Banks, Brains, and Factories

How rich information alters the economics of cooperation and work

(Based on talks given at AllDayDevops and Devops REX Paris in December 2016)

The economic model we live by grew out of humanity's effort to scale up its supply of basic needs, giving communities the means to survive, raise their standards of living, and escape poverty. Successive generations embraced and evolved methods of planned and mechanized production, with each growing its output, while preserving a tradition of asset ownership and seasonal productivity. The result today is a model of economics that strives for short-horizon profit. Now that technology makes the goal of plenty attainable, a new model may be needed for a `smart world', one that redefines the goal of the economy from growth for its own sake to something more invested in social cohesion.

Thanks to information technology, we are already moving to a new kind of service-oriented economy, one where individual intent and meaning can be built into all our interactions, at a deep level. This is a personalized, application-centric economy, using information technology in all areas to drive processes, with rich semantics. Today's legacy system, by contrast, predated the information age, and could only afford to cope with a generic form of money, managed on an aggregate scale much greater than individual concerns, by singular institutions whose goals are not well aligned with those of ordinary people. However, modern access to rich information processing, and a growing disillusionment with industrial management styles, could reverse that, allowing us to return to the original notion of group cooperation as the basis for our economy. What could this mean for us in a `smart' post-industrial world, with more diverse interests, in which basic resources are plentiful?

What binds us together, and what tears us apart?

Cooperation (when parts work together as a whole) evolved in biology as an adaptation to the economic benefits of specialization, or differentiation of roles. Cooperation begins when agents (differentiated by the promises they make) interact with one another, allowing them to make new collective promises that no individual could keep alone.

Cooperation has brought atoms together as molecules, single-celled organisms into cooperative clusters, as plants, animals, humans, societies, and even whole ecosystems. Cells, organs, bodies, families, tribes, and even countries (political unions) are all have their own separate lives as singular `agents'; but it is how they behave together that gives them a functional meaning in a larger context.

Cooperation is at the root of most if not all systemic behaviours, many of which we take for granted. That includes civil societies at all levels of development. Cooperation explains economies of scale, and unlocks the advancements made when barriers to our shared progress are overcome. The economics of cooperation are complicated, and contemporary economics too simplistic to capture its semantic nuances, but that may now be changing, due to the availability of information systems that can cope with and track rich detail quickly and cheaply. Today, we have the technology to manage an information-oriented view of economic cooperation that can unify all perspectives in a simple way.

In this essay, I shall try to explain the underpinnings of a such view of cooperation to a technology audience, based on simple promise theory concepts[1], laying out the path to a future socioeconomic viewpoint to be described in a follow-up article.

Inequality is not (only) what you think

Humans evolved to cooperate. There is strong evidence that our neo-cortical brains evolved to manage relationships, for what biologists call `reciprocal altruism', and that our species therefore cooperate as our natural state of being[2,3,4]. What is most impressive though is how we have scaled cooperative behaviour from bands of hunter-gatherer all the way up to modern cities and nation states. We should not underestimate the magnitude of that achievement[13]. Indeed, many human societies have still not made this change, even in the increasingly pervasive modern age of information[5,13].

What brings things and people together in a cooperative way? Life is filled with obstacles that are hard to overcome alone. We can assist one another both with both qualities (semantics) and quantities (bulk supply). So cooperation is a way to exceed individual limitations. Curiously, our narrative of the natural world is mainly about competition and contention, but this is a mirage. What appears competitive from one interpretation (prey versus predator, for instance), may be seen at cooperative (in balance) from a total ecosystem perspective. Our society is mainly cooperation with a little competition riding on top of the stability it provides. Thus, scales and boundaries (defining who and what is inside and outside a group or system) play very particular roles in understanding systemic cooperations of parts. These aspects are the elements of promise theory, making it a useful tool for examining social order.

Agents have to distinguish themselves to cooperate. Without being able to promise distinguishing traits (semantic information or different bulk capacities), there would be no basis for selection, and working together could only be an ad hoc sharing of the total supply of stuff. In order to cooperate, we need to recognize the capabilities of others, and be able to trade them. Thus, in terms of information, it is the inequality of agents is the basis for cooperation. Some agents may promise leadership skills, some techical ability, others may be stronger or faster than others. Cooperation is about how to draw advantage from those assets beyond the confines of the individual.

Working for trust: value and the social brain

Anthropologist-psychologist Robin Dunbar showed that humans' large brains allow us to interact with larger groups than primates with smaller brains. By plotting the sizes of primates' neocortex regions against the sizes of social groups, he showed a straight line relationship, suggesting that neo-cortical brain size co-evolved with social group size. This is Dunbar's social brain hypothesis, and it is supported by much evidence[2,3,8]. It links iterative work done by brains in recognizing and learning traits, over extended times, to the capacity needed to memorize and recognize friends and foes. This indicates that the time-investment of energy in learning is of value. Transactional encounters (like a Markov process) cannot explain large brain size, but extended relationships can.

There must be something valuable about relationships, after all the brain consumes about 20% of our energy at rest, which is a huge penalty for the meager capability of better living together. The reason nature endures this extraordinary cost is surely the unexpected survival benefits of managing these long-term relationships. Relationships are not just social, they are cognitive. As we interact over time, in many different contexts, we aggregate rich information and build up a complete picture of something, by integrating the multiple experiences into a single model that could not be discovered by single `transactional' encounters. In short, we learn to know someone or something as what we call a friend. Long term cognition, with integration of contextual characteristics, is the very complement of cooperation, allowing us to know and recognize individuality. This is why brains are the key to cooperation.

What is the true value of a relational brain? The answer is risk management and cost saving: removing the overhead of hedging and testing the waters and dancing around an untrusted party to see what it might do. Time, as marked by repeated interaction, is the fundamental currency of economic value. It is the one thing we cannot easily extend in the moment. There is thus an economic incentive to increase trust, to save time. Trust is an assessment based on a memory of the reliability of agents to perform according to our expectations. If an agent is predictable, we can trust that it will continue to behave in a way we can use for our own purposes. This is valuable. If we can't trust, we have to verify every step of the way, and that is expensive. Thus once our cognitive assessments stabilize, such that our expectations match our observations (over some duration or timescale), we save future time and effort by trusting in behavioural outcomes. Value is like Brownie points for good behaviour: the answer to the question, can I rely on you?

The capacity to form a relationship is the capacity to remember past behaviour, recognize it in another, to assess it, and to cache it as a trust score to guide future expectation. We learn adaptive responses to known capabilities and quirks, all for personal gain. This requires some modelling and pattern recognition, as well as contextual memory. For this we need brains. However, what may have once evolved only as a small social group skill has now taken on far greater implications, as we have scaled cooperation and embraced its emergent benefits.

Having acquired the capability to `truly know' other humans as friend or foe (thanks to our brains), we can do it for animals, tools, problem solving, navigational routes, strategies, and any number of other things. All we need to do is repeatedly interact with something, and our brains will work their magic, anthropomorphizing behaviours and treating all the forces in our lives as potential allies. In other words, one may regard every aspect of what we now call knowledge as just a kind of on-going quasi-social relationship, riding on top of the cognitive apparatus that evolved to strengthen cooperative animal groups.

Once equipped with a brain that builds relationships, it is only natural that it would try to apply that skill to everything. Consider, for instance, how many hours a day you spend interacting with your phone, word processor, or spreadsheet; then, compare that to the number of hours a day you spend interacting with loved ones. Work is not a task, it is a relationship that has come to dominate our lives, and takes up at least as much space as our close human relationships[8]. Humans, however, are the only primate brains that clock into factories.

Robert Axelrod[6,7] arrived at a similar conclusion about the importance of time in cooperation, by a different route. He studyied game theory models of iterated prisoners' dilemma games. He showed that value comes from repeated cooperation, i.e. extended mutually beneficial relationships, not just singular transactions. Value comes from persistence in time (e.g. loyalty or stickiness), and the scale on which we measure it is entirely the whim of each individual's personal assessment. This is consistent with Dunbar's hypothesis that there is a cost and a benefit associated with long term relationships, reflected in nature's favouring higher brain functions.

The problem with relationships is that they are expensive and time consuming to establish and maintain. Trust, after all, is built from observing how well agents keep their promises in the long run. Trust is cumulative, not transactional. If we only expect to meet a counterpart once, and engage in a single transaction, there is a temptation to exploit the situation for selfish reasons. But if we know that we are going to meet that same agent again, we can expect retaliation. Axelrod showed that even rational agents should favour generosity and fair trade (a trust relationship) in order to maximize their utility.

Our solution in the modern world of money is to introduce a proxy, by outsourcing all random encounter relationships to a trusted third party (a bank or government) who promises to honour transactions. Don't trust this vendor/customer to trade fair value? Well, trust me instead and we can save time.

The trinity of cooperation

Let's try to peel back the layers of significance, to apply cooperation to our modern economic narrative, to unravel its modern development, and to debug some of its problems. There are three broad characters at play in a story of cooperation at scale, which I have chosen to dub as `banks', `brains', and `factories'.

As we'll see, these are roles to be played by agents rather than specific entities (they are promises made by agents as distinguishing traits), but they mimic a useful separation of concerns. I shall use these as idealized concepts, without worrying about the real-world nature of these too much.

  • Brains
    promise learning, prediction, combination
    In the Dunbar sense (explained above), brains represent the curators and maintainers of on-going relationships. They observe, sense, and learn patterns of behaviour, and they can make decisions to adapt in response. They represent what makes us human, social, creative, capable of cooperating. As we shall see, brains are also where value is defined, individually. Thus they are where currencies really get their value. Brains don't have to be directly human. They can be proxies for human interests, like corporations, vendors, or even artificial intelligences.

    Brains are cognitive, learning and employ feedback loops for build a learning circuit. Trade is, of course, one of the main repetitive relationships that a brain carries out. The receipt of goods or services is a cognitive process at the scale of the trading parties.
    promise capabilities, uniqueness, work
    Factories are the sources, owners, or creators of the information that is traded by brains. They create the unique traits, the basic inhomogeneities, or inequalities that enable trading and sharing. Factories sometimes mine raw materials and sometimes combine them into composites with new meaning, generating new qualities or quantities. They are a source of semantic variation (n information science, the term semantics is used to describe interpretation and meaning).
    promise stable lending of resources
    Banks are lenders. Their roles as vaults, for storing stuff, is less important than the fact that they have the authority to lend without becoming empty. They allow us to borrow in some form of currency (sugar, bread, generic money, or goodwill) to overcome obstacles. They thus turn cooperation into a service that does not depend on friendship. Any agent that can lend a hand, without significant burden on itself, could be considered a bank.

    By being reliable, banks mediate as Trusted Third Parties, i.e. monolithic proxies for pairwise trustworthiness. If we don't trust a counterpart's money, we can still trust the bank to pay up, as long as the bank mediates all the transactions, though its authorized coins, notes, cards, or cheques, etc. Thus banks get in between the agents in direct relationships, which is sometimes useful and sometimes harmful.

    Building on specialization and recombination

    Basic issue number one: how can agents (people, machines, companies, countries) exceed their own limited capabilities as individuals, to advance into new areas? The answer is: by taking on different roles and helping each other out.

    When they/we act as autonomous units, self-sufficient agents are their own factories, drawing on their own resources and making what they need to keep their promises. Sometimes, however, agents need help from one another. Sometimes cooperation is voluntary, other times it seems imposed by necessity. Diversity or inequality of agents' capabilities favours coming together to form molecular allegiances with new properties, in a chemistry of cooperation. They can make different new kinds of promises to one another, and the ability to make and keep a promise is a valuable basis for measuring trust. This applies to all agent scales from genes to nation states.

    Agents, with similar specializations, can cluster together to scale particular functional roles, e.g. services or activities that promise specific semantic outcomes. If all agents were the same, then they would have no particular incentive to come together; all agents would float around like amoebae, competitively trying to grab their share of the surrounding nutrients.

    The separation of concerns, which accompanies specialized learning, can, however, lead to efficiency, opening new doorways. Breaking up a problem into modular parts may help to scale it efficiently. A dedicated agent, which promises its specialized skill as a service, may handle more clients by interleaving them (so-called multiplexing). This brings an economy of scale. If we double the number of clients, we may not need to double the number of specialized agents to perform the service[8], because the cost associated with switching from one skill to another (called context switching) is eliminated, and the agent can focus on learning a smaller number of skills in greater depth. In short it has to maintain a small number of learning relationships.

    Economies of scale make specialization economically sensible when workload is not saturated. Several agents might be able to work together on the same thing, but also one agent might be able to share its time between several tasks, because it can devote all of its energies to doing similar work. Thus, as cooperation grows, we may need fewer agents on balance, provided workloads are not densely packed. You work on the `dev', I'll work on the ops. You be a doctor, I'll be an engineer.

    But there is also a cost to this specialization. As agents become dissimilar, they also become more isolated. They develop their own cultures and languages. They no longer understand one another implicitly, so they need a common language to communicate promises to one another about what they can do. All this costs time. Without trust, barriers of verification are introduced (fill out this form, wait for approval, jump through this hoop), leading to a retreat from efficiency. So it's no longer just a case of getting enough people together. We also have to make sure they know each others' roles, and know how to coordinate. The scaling of intent involves some kind of management or coordination.

    Cooperation is thus a Faustian bargain. We might improve efficiency (at least at low utilization) at the expense of increased complexity. If you think that's not true, try breaking up a lump of dough and then putting it back together again without changing its behaviour, then try taking apart a human being and put it back together again without changing its behaviour! The dough does not cooperate, so there is no information associated with its configuration of parts configuration. A human body, on the other hand, is a highly intricate cooperation of parts, each promising unique roles, which contains a vast amount of interaction information at all scales.

    In the future, the equalization of society, by the broad elimination of diversity that technology affords, may force us to rethink our interaction as individuals. In a sense, our striving to make universal access to goods and services, through integrated stores like Amazon, and online services, accessible though trusted third parties behind `smartphone apps' etc, undermines a part of the social contract on which we scale the modern world. When everything is available to us at the push of a button, by vending machine, replicator, or smart third party phone app, why would we even need to talk to each other, let alone work together? Would we evolve back into social amoebae, into technological hunter gathers, interacting only with faceless third party service providers? The answer here depends on the distribution of `wealth' or capacity to act and reward disinterested parties.

    Asking for help: exceeding our limits by borrowing

    Much as we value the predictability of total information that comes with our autonomous self-sufficiency, we cannot always manage everything alone. There are places in the future that we can only reach by working together. How then do we exceed our own capabilities, as individuals, to go beyond what a single individual can do? The answer is we cooperate, or borrow capability from other agents who have the capacity to help. The risky question in a world of diversity and cooperation is thus: can I rely on you?

    Why would we choose to help someone else? If we help someone and they overcome an obstacle, do they then owe us something in return? If you help me, do I need to pay you back? The idea of compensation for effort leads us to an economic reckoning, which I'll return to below. In short, no agent would help another if it meant compromising its own survival, but would it ask for something in return? Rational economic thinking suggests that there has to be a mutual benefit, reduced costs, or at the very least least no net loss in order to pursue an interaction. But humans are not rational. We are emotional in the short term, and only approximately rational when averaged over long times and statistical ensembles.

    Agents cooperate by:

    • Trade: a permanent exchange, for independent reasons.
    • Lending and borrowing: i.e. trade something with a time delay to returning it (debt).
    • Collaboration, mutually contributing to an outcome of common value.

    There are relative perspectives at play in these definitions. External trade is just internal collaboration, when viewed at a larger scale, and vice versa. A loan is just a trade by any other name. The lender offers immediacy of action, or speed of access to a resource, and expects a slower return of (often trading some kind of `interest' for the time lost as compensation). Time is a valuable and non-recoverable resource. Alternatively, a lender might trade the promise of a skill for some other skill promised in exchange. There are thus, as usual in systems, both dynamic and semantic forms of lending and borrowing:

    • Borrowing a larger amount of a single resource (dynamic, quantitative lending).
    • Borrowing a new enabling resource (semantic, qualitative lending).

    When agents know about each other, and trust each other, they can try to work together. The incentive to cooperate comes from promising one another outcomes that yield a benefit to the other. This benefit is a highly complex calculation (it needed the evolution of powerful brains), and it is poorly captured by our contemporary idea of money. The better they know one another, the less likely they are to insist on an immediate repayment. The command and control view of factory work, throwing tasks over the wall, i.e. imposing on one another's autonomy, like an alpha male aggressor, may even be a disincentive to cooperate (as production line workers recognize and challenge). Indeed, imposition might be construed as a form of attack (an attempt to appropriate the resources of an agent without a promise). Each agent assesses benefits to cooperation individually, in an Axelrod manner, and thus each agent separately determines a notion of value. In the currency of trust, there is no universal scale of value.

    Why do we need to compensate others?

    Why do we pay for what we take? The concept of ownership plays a role here. By social convention, if I come across something of value in my territory, whether by fortune or by hard labour, we say that it is mine, and you have to give me something in return.

    This is a moral position. We did not have to arrange our society in this way. In fact, if we are honest (which we rarely are when guided by self-interest), then ultimately we steal or `appropriate' everything like pirates. All the basic resources of the planet ultimately come from the sun and from other bygone stars. The stuff is lying around for us to take, not made by anyone. Our lives are granted by the sun, but no one pays back the sun. We help ourselves to animals and plants, without paying back directly. There is a balanced ecosystem, but it runs at a net loss, propped up by the sun's energy.

    The reason we pay transactionally for things is out of an ethical sense of `fair share' between humans, i.e. that we have learned to respect the boundaries of other `agents'. That is your arm, I won't take it. That is your food, I won't take it (unless I am desperate or you violate my concept of fairness). Trust, respect, fear of retribution (tit for tat, Axelrod's studies discovered), over a long term relationship are what allows the stability of agents and their promises to continue. Thus, we pay pragmatically to maintain trust, stability, and continuity in society. This is a form of what biologists refer to as reciprocal altruism.

    Scaling cooperation with promises, not obligations

    Promise theory is a discrete approach to understanding the freedom and constraints to scale cooperation. It describes the axioms on which economic behaviour are based. Could we use it to reconnect with the roots economics has lost touch with today? The appearance of `DevOps' suggests that we might.

    Left: Impositions impose intent from outside (non-local).
    Right: Promises respect intent only from within (local).

    Promise theory allows us to compare the imposition view of command and control (`make it so!') to a promise (`look what I can do, take it or leave it') view. In turns our that, in spite of the legacy of Newtonian thinking, a promise view is much closer to our modern understanding of how interactions work in the physics too[7].

    The main problem with impositions is not ethical or political, or the fact that they violate the autonomy of agents. It is that they do not lead to certainty or predictability. In fact, often then bring the opposite (inconsistency and uncertainty), because they are non-local: the declaration of intent happens in a different location than the verification of outcome, so the outcome might actually remain unknown. Two agents can try to impose different constraints on a third, and bring confusion rather than clarity. Promises are a `bottom up' theory of local information in which the declaration of intent and the point of outcome are the same, so they maximize the availability of information. Bottom up means local and consistent, as we know from physics.

    Impositions: (obligations) are non-local, low trust, throw it over the wall...
    Promises: local, trust building, by iterative feedback, voluntary cooperation, self-sufficient and autonomously kept. No agent can make a promise about an agent other than itself.

    In promise theory, we speak only of `agents', without caring whether they are human, animal, mineral, plant, machine, etc. All we care about is what promises they `intend' to keep. So we are not limited to only human interactions, or machine interactions, or monetary interations: we can handle all of these inside a common framework. There is an anthropomorphization here, which is harmless, and sometimes helpful. It is not important whether the paper in a British pound note intends to represent money, and to pay the bearer on demand a sum of one pound or whether it is merely a proxy for someone else's intent.

    To get from individual trade to macro-economics, we `only' need to understand how to scale promises. Promise theory allows us to track and scale both semantic and dynamic aspects of interactions, as discrete networks of interaction, to see how qualitative and quantitative aspects work together. Promises kept are a quantum of fulfillment, of intention realized, i.e. of reliability, thus they are a basic accounting unit for trust. To understand trust and value, it is therefore helpful to frame matters in the discrete graphical framework of promise theory. Later, when we understand the microscopic constraints, we can see how they scale into smooth continuum relationships on the macro-level.

    Understanding the scaling of trust, by agent scaling: interior and exterior promises

    The scaling of agency simplifies trust, at a cost, because it leads to a redefinition of our expectations of cooperative behaviour, placing multiple concerns under a common umbrella. If we can wrap systems in a shell or skin, as a single organism, we can simplify the trust relationship for the consumer of a promise who interacts with them. See the figure.

    When forced to cooperate with all the agents directly, dependencies in a chain of delivery lead to an explosion of information to verify trust. The rules of promise theory show that the end user must form individual trust relationships with each of the levels of dependency, because agents can only make promises about their own behaviour, but dependencies must be independently promised. A company or product shell, on the other hand, may wrap services in a `trust box' so that a consumer only has to trust the integrity of the whole agent thing, and leave the integration to the integrator. What happens on the interior stays on the interior. Only exterior promises are exposed.

    Suppose now a `smart service' integrates a number of independent services, which the user has to work with independently; rather than assuming that the user will know all the prerequisites and be prepared, the smart wrapper could make sure to promise the user a checklist of promises it must be able to keep in order to complete its interaction with the whole chain of obstacles, leading users through the steps, like a `wizard'. Now we are in the territory of smart systems, buildings, cities, etc. This is the kind of interaction that is common in software today, but not in government or public ervices, for instance.
    If the world were composed entirely of equal agents there would be limited gain from cooperation.
    1. The cost of cooperating between peers is N reward for N2 cost of coordination.
    2. The cost of cooperating with a singled out hub or coordinator agent is N-1 reward for N-1 cost.
    3. If the capabilities can be folded `vertically' into a single autonomous agent, then the potential is for N reward at cost 1.
    Thus, if everyone were the same, with only vertical scaling, we would have no need to cooperate or help one another. We would be independent, like `amoeba', floating happily and independently, getting everything from our smartphones. Semantic inequality enabled horizontal scaling not the real problem, but quantitative stockpiling and hoarding is. When some agents continue to accumulate an unfair share of resources, preventing others from getting access, those `rich' agents become effectively cancerous parasites on society, compromising fair access. But, who decides what is fair? This is an ethical and political question: economics is inseparable from politics.

    But, ...depending on others undermines promises, and trust

    Paradoxically, there is dark side of cooperation, which is that dependency invalidates promises: because dependency delocalizes agency and information[8].

    We base trust on our repeated experience of whether agents keep their promises or not, i.e. do they give us accurate information that allows us to work with them? However, if an agent promises something conditionally, relying on an dependency it cannot meet without assistance, this is not a real promise, e.g. `I will deliver X if I get Y from elsewhere', is an empty statement unless the same agent can also promise that it will get Y. No agent can make a promise on behalf of another (indeed, this assumption about voluntary cooperation is an axiom of promise theory).

    Personal trust, between peers, which our brains evolved to process, is resource intensive, difficult and costly to scale. It requires a deep understanding of third parties, of time and space, and of distributed information, that we take for granted in our cognitive repertoire. Human agents can thus scale by clustering, and a cluster can make new promises and behave as a single collective agent. Hierarchical clustering is the basis on which we scale all promise relationships.

    If new promises can be made at each decouplable scale of agency, then trust relationships must also form at each scale. Promise theory tells us that every scale can have new promises, and hence form independent relationships (promise, accept, assess individually), and this implies independent notions of value, and independent currencies for cooperation, at each scale too. Thus, macroeconomics depends on microeconomic detail, with some degree of coupling, but is not determined by it unless that coupling is very strong, at which point it would be chaotic.

    De-personalizing trust:
    Banks, generic money, and the dehumanization of society

    Scaling cooperation could be expensive, but there is a trick that works all agents except one: centralization. Today we know this as the `SOMETHING as a service' trick. By eliminating direct human relationships (that vestigial quirk that defines our intelligence), we can speed up cooperative interactions, eliminating the trust-building phase. This is what banks and generic money do. Instead of having to trust N agents, we only have to trust the bank. The downside of this is that money does not communicate the semantics of our intent, so part of the economy transactions take place `out of band'.

    Money (like a global variable in a computer program) is the victim of contention and contextual muddle. The role of banks, and their currency (money), is thus slightly different from the popular understanding of a vault in which we keep our savings.

    This trick of centralization has been used in civilizations around the world, to bring a de-personalization of society, through the creation of institutions or so-called Trusted Third Parties. It has been a very successful strategy for redefining trust relationships and establishing political stability, through its apparent role as impartial calibrator and arbitrator of `fairness'. Use of a third party is a mechanism for weakening the couplings to individual agents, and thus also eliminating the power of trust bonds between close individuals. This is what banks are supposed to do. It has both positive and negative implications.

    Another way to de-personalize is to average away individuality by voting. Thus, democratic process is another important way in which we perform semantic averaging and define fairness.

    On the positive side, we de-personalize services in order to take power away from individuals with selfish and local concerns (like kinship and tribal allegiances that lead to unhelpful rivalries). This performs a kind of semantic averaging over the relationships that make define our modern sense of `fairness'. It helps us to live together in a civil union, and has the additional benefit of subordinating everyone to a standard that treats everyone according to the same policy (which, alas, does not imply equally). Moreover, when we exchange money tokens whose buying power (let's not call it value) are promised by a trusted third party, we now only have to trust this single entity to engage in transactions with unknown parties. We don't have to build a relationship with everyone, because the bank (or government) will guarantee our future buying power, and the continuity of future society.

    Banks promise authorized money tokens with a standardized unit of buying power (though they change this promise frequently and inconsistently, because of the invalidating effect of dependencies). If everyone trusts these money tokens, by investing in a trust relationship with the bank (as if it were a giant, if slightly obtuse brain), and everyone promises to use these tokens in transactions, then trust can be replaced by price. This has some negative side effects. To begin with, there is no deterministic relationship between price and trust. It drives a wedge between parties too, making us care less about one another, chasing buying power instead of cooperation. We come to imagine that the bank (or someone else) will take care of our neighbours so that we don't need to play a community role. Money is dissociated from cooperative trust, and re-associated with personal status. Monetary cooperation suddenly looks more like competition.

    Today (at least in my home country) banks are now providing trusted identity authentication services too, illustrating their new role in the modern world as identity information brokers. They are trust merchants, as are Google, Facebook, and others who broker central banks of authoritative `trust'. Does the fact that we are able to create a centralized system, where we replace peer trust by the automated trust implied by money and bank, mean that individual trust was actually a waste of time? Were we wrong to demand trust of one another in the first place? No, because, by taking money (as a kind of external validation of trust) for granted, we now operate under greater risk of failed expectations. We just hope that if the trust were misplaced, we could sue for compensation retroactively (tit-for-tat). What we gain is the assurance of a kind of guarantor --- a bank, central bank, or government who defines the currency, and can print as much as it likes to cover losses and paper over cracks, according to its own standard of fairness. In effect, banks meddle with the causal nature of time in interactions, by providing a buffer for `cashflow'.

    To maintain the stability of this weak `third party' quasi-trust, banks have to be unflappable sources of authority, like infinite reservoirs, as constant as the Northern star, `alphas' that dominate behaviours of the social group. They are the supertankers that do not get knocked off course by a few waves. When we borrow, and perhaps fail to repay, they must continue. The banks are supposed to sustain this kind of equilibrium with the users of their currencies. Information theory tells us that agents can never reach equilibrium unless they are dominated by a singular source or sink of essentially infinite capacity. There can only be one collective `bank agent' to calibrate a particular form of currency; though, in accordance with the scaling laws for promise agents, the bank agent may be composed of multiple cooperating distributed sub-agents, making coordinated promises. This includes the possibility of distributed algorithms for `electronic currencies' like BitCoin. But, if everything will continue no matter whether we repay a loan, why would we bother? The answer, again, is trust and the politics of fairness.

    Money can't increase trust between its users, only to the bank
    Banks and their calibrated money promise basically a language for interaction. Because money is a trust relationship with a bank (a bank, in the most general sense, promises to validate and authorize its currency), and not a trust relationship between the users of the currency it promises, it leads to only implicit third-party trust. This is rather weak, as agents are only bonded by the mediation of a third party. In chemistry, we would call such bonding through an intermediary a covalent bond.

    Consider a slightly different example based on something we would not normally consider to be a bank: a television. A television is an unflappable source of entertainment (no really). If a group of people are sitting around a television set watching a show, they are bonded implicitly through the third party, like members of a club, just as a bank binds together money users in a monetary system. The watching (lending) of content by one user does not prevent others from watch it too. The content is unflappable. Eventually, they repay this by license fees or cable charges, etc. The users or watchers have no direct peer-to-peer interaction, so their trust for one another is weak. Giving the peers more entertainment, or more `currency', only enhances their bond to the bank, i.e. their allegiance to the TV channel, not to one another.

    The calibrating influence of the bank only enables them to exchange mutually understood information (tokens like: `Sally married Geoff OMG!') and they will both understand these transactions, but they do not build deep trust between one another. They would still need to build that independently peer to peer to trust one another. Similarly, when we pay for things with money, a bank calibrates the value of the money and authorizes is, so we can understand one another, but we do not trust a vendor simply because it uses money, or because we are paid more, or because goods are cheaper or more expensive. We would still have to build up trust directly through repeated interactions over time (did the vendor give me quality for my money?). Money is a lingua franca for communication, and a proxy for weak covalent trust.

    Taking away individual trust and replacing it with a politicized form of fairness is a lot to ask of a society. The users or watchers have no direct peer-to-peer relationship, and not all societies accept this loss of autonomy equally.

    In Northern Europe, perhaps the epitome of belief in dispassionate organization[5], which calibrate fairness and represent trust through monolithic `banks' and government bureaucracies, we are proud that fairness cannot depend on family, kinship ties, or clan allegiances (though social status can often still carry privilege). Our modern allegiance is to money (not even government actually). The price is a bureaucracy that seems to care little for individuals, i.e. a dehumanized society. It is impartial, in some sense, but also inhuman, and even obtuse at times.

    Southern Europe still clings more to the values of friends and family, and allows peers to interact directly, offering individual favours. Bartering and negotiation are still a part of basic services. This might not scale as well, and is unlikely to be as uniform (i.e. what we might call fair). To Northern European eyes, this sometimes seems corrupt (looking after your close relationships), even unfair and messy, but it clings on to the humanity of individuals at the expense of impartiality. If you are willing to interact on a personal level, you can get bespoke service without the latent bureaucratic hindrances. Both forms of political order are widespread around the world[5].

    DevOps as an example of promise-oriented cooperation, without money

    Rebelling against the centralized blunt force trauma of factory imposed production lines, and trust replaced by monetary governance, peer cooperation has been rearing its head again in recent times. Over the past decade, a movement has arisen in the domain of e-commerce and IT services called `DevOps' which tries to address systemic failures of cooperation in software production and operation. The idea that developers `Devs' were the sole source of value, and that pushing their creations downstream to sales and operations, without caring to listen to their concerns, has come to be seen as not only arrogant but irresponsible, ignoring reality (an imposition).

    Whereas managers (who often act like a bank) try to exert influence through wages and monetary means, the DevOps solution was not to give the workers more money, it was to encourage them to make friends (promising one another trust-building behaviour). DevOps promoted the idea of building an interactive feedback culture (brains talking to peers, rather than factories talking through a bank). Money, after all, is not value, but an iterative relationship is. The success of this human message points to an obvious flaw in our economic expectations. Our work narrative is in conflict with our basic human instincts. Moreover, everyone secretly knew this, but the received MBA narrative of monetary control maintained a distorted view.

    Paying someone more to accept bad conditions is not a real incentive for the desired outcome, because the semantics of money are not aligned with the semantics of the outcome itself. Offering money is more likely to encourage greed or even cheating. The role of the third party gets in the way of the relationship, just as when workers in hierarchical organizations are told `you have to go through my manager to talk to me'. The de-personalization of having an intermediate agent (third party) removes a value creating loop and decouples the specific semantics from one another. Generic currencies do the same, hence the move towards private currencies, payment loyalty cards, etc.

    Sometimes the opposite trend can destroy cooperation. Recently a friend of mine told me of an example of counter-culture. In her startup, a recent hire started to create conflict over entitlement to better pay relative to another worker. This internal conflict shifted focus onto a `bank approach', which got in between the peer relationships. On the interior of a promise-agent (in this case a company), cooperation is needed to stabilize the external promises. If there is to be competition, you want it to be on the exterior, else the agent is not even a stable entity.

    DevOps has become a concept now in IT. It has the feel of a civil rights movement--- a return to human values protesting over a culture of management by third party imposition. People want to be brains not factories. Managing at the macroscopic scale of averages will never be good for microscopic concerns. DevOps is a rehumanization of the scaled production pipeline, but introducing more feedback. It is a cognitive (brain) learning model that builds trust. The traditional (factory) imposition model of `throw it over the wall' has fallen short of delivering trusted services, as promise theory would predict, from the lack of feedback and cooperation.

    Of course, it is not just developers and operators who have this trust gap. The semantic richness of modern information organizations means there are many interfaces between parties who need to cooperate, to align their interior promises with the exterior promises of their teams or clusters---not just Dev and Ops.

    The focus on relationships, and brain approaches, has extended beyond pipelines to separations of concerns in cooperative networks (software ecosystems), through `microservice architectures' that are not for scaling technology, but rather human cooperation. Dunbar's limits tell us how many of these we can realistically manage as humans. The concept of microservices, for semantic decomposition, enable specialization while keeping tight relationships. It is not about reuse or efficiency of scale as much as it is about value generation.

    When trust goes into recession, depression follows

    However we choose to manage cooperation, it is necessarily a fragile affair. When one or more of the three pillars of cooperation fail, the result is a dynamical collapse of a system of promises, leading to a recession of trust. In the 1930s economists witnessed at first hand this recession of cooperation, precipitated by an economic system based on interdependent currencies (which make promises invalid) and fending global imbalances with only local remedies. The entire edifice of social cooperation came to a halt.

    In a depression, even the pressures and incentives, imposed by a central, bank become mistrusted by its client agents, and they become unwilling to use its currency (money), planning instead to wait and hoard for a better price, or more favourable deal. Hoarding of cash, as a fungible buffer, became seen as an insurance against the expectation that others would break their promises, either by willful intent or by force majeure. This could turn to tragedy in regions with hyperinflation, where savings would evaporate back into the imaginary fire from whence money originally came. Protectionism shoots itself in the foot.

    The problem in a depression is thus not one of fiscal inequality, per se, but rather of hoarding, preventing others from accessing supplies they need to replenish. Supplies are perishable over some timescale, so the effect of a depression is tied to a slow-down on this particular timescale of needs relative to supplies. In the modern world, where the mantra of growth is taken as an axiom, we are force fed goods we don't strictly need, to sustain the desire for profit. It seems hardly any wonder that trust is subject to sudden collapse when we are woken up from our daily sleepwalk by inclement financial weather.

    The general purpose money issued by nation states, from their central banks, is used as a proxy for all kinds of social contracts. However, because the money carries no recognizable intent, money transactions muddle together issues that should be kept separate. Money couples semantically independent issues together, across multiple scales, and strong coupling is the basic cause of deterministic chaos[8].

    Generic money is only an approximate guiding hand for social interaction, analogous to herding cats with a red cape, in an economic charade of spectacular semantic complexity and interdependency. This makes it not only difficult to understand but also highly unstable, because it brings the kind of strong coupling that quickly leads to chaos. The total system may be overconstrained in some areas, and under-constrained in others. Money and resources are not scale-free (conformal), but the neo-classical economy is assumed to be, by its model of artificial growth and equilibrium.

    Money can't really measure the value of an outcome, but the neo-classical market narrative pretends that it does and that markets can respond instantaneously with changes of price, propagating this information instantaneously and self-stabilizing the markets. The extent to which money carries value depends on what prices each individual associated with items in the first place. That is context dependent. We may sell a family heirloom to pay the rent. It would be absurd to compare their value on a single scale, but that is what we do with generic money. Economic theory pretends to assume that value and price are strongly correlated by market forces, but food is not the same as taste; this could only be true in highly idealized circumstances. Indeed, for all the money we stockpile, inflation can wipe out the value of money, without altering the value of things.

    The flow of money is only a form of information, not a transfer of universal value. It has to operate at a finite speed. The trust that matters to society is associated with peer relationships, not with money transactions, or with trust in the bank; but, when trust in the bank and its money are lost, parties stop talking with their money, and this weakens everyone's trust in everyone else even more. This is what we mean by a depression. Cooperation grinds to a halt and parties put up barriers and more costs to cooperating (`austerity').

    During financial crises, governments have often tried to control spending by imposing austerity, believing that they are buying a kind of insurance at fixed value. However, by making it difficult to spend money, the problems of receding trust are make much worse. Since money is the main form of dialogue in finance, preventing parties from talking only paralyzes the economy. Imagine if you had a problem to solve and the response was to limit access to the phone, Internet, and email. How would you fix the issue?

    This seems to be a deep flaw in modern economic theory. Money is only a crude hammer, but we try to hit every problem with it. Finance and macro-economics are deeply coupled, with Byzantine complexity, yet as financial blacksmiths hammer away with their single tool, macro-economics tries to govern the effect by annealing and quenching the financial flows, averaging away the timescales of their causal effects, to manufacture a totally artificial quasi-equilibrium. The imagined end-state of such a process could only approximate the actual economy on a timescale much greater than than the monetary effects one is trying to govern.

    The curious case for interest

    Debt, and `interest' paid on debt, are another feature of the modern monetary economy that do not make much sense between trusted friends, yet they dominate the economy today. The closer the relationship, the less concerned we are about repayment. Lenders are strong, loaners are weak. The de-personalization through money led to a sense of legitimacy for charging of rent and compound interest, depending on the inconvenience, and potential loss due to inflation, suffered by a lender, also as a disincentive to unbalanced parasitic borrowing. In Islamic banking, charging of interest is considered morally unacceptable.

    This subject is too large to fit into this margin, however, a full analysis of the rationality of interest and inflation, including semantics, in the context of promise theory, would be a worthy underpinning for an analysis of a cooperative smart economy.

    The needs of the economy outweigh the needs of the community or the human

    An economy that only delivers stability on a timescale much greater than human behaviour is of no value to its population. It might have a theoretical benefit to a society's long term survival, but it asks individual humans to sacrifice their needs for an abstract goal. This is similar to the dilemma we face with global warming and ozone depletion: why should individuals sacrifice their lives for an abstract agent, like the planet or the economy. This is a questionable and unfair request to make of anyone, even for the benefit of future generations. In a world of rich information, we ought the be able to find a suitable compromise to both issues, with twice the moral payback.

    We have only begun to use rich information to track economics and financial concerns. Internally, in markets, high frequency traders, and debt-traders markets, buried in a Byzantine financial system, already use some of these ideas to address the concerns of banks and investors in bulk, but these also tend to sacrifice individuals on the alter of bulk profit.

    What possibilities exist in a service economy, where we can trace the semantics of intent and the interactions of multiple parties, with all dependencies, quickly and in a controlled way? Could we then enable micro-adjustments to trade, large and small, through a variety of monetary currencies, just as a highly unstable fighter jet performs a million micro-adjustments to its flight envelope just to stay in the air while maximizing its maneuverability[8]. This is already beginning to happen.

    Timescales and promise-keeping are intrinsically linked

    Complex currency semantics arise when promises of goods and services are composed from many parts, each with networked dependencies. A good example of this is how airlines price the seats on flights. They try to predict the future cost based on a variety of promises that may or may not be kept, such a fuel price, demand, etc. It might seem that a seat on an empty flight should be cheap, but costs do not scale continuously. The cost of carrying a single passenger in terms of weight, versus weight of fuel depends on them keeping their promise of baggage allowance, body weight (which they do not promise), and the price of fuel, whose promise changes on a timescale much shorter than ticket sales. This is combined with logistical costs of having planes promised to be at different locations for availability, which in turn depends on weather conditions and a variety of factors that make the pricing a gamble. Like weather prediction, detailed information might enable a brute-force calculation, still with some uncertainty. All these considerations lead to an accumulation or orders that is by no means a Markov process: expectations for the final flight depend on the order and time at which ticket purchases come in. Costs may be unfairly placed on certain passengers at the time of booking, because the semantics of purchase are to promise a price up front, instead of later when the costs are actually known.

    In many cases, as long as there is sufficient stability in the prices promised, fluctuations can be evened out over a timescale much larger than the timescale of ticket purchases and flights. Thus stability is ensured by the thermal reservoir model again, where the size of the reservoir defines a critical scale for being able to . Market monopolies that can aggregate all orders in a single bank buffer will have greater stability.

    Scales (timescales) play a central role in the ability to make predictions, even with promises. Moreover, we know that promises will not always be kept, so we must have sufficient bulk redundancy to even out (stabilize) fluctuations, in both dynamics and semantics. Human responses are emotional in the short run, but may approximate `rational' when averaged over long timescales and statistical populations (ensembles), by semantic averaging[7]. Thus, if we attempt to model the economy in terms of rational agents, it will lead to management over a timescale much great than that of individual concerns. The economy will not serve us as individuals. Who then will it serve?

    Promise theory should make it clear that the scaling of agents is a discrete matter, in the short term, not a continuous one. Discreteness of agents in space and time implies a timescale, which affects interactions and outcomes accordingly. We may approximate a fluctuating currency system as a continuum near-equilibrium model, for the total (global) system, if we are allowed to buffer cash flow with a large singular dominant reservoir of funds to stabilize debt, to average out the discrete fluctuations, and neutralize specific semantic failure modes. But this does not scale down to individual currencies interacting with neighbouring economies, as that is (by definition) a non-equilibrium scenario on any relevant timescale.

    Our notions of banks, factories, and brains are too simple for the information age. The generic money we pretend is neither universal nor really value-carrying. Promise theory shows us that our money is really already many different kinds of money, for addressing concerns at multiple scales, and the fact that modern economics tries to makes them all the same is a fundamental source of instability, leading to strong coupling and cascade failures.

    Moves away from this generic money, towards interaction specific money, are all around us in the information age, with store loyalty cards, flight miles, and all kinds of vouchers that are tied to specific sources. Although vouchers are usually per company, or institution, the ability to track rich information allows every good or service to maintain its own currency. We have that capability, but it seems too much for our simple minds, because we know Dunbar's limits affect all out relationships, not just human ones.

    Towards a smarter, semantic economic soundtrack for our lives

    The challenge we face in changing the way our economy works is that we have implicated ourselves and our lives so deeply into a system that was designed to produce greater output, to lift society out of poverty and to advance our living standards, that the entire meaning of our lives has become the enactment of this process, of going to the factory to make the money. Our major relationships are no longer with family and friends but with our work. Goals, process, and workforce have absorbed life, relationships, and population.

    We humans have been the originators of the ideas and the goals, the workforce by which the production is carried out. Our life relationships have been dominated by our work. But, as systemic automation takes over the production of goods and services, our personal time will no longer be dominated by the pursuit of feeding this abstract growth. Generations have built a society out of suckling at the teats of factory workplaces, suppressing brains in favour of factory life. If that goes away, what could we do to fill that gap in our lives?

    We shall need brains, not just factories, to maintain trust and human values, else we shall see the values we take for granted whittled away by the increasing de-personalization that comes from decoupling people from one another. Soon we may only be coupled through impartial services, via our `smart' phones. Some might speculate whether artificial intelligences, and `cognitive computing' could take over the burden of trust relationships at high speed. Indeed, some of this is happening in high speed trades. But in these cases, the goal is to exploit systems, like strip-mining for numerical profit. The relationships are not based on a creation of mutual value. This will be the challenge of a better economy.

    Economic cooperative behaviour as a scaled cognitive process

    Macroscopic boundary conditions modulate microscopic behaviour, while microscopic fluctuations potentially destabilize long-term macroscopic conditions. Because there is long/short-term memory, economic behaviour is not a Markov process.

    Promise theory clarifies the role of value as an aspect of our cognitive interaction. Each `brain' decides its own value. Yet normally we try to use a single currency, whose imposed `trading worth' is calibrated by a central bank. The calibration is adjusted to try to address many different concerns imposed from outside, but we know that multiple impositions lead to conflicts of interest. Suppose instead, we actually separated the currencies used for different relationships, just as we separate currencies for different superagents (countries, corporate loyalty programs, etc). It would be a lot to manage (but that's what we have computers for). by decoupling currencies from the single general purpose `Dollar' or `Euro', etc., we could assure the stability and fairness of each quite easily.

    Recapturing our understanding of meaning (semantics), and aligning around a shared sense of its value, is the key to not losing our sense of self, and remaining human. System design thinking is motivated to eliminate the individual, for reliability. If a system relies on individuals who are unique, then they are single points of failure that make it fragile. If the individuals are redundant, or interchangeable, then there is systemic robustness, but their individual value is averaged away. When we design robust systems (on which everyone can rely) the paradox is that we become unimportant as individuals. The system might now outlast any individuals promises, and absorb failures to keep the promises, but it deliberately makes individuals irrelevant (and their happiness and quality of life are not factored into the goals of the system). Thus the economic success of a system automatically dehumanizes its goals. As Spock put it, in Star Trek: The Wrath of Khan, `The needs of the many outweigh the needs of the few, or the one'. But this need to disenfranchise individuals for the many leads to disgruntled parties. When the needs of the system (often represented by an `owner') are not aligned with the needs of all (or, worse still, penalize certain parties) then the system merely ends up not working in their favour.

    The way we eliminate or reconnect personal issues must be handled fairly and with sensitivity. When impersonal organizations are judged to be obtuse, it is often because they de-personalize users without de-personalizing the services that serve them. When we install petty kings and dictators into service roles (the disgruntled bank clerk, the self-righteous tax officer, etc), whose promise was supposed to be simple and impartial, they fail to keep those promises, abusing their uniqueness as a way of clinging onto individual power, and thwarting others. One of the judgements that still begs us for our human intelligence is when we should suppress our humanity, and when we should use it for compassionate exception, but preferably never as a weapon.

    In a modern information society, this is not a tradeoff we should have to make. Our ability to deal with information should render this problem trivial: we ought to be able to do better. If the goal of civil society is to work for (all) humans, the system's goals need to be compassionately aligned with all its contributors' needs. Clearly that is not the case in contemporary capitalism, where shareholders and privileged landowner `classes' are favoured over mere `workers' in a kind of return to feudal ownership (whatever the host country's formal claims about freedom and democracy), exacting tributes, rents, and even interest payments. The system is engineered to cumulatively favour the rich over the poor, because it deals only with numerical quantitative measures, discarding qualitative semantics.

    Thus ownership is a complicating semantic that may work against the needs of the whole. We pay because of the semantics of ownership, and owners feel entitled by history, kinship ties, dynasties etc, to maintain their control and privilege[5].

    Modernizing money to align with human values?

    Semantics are the key to functional economics (and an information rich economy) because `fair distribution' can be weighted by sense of purpose (intent). If everyone can agree on a division of promised roles, then there need be no loss of trust, and no need for total centralization in a single entity. In IT we call this `microservices'. Centralization can be particular to the role (a brain for every purpose). Agents can take a longer term view that, by reciprocal altruism, they can eventually benefit more. Today the transactional `growth' economy and the trust economy are two different things. In a service economy, they can be the same. Intent and voluntary cooperation now play their roles and take on new significance.

    Each agent interaction scale (by cluster aggregation) has independent concerns and timescales: people, companies, towns, nations. Why do monetary authorities mix all their trust issues together, through currency incentives? The main reason is that currencies were designed prior to the information age. Each could have a different currency for cooperation.

    Why lump all these concerns into a single instrument? Why not have a different currency for every imaginable interaction, and manage exchange value only on a need-to-know basis? This kind of approach to separation of concerns is basic lore in modern information technology, but it is more of a belief system than a scientifically motivated `truth'. In fact, this is starting to happen, as every independent economic centre creates its own `app' or chargecard.

    However, the efficiencies of modularization are not well understood, and mistakes have been made across different fields, including the lessons of the Garden City Movement in town planning, which brought massive traffic congestion at the expense of semantic separation. We already have a different smart-app for every service, but money was invented long before modern information semantics were developed. If some lingua franca currency needs to be used, let each scale figure out the exchange system.

    It would be an interesting exercise of humanity to ask the question: why would someone give me something for nothing, or for a long-term IOU? Do we need to revisit the issue of debt forgiveness? Could we transform a punitive model into a constructive one, i.e. one that restores dignity and social participation, based on societal values rather than fiscal ones? In a world of plenty, society can provide anyone with a buffer for survival. The human issue is more complicated though: it is not enough for humans merely to survive. Our expensive brains crave relationships and dignity.

    The way taxes are imposed today is Byzantine and dehumanizing. If taxation were managed as a rich information service, i.e. as a smart payment for social services, including borrowing, then all these issues could all be combined into something analogous to the yearly taxes as as ritual reckoning at the end of each year. Those who abused their access to `fair share usage' could be policed and made to redress their debt to society on a longer timescale. Clearly, the new possibilities for aggregating information suggest that we may have to redraw the lines between what is private and what is state.

    The need to be involved in the passing of money tokens seems almost quaint in this modern world. In smart cities around China, for example, electronic payment has been a reality for years. People hardly need to think about the existence of money, as long as they don't run out of buying power. The details of payment are more of a confirmation ritual than a financial transaction. Electronic payment, by phone, allows people to pay practically anything, even with no handling fee. These service providers can create and exchange currencies transparently to individuals in an information rich world. The only question is: will this happen in a way that makes us dependent on a fragile system, or will it reflect and reinforce the values of trust that our brains evolved to handle. Will it be smart for us?

    Generic money cannot capture the semantics of its usage case-by-case, but a service relationship with rich semantic information can. Thus micro-currencies allow smarter adaptation and decoupling of risk.

    Identity management (definition of boundaries) is the chief information semantic that leads to inequality, ownership, and all of the qualities modernity relies on. In a world of rich information, identity management is easy and we can keep track of much smaller groups again. Thus information capability could undermine the civil achievements of equalizing or creating parity between families, tribes, and other closed groups. When technology was poor, it made sense to make everyone the same, a simple (even simplistic) view of fairness.

    Explicit answers to all these issues require a sequel to this, already long, piece.

    The only thing we can't scale is individuality

    As our goals get bigger, we have been able to scale the work needed to realize the goals by increasing a workforce. But in the information age, automation will be the main answer to scaling production, leaving swathes of workers in current jobs unemployed, without possibility of retraining. We can scale almost anything, but we cannot scale the individuality of a single human being. When we no longer fit into any of the slots that need filling, we will have become properly redundant.

    What can we do to adjust the course of future society to prepare for this? To begin with, we will need to learn how to treat individuality as an asset rather than a liability. Then, we need to fill the hole in our lives that is opening up from the mismatch of what a human can promise and what the economy expects. We need to re-engineer the economy to address the needs of individual humans, not merely firms and nation states. As automation and transport logistics improve, the need for globalization will be partially reversed. Only non-recyclable raw materials will need to move significant distances on a regular basis. The flow of `human capital' or skill is another economy that is totally separate from money, as we understand it today. It is handled by immigration policy, where cultural semantics are often handled bluntly and insensitively.

    Finally, time is the resource that cannot currently be replenished. Individually, humans have finite lifetimes, which cannot be extended significantly or on demand. An economy has to support our well-being on this timescale, but the increasing speed of the supply chains in metropolitan societies effectively allows us to extend lifetimes relative to our preferred work rate. Our wants and desires (market demands and thus economics) are intrinsically pinned to a timescale of human cultural and generational norms, but that is speeding up. There has been a tendency for individual concerns to be ignored because they were not scalable or sustainable. We have purposely dehumanized the world in order to make it last longer, serving the economy, not ourselves as individuals. But this can change thanks to rich information technologies.

    The real problem we face in society is that, as we scale from a single human to a collective, the `brain' that learns the behaviours at collective scale, and the promises it makes, are progressively less intelligent, less nuanced, and the behaviours are designed to be less complicated, so scaling up means dumbing down. In the future, we might even apply artificial intelligence to manage trust relationships at scale, thus writing humans out of the story altogether. But then, we would have transformed our species from social animals back almost to the point of passive amoebae, being fed by a nutrient stream through a technological feeding tube.

    Going it alone?

    What keeps us together, and what tears us apart? The answer to both questions might be how we use our technology. Technology connects us in countless ways, but paradoxically it also makes us increasingly independent as individuals, . If it comes in between us as human beings, it can separate us from the social contracts that brought us to where we are.

    What will be our economic system in a world of plenty, where factories are largely automated? Will having everything, at the touch of a smartphone, release us from the need to form trust relationships by making mutual promises? Will we only interact with `trusted' third parties, companies and institutions? How will these be maintained without maintaining a strong cognitive adaptability to our environment within the social fabric. I am an optimist on this point. We will not give up our humanity so easily, as argued here:

    Striving for `universal equality' is certainly not an answer to the issues we face. This would only bring about the loss of culture, individualism, and all the things we value in relationships. However, basic human rights, and giving everyone an appropriate buffer for absorbing shocks, in order to maintain local stability, and to eliminate suffering, would be an obvious win. We also need to venture funds to promote future exploration. Innovation and exploration are human nature too. Will our roles as social animals (mostly free of aggressive conflict) be supported or undermined by whatever system replaces generic money? Our civil society still relies on many old assumptions, but if too many of the underpinnings of the social contract are removed, a society can still disintegrate either by rising costs (unlikely) or by a lack of mutually beneficial social norms (`lawlessness')[10].

    It is deliciously ironic that DevOps has emerged as a global movement re-emphasizing the importance of peer trust, in the last decade in the world of IT. The very enabler of our `dehumanized' impartial system has found that the solution to its systemic issues is to build human trust along side automated delivery: a recognition that a return to actual relationships with feedback and long term mutual understanding is beneficial and can work to everyone's satisfaction---and not only to overcome obstacles, but for our human happiness.

    Amongst the lessons to be learned from DevOps:

    • Throwing central money at the problem did nothing to improve the promised outcomes, but may have fuelling greed amongst developers, distracting them to feel financially special rather than cooperative with business goals.
    • Better cooperation for semantic quality and well being between dependency chain links in a production line.
    • Dividing a problem into human-sized chunks called microservices, reconnecting individual dignity, sense of ownership, and responsibility to brains rather than faceless factories.

    Juggling work and happiness

    Economist Richard Layard has long been asking the question whether profit is really the motive we should be striving for. In his book Happiness[12], he suggests that a better goal (and an important one for our future) is to live happy lives now, not merely invest in the promise of future riches. Moreover, we should not forget the lessons of anthropologist Joseph Tainter, who wrote about the role of mistrust in bringing about an economic collapse in numerous past societies throughout history[11].

    Technology is both connecting us together over wider areas, and undermining our need for one another, by equalizing the world. Technology places us in front of an audience that far exceeds our Dunbar limits. The growth economy is homogenizing and dumbing down the diversity of the world, while it strip-mines and concentrates its trust tokens with escalating unfairness. Shops around the world are filled with the same offers, but the access and entitlement to a share of it is approaching catastrophic imbalance. Only at the level of nation states are there still minor variations in culture and raw resources worth trading for. When recycling becomes efficient, we could strike many raw materials from the list too.

    To respect the species we evolved to be, humanity needs its brains, along side banks, and factories for a long time to come, but the shape taken by these agents may not be recognizable to us for much longer.

    MB Oslo Sun Jan 1 13:40:19 CET 2017, minor edits 5-11th Jan.


    1. M. Burgess, Thinking in Promises, 2015
    2. R. Dunbar, Grooming, Gossip and the Evolution of Language, 1996
    3. W.X. Zhou and S. Sornette and R.A. Hill and R.I.M. Dunbar, Discrete hierarchical organization of social group sizes, Proc. Royal Soc., 2004
    4. R. Dawkins, The Selfish Gene, 1976
    5. F. Fukiyama, The Origins Of Political Order, 2011.
    6. R. Axelrod, The Evolution of Cooperation, 1984
    7. R. Axelrod, The Complexity of Cooperation: Agent-based Models of Competition and Collaboration, 1997
    8. M. Burgess, In Search of Certainty, 2013
    9. S. Keen, Debunking Economics, 2011
    10. J. Varoufakis, The Global Minotaur, 2011, 2015
    11. J. Tainter, The Collapse of Complex Societies, 1988
    12. R. Layard, Happiness, 2005
    13. J. Diamond, Guns, Germs, and Steel, 1997.

    NOTE ADDED 22 January 2017 - After writing this piece, I was recommended the excellent book, The Zero Marginal Cost Society, by Jeremy Rifkin, which echoes and deepens several of the arguments in this piece, and is highly recommended.

    NOTE ADDED 2 March 2017 - After writing this piece, I was recommended the excellent book, The Memory Bank, by Keith Hart, which (far ahead of its time) presages and deepens several of the arguments in this piece, and is highly recommended.


    I am grateful to Lisa Caywood, Kevin Cox, Patrick Debois, Bill Janeway, Robert Johnson, Ole Martin Løvvik, Tim O'Reilly, and Lynn Xu for helpful comments.