The dawn of the second knowledge age

profit

The myth of progress

Capitalism, via the construction of abstract tokens as interest bearing debt, maximises the efficiency of the accumulation of abstract tokens, specifically it maximises the accumulation of abstract tokens in the hands of those institutions and individuals that claim to have the “authority” to issue debt and that design the social rules around the transfer, repackaging, and annulment of debt.

As long as access to capital affords individuals the “right” to make decisions that significantly impact on the lives of others, the distribution of debt and ownership rights related to land and means of production in society have a huge influence over the well-being of communities and families. This state of affairs is highly problematic, as all humans have limited cognitive capacity and limited ability to understand the lives and needs of other people.

The more capital an individual accumulates, the more their decisions start to impact the lives of hundreds, thousands, and in some cases millions and billions of people.

At the same time, we know that no human can maintain more than 150 relationships with other people, and that all our assumptions about the lives and needs of humans are based on the very small set of people that we relate to. By definition, we don’t understand all the people that we “don’t relate to”. In our busy civilised and hyper-social lives we come across far more than 150 people (Dunbar’s number). We interact within them on a transactional anonymous basis, and we may read about their lives, but it is impossible for us to fully understand their context, as we have not walked in their shoes from the first day in their lives, and thus lack the experience, the insights, and the tacit knowledge that shapes their unique world-views.

Thus, making decisions that potentially affect the lives of many hundred to several billion people without explicit consent of all those potentially affected, must be considered the pinnacle of human ignorance and is a strong indicator of a lack of compassion.

Of course many societies acknowledge the dangers associated with big social power gradients and like to present themselves as democratic, pointing to regular elections and legislation that is developed by democratically elected representatives, claiming that this allows all citizens to contribute to important decisions that shape the operating model of society. There are numerous problems with this naive claim:

  1. At national, regional, and even municipal level, each elected office holder represents a number of people that far exceeds the human cognitive limit of 150.
  2. In many societies citizens only have very limited ability and opportunity to contribute to discussions and important decisions that will affect their lives beyond the participation in elections every few years.
  3. In most societies immense amounts of decision making powers are concentrated at the national, regional, and municipal levels, and the decision making powers at the level of local communities are quite limited. Social power gradients manifest in pyramidal organisational designs within government institutions and corresponding communication structures.
  4. In a rapidly changing world that is affected by human induced climate change and ecological collapse, elections every few years represent a feedback loop that is far too slow for preventing further damage to the highly complex ecological systems that enable human existence on this planet.
  5. All modern “civilised” societies rely on the construction of abstract tokens as interest bearing debt and related tools (interest rates, government bonds, etc.) as a key means for influencing economic activity within their jurisdiction. This delegates significant decision making powers to small privileged elites who are granted preferential access to financial capital.
  6. Capitalised profit maximising corporations are not subject to democratic governance. Instead shareholders and their representatives have the ability to implement whatever organisational design they deem most appropriate for profit maximisation – usually a pyramidal management structure that treats employees and suppliers as resources to be exploited. Once a profit maximising corporation has acquired a monopoly position in a particular domain, it even treats customers as a resource to be exploited. In most jurisdictions the cost of penalties for ignoring or subverting antitrust legislation is negligible.
  7. Just like in many government organisations the number of employees in corporations often far exceeds the human cognitive limit of 150. As a result not only do shareholders, who are often not employed by the organisation, wield a huge influence, but the appointed top level managers regularly make decisions that affect many hundred or thousands of employees.
  8. The fixation on governance via abstract monetary metrics and controls (debt creation, budget allocation, interest rates, various forms of taxation, tax rates, etc.) leads to extremely over-simplified models of economic activity and contributes to an illusion of control, whilst creating huge blind spots for externalities that can’t be articulated in monetary metrics. Concepts such as the triple bottom line are well meaning but woefully inadequate attempts for shedding light on the blind spots created by the heavy reliance on monetary metrics. Ignorance is not a good foundation for decision making.
  9. An honest analysis of the measurable “achievements” of democratic governance as exemplified in current practices in the so-called “developed” world paints a dim picture of collective human intelligence: human activities have triggered the sixth mass extinction event on this planet, billions of humans are malnourished, our ecological footprint has been unsustainable for several decades, and the climate is changing at a rate that is orders of magnitudes faster than all earlier climatic changes in the history of the planet. And even in the light of all these results, monetary metrics are still used as the foundation for economic discussion and decision making.

The systematic analysis of earlier human “civilisations” (societies with cities, written language, and money) conducted by historian Joseph Tainter shows us that so far all “civilisations” have eventually “collapsed”, i.e. they have resulted in a much less centralised and less resource intensive organisation of human affairs.

For observations on the obsolescence of the current capitalist economic operating system in particular, I recommend listening to this very timely interview with anthropologist David Graeber, the author of Debt, The First 5,000 Years.

“Normal” is like standing on the railway tracks, looking at the coming train and asking how fast it is approaching. – David Graeber

The information age

info

The history of human civilisations to-date can be described as the information age, in which decisions within human societies are driven significantly by information encoded in written rules and in monetary metrics. The information age predates the invention of digital computers by several thousand years.

The physical manifestation of written language and abstract monetary tokens induces and reinforces an illusion of:

  • permanence (written words can survive many generations),
  • universal applicability (written words can be transported across large distances without distortion),
  • precision (written numbers allow quantification to quasi arbitrary levels of precision), and even an illusion of
  • shared understanding (via our associative memory familiar written words from unfamiliar people remind us of our personal experiences, and it is easy to forget that others may associate different experiences with the same words).

Written conventions and the fungibility of money equipped “civilised” societies not only with a tool for trade and complex transactions, but also with a tool for storing “value”. The act of quantification of value relies on a tacit consensus amongst the users of an abstract currency. A civilised society allows selfish and unscrupulous people to accumulate money by negotiating hard when selling to strangers and when buying from other strangers. The invention of interest bearing debt offered further “leverage”.

Money can be described as the abstract tool that has allowed humans to extend and scale the dominance hierarchies found in other primate societies to groups of many thousands and millions of people.

Via the reliance on money “civilised” societies actively encourage hoarding of information and resources. In “civilised” societies three types of human behaviour can be observed in the context of economic interactions:

  1. Altruism
  2. Reciprocal altruism
  3. Profit maximisation

Most people rely on one of these three strategies as their default mode of operation when dealing with friends and family, and with a potentially different default mode of operation when dealing with strangers. “Civilised” societies systematically disadvantage altruistic and compassionate people in favour of profit maximisers who are superficially charming and who can get away with creative interpretation of social rules.

Economics can be described as the discipline that attempts to legitimise the behaviour of profit maximisation alongside reciprocal altruism.

It should not really be surprising that to-date all “civilised” societies have eventually ended in collapse. Hoarding of information is not conducive to collective intelligence, and hoarding of resources leads to increasingly resource intensive cultural practices and to a growing ecological footprint.

In our times the close link between information hoarding and resource intensive cultural practices is exemplified by phrases such as:

  • Web 2.0 is about controlling data
  • The user [information producer] is the product
  • Monetisation of data
  • Data is the new oil

In the heat of civilised busyness it is easy to overlook that fact that money itself is simply abstract data, and that an objective such as “monetisation of data” encourages companies to develop absurd services that don’t serve any human need beyond the aggregation of capital on behalf of those who are obsessed with hoarding money.

Digital computers have accelerated the production of economic inequalities and have led to entire “industries” that attempt to monetise data, and which, in the pursuit of this objective, further increase resource and energy consumption. Bitcoin and similar cryptocurrencies epitomise this trend.

The global Bitcoin network is consuming more than seven gigawatts of electricity. Over the course of a year that’s equal to around 64 TWh or terawatt hours of energy consumption. That’s more than the country of Switzerland uses over the same time period (58 TWh per year).The Verge, 4 July 2019

Across the board, all “civilisations”, past and present, consist of organised groups of people that are so large that many interactions are “transactions” between people that don’t know much if anything about each other.

Life in “civilised” societies routinely puts people in situations of cognitive overload. People are forced to get used to the stress of transacting with anonymous strangers and are subject to social pressures to conform to norms and demands that have been decided in far away places, by rulers and bureaucrats who have no understanding of the local context in specific parts of their “empire”.

The knowledge age

knowledge-soc

In contrast to the myths about “human nature” that power civilisations, human babies are naturally inclined to help strangers, without any need for coercion or external “incentives”:

…helping [unrelated] others with simple physical problems is a naturally emerging human behaviour …at fourteen to eighteen months of age, before most parents have seriously started to expect their children, much less train them, to behave pro-socially. …parental rewards and encouragement do not seem to increase infants’ helping behaviour.

Parents take heed: the parental encouragement did not affect the infant’s behaviour at all; they helped the same amount with or without it.

… the infants were so inclined to help in general that to keep the overall level of helping down – so that we could potentially see differences between conditions – we had to provide a distracter activity in which they were engaged when the opportunity to help arose. Nevertheless, in the vast majority of cases, they pulled themselves away from this fun activity – they paid a cost – in order to help the struggling adult.

– Michael Tomasello, Why We Cooperate, Boston Review Books, 2009

Prior to the information age, for several hundred thousand years humans lived in much smaller groups without written language, money, and cities. The archaeological evidence available and also the evidence from “uncivilised” indigenous cultures that have survived until recently in a few remote places point towards an interesting commonality in the social norms of such societies:

The strongest social norms in pre-civilised societies were norms that prevented individuals from gaining power over others.

The key to the social co-operation in complex stateless societies is that they must effectively deal with the “free-rider” problems inherent in groups made up of ego-directed people. Overcoming these collective action problems is essential to understanding the evolution of social complexity in our species. These more successful stateless societies create social organisations that allow individual members of the group to benefit in ways that they cannot in smaller population sizes.

The lack of coercion in complex stateless societies is a key feature of this social phenomenon… unlike leaders in state societies, those in stateless ones do not possess coercive power over others. This is an extremely important observation: the emergence of of complex stateless societies was not a costly process in which the vast bulk of people were forced to give up resources or labor to ego-directed aggrandizers… ad-hoc managerial leadership will emerge to deal with the free-rider problems, on the one hand, and the need to reward co-operators, on the other. This is a kind of leadership created by the group; it is not forced on the group either by aggrandizers or by environmental stresses.

Informal social coercion exists in all stateless societies, and is manifested in taboo, black magic, and so forth. However, stateless societies are notable for their absence of institutionalised elites with power to obligate others without a substantial consensus among the community… power in stateless societies by leaders is ad hoc and granted or withdrawn by the community at large.

…people in small groups create rules and norms to govern the production and exchange of resources, behaviors that makes sustained economic co-production possible. These norms and rules are structured through various kinds of ritual and taboo. These rituals schedule tasks, reward co-operators, and enhance pay-off for prosocial behaviour by all members of the group. They maintain fairness and punish non-co-operators.

…small groups provide a context in which most people know each other. The ability to create social histories of most people and to use these reputations in future interactions is possible in small-group contexts in ways not possible in large groups.

people in successful groups recognise both the individual and the collective advantage of co-operation, and some individuals in the group are willing to absorb some costs onto themselves to maintain norms of fairness in exchange for prestige,

– Charles Stanish, The Evolution of Human Co-operation : Ritual and Social Complexity in Stateless Societies, Cambridge University Press, 2017

Unfortunately the language used by social scientists, including anthropologists, is biased by our culture, and makes use of terms such as ‘leaders’ and ‘prestige’, which carry some semantics in “civilised” societies that do not apply in pre-civilised societies.

The notion of ‘leadership’ as described in the extract above refers refers to an appreciation for valuable domain specific tacit knowledge and skills, and to the trust that is extended to individuals with empirically validated valuable knowledge and skills.

Similarly the notion of ‘prestige’ described in the extract above refers to individuals who consistently act in altruistic ways and contribute their knowledge in ways that benefit the group, who as a result enjoy the trust of many members of the group.

To date the vast majority of anthropological research ignores the role of neurodiversity in shaping human societies. Social scientists routinely assume neurotypical social motivations when observing and interpreting human behaviours. Taking into account that neurodivergent and especially autistic people may not at all be interested in ‘prestige’ in the sense of social status, but are rather motivated by a strong sense of curiosity and individual agency, allows for a more nuanced interpretation.

Regardless of cultural context, the curiosity and unusual sensory abilities of autistic and otherwise neurodivergent individuals result in deep domain specific knowledge and related specialised skills. Some of the acquired knowledge and skills may turn out to be valuable to society and attract the attention of others. In pre-civilised societies neurodivergent individuals will likely have been recognised as trustworthy carriers of valuable knowledge and competencies, the easily transferable parts of which will then have been preserved and propagated to others via cultural transmission.

In pre-civilised societies valuable knowledge was shared and carefully transmitted to future generations. In the absence of written language the knowledge transmission process involved all senses and intensive interaction between recognised masters and motivated novices.

Great climbers and highly skilled hunters, as well as those that excel in other locally valued domains, are sought out, deferred to, and naturally emerge as influential across a wide range of domains. Such respected individuals are rarely ill-tempered or erratic, and instead they are often renowned for their generosity. This phenomenon occurs even in societies that are highly egalitarian, possessing no formal leadership roles or hierarchy.

… once humans became good cultural learners, they needed to locate and learn from the best models. The best models are those that who seem to possess the information most likely to be valuable to learners, now or later in their lives. To be effective, learners must hang around their chosen models for long periods and at crucial times. Learners also benefit if their models are willing to share nonobvious aspects of their practices, or at least not actively conceal the secrets of their success.

… humans reliably develop emotions and motivations to seek out particularly skilled, successful, and knowledgeable models and then are willing to pay deference to those models in order to gain their cooperation, or at least acquiescence, in cultural transmission. This deference can come in many forms, including giving assistance, gifts and favours, as well as speaking well of them in public.

– Joseph Henrich, The Secret of Our Success : How Culture is Driving Human Evolution, Princeton University Press, 2015

The extract above underscores that autistic individuals will likely have played a key role in knowledge transmission, as they tend to be the ones who are incapable of keeping hidden agendas and consistently ‘willing to share nonobvious aspects of their practices’ with others.

Literally hundreds of experiments in dozens of countries using a variety of experimental protocols suggest that, in addition to their own material payoffs, people have social preferences: subjects care about fairness and reciprocity, are willing to change the distribution of material outcomes among others at a personal cost to themselves, and reward those who act in a pro-social manner while punishing those who do not, even when these actions are costly.

Initial skepticism about the experimental evidence has waned as subsequent experiments with high stakes and with ample opportunity for learning failed to substantially modify the initial conclusions.

This shift in the view of human motives has generated a wave of new research. First, and perhaps most important, a number of authors have shown that people deviate from the selfishness axiom and that this can lead to radical changes in the kinds of social behavior that result. For example, Fehr and Gächter (2002) have shown that social preferences leading to altruistic punishment can have very important effects on the levels of social cooperation (Ostrom et al. 1992).

– Joseph Henrich et al., Foundations of Human Sociality, Oxford University Press, 2004

Given what we know about neurodiversity and autistic people, the above results are unsurprising, and entirely consistent with the level of attention that autistic people tend to pay to social justice and fairness, irrespective of whether these attributes are valued by the surrounding culture or not. Given the neurotypical human tendency for over-imitation, any fairness norms invented by trustworthy autistic carriers of valuable knowledge will easily be absorbed into the cultural repertoire of the group.

… once culture gets off the ground it enables adaptation to new niches, situations, climates, and ecologies in a vastly more efficient way than can be achieved by ordinary natural selection… Societies with culture… quickly adapt to circumstances of any kind, … without waiting for the cumbersome process of natural selection to do its work.

– Robert A. Paul, Mixed Messages : Cultural and Genetic Inheritance in the Constitution of Society, University of Chicago Press, 2015

The combination of neurodiversity and the human capacities for collaboration and cultural transmission that defined the knowledge age enabled humans to thrive for many hundred thousand years in a diverse range of circumstances – until humans invented the ingredients of “civilisation”, which, via the introduction of written language and money, shifted attention away from valuable knowledge to the accumulation of social power.

Whereas pre-civilised societies appreciated the talents of autistic and otherwise neurodivergent people, the tools of “civilised” societies provide irresistible opportunities for ego-directed aggrandizers, which I am tempted to describe as “human primates” who are only interested in the acquisition of social power and related status symbols.

The knowledge age 2.0

knowledge2.0

“Civilisation” can be thought of as a social operating system that is afflicted by a collective learning disability induced by primate dominance hierarchies, which dampen feedback loops and flows of valuable knowledge. The result is a cultural inertia that perpetuates social power gradients and that discriminates against the discoverers of new knowledge that might undermine established social structures.

The exciting aspect about the human capacity for culture is that via a series of accidental discoveries and inventions, we have created a global network for sharing valuable knowledge, as well as opinions and misinformation. It apparently takes a virus like SARS-CoV-2 to put this network to good use, and to shift “civilised” cultural norms away from profit maximisation and back towards sharing knowledge for collective benefit.

I’ll hand over to one of my autistic peers for a synopsis:

It is fascinating to notice that SARS-CoV-2 has very rapidly induced cultural changes that affect the foundations of “civilisation”:

  1. Cities – explicitly designed to facilitate rapid sequences of human interactions in anonymous contexts, have been forced to adopt and enforce rules for physical distancing and limiting social interaction.
  2. Written language – when used as a tool for propaganda and distortion, now contributes to the spread of the virus, and yet can play a critical role when used for sharing valuable knowledge.
  3. Money – when used as a tool to protect social power gradients and profits, now has become a negative indicator that signals a lack of trustworthiness.

It is clear that the future of human societies now critically depends on cultural evolution of these foundations. Concepts such as cities and written language as well as quantitative metrics may survive, but their scope of applicability and the operational rules and rituals associated with them may be transformed to such an extent that we will invent new words to clearly distinguish between the old semantics of the information economy and the new semantics of the emerging knowledge age.

In a world increasingly not only connected by trade in goods, but also by exchange of violence, information, viruses, emissions, the importance of social preferences in underwriting human cooperation, even survival, may now be greater even than it was amongst that small group of foragers that began the exodus from Africa 55,000 years ago to spread this particular cooperative species to the far corners of the world.

– Samuel Bowles and Herbert Gintis, A Cooperative Species : Human Reciprocity and its Evolution, Princeton University Press, 2013

Planetary intelligence is achieved by creating a feedback loop of mutual learning between the rapid learning cycles (mutations) of viruses and learning cycles at human scale, which are now amplified via a global digital network at super-human scale. Humans are learning the hard way that messing with that network for misinformation and attempts of hierarchical control works against humans and the entire planetary ecosystem.

Once ego-directed aggrandizers with “leadership aspirations” are again recognised as the biggest threat to society, our capacity for culture may again make us more intelligent than the other primates. We can reorient towards a kinder human scale world that nurtures a global knowledge commons and that celebrates mutual aid.

Experiencing the beauty of collaboration at human scale

Timeless patterns of human limitations

The title of this post is the title of a book project I am working on, to provide organisations with a useful sense of direction, giving them the option to snap out of busyness as usual mode when they are ready. Whether this then happens in a timely manner may vary from case to case. It is not something that anyone has much control over.

getting-started.png

As the title suggests, the book is about collaboration, about scale, and about humans, about beauty, and about limits. It has been written from my perspective as an autistic anthropologist by birth and a knowledge archaeologist by autodidactic training.

I attempt to address the challenges of ethical value creation in the Anthropocene. There is no shortage of optimistic books that celebrate human achievements and there is also no shortage of pessimistic books that proclaim the end of the human species. In contrast I approach the Anthropocene from the fringe of human society, from the perspective of someone who does not relate to abstract human group identities.

Evolutionary biologist David Sloan Wilson observes that small groups are the organisms within human society – in contrast to individuals, corporations, and nation states. The implications for our “civilisation” are profound. It is time to curate and share the lessons from autistic people, and help others create good company by pumping value from a dying ideological system into an emerging world.

Since the very beginning civilisation has always been more about a myth of progress than about anything that benefits local communities and families. – except perhaps for the benefit of not being killed as easily by a neighbouring horde of more or less civilised people. Once the history of civilisation is understood as series of progress myths, where each civilisation looks towards earlier or competing civilisations with a yardstick that is tailored to prove that its own myths and achievements are clearly superior to anything that came before, it is possible to identify the loose ends and the work-arounds of civilisation that are usually presented as progress.

The result is a historical narrative that makes for slightly less depressing reading than 10,000 years of conflict and wars. Instead, human history can be understood as a series of learning experiences that present us with the option to break out of the tired, old, and increasingly destructive pattern once it has been recognised. Whether our current global civilisation chooses to complete the familiar pattern of growth and collapse in the usual manner is a question that is up to all of us.

Regardless of what route we choose, on this planet no one is in control. The force of life is distributed and decentralised, and it might be a good idea to organise accordingly.

To understand why beauty, human scale, collaboration, and limits are essential for human sanity, we only need to look at the ugly reality of super-human scale institutions that currently surround us.

Super-human scale

If you want to read a good book on economics, pick up The Value of Everything: Making and Taking in the Global Economy by Mariana Mazzucato, and if you enjoy engaging your brain while reading you can add The New Economics of Inequality and Redistribution by Samuel Bowles for good measure.

For concrete examples of making and taking in the global economy look no further than the wheeling and dealing that Vandana Shiva’s examines in her work. She makes astute observations on the role that Microsoft and other global technology companies play in rolling out intensive industrial agriculture to all corners of the planet.

Beyond the tactic of economic arm-twisting global corporations have perfected the art of accounting, shifting 40% of their profits ($600 billion annually) into tax havens with tax rates from 0% to less than 10%. Governments that represent their people rather than corporate interests would legislate against this practice – but they don’t.

Here is an excellent paper by A. Pluchino. A. E. Biondo, and A. Rapisarda on the intelligence of the economic game and the logic of capital, not even considering the effects of psychopathic social gaming. Synopsis:

On random factors that influence “social success”

there is nowadays an ever greater evidence about the fundamental role of chance, luck or, more in general, random factors, in determining successes or failures in our personal and professional lives. In particular, it has been shown that scientists have the same chance along their career of publishing their biggest hit; that those with earlier surname initials are significantly more likely to receive tenure at top departments; that one’s position in an alphabetically sorted list may be important in determining access to over-subscribed public services; that middle name initials enhance evaluations of intellectual performance; that people with easy-to-pronounce names are judged more positively than those with difficult-to-pronounce names; that individuals with noble-sounding surnames are found to work more often as managers than as employees; that females with masculine monikers are more successful in legal careers; that roughly half of the variance in incomes across persons worldwide is explained only by their country of residence and by the income distribution within that country; that the probability of becoming a CEO is strongly influenced by your name or by your month of birth; and that even the probability of developing a cancer, maybe cutting a brilliant career, is mainly due to simple bad luck.

On the randomness of the distribution of rewards when the logic of capital is applied

In particular, the most successful individual, with Cmax = 2560, has a talent T = 0:61, only slightly greater than the mean value mT = 0:6, while the most talented one (Tmax = 0:89) has a capital/success lower than 1 unit (C = 0:625). As we will see more in detail in the next subsection, such a result is not a special case, but it is rather the rule for this kind of system: the maximum success never coincides with the maximum talent, and vice-versa. Moreover, such a misalignment between success and talent is disproportionate and highly nonlinear. In fact, the average capital of all people with talent T > T is C 20: in other words, the capital/success of the most successful individual, who is moderately gifted, is 128 times greater than the average capital/success of people who are more talented than him. We can conclude that, if there is not an exceptional talent behind the enormous success of some people, another factor is probably at work. Our simulation clearly shows that such a factor is just pure luck.

Meanwhile, while society is stuck in a broken system, what is the best way of allocating R&D funding?

The European Research Council has recently given to the biochemist Ohid Yaqub a 1:7 million US dollars grant to quantify the role of serendipity in science. Yaqub found that it is possible to classify serendipity into four basic types and that there may be important factors affecting its occurrence. His conclusions seem to agree with the believing that the commonly adopted – apparently meritocratic – strategies, which pursuit excellence and drive out diversity, seem destined to be loosing and inefficient. The reason is that they cut out a priori researches that initially appear less promising but that, thanks also to serendipity, could be extremely innovative a posteriori.

… if the goal is to reward the most talented persons (thus increasing their final level of success), it is much more convenient to distribute periodically (even small) equal amounts of capital to all individuals rather than to give a greater capital only to a small percentage of them, selected through their level of success – already reached – at the moment of the distribution. On one hand, the histogram shows that the “egalitarian” criterion, which assigns 1 unit of capital every 5 years to all the individuals is the most efficient way to distributed funds,

The model shows the importance, very frequently underestimated, of lucky events in determining the level of individual success. Since rewards and resources are usually given to those that have already reached a high level of success, mistakenly considered as a measure of competence/talent, this result is even a more harmful disincentive, causing a lack of opportunities for the most talented ones. Our results are a warning against the risks of what we call the “naive meritocracy” which, underestimating the role of randomness among the determinants of success, often fail to give honors and rewards to the most competent people.

Robert Reich does a good job of highlighting the systemic dysfunction, but I cringe when I hear the undercurrent of the American Dream of “getting ahead” by working hard and the related myth that capitalism can be fixed by appropriate levels of regulation and democratic oversight. It seems no one dares to tell the truth to US audiences.

Critical social scientists regularly point out that the entire discipline of psychology is best understood as the study of human behaviour in WEIRD (Western Educated Industrialised Rich Democratic) cultures. Its identical twin is the discipline of marketing. The cultural bias is extreme. Here is just one example of the flaky foundations and of the bias. I have started to extend WEIRD to WEIRDT : Western Educated Industrialised Rich Democratic Theatre. Everything in this theatre is about perception – there is no substance or connection to the physical and ecological context outside the theatre.

But the world outside the theatre still exists. This commentary from Noam Chomsky and the timeless quotes from David Hume apply. The notion of governance as perception management and of politics as a theatre of opinions is what I try to highlight in this article on the CIIC blog. Noam Chomsky is correct in pointing out that in the super-human scale democratic theatre the power lies with the governed once the veil of secrecy is blown away. This is extremely important to realise in our society of ubiquitous surveillance and security agencies from state actors. As Noam Chomsky points out, security agencies are designed to secure the interests of the theatre and not the interests of the population. Anyone who still believes that any security agency or secret service is doing a useful job for any society has been conned by the theatre.

Transparency is the ultimate disinfectant in the digital era. But as long as the population believes in the myth of the necessity for state secrets and corporate secrets – which by definition are secrets with super-human scale / scope, the power of transparency will remain dormant. All super-human scale secrets are instruments of systematic abuse. The sooner this is widely understood the sooner the theatre can be confined to the dustbin of history. My dad worked in the diplomatic service, and even though I was one or more levels removed from the content, the ridiculous and delusional self-importance of the diplomats and their ignorance of physical reality was obvious to a 10-year old. As Douglas Rushkoff observes, “Operation Mind Fuck was too successful, but there is a way to bring back a little bit of hope into what we are doing.”

Beliefs in money, debt, institutions, and systems are better thought of as behavioural patterns (habits) than as beliefs that people are genuinely comfortable with. Even most actors within the theatre are suffering from severe cognitive dissonance. Habits that don’t serve us well are usually referred to as addictions. The challenge for the people stuck in the theatre within corporations and other super-human scale institutions lies in overcoming addiction and story withdrawal symptoms.

When the majority of people start to understand that all our super-human scale organisations are part of the theatre transparency can be deployed as a disinfectant for social diseases.

Human scale

The operating models of Buurtzorg and other non-hierarchical and distributed collaborative organisations like S23M are concrete examples of understandable and relatable human scale organisations.

The fact that human scale social operating systems can be constructed on top of corrupt infrastructure is a powerful message. In particular autistic people are increasingly asking me about S23M’s transparent operating model, and I am more than happy to assist them in setting up neurodiventures that provide them with some level of protection from the toxic and delusional theatre around them. By focusing on the human scale outside the theatre we can reconnect with the physical and ecological niche that supports our human needs.

The book project I am working on won’t provide all the answers – that is impossible – but it can equip small groups of people with:

  • critical thinking tools and a language framework that encourages creativity and that assists learning and discovery,
  • as well as an ethical framework that promotes collaboration and knowledge sharing instead of competition.

As part of the project it is necessary to provide an unvarnished account of human history to date. The table of content provides an indication of scope and framing:

  1. Human origins
  2. Human scale patterns
  3. The human lens
  4. Learning
  5. Super-human scale patterns
  6. Loose end : Loss of control
  7. Work-around : Automated labour
  8. Industrial society
  9. Loose end : Exponential growth
  10. Work-around : Computing
  11. Information society
  12. Loose end : Loss of tacit knowledge
  13. Work-around : Busyness
  14. Liquidation society
  15. Loose end : Loss of semantics
  16. Thought experiment : Knowledge society
  17. Addressing the loose ends
  18. Human scale patterns, second edition
  19. Transitioning to human scale
  20. Conclusion

Tools for creating learning organisations

If individual learning seems difficult at times, organisational learning seems elusive or impossible most of the time. In my experience the following tools allow knowledge to flourish at human scale – in the open creative spaces between disciplines and organisational silos:

The SECI knowledge creation spiral is a useful conceptual tool for understanding and improving learning and knowledge flows within organisations. The four SECI activity categories (socialisation, externalisation, combination, internalisation) can be used to describe learning at all levels of organisational scale.

seci

MODA+MODE is a conceptual framework for creating learning organisations that extends the concepts of continuous improvement and the SECI spiral into the realm of knowledge intensive industries, transdisciplinary research and development, and socio-technological innovation. MODA+MODE uses the SECI knowledge creation spiral to release the handbrake on tacit knowledge and creativity by focusing on:

  • sharing and validating knowledge,
  • making knowledge explicit and accessible to humans and software tools,
  • combining shared knowledge in creative ways,
  • transdisciplinary research and development across organisational boundaries.

mm.png

Open Space Technology is a very simple and highly scalable technique for powering a continuous SECI knowledge creation spiral that breaks through the barriers of organisational boundaries, established silos and management structures.

ost

The human lens provides thirteen categories that are invariant across cultures, space, and time – it provides a visual language and reasoning framework for transdisciplinary collaboration. The human lens allows us to make sense of the world and the natural environment from a human perspective, to evolve our value systems, and to structure and optimise human economic endeavours.

humanlens

The human lens is comprised of:

  1. The system lens, to support the formalisation and visual representation of knowledge and resource flows in complex socio-technological systems based the three categories of resources, events, and agents (the REA paradigm, an accounting model developed by E.W. McCarthy in 1982 for representing activities in economic ecosystems). The system lens can be applied at all levels of organisational scale, resulting in fractal representations that reflect the available level of tacit knowledge about the modelled systems.
  2. The semantic lens, to support the formalisation and visual representation of values and economic motivations of the agents identified in the systems lens. The semantic lens provides a configuration framework for articulating economic, ethical, and cultural value systems as well as a reasoning framework for evaluating socio-technological system design scenarios and research objectives with the help of the five categories of social, designed, symbolic, organic, and critical.
  3. The logistic lens, to support the formalisation and visual representation of value creating activities and heuristics within socio-technological systems. The logistic lens provides five categories for describing value creating activities: grow (referring to the production of food and energy), make (referring to the design, engineering, and construction of systems), sustain (referring to the maintenance of production and system quality attributes), move (referring to the transportation of resources and flows of information and knowledge), and play (referring to creative experimentation and other social activities). The logistic lens can be used to model and understand feedback loops across levels of scale (from individuals, to teams, organisations, and economic ecosystems) and between economic agents (companies, regulatory bodies, local communities, research institutions, educational institutions, citizens, and governance institutions). The categories of the logistic lens assist in the identification of suitable quantitative metrics for evaluating performance against the value system articulated via a configuration of the semantic lens.

The 26 MODA+MODE backbone principles provide a baseline set of thinking tools to avoid getting entrapped in a single paradigm. Thinking tools are the mental image schemas, frames, and reasoning tools, and also the behavioural patterns that help us to validate knowledge, ask new questions, and form and explore new ideas – the hugely diverse set of tools that different people tap into as part of the creative process. The backbone principles have been sourced from a range of sciences and engineering disciplines, including suitable mathematical foundations.

backbone.png

The 8 prosocial core design principles developed by Elinor Ostrom, Michael Cox and David Sloan Wilson guide the application of evolutionary science to coordinate action, avoid disruptive behaviours among group members, and cultivate appropriate relationships with other groups in a multi-group ecosystem. The pro social design principles provide a good starting point for implementing concrete policies and systems that are specifically adapted to the needs of neurodiverse groups of people and collaboration in transdisciplinary research and development environments.

prosocial.png

Neurodivergence is at the core of creativity.

At the moment too many organisations and people are either completely paralysed by fear or are running around like headless chickens as part of busyness as usual. Last weekend I found this great clip from Jonathan Pie in my Twitter feed. You can laugh and cry at the same time.

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.

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.

Poll on current priorities of IT organisations in the financial sector

As part of research on the banking sector, I have set up a poll on LinkedIn on the following question:

Which of the following objectives is currently the most relevant for IT organisations in the financial sector?

  • Improving software and data quality
  • Outsourcing new application development
  • Outsourcing legacy software maintenance
  • Improving time to market of new products
  • Reducing IT costs

The poll is intended as a simple pulse-check on IT in banking, and I’ll make the results available on this blog.

Please contribute here on LinkedIn, in particular if  you work in banking or are engaged in IT projects for a financial institution. Additional observations and comments are welcome, for example insights relating to banks in a particular country or geography.

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. .

Participate: Tweeting in the format URL relationship URL

Twitter has emerged as a very powerful medium for propagating ideas and thoughts. Possibly Twitter is the ideal data input tool for harnessing the collective insights of the humans and systems that are connected to the web – effectively a significant proportion of all humans and virtually every non-trivial system on the planet.

By simply adopting a convention of twittering important insights in the format <some URL> <some relationship> <some other URL>, users can incrementally, one step at a time, create a personal model of the web. These personal models can grow arbitrarily large, and Twitter is certainly not the appropriate tool for visualising, modularising and analysing such models. But arguably, Twitter is the most elegant and simplest possible front end for capturing atoms of knowledge.

Note that URLs used on Twitter typically point to a substantial piece of information, and not a simple word or sentence. Often a URL references an entire article, a web site, or a non-trivial web-based system. These articles, web sites or systems can be considered semantic identities in that specific users (or groups of users) associate them with specific semantics (or “meaning”). Hence tweets in the <some URL> <some relationship> <some other URL> format suggested above represent connections between two semantic identities. A set of such tweets amounts to the construction of a mathematical graph, where the URLs are the vertices, and the relationships are the edges.

If we add functions for transforming graphs into the mix, and considering that we are connecting representations of semantic identities, we end up in the mathematical discipline of model theory. Considering further that Twitter models are user specific, and that the semantics that users associate with a URL are not necessarily identical – but rather complementary, we can further exploit results from the mathematics of denotational semantics. For the average user there is no need to worry about the formal mathematics, and it is sufficient to understand that the <some URL> <some relationship> <some other URL> format (I will use #URLrelURL on Twitter when referencing this format) allows the articulation of insights that correspond to the atoms of knowledge that humans store in their brains.

With appropriate software technology it is extremely easy to translate sets of #URLrelURL tweets into a proper mathematical graph, and into a user specific semantic model. These models can then be analysed, modularised, visualised, compared, and transformed with the help of machine & human intelligence. Amongst other things, retweets can be taken as an indication of some degree of shared understanding in relation to a particular insight. Further qualification of the semantic significance of specific tweets can be calculated from the connections between Twitter users, and from analysis of the information/functionality offered by the two connected URLs.

The most interesting results are unlikely to be the individual mental models that are recorded via #URLrelURL tweets, but will rather be the overlay of all the mental models, leading to a complex graph with weighted edges, which can be analysed from various perspectives. This graph represents a much better organisation of semantic knowledge than the organisation of information delivered by systems like Google search.

Instead of processing semantic models, Google search must process entire web sites with arbitrary syntactic content, with no indication of which pairs of URLs constitute insights useful to humans. Google can only indirectly infer (and make assumptions about) the semantics that humans associate with URLs by applying statistics and proprietary algorithms to syntactic information.

In contrast, the raw aggregated #URLrelURL tweet model of the world captures collective human semantics, and any additional machine generated #URLrelURL insights can be marked as such. The latter insights will not necessarily be of less value, but it will be reassuring to know that they are firmly grounded in the collective semantic perspective of human web users.

Making this semantic perspective accessible to humans and to software via appropriate search, visualisation, and analysis tools will constitute a huge step forwards in terms of learning, effective collaboration, quality of decision making, and in terms of eliminating the boundary between biological and computer software intelligence.

Therefore, please join me in capturing valuable nuggets of insight in the format of
<some URL> <some relationship> <some other URL> tweets.

Example:

http://gmodel.org #gmodel can be used to #translate twitter models into #semantic #models http://bit.ly/em60Tw