Beyond busyness as usual

I have always been irritated by people for whom business is first and foremost about “monetisation”. Extrinsically motivated busyness people are incapable of understanding any non-trivial innovation. The worship of monetisation often goes hand-in-hand with the introduction of so-called “organisational values” as hollow slogans, with no thoughts spared for how these values are going to be enacted, and how they might create something that people within and beyond the organisation actually recognise and appreciate as valuable.

Existing approaches like the highly popular business model canvas or the OMG’s business motivation model miss the bigger picture of cultural evolution in the context of zero marginal cost communication and assume a very traditional business mindset.

Value systems

We live in a context of rapid and multidimensional cultural evolution. A few years ago the need for agreeing of what constitutes a useful direction and the need for assessing progress prompted me to design a simple modelling language for purpose and value systems.

semantic lens

The semantic lens is a simple tool for agreeing what is considered valuable, and it assists in identifying suitable metrics for keeping track of output or progress. As a nice side effect the metrics encouraged by the semantic lens prevent results from being dumbed down prematurely to easily corruptible monetary numbers.

example-semantic-lens

Example of an instantiated semantic lens

The semantic lens is a visual language for describing human motivations. Four of the five core concepts directly relate to the outputs of human creativity, and nature, and the fifth concept, is directly connected to the first four concepts. The element of critical self-reflection invites the questioning of established values and the consideration of alternative candidate values.

A configured semantic lens assists in surfacing the cultural context and assumed value system that underpins the value proposition of a potential innovation. In the absence of an explicit value system it is impossible to reason about innovation in any meaningful way – the discussion is limited to thinking within the established cultural box and very easily deteriorates into a discussion of “ingenious ways of monetising data”.

s23m-semantic-lens

The S23M semantic lens explains why S23M exists

  1. Critical self-reflection : regarding all other elements of the semantic lens (in no particular order) towards sustainability, resilience, and happiness
  2. Symbols : Co-creating organisations and systems which are understandable by future generations of humans and software tools
  3. Nature : Maximising biodiversity
  4. Artefacts : Minimising human generated waste
  5. Society : Creating a more human and neurodiversity friendly environment
    • Generating more trust – less surprising misunderstandings, 
more collaborative risk taking, less exploitation, more mutual aid
    • Generating more learning – more open knowledge sharing, 
less indirect language, less hierarchical control, deeper understanding
    • Generating more diversity – more appreciation of difference, less coercion, more curiosity
s23m-feedback-loops

We are in the business of strengthening / weakening specific feedback loops

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 in the living world.

Value creating activities

To go beyond motivations and intent, and to describe the value creating activities within an economic system, or the activities of a specific organisation or individual economic agent, requires a dedicated modelling language beyond the semantic lens.

logistic lens

Understanding the human value creation process is not helped by the multitude of completely arbitrary and internally overlapping categorisation schemes that economists and business people use to talk about industries and sectors. The logistic lens has the potential to put an end to the distracting proliferation of jargons via five simple categories. In the logistic lens models can be nested in a fractal structure as needed to reflect the reality of complex systems.

Four of the five core concepts of the logistic lens deal with activities that produce observable results in the physical and natural environment, and all human cultural activities that are one or more levels removed from being measurable in the physical and natural environment are confined to the culture concept.

  1. Energy and food production provide the fuel for all our human endeavours.
  2. Design and engineering are the focus of many human creative endeavours, and have resulted in the tools that power our societies.
  3. Transportation and communication allow human outputs, both in terms of concrete and abstract artefacts, to be shared and made available to others, and allow resources and knowledge to be deployed wherever they are needed.
  4. Maintenance and quality related activities are those that are needed to keep human societies and human designed technologies operational.
example-logistic-lens

Example of an instantiated logistic lens to structure and optimise activities within a given culture

Economic progress and value creation can be understood in terms of the cultural activities of playing and learning, and related design and engineering activities that lead to technological innovation.

Truly disruptive innovations have the characteristic of not only resulting in a new player in the economic landscape, but they also trigger or tap into a shift in value systems. Thus the semantic lens is a useful gauge for identifying and exploring potentially disruptive innovations.

Taken together the semantic and logistic lenses provide a very small and powerful language for reasoning about human behaviour and human creativity – even beyond the confines of established social norms and best business practices.

Innovation and cultural change can only be transformative if it substantially redefines social norms and so-called best practice.

The big human battle of this century

The big human battle of this century is going to be the democratisation of data and all forms of knowledge, and the introduction of digital government with the help of free and open source software

Whilst undoubtedly the reaction of the planet to the explosion of human activities with climate change and other symptoms is the largest change process that has ever occurred in human history in the physical realm, the exponential growth of the Internet of Things and digital information flows is triggering the largest change process in the realm of human organisation that societies have ever experienced.

The digital realm

The digital realm

Sensor networks and pervasive use of RFID tags are generating a flood of data and lively machine-to-machine chatter. Machines have replaced humans as the most social species on the planet, and this must inform the approach to the development of healthy economic ecosystems.

Internet of Things

Sensors that are part of the Internet of Things

When data scientists and automation engineers collaborate with human domain experts in various disciplines, machine-generated data is the magic ingredient for solving the hardest automation problems.

  • In domains such as manufacturing and logistics the writing is on the wall. Introduction of self-driving vehicles and just a few more robots on the shop floor will eliminate the human element in the social chatter at the workplace within the next 10 years.
  • The medical field is being revolutionised by the downward spiral of the cost of genetic analysis, and by the development of medical robots and medical devices that are hooked up to the Internet, paving the way for machine learning algorithms and big data to replace many of the interactions with human medical professionals.
  • The road ahead for the provision of government services is clearly digital. It is conceivable that established bureaucracies can resist the trend to digitisation for a few years, but any delay will not prevent the inevitability of automation.

The social implications

Data driven automation leads to an entirely new perspective on the purpose of the education system and on the role of work and employment in society.

Large global surveys show that more than 70% of employees are disengaged at work. It is mainly in manufacturing that automation directly replaces human labour. In many other fields the shift in responsibilities from humans to machines initially goes hand in hand with the invention of new roles and loss of a clear purpose.

Traditional work is being transformed into a job for a machine. Exceptions are few and far between.

Data that is not sufficiently accessible is only of very limited value to society. The most beneficial and disruptive data driven innovation are those that result from the creative combination of data sets from two or more different sources.

It is unrealistic to assume that the most creative minds can be found via the traditional channel of employment, and it is unrealistic that such minds can achieve the best results if data is locked up in organisation-specific or national silos.

The most valuable data is data that has been meticulously validated, and that is made available in the public domain. It is no coincidence that software, data, and innovation is increasingly produced in the public domain. Jeremy Rifkin describes the emergence of a third mode of commons-based digitally networked production that is distinct from the property- and contract-based modes of firms and markets.

The education system has a major role to play in creating data literate citizen-scientists-innovators.

The role of economics

It is worthwhile remembering the origin of the word economics. It used to denote the rules for good household management. On a planet that hosts life, household management occurs at all levels of scale, from the activities of single cells right up to processes that involve the entire planetary ecosystem. Human economics are part of a much bigger picture that always included biological economics and that now also includes economics in the digital realm.

To be able to reason about economics at a planetary level the planet needs a language for reasoning about economic ecosystems, only some of which may contain humans. Ideally such a language should be understandable by humans, but must also be capable of reaching beyond the scope of human socio-economic systems. In particular the language must not be coloured by any concrete human culture or economic ideology, and must be able to represent dependencies and feedback loops at all levels of scale, as well as feedback loops between levels of scale, to enable adequate representation of the fractal characteristic of nature.

The digital extension of the planetary nervous system

In biology the use of electrical impulses for communication is largely confined to communication within individual organisms, and communication between organisms is largely handled via electromagnetic waves (light, heat), pressure waves (sound), and chemicals (key-lock combinations of molecules).

The emergence of the Internet of Things is adding to the communication between human made devices, which in turn interact with the local biological environment via sensors and actuators. The impact of this development is hard to overestimate. The number of “tangible” things that might be computerized is approaching 200 billion, and this number does not include large sensor networks that are being rolled out by scientists in cities and in the natural environment. Scientists are talking about trillion-sensor networks within 10 years. The number of sensors in mobile devices is already more than 50 billion.

Compared to chemical communication channels between organisms, the speed of digital communication is orders of magnitude faster. The overall effect of equipping the planet with a ubiquitous digital nervous system is comparable to the evolution of animals with nervous systems and brains – it opens up completely new possibilities for household management at all levels of scale.

The complexity of the Internet of Things that is emerging on the horizon over the next decade is comparable to the complexity of the human brain, and the volume of data flows handled by the network is orders of magnitudes larger than anything a human brain is able to handle.

The global brain

Over the course of the last century, starting with the installation of the first telegraph lines, humans have embarked on the journey of equipping the planet with a digital electronic brain. To most human observers this effort has only become reasonably obvious with the rise of the Web over the last 20 years.

Human perception and human thought processes are strongly biased towards the time scales that matter to humans on a daily basis to the time scale of a human lifetime. Humans are largely blind to events and processes that occur in sub-second intervals and processes that are sufficiently slow. Similarly human perception is biased strongly towards living and physical entities that are comparable to the physical size of humans plus minus two orders of magnitude.

As a result of their cognitive limitations and biases, humans are challenged to understand non-human intelligences that operate in the natural world at different scales of time and different scales of size, such as ant colonies and the behaviour of networks of plants and microorganisms. Humans need to take several steps back in order to appreciate that intelligence may not only exist at human scales of size and time.

The extreme loss of biodiversity that characterises the anthropocene should be a warning, as it highlights the extent of human ignorance regarding the knowledge and intelligence that evolution has produced over a period of several billion years.

It is completely misleading to attempt to attach a price tag to the loss of biodiversity. Whole ecosystems are being lost – each such loss is the loss of a dynamic and resilient living system of accumulated local biological knowledge and wisdom.

Just like an individual human is a complex adaptive system, the planet as a whole is a complex adaptive system. All intelligent systems, whether biological or human created, contain representations of themselves, and they use these representations to generate goal directed behaviour. Examples of intelligent systems include not only individual organisms, but also large scale and long-lived entities such as native forests, ant colonies, and coral reefs. The reflexive representations of these systems are encoded primarily in living DNA.

From an external perspective it nearly seems as if the planetary biological brain, powerful – but thinking slowly in chemical and biological signals over thousands of years, has shaped the evolution of humans for the specific purpose of developing and deploying a faster thinking global digital brain.

It is delusional to think that humans are in control of what they are creating. The planet is in the process of teaching humans about their role in its development, and some humans are starting to respond to the feedback. Feedback loops across different levels of scale and time are hard for humans to identify and understand, but that does not mean that they do not exist.

The global digital brain is currently still in under development, not unlike the brain of a human baby before birth. All corners of the planet are being wired up and connected to sensors and actuators. The level of resilience of the overall network depends on the levels of decentralisation, redundancy, and variability within the network. A hierarchical structure of subsystems as envisaged by technologist Ray Kurzweil is influenced by elements of established economic ideology rather than by the resilient neural designs found in biology. A hierarchical global brain would likely suffer from recurring outages and from a lack of behavioural plasticity, not unlike the Cloud services from Microsoft and Amazon that define the current technological landscape.

Global thinking

The ideology of economic globalisation is dominated by simplistic and flawed assumptions. In particular the concepts of money and globally convertible currencies are no longer helpful and have become counter-productive. The limitations of the monetary system are best understood by examining the historic context in which money and currencies were invented, which predates the development of digital networks by several thousand years. At the time a simple and crude metric in the form of money was the best technology available to store information about economic flows.

As the number of humans has exploded, and as human societies have learned to harness energy in the form of fossil fuels to accelerate and automate manufacturing processes, the old monetary metrics have become less and less helpful as economic signals. In particular the impact of economic externalities that are ignored by the old metrics, both in the natural environment as well as in the human social sphere, is becoming increasingly obvious.

The global digital brain allows flows of energy, physical resources, and economic goods to be tracked in minute detail, without resorting to crude monetary metrics and assumptions of fungibility that open the door to suppressing inconvenient externalities.

A new form of global thinking is required that is not confined to the limited perspective of financial economics. The notions of fungibility and capital gains need to be replaced with the notions of collaborative economics and zero-waste cyles of economic flows.

Metrics are still required, but the new metrics must provide a direct and undistorted representation of flows of energy, physical resources, and economic goods. Such highly context specific metrics enable computational simulation and optimisation of zero-waste economics. Their role is similar to the role of chemical signalling substances used by biological organisms.

Global thinking requires the extension of a zero-waste approach to economics to the planetary level – leaving no room for any known externalities, and encouraging continuous monitoring to detect unknown externalities that may be affecting the planetary ecosystem.

The future of human economics

The real benefits of the global digital brain will be realised when massive amounts of machine generated data become accessible in the public domain in the form of disruptive innovation, and are used to solve complex optimisation problems in transportation networks, distributed generation and supply of power, healthcare, recycling of non-renewable resources, industrial automation, and agriculture.

Five years ago Tim O’Reilly predicted a war for control of the Web. The hype around big data has let many organisations forget that the Web and social media in particular is already saturated with explicit and implicit marketing messages, and that there is an upper bound to the available time (attention) and money for discretionary purchases. A growing list of organisations is fighting over a very limited amount of potential revenue, unable to see the bigger picture of global economics.

Over the next decade one of the biggest challenges will be the required shift in organisational culture, away from simplistic monetisation of big data, towards collaboration and extensive data and knowledge sharing across disciplines and organisational boundaries. The social implications of advanced automation across entire economic ecosystems, and a corresponding necessary shift in the education system need to be addressed.

The future of humans

Human capabilities and limitations are under the spot light. How long will it take for human minds to shift gears, away from the power politics and hierarchically organised societies that still reflect the cultural norms of our primate cousins, and from myopic human-centric economics, towards planetary economics that recognise the interconnectedness of life across space and time?

The future of democratic governance could be one where people vote for human understandable open source legislation that is directly executable by intelligent software systems. Corporate and government politicians will no longer be deemed as an essential part of human society. Instead, any concentration of power in human hands is likely to be recognised as an unacceptable risk to the welfare of society and the health of the planet.

Earth

Earth

Humans have to ask themselves whether they want to continue to be useful parts of the ecosystem of the planet or whether they prefer to take on the role of a genetic experiment that the planet switched on and off for a brief period in its development.

Death by Standardisation

Standardisation is a double-edged sword. Compliance with standards is best restricted to those standards that really make a difference in a specific context.

Even innocent standardisation attempts such as enforcing a shared terminology across an organisation can be counter-productive, as it can lead to the illusion of shared understanding, whereas in practice each organisational silo associates different meanings with the terminology.

There is no simplistic rule of thumb, but the following picture can help to gain a sense of perspective and to avoid the dreaded death zone of standardisation.

Death by Standardisation

The story of life is language

This post is a rather long story. It attempts to connect topics from a range of domains, and the insights from experts in these domains. In this story my role is mainly the one of an observer. Over the years I have worked with hundreds of domain experts, distilling the essence of deep domain knowledge into intuitive visual domain-specific languages. If anything, my work has taught me the skill to observe and to listen, and it has made me concentrate on the communication across domain boundaries – to ensure that desired intent expressed in one domain is sufficiently aligned with the interpretations performed in other domains.

The life of language and the language of life can’t be expressed in written words. Many of the links contained in this story are essential, and provide extensive background information in terms of videos (spoken language, intonation, unconscious body language, conscious gestures), and visual diagrams. To get an intuitive understanding of the significance of visual communication, once you get to the end of the story, simply imagine none of the diagrams had been included.

Drawing Hands, 1948, by the Dutch artist M. C. Escher

It may not be evident on the surface, but the story of life started with language, hundreds of millions of years ago – long before humans were around, and it will continue with language, long after humans are gone.

The famous Drawing Hands lithograph from M. C. Escher provides a very good analogy for the relationship between life and language – the two concepts are inseparable, and one recursively gives rise to the other.

At a fundamental level the language of life is encoded in a symbol system of molecular fragments and molecules – in analogy to an alphabet, words, and sentences.

The language of life

TED – Craig Ventor on creating synthetic life

Over the last two decades molecular biologists and chemists have become increasingly skilled at reading the syntax of the genetic code; and more recently scientists started to work on, and have successfully prototyped techniques to write the syntax of the genetic code. In other words, humans now have the tools to translate bio-logical code into digital code as well as the tools to translate digital code back into bio-logical code. The difference between the language of biology and the language of digital computers is simply one of representation (symbolic representations are also called models). Unfortunately, neither the symbols used by biology (molecules), nor the symbols used by digital computers (electric charges), are directly observable via the cognitive channels available to humans.

However, half a century of software development has not only led to convoluted and unmaintainable legacy software, but also to some extremely powerful tools for translating digital representations into visual representations that are intuitive for humans to understand. We no longer need to deal with mechanical switches or punch cards, and modern user interfaces present us with highly visual information that goes far beyond the syntax of written natural language. These visualisation tools, taken together with the ability to translate bio-logical code into digital code, provide humans with a window into the fundamental language of life – much more impressive in my view than the boring magical portals dreamed up by science fiction authors.

TED – Bonnie Bassler on how bacteria communicate

The language of life is highly recursive. It turns out that even the smallest single-celled life forms have developed higher-level languages, to communicate – not only within their species, but even across species. At the spacial and temporal scale that characterises the life of bacteria, the symbol system used consists of molecules. What is fascinating, is that scientists have not only decoded the syntax (the density of molecular symbols surrounding  the bacteria), but have also begun to decode the meaning of the language used by bacteria, for example, in the case of a pathogen, communication that signals when to attack the host.

The biological evidence clearly shows, in a growing number of well-researched examples, that the development of language does not require any “human-level” intelligence. Instead, life can be described as an ultra-large system of elements that communicate via various symbol systems. Even though the progress in terms of discovering and reading symbol systems is quite amazing, scientists are only scratching the surface in terms of understanding the meaning (the semantics) of biological symbol systems.

Language systems invented by humans

From muddling to modelling

Semantics is the most fascinating touch point between biology and the mathematics of symbol systems. In terms of recursion, mathematics seems to have found a twin in biology. Unfortunately, computer scientists, and software development practitioners in particular, for a long time have ignored the recursive aspect of formal languages. As a result, the encoding of the software that we use today is much more verbose and complex than it would need to be.

From code into the clouds

Nevertheless, over the course of a hundred years, the level of abstraction of computer programming has slowly moved upwards. The level of progress is best seen when looking at the sequence of the key milestones that have been reached to date. Not unlike in biology, more advanced languages have been built on top of simpler languages. In technical terms, the languages of biology and all languages invented by humans, from natural language to programming languages, are codes. The dictionary defines code as follows:

  1. Code is a system of signals used to send messages
  2. Code is a system of symbols used for the purpose of identification or classification
  3. Code is a set of conventions governing behaviour

Sets – the foundation of biological and digital code

Mathematically, all codes can be represented with the help of sets and the technique of recursion. But, as with the lowest-level encoding of digital code in terms of electric charges, the mathematical notation for sets is highly verbose, and quickly reaches human cognitive limits.

The mathematical notation for sets predates modern computers, and was invented by those who needed to manually manipulate sets at a conceptual level, for example as part of a mathematical proof. Software programming and also communication in natural language involves so many sets that a representation in the classical mathematical notation for sets is unpractical.

The importance of high-quality representation of symbols is often under-rated. A few thousand years ago humans realised the limitation of encoding language in sounds, and invented written language. The notation of written language minimises syntactical errors, and, in contrast to spoken language, allows reliable communication of sequences of words across large distances in space and time.

The challenge of semantics

The impossibility of communicating desired intent

Software development professionals are becoming increasingly aware of the importance of notation, but interpretation (inferring the semantics of a message) remains an ongoing challenge. Adults and even young children, once they have developed a theory of mind, know that others may sometimes interpret their messages in a surprising way. It is somewhat less obvious, that all sensory input received by the human brain is subject to interpretation, and that our own perception of reality is limited to an interpretation.

The curse of software maintenance

Interpretation is not only a challenge in communication between humans, it is as much a challenge for communication between humans and software systems. Every software developer knows that it is humanly impossible to write several hundred lines of non-trivial program code without introducing unintended “errors” that will lead to a non-expected interpretation by the machine. Still, writing new software requires much less effort than understanding and changing existing software. Even expert programmers require large amounts of time to understand software written by others.

The challenge of digital waste

We have only embarked down the road of significant dematerialisation of artefacts in the last few years, but I am somewhat concerned about the semantic value of many of the digital artefacts that are now being produced at a mind-boggling rate. I am coming to think of it as digital waste – worse than noise. The waste involves the time involved in producing and consuming artefacts and the associated use of energy.

Sharpening your collaborative edge

Of particular concern is the production of meta-artefacts (for example the tools we use to produce digital artefacts, and higher-level meta-tools). The user interfaces of Facebook, Google+ and other tools look reasonable at a superficial level, just don’t look under the hood. As a result, we produce the digital equivalent of the Pacific Garbage Patch. Blinded by shiny new interfaces, the digital ocean seems infinite, and humanity embarks on yet another conquest …

Today’s collaboration platforms not only rely on a central point of control, they are also ill-equipped for capturing deep knowledge and wisdom – there is no semantic foundation, and the tools are very limited in their ability to facilitate a shared understanding within a community. The ability to create digital artefacts is not enough, we need the ability to create semantic artefacts in order to share meaningful information.

How does life (the biological system of the planet) collectively interpret human activities?

TED – Naomi Klein : Addicted to risk

As humans we are limited to the human perspective, and we are largely unaware of the impact of our ultra-large scale chemical activities on the languages used by other species. If biologists have only recently discovered that bacteria heavily rely on chemical communication, how many millions of other chemical languages are we still completely unaware of? And what is the impact of disrupting chemical communication channels?

Scientists may have the best intentions, but their conclusions are limited to the knowledge available to them. To avoid potentially fatal mistakes and misunderstandings, it is worthwhile to tread carefully, and to invest in better listening skills. Instead of deafening the planet with human-made chemicals, how about focusing our energies on listening to – and attempting to understand, the trillions of conversations going on in the biosphere?

Gmodel – The Semantic Database

At the same time, we can work on the development of symbolic codes that are superior to natural language for sharing semantics, so that it becomes easier to reach a shared understanding across the boundaries of the specialised domains we work in. We now have the technology to reduce semantic communication errors (the difference between intent and interpretation) to an extent that is comparable to the reduction of syntactic communication errors achieved with written language. If we continue to rely too heavily on natural language, we are running a significant risk of ending the existence of humanity due to a misunderstanding.

Life is language

Life and languages continuously evolve, whether we like it or not. Life shapes usand we attempt to shape life. We are part of a dynamic system with increasingly fast feedback loops.

Life interprets languages, and languages interpret life.

Language is life.

Sharpening your collaborative edge

All animals that have a brain, including humans, rely on mental models (representations) that are useful within the specific context of the individual. As humans we are consciously aware of some of the concepts that are part of our mental model of the world, and we can use empirical techniques to scratch the surface of the large unconscious parts of our mental model.

When making decisions, it is important to remember that there is no such thing as a correct model, and we entirely rely on models that are useful or seem useful from the perspective of our individual view point, which has been shaped by our perceptions of the interactions with our surroundings. One of the most useful features of our brains is the subconscious ability to perceive concrete instances of animals, plants, and inanimate objects. This ability is so fundamental that we have an extremely hard time not to think in terms of instances, and we even think about abstract concepts as distinct things or sets (water, good, bad, love, cats, dogs, …). Beyond concepts, our mental model consist of the perceived connections between concepts (spacial and temporal perceptions, cause and effect perceptions, perceived meaning, perceived understanding, and other results of the computations performed by our brain).

The last two examples (perceived meaning and understanding) in combination with the unconscious parts of our mental model are the critical elements that shape human societies. Scientists that attempt to build useful models face the hard tasks of

  • making parts of their mental model explicit,
  • designing measurement tools and experiments to validate the usefulness of their models,
  • and of reaching a shared understanding amongst a group of peers in relation to the usefulness of a model.

In doing so, natural scientists and social scientists resort to mathematical techniques, in particular techniques that lead to models with predictive properties, which in turn can be validated by empirical observations in combination with statistical techniques. This approach is known as the scientific method, and it works exceptionally well in physics and chemistry, and to a very limited extent it also works in the life sciences, in the social sciences, and other domains that involve complex systems and wicked problems.

The scientific method has been instrumental in advancing human knowledge, but it has not led to any useful models for representing the conscious parts of our mental model. This should not surprise. Our mental model is simply a collection of perceptions, and to date all available tools for measuring perceptions are very crude, most being limited to measuring brain activity in response to specific external stimuli. Furthermore, each brain is the result of processing a unique sequence of inputs and derived perceptions, and our perceptions can easily lead us to beliefs that are out of touch with scientific evidence and the perceptions of others. In a world that increasingly consists of digital artefacts, and where humans spend much of their time using and producing digital artefacts, the lack of scientifically validated knowledge about how the human brain creates the perception of meaning and understanding is of potential concern.

The mathematics of shared understanding

However, in order to improve the way in which humans collaborate and make decisions, there is no need for an empirically validated model of the human brain. Instead, it is sufficient to develop a mathematical model that allows the representation of concepts, meaning, and understanding in a way that allows humans to share and compare parts of mental models. Ideally, the shared representations in question are designed by humans for humans, to ensure that digital artefacts make optimal use of the human senses (sight, hearing, taste, smell, touch, acceleration, temperature, kinesthetic sense, pain) and human cognitive abilities. Model theory and denotational semantics, the mathematical disciplines needed for representing the meaning of any kind of symbol system, have only recently begun to find their way into applied informatics. Most of the mathematics were developed many years ago, in the first half of the 20th century.

To date the use of model theory and denotational semantics is mainly limited to the design of compilers and other low-level tools for translating human-readable specifications into representations that are executable by computing hardware. However, with a bit of smart software tooling, the same mathematical foundation can be used for sharing symbol systems and associated meanings amongst humans, significantly improving the speed at which perceived meaning can be communicated, and the speed at which shared understanding can be created and validated.

For most scientists this represents an unfamiliar use of mathematics, as meaning and understanding is not measured by an apparatus, but is consciously decided by humans: The level of shared understanding between two individuals with respect to a specific model is quantified by the number of instances that conform to the model based on the agreement between both individuals. At a practical level the meaning of a concept can be defined as the usage context of the concept from the specific view point of an individual. An individual’s understanding of a concept can be defined as the set of use cases that the individual associates with the concept (consciously and subconsciously).

These definitions are extremely useful in practice. They explain why it is so hard to communicate meaning, they highlight the unavoidable influence of perception, and they encourage people to share use cases in the form of stories to increase the level of shared understanding. Most importantly, these definitions don’t leave room for correct or incorrect meanings, they only leave room for different degrees of shared understanding – and encourage a mindset of collaboration rather than competition for “The truth”. The following slides provide a road map for improving your collaborative edge.

Sharpening Your Collaborative Edge

After reaching a shared understanding with respect to a model, individuals may apply the shared model to create further instances that match new usage contexts, but the shared understanding is only updated once these new usage contexts have been shared and agreement has been reached on model conformance.

Emerging technologies for semantic modelling have the potential to reshape communication and collaboration to a significant degree, in particular in all those areas that rely on creating a shared understanding within a community or between communities.

No one is in control, mistakes happen on this planet

No one is in control, mistakes happen on this planet

As humans we heavily rely on intuition and on our personal mental models for making many millions of subconscious decisions and a much smaller number of conscious decisions on a daily basis. All these decisions involve interpretations of our prior experience and the sensory input we receive. It is only in hindsight that we can realise our mistakes. Learning from mistakes involves updating our mental models, and we need to get better at it, not only personally, but as a society:

Whilst we will continue to interact heavily with humans, we increasingly interact with the web – and all our interactions are subject to the well-known problems of communication. One of the more profound characteristics of ultra-large-scale systems is the way in which the impact of unintended or unforeseen behaviours propagates through the system.

The most familiar example is the one of software viruses, which have spawned an entire industry. Just as in biology, viruses will never completely go away. It is an ongoing fight of empirical knowledge against undesirable pathogens that is unlikely to ever end, because both opponents are evolving their knowledge after each new encounter based on the experience gained.

Similar to viruses, there are many other unintended or unforeseen behaviours that propagate through ultra-large-scale systems. Only on some occasions do these behaviours result in immediate outages or misbehaviours that are easily observable by humans.

Sometimes it can take hours, weeks, or months for  downstream effects to aggregate to the point where they cause some component to reach a point where an explicit error is generated and a human observer is alerted. In many cases it is not possible to trace down the root cause or causes, and the co-called fix consists in correcting the visible part of the downstream damage.

Take the recent tsunami and the destroyed nuclear reactors in Japan. How far is it humanly and economically possible to fix the root causes? Globally, many nuclear reactor designs have weaknesses. What trade-off between risk levels (also including a contingency for risks that no one is currently aware of) and the cost of electricity are we prepared to make?

Addressing local sources of events that lead to easily and immediately observable error conditions is a drop in the bucket of potential sources of serious errors. Yet this is the usual limit of scope of that organisations apply to quality assurance, disaster recovery etc.

The difference between the web and a living system is fading, and our understanding of the system is limited to say the least. A sensible approach to failures and system errors is increasingly comparable to the one used in medicine to fight diseases – the process of finding out what helps is empirical, and all new treatments are tested for unintended side-effects over an extended period of time. Still, all the tests only lead to statistical data and interpretations, no absolute guarantees. In the life sciences no honest scientist can claim to be in full control. In fact, no one is in full control, and it is clear that no one will ever be in full control.

Traditional management practices strive to avoid any semblance of “not being in full control”. Organisations that are ready to admit that they operate within the context of an ultra-large-scale system have a choice between:

  • conceding they have lost control internally, because their internal systems are so complex, or
  • regaining a degree of internal understandability by simplifying internal structures and systems, enabled by shifting to the use of external web services – which also does not establish full control.

Conceding the unavoidable loss of control, or being prepared to pay extensively  for effective risk reduction measures (one or two orders of magnitude in cost) amounts to political suicide in most organisations.

The impossibility of communicating desired intent

Communication relies on interpretation of the message by the recipient

Communication of desired intent can never be fully achieved. It would require a mind-meld between two individuals or between an individual and a machine.

The meaning (the semantics) propagated in a codified message is determined by the interpretation of the recipient, and not by the desired intent of the sender.

In the example on the right, the tree envisaged in the mind of the sender is not exactly the same as the tree resulting from the interpretation of the decoded message by the recipient.

To understand the practical ramnifications of interpretation, consider the following realistic example of communication in natural language between an analyst, a journalist, and a newspaper reader:

Communication of desired intent and interpretation

1. intent

  • Reiterate that recurring system outages at the big four banks are to be expected for at least 10 years whilst legacy systems are incrementally replaced
  • Indicate that an unpredictable and disruptive change will likely affect the landscape in banking within the next 15 years
  • Explain that similarly, 15 years ago, no one was able to predict that a large percentage of the population would be using Gmail from Google for email
  • Suggest that overseas providers of banking software or financial services may be part of the change and may compete against local banks
  • Indicate that local banks would find it hard to offer robust systems unless they each doubled or tripled their IT upgrade investments

2. interpretation

  • Bank customers must brace themselves for up to 15 years of pain
  • The big four banks would take 10 years to upgrade their systems and another five to stabilise those platforms
  • Local banks would struggle to compete against newer and nimbler rivals, which could sweep into Australia and compete against them
  • Local banks would find it hard to offer robust systems unless they each doubled or tripled their IT upgrade investments

3. intent (extrapolated from the differences between 1. and 2.)

  • Use words and numbers that maximise the period during which banking system outages are to be expected
  • Emphasise the potential threats to local banks and ignore irrelevant context information

4. interpretation

  • The various mental models that are constructed in the minds of readers who are unaware of 1.

Adults and even young children (once they have developed a theory of mind) know that others may sometimes interpret their messages in a surprising way. It is somewhat less obvious to realise that all sensory input received by the human brain is subject to interpretation, and that our own perception of reality is limited to an interpretation.

Next, consider an example of communication between a software user, a software developer (coder), and a machine, which involves both natural language and one or more computer programming languages:

Communication of desired intent including interpretation by a machine

1. intent

  • Request a system that is more reliably than the existing one
  • Simplify a number of unnecessarily complex workflows by automation
  • Ensure that all of the existing functionality is also available in the new system

2. interpretation

  • Redevelop the system in newer and more familiar technologies that offer a number of technical advantages
  • Develop a new user interface with a simplified screen and interaction design
  • Continue to allow use of the old system and provide back-end integration between the two systems

3. intent

  • Copy code patterns from another project that used some of the same technologies to avoid surprises
  • Deliver working user interface functionality as early as possible to validate the design with users
  • In the first iterations of the project continue to use the existing back-end, with a view to redeveloping the back-end at a later stage

4a. interpretation (version deployed into test environment)

  • Occasional run-time errors caused by subtle differences in the versions of the technologies used in this project and the project from which the code patterns were copied
  • Missing input validation constraints, resulting in some operational data that is considered illegal when accessed via the old system
  • Occurrences of previously unencountered back-end errors due to the processing of illegal data

4b. interpretation (version deployed into production environment)

  • Most run-time errors caused by subtle differences in the versions of the technologies have been resolved
  • Since no one fully understands all the validation constraints imposed by the old system (or since some constraints are now deemed obsolete),  the back-end system has been modified to accept all operational data received via the new user interface
  • The back-end system no longer causes run-time errors but produces results (price calculations etc.) that in some cases deviate from the results produced by the old version of the back-end system

In the example above it is likely that not only the intent in step 3. but also the intent in step 1. is codified in writing. The messages in step 1. are codified in natural language, and  the messages in step 3. are codified in programming languages. Written codification in no way reduces the risk of interpretations that deviate from the desired intent. In any non-trivial system the interpretation of a specific message may depend on the context, and the same message in a different context may result in a different interpretation.

Every software developer knows that it is humanly impossible to write several hundred lines of non-trivial program code without introducing unintended “errors” that will lead to a non-expected interpretation by the machine. Humans are even quite unreliable at simple data entry tasks. Hence the need for extensive input data validation checks in software that directly alert the user to data that is inconsistent with what the system interprets as legal input.

There is no justification whatsoever to believe that the risks of mismatches between desired intent and interpretation are any less in the communication between user and software developer than in the communication between software developer and machine. Yet, somewhat surprisingly, many software development initiatives are planned and executed as if there is only a very remote chance of communication errors between users and software developers (coders).

In a nutshell, the entire agile manifesto for software development boils down to the recognition that communication errors are an unavoidable part of life, and for the most part, they occur despite the best efforts and intentions from all sides. In other words, the agile manifesto is simply an appeal to stop the highly wasteful blame culture that saps time, energy and money from all parties involved.

The big problem with most interpretations of the agile manifesto is the assumption that it is productive for a software developer to directly translate the interpretation 2. of desired user user intent 1. into an intent 3. expressed in a general purpose linear text-based programming language. This assumption is counter-productive since such a translation bridges a very large gap between user-level concepts and programming-language-level concepts. The semantic identities of user-level concepts contained in 1. end up being fragmented and scattered across a large set of programming-language-level concepts, which gets in the way of creating a shared understanding between users and software developers.

In contrast, if the software developer employs a user-level graphical domain-specific modelling notation, there is a one-to-one correspondence between the concepts in 1. and the concepts in 3., which greatly facilitates a shared understanding – or avoidance of a significant mismatch between the desired intent of the user 1. and the interpretation by the software developer 2. . The domain-specific modelling notation provides the software developer with a codification 3. of 1. that can be discussed with users and that simultaneously is easily processable by a machine. In this context the software developer takes on the role of an analyst who formalises the domain-specific semantics that are hidden in the natural language used to express 1. .