Creating human-scale learning organisations

seci

Progress in healthcare delivery and many other domains and industries is contingent on organisations that are capable of absorbing and learning from increasing volumes of data, and capable of integrating the resulting knowledge with the tacit domain expertise and contextual awareness of humans. This article introduces the notion of human scale technology to describe the characteristics that enable tacit knowledge and digitized explicit knowledge to flow between humans and software systems, and it elaborates the role that agent based formal semantic models and non-hierarchical governance structures can play in this context.

Introduction

In the healthcare sector for example, services are coordinated and delivered via medical practitioners, via specialised clinical staff and administrative staff, and via a growing number of supporting software systems. Whilst the level of automation is rising in many domains [1], human tacit knowledge, situational awareness, and the ability to develop trusted relationships amongst peers and with patients are critical elements of optimal service delivery. The overall confidence of patients in the healthcare delivery system is a function of the levels of trust in clinicians and in the systems and tools used by clinicians and patients.

1. Learning organisations

Feedback loops of information flows between agents are the atoms of organisational learning. The SECI (socialisation, externalisation, combination, internalisation) model [2][3] is a useful conceptual tool for extending the concept of continuous improvement into the realm of knowledge intensive organisations.

Concrete SECI knowledge flows can be visualised and formalised with the resources, events, and agents (REA) paradigm [4], leading to representations that are easily understandable by humans and at the same time easily processable by software tools, as illustrated in figure 1.

human lens - example
Figure 1. Extract from a visual semantic model expressed through the human lens

A national or regional healthcare delivery system is an example of one of the most complex systems operated by humans. Some aspects of such systems are the result of deliberate design, whereas most aspects are the result of cultural evolution under externally imposed constraints. The learning potential of human institutions is defined by the tacit knowledge of the people that are part of the institution, by the understandability and adaptability of the designed aspects of the institution (including policies and technological systems), and by external constraints that are imposed on the institution (in particular access to resources).

1.1. Complexity level 0

One of the simplest possible learning systems is a system of two agents a1 and a2 that can process three categories of events e1, e2, and e3 and store information about these events in a suitable information resource structure r:

  1. Agent a1 triggering event e1, and agent a2 storing information about the occurrence of e1 in r, replacing all prior stored information about events
  2. Agent a1 triggering event e2, and agent a2 storing information about the occurrence of e2 in r, replacing all prior stored information about events
  3. Agent a1 triggering event e3, and agent a2 responding with r – information about the stored event

The learning challenge at this level of complexity is limited to the error rates of the communication channel between the two agents.

1.2. Complexity level 1

A learning system of some complexity from the perspective of human cognitive limits is a system of two software agents that can process many different categories of events, store structured information about a large number of events, and respond to events in context specific ways [5][6].

1.3. Complexity level 2

A learning system of medium complexity from the perspective of human cognitive limits is a system of a human agent and a software agent that can process many different categories of events, remember both structured and unstructured information about a large number of events, and respond to events in context specific ways [7][8].

1.4. Complexity level 3

An example of a highly complex learning system is a system of two human agents that can process a very broad range of different categories of events, remember both structured and unstructured information about these events, and respond to events in highly context specific ways.

1.5. Complexity level 4

Some of the most complex learning systems involve multiple groups of human agents, and all the interactions between these groups and within these groups. Such learning systems can only be understood by introducing viewpoints, perspectives, and agent motivations as first class modelling concepts [9][10][11].

1.6. Complexity level 5

The most complex learning systems involve multiple groups of human and software agents, including software agents that perform above human cognitive limits, and all the interactions between these groups and within these groups. Such learning systems can only be understood if software agents are able to make their knowledge accessible in human scale representations that respect human cognitive limits [12].

2. Agent based modelling

As highlighted by the SECI cycle, knowledge within an institution accumulates in two places: within the heads of people (tacit), and within knowledge artefacts and software systems (explicit). The MODA + MODE meta paradigm [13][14] is concerned with supporting the SECI cycle with transdisciplinary cultural practices and tools. The core of MODA + MODE consists of two parts:

  1. A set of 26 backbone principles (thinking tools) for creating learning organisations and understandable systems that transcend established discipline boundaries.
  2. The human lens, which is a metalanguage for describing the semantics of complex system behaviour at all levels of scale.

The categories of the human lens are invariant across cultures, space, and time, and hence they are suitable structural elements of a metalanguage for specifications of formal domain specific languages [5] in a multi-agent context, which are needed to formalise the descriptions of systems at complexity levels 4 and 5.

3. Human scale technologies

For at least two decades now software developers and their employers have neglected the role of understandability for humans. The result is a web of technological dependencies that no one understands and that cannot easily be analysed in terms of potential risks [15][16].

The risks associated with opaque systems are not limited to classical software systems. Artificially intelligent (AI) systems, and the way in which they are currently designed, further grow the web of dependencies, complete with naïve and simplistic assumptions about human nature and economics baked in [17].

Human scale computing [18] can be understood as the elaboration of the role of cognitive characteristics of humans within ergonomics. Human cognitive limits must become a primary concern in the design of human institutions and technologies [12], in much the same way that human scale physical dimensions and characteristics have shaped the discipline of 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.

Our current technologies and communication tools hardly meet any of the human scale computing criteria to a satisfactory level. I believe that the human lens is an appropriate foundation for further work towards human scale computing.

4. Organisational structures

All effective approaches for continuous improvement [19] (such as Kaizen, Toyota Production System, Waigaya, etc.) and innovation (Open Space [20], Manifesto for Agile Software Development, collaborative design, etc.) share one noteworthy common principle:

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

This observation is backed up by evidence from thousands of organisations that strive to improve or establish a culture of innovation. 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.

Research of highly competitive Western cultures [21] demonstrates that the social game known as capitalistic economics is a game of luck. Within that game, success has nothing to do with value creation for society and everything to do with social manipulation skills and lack of empathy [22][23][24].

As long as our economic paradigm rewards social gaming, individuals can improve their odds of success by adopting psychopathic behavioural patterns, and by claiming and taking credit for the work of others. Depending on one’s level of empathy, beyond the façade of social success, mental health suffers more or less in the process.

An alternative approach is for a team to agree on non-conventional measures of success, and to work together as a collaborative team to share knowledge, resources, opportunities and success, and by removing all forms of in-group competition and hierarchical structures, to shift the odds for an entire group of people. Given the level of unproductive in-group competition in hierarchical teams [25], non-hierarchical teams have a clear collaborative edge and are well positioned to thrive [26].

The team approach is better for human mental and physical health [27], and it also allows a group to be more selective in terms of where to look for opportunities and how to contribute to society. Problems with hierarchical forms of organisation result from cultural inertia [28] and from the extent to which humans are culturally programmable [29][30].

5. Competency networks

The competency network within the organisation is the union of all the multi-dimensional domain-specific competency rankings that individuals allocate to the other members within the group [31]. It is the only social structure that directly supports the purpose of an organisation.

The existence of competency networks 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 [28] can keep fear, mistrust, and in-group competition alive, and easily leads to the emergence of new oppressive hierarchical structures.

All healthy and resilient institutions have a well-functioning competency network [26][32]. A good way to understand competency networks is via the notion of trustworthiness and the nurturing and maintenance of trusted relationships [33].

A competency network is the graph of experience-based pair-wise trustworthiness ratings in relation to various domains between the members of a group.

The trustworthiness ratings in a competency network are tied to specific pairs of individuals, and by definition they are not directly transferable and never aggregated into any global ranking. The notion of competency networks is inspired by the correlation between software system structures and the communication patterns between human software developers observed by Mel Conway in 1967 [34].

6. Conclusion

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

For the first time, the age of digital networks enables us to construct cognitive assistants that help us to nurture and maintain globally distributed human scale competency networks – networks of mutual trust. It is time to tap into this potential and to combine it with the potential of zero-marginal cost [35] global communication and collaboration.

All successful non-hierarchical organisations replace management hierarchies with a simple advice process [26] that establishes the vital feedback loops that enable the organisation to learn and adapt in a timely manner, even in a highly dynamic context.

References

  1. Moreno-Conde A, et al. Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis. J Am Med Inform Assoc, Oxford University Press, 2015;22: pp. 925–934.
  2. Takeuchi, Nonaka, The New Product Development Game [Internet], Harvard Business Review, https://hbr.org/1986/01/the-new-new-productdevelopment-game, 1986.
  3. Nonaka, Toyama, Hirata, Managing Flow: A Process Theory of the Knowledge-Based Firm, Palgrave Macmillan, 2008.
  4. Hruby P., Model-Driven Design Using Business Patterns, Springer, 2006.
  5. Bettin J. and Clark T., Advanced modelling made simple with the Gmodel metalanguage. Proceedings of the First International Workshop on Model-Driven Interoperability, ACM, 2010; pp. 79-88.
  6. Bettin J. and Clark T., Gmodel, a language for modular meta modelling. In Australian Software Engineering Conference, KISS Workshop, 2009.
  7. Murray J.H., Inventing the Medium: Principles of Interaction Design as a Cultural Practice, MIT Press, 2011.
  8. The potential and limits of clinical decision support systems, CIIC unConference [Internet]. Auckland, Sept 2016: https://ciic.s23m.com/expected-results/ciic-3-september-2016-auckland/.
  9. Senge P.M., The Fifth Discipline: The Art & Practice of The Learning Organization, Currency, 1990.
  10. Neurodiversity – The Core of Creativity, CIIC unConference [Internet]. Auckland, December 2016: https://ciic.s23m.com/expected-results/ciic-3-december-2016-auckland/.
  11. Design and development of tools for effective self-care, CIIC unConference [Internet]. Auckland, March 2016: https://ciic.s23m.com/expected-results/ciic-5-march-2016-auckland/ .
  12. The essence of humanity: interaction and collaboration of humans and intelligent machines, CIIC unConference [Internet]. Auckland, Sept 2017: https://ciic.s23m.com/expected-results/ciic-16-september-2017-auckland/.
  13. Bettin J., Model Oriented Domain Analysis & Engineering, Thinking Tools for Interdisciplinary Research, Design, and Engineering, CIIC Blog [Internet]. Auckland, August 2017: https://coininco.files.wordpress.com/2017/08/moda-and-mode-lenses-and-principles.pdf.
  14. Bettin J., Model Oriented Domain Analysis & Engineering. Domain Engineering Product Lines, Languages, and Conceptual Models. Berlin Heidelberg: Springer Verlag, 2013; pp. 263-290.
  15. Foote B., Yoder J., Big Ball of Mud, Fourth Conference on Patterns Languages of Programs, Monticello, Illinois, 1997.
  16. Bettin J., Software, Engineering, Artefacts, Language [Internet]. Proceedings of the SEMAT (Software Engineering Method and Theory) Workshop Zurich, March 2010: http://semat.org/proceedings-of-the-semat-zurich-workshop .
  17. O’Neil C., Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Crown, 2016.
  18. Human scale computing, CIIC unConference [Internet]. Auckland, June 2017: https://ciic.s23m.com/expected-results/ciic-3-june-2017-auckland/
  19. Deming W. E., Out of the Crisis, Massachusetts Institute of Technology, 1982.
  20. Owen H., Open Space Technology: A User’s Guide, Berrett-Koehler Publishers, 2008.
  21. Pluchino A., Biondoy A. E., Rapisardaz A., Talent vs Luck: the role of randomness in success and failure, arXiv:1802.07068v2 [physics.soc-ph], February 2018.
  22. Babiak P., Hare R., Snakes in suits: When psychopaths go to work, Harper Business, 2006.
  23. Long S., The perverse organisation and its deadly sins, Karnac Books , 2008.
  24. Long S., Socioanalytic methods – Discovering the hidden in organisations and social systems, Routledge, 2013.
  25. Graeber D., Bullshit Jobs : A Theory, Penguin, 2018.
  26. Laloux F., Reinventing Organizations, Nelson Parker, 2014.
  27. Bowles S., Gintis H., A Cooperative Species, Princeton University Press, 2011.
  28. Kegan R., Laskow Lahey L., Immunity to change – How to overcome and unlock the potential in yourself and your organisation, Harvard Business Review Press, 2009.
  29. Tomasello M., The Cultural Origins of Human Cognition, Harvard University Press, 1999.
  30. Girard P., Pavlov V., Wilson M. C., Belief diffusion in social networks [Internet]. University of Auckland, 2015 : https://www.cs.auckland.ac.nz/~mcw/Research/Outputs/GPW2015.pdf.
  31. Bettin J, Designing filtering, trust building, thinking, and learning tools for distributed high-performance teams, Proceedings of the HINZ Conference Rotorua, November 2017.
  32. Wilson D. S., Does Altruism Exist?, Yale University Press, 2015.
  33. Bettin J, Elliffe M., Improving Interoperability and Trustworthiness of Healthcare Data Repositories. Proceedings of the HINZ Conference Auckland, November 2016.
  34. Conway M, Conway’s Law, 1967 : http://melconway.com/Home/Conways_Law.html.
  35. Rifkin J. The Zero Marginal Cost Society. St Martin’s Press, 2013.

 


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Drop the mask to create collaborative edges

Drop-the-mask

Human societies are characterised by abstract group identities, from local communities, to favourite sports teams, employers, professions, social class, languages, dialects, tribes, countries, online groups, brand loyalty, etc.

Every identifiable group identity is characterised by specific behavioural cultural norms, only some of which are explicitly stated and acknowledged. People who identify with a group are expected to conform with the explicit and implicit behavioural code.

What is only rarely talked about in mainstream society is the effort that it takes individuals to conform to a multitude of group identities, especially if the social norms associated with different identities are incompatible, and to some extent contradict each other.

Autistic people, who do not subconsciously pick up implicit social norms and the meaning of non verbal social signals are acutely aware of the mental effort needed to conform to social norms and of the inconsistencies and conflicts between various norms. As a result, autistic people are much less motivated to subscribe to any group identity, and experience any group demands for conformance with arbitrary rules that serve no obvious purpose (other than a confirmation of identity) as a significant mental burden.

The masking that goes hand in hand with attempts to comply with arbitrary and often implicit social norms is increasingly recognised by the autistic community as a key reason for autistic burn-out and suicide. However, trends in mental health statistics in the wider population hint at a problem far beyond the autistic community.

It is well established that growing levels of social inequality correlate with a rise in mental health issues, and the root cause may well relate to the formation of increasingly absurd group identities and associated signals of social status that make it acceptable to exclude the less fortunate. Research by anthropologist David Graeber confirms that masking also takes a significant toll on those who copy and comply with social norms without any conscious effort:

“Those who work bullshit jobs are often surrounded by honor and prestige; they are respected as professionals, well paid, and treated as high achievers—as the sort of people who can be justly proud of what they do. Yet secretly they are aware that they have achieved nothing; they feel they have done nothing to earn the consumer toys with which they fill their lives; they feel it’s all based on a lie—as, indeed, it is.”

The last 100 years can be described as the age of advertising and marketing, which is fuelled by industrialised production of group identities – think brands, and by in-group competition – think financial capitalism. From evolutionary biology we know that in-group competition has negative survival value – it is the opposite of intelligent behaviour.  It seems that this insight is finally filtering through to the social world of busyness as usual.

Especially at work, it is time to drop the mask

A couple of weeks ago, as part of Techweek NZ, I  reflected on the insights gathered by the CIIC community over the last three years, and talked about the challenges that transcend the established silos of industry, government and academia. The discussions with audiences in Auckland, Tauranga, Wellington, and Christchurch confirmed that it is time to shift from a culture of sick busyness to an appreciation of the value of life. You can watch the synopsis of a corresponding webinar below.

In New Zealand we can read about shocking mental health statistics every week, and we have significant problems with workplace bullying in the healthcare sector and in other industries.

Advanced automation gives us the choice between imagining, creating, and living in cruel worlds and imagining, creating, and living in compassionate worlds.

We have a choice!

Inclusive culture is minimalistic. Adopting a small backbone of explicit first principles that have a track record of encouraging trust building and learning helps.

The CIIC community is looking forward to your perspectives and insights on human, non-human, and ecosystem health.

Join us on 9 June and on 16 June 2018 in open space to explore the challenges and elements of potential solutions at AUT Colab in Auckland and at RMIT in Melbourne. At each workshop we discuss one or more wicked problems that have been submitted by participants.


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Human, non-human, and ecosystem health

ecosystem

At the March CIIC workshop it was great to see a growing number of regular participants, as well as a growing number of students and researchers from AUT. The results of the discussions on topics at the intersection of agriculture and health are available here.

For the next CIIC workshops on 9 June (Auckland) and 16 June (Melbourne) we are encouraging submissions of questions that help explore the full value of human, non-human, and ecosystem health from different viewpoints and perspectives.

Ultimately the most valuable export goods of New Zealand and Australia could be health related products and services. Such services and products will in many cases be based on techniques and outputs from the agriculture sector, but they may also include other goods and services.

In this context the interests of the wider population in terms of access to high quality and healthy food and ecological sustainability significantly overlap with the export interests of farmers and the agriculture sector.

The longer term perspective and in particular the ecological viewpoint on health extends far beyond the mandate of current human healthcare providers and the perspectives within the agriculture sector.

Ecosystem health is concerned in particular with values that are often sidelined by reference to established economic dogma and by interests of powerful economic players:

  1. Happiness and mental health
  2. Non-human health in the broadest sense
  3. Biodiversity
  4. Knowledge about food production and preparation
  5. Democratic production
  6. Ethical considerations, transparency and fairness
  7. Communal rituals

The following documents and reports from the International Panel of Experts on Sustainable Food Systems (IPES-Food) contain relevant background information:

 

gulf.jpg

The Hauraki Gulf provides a “good” example of an ecosystem that is under unprecedented pressure from pollution and over-fishing. The recent Hauraki Gulf Forum’s stock take concludes that the gulf is now deteriorating faster than any management efforts could tackle the degradation.

We looking forward to your perspectives and insights on human, non-human, and ecosystem health.

Join us on 9 June and on 16 June 2018 in open space to explore the challenges and elements of potential solutions at AUT Colab in Auckland and at RMIT in Melbourne. At each workshop we discuss one or more wicked problems that have been submitted by participants.


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Urban farming – the future of agriculture?

Leading up to the next CIIC workshop on 3 March, here is a good documentary on the potential of urban farming and vertical farming – and by implication, on the threats to established business models in the agriculture and food production sector:

The examples provided highlight the need for transdisciplinary research and design. The following commentary is worthwhile bearing in mind, as vertical farming is not a silver bullet. Urban farming needs to be considered from a holistic perspective:

… this technique has a huge place to play in the world’s future food production. Cities get so clogged up with trucks delivering small quantities of fresh leafy greens to small stores and restaurants every single day. Imagine if all that food was grown nearby and stores and restaurants could order only what they were selling, and to have bike or drone couriers bring fresh stuff to them as they run out, resulting in no waste, and in huge reductions in congestion and fossil fuel.

The way to go is vertical farms for fast rotting/fresh daily food needs with low light/fertiliser requirements, and greenhouse hydroponics, using natural sunlight – and supplementing if needed, for plants with high water and fertiliser needs. The answers have to be varied. Attempts to promote one system of farming as “the solution” are bound to fail, and will be written off instead of being developed to their full potential. That would be incredibly sad.

It is theoretically possible to grow peppers and tomatoes and other plants with very high light and nutrient needs indoors under lights. But there are massive amounts of desert land on the planet where sunlight is not in short supply. It makes no sense to try to grow these plants in tiny spaces under electric lights when we could just use available sunlight.

Another consideration is that cereal crops don’t come off the plant ready to eat. They need to be hulled and processed, and the majority of the plant is waste straw that needs to be processed or disposed of in a useful way. [A range of production systems are needed to accommodate the needs of all the crops that are valuable ingredients of human diets]

urban farming

Urban farming can take many forms. Higher Ground Farm manages two rooftop farms in Boston.  One is a commercial rooftop farm located on the Boston Design Center in the Seaport District of Boston, growing greens, herbs, tomatoes, and other vegetables for sale to Boston restaurants.  The other is on Boston Medical Center, growing fresh produce for the hospital’s patients, staff, and visitors and for their on-site food bank.

Taking a clue from nature to solve plastic pollution

The intersection between agriculture and healthcare extends beyond nutrition and food production. The Humble Bee is a very interesting concept that holds great potential for non-trivial innovation.

OLYMPUS DIGITAL CAMERA

By understanding the potential of the bees from a product and market perspective, Humble Bee is creating a clear link between an environmental problem and a major market opportunity.

Humble Bee’s product aims to replace some of the polymers clogging up the oceans and the chemicals used in plastics manufacturing, which are in the process of being banned. Or, put another way, we are working to alleviate the market pain of getting ahead of compliance, whilst maintaining product performance.

A co-ordinated community response to the Guava Moth

One of our readers in Auckland recommends addressing the damage done by the Guava Moth, which is devastating feijoa crops. Most soft fruit and will require a co-ordinated community response.

guava moth

The guava moth is not a new pest – it was first found in Northland in 1997 and over the past few years has become a problem in pockets around Auckland and further south. In the Auckland region this year [2017], there has been an explosion in the population of the guava moth, Auckland Council’s biosecurity team said.

Alice Rennie is a keen gardener in the Auckland suburb of Half Moon Bay and knows all about the difficulties that the guava moth can cause. Her small garden is a treasure trove of fruit trees and vegetables and should be providing her with ample produce throughout the year.

“Guavas, feijoas, apples, plums, citrus, limes, orange, mandarin, lemon and we also have berries. Most of my food and fruit and veggies comes from the garden. And I noticed about four years ago that the plums had tiny weeny holes in them and when I cut them open I found bugs,” she said.

If you are working on a wicked problem and would like to share your knowledge and questions with other innovators, domain experts and researchers in an interdisciplinary forum, join us at the next CIIC workshop on 3 March at AUT Colab in Auckland and at RMIT in Melbourne. At each workshop we discuss one or more wicked problems that have been submitted by participants.

 


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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.

nz-agriculture

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.

nutrition

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.

nz-healthcare.png

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!


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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.

trust

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

learning.png

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.

distributed

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.


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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.

Mathematics

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.

DN160212_DSC0107.NEF

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.

trust2


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