis of the thinking tools that predate human language:

  1. Humans and a some other animals are capable of shared attention. I can look at something and detect that another animal is looking at the same thing, and I understand that we are both seeing the same thing, whilst realising that we may have wildly different perspectives on the thing (associations with past experiences) that we see. Someone who has never seen or heard of a gun may not know that it can kill. I can also observe two people who are looking at some object, and I understand that their minds are focused on that object.
  2. Beyond awareness of shared attention humans have evolved limbs that allow us to point to things, to further disambiguate and make it more obvious what we are focusing on.
  3. Humans and other animals create mental representations (= models) of the things we interact with.
  4. Furthermore humans and some animals can identify commonalities between things (abstract/generalise) and create mental models of groups of similar things (= categories).
    • … and can identify spatial relationships between things (containment and connectors) and create mental models of these relationships (= graphs).
    • … and can identify changes over time (movement of things) and create mental models of patterns of movements (= operations).
  5. Humans and perhaps also some animals can apply their pattern recognition and abstraction abilities to operations, leading to mental representations that contain abstract operations.
  6. Humans and perhaps also some animals rely on their mental models to conduct extensive simulations to predict events and arrive at decisions. In some domains this happens subconsciously and very fast, and in other domains we are capable of slower and deliberate conscious simulations.

We and other animals can do all of these things without talking! No spoken or written language is required. Mental models and reasoning clearly came first. Human language came second.

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.

shared understanding

We probably developed the technique of validation by instantiation (pointing to multiple examples) long before language, to establishing a reasonable level of shared understanding of life-critical abstract concepts. Perhaps it became convenient to associate some of these abstract concepts with reasonably distinct kinds of grunts, and this then eventually led to how we speak.

Some philosophers – and software developers – are so infatuated with language and syntax that they can become blind to how the physical world is shaping our mental worlds.

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

As soon as spoken language entered our world, initially as a serialisation format for communicating simple references to things within our local context, things started to get messy. We started to reference abstract things, references to references, and experiences that occurred many years ago. From that time onwards I suspect the number of misunderstandings in communication grew exponentially.

misunderstandingTheory of mind has likely evolved in tandem with language, it allowed us to create rough and speculative models of what might go on in another mind. But 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.

Validation by instantiation would now only come into play when harsh reality pointed people to concrete misunderstandings. Something like the 80 / 20 rule will have been good enough for language to be a useful and viable tool. Making correct assumptions 80% of the time was good enough for day to day life.

Written language

The seeds for storytelling had been sowed. The first human hive minds emerged.

Written language made things even worse in terms of the scope of the social delusion. People had opportunities to “read” large volumes of information out of context in space and time. Many of the written words of old and distant texts seemed familiar – as needed with the help of a translator (another source of potential misunderstandings), and people started importing many thousands of references to very unfamiliar abstractions into their mental models on top of their first hand experiences.

bible.jpgWe all know that human imagination knows no/few limits, but at the same time we like to believe that we “understand” what others have written, without necessarily realising the contradiction. 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.

Human culture increasingly became defined by myths residing within hive minds. At that stage a few people started scratching their heads about weird human behaviours and the beliefs that underpinned the observed behaviours. In today’s society, within the pathology paradigm of Western medicine, such people would be labelled autistic.


At all times throughout human history a few people would have realised that human language has severe limitations in terms of ambiguity and precision. This led to the development of number systems and mathematical tools. The industrial revolution and the computer revolution would not have been possible without reliable mathematical tools.

category theory.png

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. And since mathematicians were at work, they used recursion to made sure that this symbol system is fit for representing and reasoning about all other kinds of symbol systems and syntaxes.

Denotational semantics is based on the simple observation that with our current technologies we can easily construct sufficiently unique symbolic tokens that allow us to abstract the use of human understandable symbol systems into corresponding machine readable symbol systems and to store the references between the two.

categoriesCategory 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.

Visualising the language of thought

… and integrating it with digital computers:

In the Cell technology implementation of mathematical foundations we use unique tokens (semantic identities) to denote symbols and symbolic language systems.

We recognise that all language (and especially meaning/semantics) is created and shaped by individual symbol system users. All conceptual references are associated with exactly one agent, accounting for the fact that we all have unique mental models and that the cultural hive mind is a social delusion.

We rely on validation via instantiation to establish a reasonable level of shared understanding between agents (human and software), and on information quality (IQ) logic in combination with Bayesian logic to estimate the level of shared understanding in relation to specific contexts/domains.

Design of trustworthy protocols

… for interacting with humans and machines:

The foundational and irreplaceable role of the language of thought for reasoning and decision making explains why model building is so important. With spoken and written language and digital “storytelling on steroids” enabled by zero marginal cost communication modern humans have painted themselves into a dangerous corner – the goal of communication has shifted from knowledge validation and achieving shared understanding towards the transmission of seductively simplistic beliefs.

Mathematics, the arts, and music are human scale tools for communicating the essence of complex patterns of mental states (knowledge, feelings, and awareness of agency and motivations) that don’t survive simplistic attempts of serialisation and de-serialisation via stories. The outputs of mathematics, the arts, and music are highly generative, they can’t be described in any simple story. Instead they open up and invite a multitude of complementary interpretations.


The interpretation or application of mathematical theories has become a critical part of a growing number of knowledge intensive disciplines. The essence of the scientific method is the combination of the atoms of the language of thought with the technique of validation via instantiation. The arts and music are essential complementary communication and exploration tools for feelings, agency, and motivations.

Of course creative storytelling also allows for a multitude of interpretations, but whenever storytelling and related tools of persuasion are used to transmit and replicate beliefs, as is usually the case in politics and marketing, interpretation out of context becomes problematic, and critical validation becomes essential to minimise misunderstandings and attempts at deception.

If we value the creation of cultures of thinking, then the risks of deceptive storytelling need to be acknowledged, and exploration and critical validation of knowledge, feelings, agency, and motivations must be encouraged.

Join us at the next CIIC unconference on 2 December to explore the design of new types of dialogues and interaction patterns specifically for knowledge validation and trust building in our increasingly interdisciplinary world.


Dates and times


Addiction and story withdrawal


The abstract origins of economic beliefs

Most contemporary economic beliefs can be traced back to one of three influential political philosophies and related sets of economic principles, some of which of course have an extended history that predates the philosophies that popularised them over the course of the last three centuries.

Capitalism – the story told in the 18th century by Adam Smith about the fourth and “final” stage of economic organisation (“commercial interdependence”). In Smith’s philosophical perspective the primal moving agency is “human nature” driven by the desire for self-betterment, guided by the faculties of reason.

Liberalism – the story that developed out of ideas articulated by Thomas Hobbes and John Locke during the period of the English Civil Wars, the Glorious Revolution, the American Revolution, and the French Revolution in the 17th and 18th centuries.

Marxism – the 19th century story of Marx’ radical critique of philosophy, in which he examines a vast array of philosophical concepts from earlier thinkers in their dynamic relations to each other and tries to map these concepts to historical, social, political, and economic realities.

Individual economic beliefs

The level of influence of the above economic storytellers is easily comparable to influence of the storytellers of the big religions, far higher than the influence of the stories told by politicians and corporate “leaders”. Experience quickly teaches people that political and economic power corrupts, but personal experience is of limited help when it comes to assessing the validity or usefulness of the basic laws of economics and moral principles that have been handed down to us by the most popular and “timeless” storytellers and heros.

In contrast to scientific knowledge about the physical and natural world, economic understanding does not relate to a set of stable fundamental principles, but to perceptions of the prevalence of the various economic beliefs held by others, and of dynamic shifts in the prevalence of widely held beliefs.

It is important to distinguish between compelling and persuasive economic storytelling and the economic realities we encounter on a daily basis as individual economic agents.

The stories of capitalism, liberalism, and Marxism play a significant role in establishing a level of shared understanding regarding the most widely held beliefs, or more precisely, they create individual delusions of a level of shared understanding – which economists like to think have a reasonable overlap with actually held beliefs.

Amongst the infinite possibilities of beliefs about human behaviour, the following are worthwhile paying attention to:

  1. Evidence based beliefs (knowledge) about commonalities and variabilities in human behaviour from biology, neurology, anthropology, psychology, sociology, and historic accounts – with the caveat that evidence gathering in the social sciences is often indirect and may be subject to significant cultural bias
  2. Evidence based beliefs resulting from going about our daily economic activities
    • Usefulness of money (a human created abstraction) in facilitating economic transactions amongst multiple parties
    • Usefulness of debt (another human created abstraction) for scaling up the usefulness of money for large scale and long term coordination of economic activity – the belief in debt is equivalent with the belief in money as a long-term store of value
    • Trustworthiness of the institutions entrusted with issuing money / debt
    • Trustworthiness of cash tokens and the software systems entrusted with executing digital transactions

Further beliefs peddled by big economic storytellers and politicians that we individually may subscribe to also influence individual social behaviour, but tend to be constructed on top of the above two sets of “foundational” beliefs. Most arguments and heated debates about economics do not question a shared understanding of “fundamental economic reality”.

Evidence based reality check

Upon closer examination our confidence in the beliefs resulting from going about daily economic activities may not be warranted:

  1. Global growth of distributed peer production (for example open source software), co-operatives (USD2.5T annual turnover)  and not for profit organisations (for example 9.2% of all wages and salaries paid in the US) negates the assumption of money as a universal or primary prerequisite for multi-party transactions.
  2. The history of debt is a history of bubble bath economics and large scale social conflicts.
  3. Every generation lives through at least one disillusionment with the issuers of debt – historically a role played by banks and national governments, but which may change as digital currencies proliferate.
  4. Across the globe, major national currency crashes are quite frequent, even if they don’t happen every year in our backyard. At a local level, banking and payment system outages happen every few days or every few weeks.

Our beliefs in money, debt, institutions, and systems are better thought of as behavioural patterns (habits) than as beliefs that we are genuinely comfortable with.

Habits that don’t serve us well are usually referred to as addictions. We are addicted to:

  1. The convenience of money – but our mental health suffers in a context dominated by anonymous transactions
  2. The availability of debt and material consumption – which has resulted in an economy of bullshit jobs and a glut of human waste that threatens the ecosystems which provide our livelihood
  3. The spectacle of competitive social games (being the “smartest” investor, owing the biggest “status symbols”, having the most “followers” and “likes”, voting for politicians that promise even better games, …) – and yet we teach our children to share and be kind, and we help out our friends in need
  4. The distractions of digital technology – and we regret that we don’t have more time for all the other activities that our bodies enjoy and are designed for

Once we acknowledge our economic habits as addictions, the big economic stories start to appear in a different light. For example we may notice that these stories predate all advanced forms of automation and the impact of zero marginal cost in a digital economy.

The emancipation of Humans from labour – does not mean a redundancy of Humans, in fact its means the freedom of Humans from labour to discover what it means to be human in the 21st Century.

This is a future which needs us to embrace the awesome capacity of humans – for discovery, for expeditions into the unknown, to mine the future, to care, create, dream.

from Beyond Labour

To facilitate a rational discussion of commonalities and variabilities of economic stories I have extracted the foundational principles from original texts and from Encyclopaedia Britannica and have used MODA + MODE techniques for categorising the underlying motivations.  Follow the links embedded below to see the results.

The assumptions of capitalism are at the heart of our addiction. In fact, since 2002, the Chinese have consistently been one of the strongest proponents of capitalism compared with other publics around the world, even more so than Americans and Western Europeans.

The assumptions underlying neoliberal capitalism as practised by many Western democracies exclude many liberal ideals and instead include a number of important conclusions from the Marxist analysis of capitalism and human social behaviour – some of which have since been backed up by scientific evidence.

neoliberal capitalism.png

The idea of radical self-expression is, at least under the constraints of capitalism, a right-wing, Randian ideal, and could easily be the core motto of any of the large social media companies in Silicon Valley, who profit from people investing unpaid labor into cultivating their digital representations. It is in their interest that we are as self-interested as possible, since the more we obsess over our digital identity, the more personal information of ours they can mine and sell. …

from Burning Man’s tagline and central principle is radical self-expression

Neoliberal capitalism systematically exploits human psychological weaknesses to fuel our addictions – as a result the needs of capital have become more important than basic human biological needs.

It is misleading to develop growth-oriented policy around the expectation that decoupling is possible. We also note that GDP is increasingly seen as a poor proxy for societal wellbeing. GDP growth is therefore a questionable societal goal. Society can sustainably improve wellbeing, including the wellbeing of its natural assets, but only by discarding GDP growth as the goal in favor of more comprehensive measures of societal wellbeing.

from Is Decoupling GDP Growth from Environmental Impact Possible?

Updating the picture above to reflect the rise in new social modes of production that are not covered by the three big economic stories hints at the crutches that allow us to maintain our addictions.

updated neoliberal capitalism.png

Good stories in the conventional sense have a deceptive and addictive quality, making reality or the future seem more bearable and attractive even if our senses and body tells us otherwise. We don’t need any further “good” stories, we need something that serves us better than stories.

Overcoming addiction and story withdrawal symptoms

Instead of attempting to subjugate distributed peer production and not for profit organisations to the “rules” dictated by our addictions, we can adopt a new frame of reference and perceive these growing forms of organisation as tools and therapies for weaning us off our self-destructive habits. Similarly, instead of designing technologies that deliver bigger and bigger addictive hits, we can start designing technologies that actively help us overcome our culturally induced cravings.

“Good” storytelling acts in ways similar to alcohol, it is relatively harmless at small doses, but it is destructive in larger quantities and extremely tempting for some.

Addiction to popular economic stories has become a serious social and environmental problem of planetary scale. The larger an institution, the greater the level of deceptiveness of the stories it propagates. Conversely, the smaller an institution, the lower the level of deceptiveness of the stories it propagates. The most dangerous stories are those told by nation states and large multinational organisations.

Across 30 nations surveyed by Pew Research Center both in 2013 and this spring, a median of 38% now say U.S. power and influence poses a major threat to their country, up 13 percentage points from 2013.

from Globally, more people see U.S. power and influence as a major threat

To truly leave behind our addiction to stories we need to reinvent interaction and communication at a very fundamental level – even more fundamental than money, debt, and economic transactions.

The scientific revolution taught us that conducting carefully designed experiments, evidence based reasoning, model building, and independent peer review is a far superior approach to understanding the world than reliance on old and potentially deceptive stories.

When it comes to coordinating economic activity at scale, imagine relying on evidence based models and digital automation rather than relying on the language of politicians and corporate executives. For example, rather than discussing the benefits of a large infrastructure investment scenario in classical financial terms, we are better served by creating a model of the resource and information flows within wider economic ecosystem based on input from the agents within the ecosystem, and then running extensive simulations to explore the effects.

To some extent economic decision making already relies heavily on models and exploratory simulations. The key differences between an approach dominated by storytelling and an approach dominated by model creation and validation:

  1. Storytelling and old-style economic modelling focuses heavily on financial modelling, which is entirely dependent on the addictions that stand in the way of the next leap in cultural evolution. In contrast, new-style economic modelling can focus on the multi-dimensional values that we really care about.
  2. Storytelling relies heavily on techniques of persuasion whereas model sharing and validation relies heavily on data and evidence provided by all the stakeholders that are part of the wider context.
  3. The linear format of storytelling prematurely leads to a narrow focus and detracts from the exploration of entire families of potentially highly creative solutions.

Beyond addiction

A healthy diversity of new economic ecosystems can be built directly on our evolving scientific understanding of the commonalities and variabilities within human social behaviour at all levels of scale.

As a result we will rediscover the original Greek meaning of economics as household management and the innate collaborative tendencies of humans that are so easily overshadowed by cultural programming.

We will also likely begin to understand the significant risks resulting from the combination of cultural transmission and the development of written language – and more recently the development of networked digital technologies.

We can make use of powerful thinking tools for interdisciplinary research, design, and engineering to:

  1. articulate, share and validate what we value in our lives
  2. reason about the cultural conditioning that may stand in the way of progress
  3. identify useful multidimensional metrics that relate directly to our values and to the ecological state of environment we live in
  4. create organisations and highly automated human scale systems that partner with us to achieve our individual and collective goals


Join us for the next CIIC workshop on 16 September in Auckland, to build on the results from the last two workshops which focused on neurodiversity and on human scale computing , and to explore the essence of humanity and how to construct organisations that perform a valuable function in the living world!

Dates and times


Finding a niche in the living world


At the next CIIC workshop in September we can build on the results from the last two workshops in Melbourne and Auckland which focused on neurodiversity and on human scale computing to explore the essence of humanity and how to construct organisations that perform a valuable function in the living world.

redefining-intelligence.pngThe historic record of societies [and large organisations] being aware of the limitations of their culture is highly unimpressive. Today society is heading down a very familiar track, packaged in new tools and rituals. In a world of zero marginal cost the economics of scarcity directly lead to an abundance of waste:

  • Competing to produce and consume more and more stuff has become a liability.
  • Collaborating to produce less and less waste is becoming the imperative.
  • Time to relearn very old wisdom and constrain any attempts to gain power over others. Reference: Samuel Bowles, Herbert Gintis, A Cooperative Species: Human Reciprocity and Its Evolution, 2011

Redefining intelligence is our chance to break out of self-destructive patterns of behaviour. It is a first step towards a better understanding of the positive and negative human potential within the ecological context of the planet. Advanced automation gives us the choice between imagining, creating, and living in cruel worlds and imagining, creating, and living in compassionate worlds.

Notions such as “innovation” need to be carefully re-examined. There is no shortage of global and local wicked problems to be solved. We are past the point where solutions can be found without significant cultural evolution. The situation requires all hands on deck, including both human and future machine creativity, to prevent humanity from reaching its full negative potential (experimental evidence , example of the current level of “civilisation”).

It would be nice to think that we are getting past the stage where cruel experiments on animals and humans are needed to demonstrate that collaboration within a diverse group consistently delivers outcomes that are superior to the results of competition.

Explicit representation of cultural values

In the absence of an explicit value system it is impossible to reason about innovation in any meaningful way.

At S23M we have defined an explicit semantic lens that defines what we value and how we make sense of the world and the natural environment from a human perspective. The S23M semantic lens is supported by 26 principles that form the backbone of our operating model, and which assist us in building out a unique niche.

Innovation can only be transformative if it substantially redefines social norms and so-called best practice. The following 12 of the 26 MODA + MODE principles are rarely encountered in other organisations:

  1. Give minorities and outsiders access to private means of communication
  2. Adapt the cognitive load generated by technology to human cognitive limits
  3. Recognise neurological differences as authentic and valuable sources of innovative potential
  4. Value metrics from the physical and biological world more than human opinions
  5. Value the strength of shared beliefs and corresponding evidence more than the number of shared beliefs
  6. Formalise the results of commonality and variability analysis
  7. Develop visual domain specific languages to describe familiar domains in unambiguous terms
  8. Understand that all information is dependent on perspective and viewpoint
  9. Understand that power gradients stand in the way of transformation
  10. Aim for optimal conflict in a supportive and trusting team environment
  11. Recognise paradoxes and disagreements as the essence of continuous improvement
  12. Use modular distributed design to promote reuse without compromising resilience

Power gradients = sand in the gears of transformation

A very encouraging development over the last decade is the emergence of sizeable organisations that do not operate a traditional command and control hierarchy. A few examples:

The common thread the runs through these organisations is the creation and maintenance of deep relationships with a community of customers and suppliers.

The essence of humanity

At the upcoming CIIC unconference in September 2017 we will explore the essence of humanity and how to construct organisations that perform a valuable function in the living world.


  • What do you believe makes all of us human?
  • Are you thinking about a new venture and would appreciate constructive feedback from a neurodiverse group of innovators with a range of different backgrounds?
  • Are you struggling with a conflict between your personal values and the values of the organisations that you need to interact with?
  • What kind of positive changes would you like to see in society and in the technologies we use?

Join us in Auckland at AUT or in Melbourne at RMIT in open space! As usual, you are invited to submit concrete problem statements for discussion.

From the busyness of innovation to the creation of value


The failure rates of highly leveraged – investor powered – start-ups is greater than 90%, and less than 50% of all new businesses survive past the five-year mark. The extremely poor survival rate of leveraged start-ups highlights the main motivation for the popular “fail fast” mantra. The vast majority of so-called “investors” are impatient, laser focused on monetisation, and rely on social proof of their decisions rather than on a deeper level of understanding of economic value creation and the future role of human cultural evolution.

Obsession with social status has been a core characteristic of all civilisations to date. The three ingredients that power the fractal boom and bust life cycle of civilisation are:

  1. cities (large aggregations of interacting people),
  2. written language (symbolic systems that facilitate the preservation and propagation of cultural rituals across space and time), and
  3. money (an abstract metric for what a culture perceives as valuable).

All three ingredients of civilisation are highly problematic:

To date cities are groups of people that rely on energy and resources from the outside in order to survive and thrive. By definition cities are unsustainable infrastructures that source their inputs from agriculture, mining, and energy production beyond the city boundary. In order for cities to become sustainable all their sources of inputs must become sustainable. This implies closed loop zero-waste value cycles between a city and its surrounding sources of food, resources,  and energy.

The linear languages we use have severe weaknesses that trip us up on a regular basis:

When we’re trying to communicate it in simple language… we’re essentially lying about the nature of the world

Global metrics related to environmental destruction, loss of biodiversity, and climate change highlight the extent to which our cities have become unsustainable and the extent to which the metric of money is much too simplistic for measuring economic progress – or any change that legitimately deserves the label of innovation. Money is a very crude metric of social proof, and nothing more. Money works reasonably well at a local level as long as people are concerned about living healthy lives within their local mutual support network, and are not preoccupied with abstract social games and delusions.

Unfortunately, a closer analysis of human civilisations to date throughout all of human history uncovers that the ingredients of civilisation predictably lead to social delusions and to unsustainable extraction of resources. This leads to the uncomfortable conclusion that written language and money are inadequate tools for guiding civilisation onto a sustainable path. The global magnitude of unsustainable metrics of resource use and waste production confronts humanity with a major choice:

  1. Either hide behind our social delusions via the continued use of inadequate metrics and languages to continue with the busyness of monetisation as usual;
  2. or find the courage to confront the externalities of civilisation head on,
    • by assessing human collective behaviour via metrics from the physical and living world around us,
    • and by shifting the use of pervasive digital technology from perpetuating simplistic delusions to developing powerful new visual languages for sharing and validating knowledge and for improving our mutual level of understanding.

Shallow innovation, material consumption, and the spectacle of social games are not making human societies any happier. Busyness as usual is decreasing the prospects of  a healthy and enjoyable living environment for future generations of humans and other larger living creatures.

The addictive nature of money and related social delusions have severely distorted perceptions of risk, courage, and value throughout human history. The distortions clearly visible in the cultures of the “developed” world:

  1. Continuing belief in a stupefyingly simplistic metric of money as a universal tool for assessing value
  2. Replacement of independent critical thought and understanding our purpose in life with a mindless pursuit of perpetual busyness
  3. The pathologicalisation of  refusal or inability to submit to busyness, via discrimination against neurodivergent behaviour, and the via invention of various  psychological “disorders”
  4. The normalisation of narcissistic behaviours
  5. A false perception of financial investors as courageous risk takers
  6. Celebration of start-up “ventures” with a 90% failure rate
  7. The persistence of Edward W. Deming’s deadly diseases of management
  8. Continuing belief in social hierarchies as an inevitable aspect of human nature, and as an essential tool for coordinating economic activity
  9. An anthropocentric definition of intelligence and related flawed reasoning, to justify human exceptionalism, and to discount the value of biological diversity
  10. A naive perception of most human technologies as elements of progress, enabled by human exceptionalism

Reframing the collapse of civilisation into a reinvention of value creation

It is only once we fully appreciate the extent to which our culture is distorting our perceptions of potential value that we can begin to embark on a path into a less depressing future. In order to succeed we must reinvent the foundations of civilisation. If cities, written language, and money are inadequate, then we have to critically reexamine and carefully reinvent:

  1. How we organise our living spaces and related flows of resources and energy
  2. The tools of human collaboration
  3. The metrics that we use to assess progress

Reinventing human collaboration

This is perhaps the most difficult challenge, as it assumes that we are ready to acknowledge and deconstruct our cultural delusions. Where do we start?

Scientific evidence tells us that typical human nature is collaborative and not competitive, and that only around 1% of the population suffer from an innate psychopathic lack of empathy. We can relearn how to best deal with psychopathic tendencies from pre-historic hunter gatherer societies which did not have cities, written language and money. The following avenues deserve attention:

  • Replacement of  incentives for gaining individual social status with incentives for collaboration and mutual support. In this context we have the opportunity to realign collaboration with our cognitive limits, and to refocus collaboration on small social groups of around 100 individuals.
  • Making use of digital user interfaces to develop intuitive and less ambiguous non-linear visual languages, to share knowledge, and to nurture shared understanding.
  • Making use of zero marginal cost ubiquitous communications tools, to remix physical and virtual communities, and to maximise the supports available to neurodivergent individuals.

semantic lensTo perform this transformation requires a semantic lens to reason and make sense of the world and the natural environment from a human perspective. Such a modelling language for purpose and value systems can be constructed from five simple ingredients that influence our motivations and human cultural evolution:

  1. nature (our context) :
    container of our lives;
    source of food and primary sensory experiences
  2. artefacts (our tools and creative output in the physical realm) :
    building blocks of our material world;
    source of secondary sensory experiences
  3. symbols (our tools and creative output in the abstract realm) :
    building blocks of our languages and thoughts;
    source of perceptive filters
  4. societies (our subconscious (re)production of movements, artefacts and symbols) :
    building blocks of our behavioural patterns for interacting with the world and with other humans;
    source of social rules and norms
  5. critical perspectives (our conscious observation of all human behaviour) :
    building blocks of critical and divergent thoughts ;
    source of adaptive behaviour

The semantic  lens encourages us to surface and critically examine implicit and unconscious assumptions about our lives.

Reinventing the measures of economic progress

Once we look beyond the simplistic and culturally biased lens of money, we can focus on domain specific metrics from the physical and living biological world to quantify various forms of waste and inequality in terms of access to food, knowledge, resources, and energy.

logistic lensHuman value creation (flows of food, knowledge, resources, waste, and energy and related metrics) within a given culture can be understood and visualised via a logistic lens consisting of five simple categories:

  1. energy and food production : collect / grow, store, release / eat
  2. engineering : design, make, reuse, recycle, automate
  3. transportation : move, communicate
  4. maintenance : maintain system quality attributes
  5. culture : play, learn

The logistic lens in conjunction with the semantic lens provide us with a language for reasoning about economic progress, positive and negative externalities, and about possibly desirable future states.

system lensWhen the core concepts of the semantic and logistic lenses are not only used in the abstract, but are instantiated and visualised in conjunction with a system lens to reflect the concrete interactions between individuals and the concrete concerns of specific communities, the resulting dialogue is grounded in observations and data from physical and living systems, and is not obfuscated by fungible monetary metrics.

A simple rule of thumb for evaluating new products and ventures: Any innovation that does not create significant positive externalities in the logistic lens is destined to fail. A simplistic monetisation use case is no longer good enough.

Reinventing our living spaces and logistics

The advent of ubiquitous global communication and increasingly advanced levels of automation of industrial production provides the substrate for re-imagining our living spaces and daily activities.

Cities and their surroundings may be re-conceived as sustainable living ecosystems, and the thrust of human inventiveness may shift away from new gadgets and digital slot machines to new forms of reducing, reusing, and recycling waste, and to phasing out our reliance on brittle and unsustainable sources of food, resources, and energy.

The coordinated effort that is needed to address climate change and environmental destruction in a timely matter is only possible on a new foundation for human civilisation. Established institutions are losing relevance and are starting to give way to emergent structures that operate in ways that can not be understood via the outdated  illusion of economic “growth”. Earlier today Paul B. Harzog reiterated Buckminster Fuller’s message in the following  words:

Let us come together to build the future we want, and let the dying infrastructure around us decay organically. It is its own obsolescence.

Join our efforts on 3 June in Auckland and Melbourne

The upcoming CIIC unconference in June 2017 is an opportunity to work on the reinvention of human collaboration. A whole number of people have already made significant progress in this direction. Join us in Auckland or in Melbourne!

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 in 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