Chapter 1. Promises and Impositions
Imagine a set of principles that could help you understand how parts combine to become a whole, and how each part sees the whole from its own perspective. If such principles were any good, it shouldn’t matter whether we’re talking about humans in a team, birds in a flock, computers in a data center, or cogs in a Swiss watch. A theory of cooperation ought to be pretty universal, so we could apply it to both technology and the workplace.
Such principles are the subject of Promise Theory. The goal of Promise Theory is to reveal the behaviour of a whole from the sum of its parts, taking the viewpoint of the parts rather than the whole. In other words, it is a bottom-up constructionist view of the world. You could describe it as a discipline for documenting system behaviours from the bottom up.1
The idea of using promises as an engineering concept came up in 2004, as I was looking for a model of distributed computing to describe CFEngine. The word promise seemed a good fit for what I needed: a kind of atom for intent that, when combined, could represent a maintainable policy. However, it quickly became clear (having opened Pandora’s box on the idea) that there was something more general going on that needed to be understood about promises. Promises could also be an effective way of understanding a whole range of related issues about how parts operate as a whole, and it promised2 something not previously taken seriously: a way to unify human and machine behaviours in a single description.
Unlike some other modelling methods, such as in business and computer science, Promise Theory is not a manifesto, nor is it a political statement or a philosophical agenda. The magic lies in the application of a simple set of principles. It is little more than a method of analysis and engineering for picking systems apart into their essential pieces and then putting them back together. Along the way, we find a method of representing and questioning the viability of our intended outcomes. For some people, this is what computer programming is about, and there have been many philosophies around this, like OO, SOA, UML, and so on. Many of these have failed because they set philosophical agendas ahead of understanding.
The purpose of this book is to ask what can an understanding in terms of promises tell us about cooperation in human-machine systems, organizations, and technology, and how can we apply that understanding to the real-life challenges of working together?
From Commands to Promises
The cultural norm, at least in Western society, is to plan out intended outcomes in terms of the commands or steps we believe we need to accomplish in order to get there. We then program this into methods, demanding milestones and deliverables to emphasize an imperative approach to thinking. This is because we think in stories, just as we relate stories through language. But stories are hard to assess. How do we know if a story succeeded in its intent?
If, on the other hand, we change focus away from the journey to think in terms of the destination, or desired outcome, assessment and success take on a whole new meaning.
Let’s look at an example. Consider the following instructions for cleaning a public restroom:
Wash the floor with agent X.
Mop and brush the bowls.
Put towels in the dispenser.
Do this every hour, on the hour.
Now let’s convert this into a promise formulation:
I promise that the floor will be clean and dry after hourly checks.
I promise that the bowls will be clean and empty after hourly checks.
I promise that there will be clean towels in the dispenser after hourly checks.
I promise that there will be soap in the dispenser after hourly checks.
What’s the point of this? Isn’t the world about forces and pushing changes? That has been the received learning since the time of Newton, but it is an oversimplification, which is not accurate even in modern physics.
The first thing we notice is that some agent (a person or robot) has to make the promise, so we know who is the active agent, and that by making the promise, this agent is accepting the responsibility for it. The second thing we notice is a lack of motivation to make the promise. Cooperation usually involves dialogue and incentive. What is missing from these promises is a counterpart like: I promise to pay you if the bowls are clean. Thus a promise viewpoint leads to a question: how do we document incentives?
Why Is a Promise Better than a Command?
Promises expose information that is relevant to an expected outcome more directly than impositions because they always focus on the point of causation: the agent that makes the promise and is responsible for keeping it.
Commands and other impositions fail us in two ways: they tell something how to behave instead of what we want (i.e., they document the process rather than the outcome), and they force you to follow an entire recipe of steps before you can even know what the intention was.
So, why is a promise better than a command? A promise expresses intent about the end point, or ultimate outcome, instead of indicating what to do at the starting point. Commands are made relative to where you happen to be at the moment they are issued, so their relevance is limited to a brief context. Promising the end state is independent of your current state of affairs (see Figure 1-1).
Promises also express intent from a viewpoint of maximum certainty. Promises apply to you (self) — the agent making them. By the definition of autonomy, that self is what every agent is guaranteed to have control over. Impositions or commands are something that apply to others (non-self). That, by definition, is what you don’t control.
It’s possible to promise something relative to a starting point: “I promise to stand on my head right now.” “I will leave the house at 9:00 a.m.” But promises represent ongoing, persistent states, where commands cannot. They describe continuity.3
Autonomy Leads to Greater Certainty
A promise is a claim made by someone or something about an intended outcome. It is not an insistence on action, or an attempted coercion. It is an expression of what I’ll call voluntary behaviour. As we look at more examples, the sense in which I use the term voluntary should become clearer. Another word for the same thing would be autonomous.
An agent is autonomous if it controls its own destiny (i.e., outcomes are a result of its own directives, and no one else’s). It is a “standalone” agent. By dividing the world up into autonomous parts, we get a head start on causality. When a change happens, we know that it happens within an autonomous region; it could not happen from the outside without explicitly promising to subordinate itself to an outside influence.
The Observer Is Always Right
When we make a promise, we want to communicate to someone that they will be able to verify some intended outcome. Usually a promise is about an outcome that has yet to happen (e.g., “I promise to get you to the church on time for your wedding”). We can also promise things that have already happened, where it is the verification that has yet to happen (e.g., if your accounts department says, “I promise that I paid the bill on time”); the outcome has already happened, but the promisee has not yet verified the claim.
Why is this important? In fact, every possible observer, privy to relevant information, always gets to make an independent assessment of whether a promise was kept or not. The promisee might be the one especially affected by the promise outcome, but is not the only one who can form an opinion.
For example, suppose Alice promises Bob that she paid him some money. Bob was not there when she transferred the money to his account, so he cannot assess the promise until he checks his account. However, Carol heard the promise as a bystander, and she was present when Alice made the transfer. Thus, she can assess the promise as kept. Bob and Carol thus differ in their assessments because they each have access to different information.
This idea, that each autonomous agent has its own independent view, means that agents form expectations independently, too. This, in turn, allows them to make judgements without waiting to verify outcomes. This is how we use promises in tandem with trust. Every possible observer, with access to part of the information, can individually make an assessment, and given their different circumstances, might arrive at different conclusions.
This is a more reasonable version of the trite business aphorism that “the customer is always right.” Each observer is entitled to his own viewpoint. A useful side effect of promises is that they lead to a process of documenting the conditions under which agents make decisions for later use.
Culture and Psychology
There are cultural or psychological reasons why promises are advantageous. In the imperative version of the restroom example, you felt good if you could write down an algorithm to bring about the desired end state, even once. Your algorithm might involve a checklist of items, like scrubbing bowls, using a special detergent, and so on. Writing down the steps feels pedagogical because it tells you how. It might be good for teaching someone how to keep a promise in the future, but it does not make clear what the final outcome should be, or whether there is more than one way to achieve the outcome. Thus, without a promise, one cannot assess the algorithm. With a promise, we can be clear about the desired end state, and also discuss alternative ways to bring it about.
The how is the designer part. What about the running and maintenance part of keeping a promise over time, and under difficult circumstances? In the world of information technology, design translates into “development” and maintenance translates into “operations,” and understanding both together is often called DevOps.
If you imagine a cookbook, each page usually starts with a promise of what the outcome will look like (in the form of a seductive picture), and then includes a suggested recipe. It does not merely throw a recipe at you, forcing you through the steps to discover the outcome on trust. It sets your expectations first. In computing programming, and in management, we are not always so helpful.
Promises fit naturally with the idea of services.4 Anyone who has worked in a service or support role will know that what you do is not the best guide to understanding: “Don’t tell me what you are doing, tell me what you are trying to achieve!” What you are actually doing might not be at all related to what you are trying to achieve.
Nonlocality of Obligations
A major issue with impositions, especially “obligations,” is that they don’t reduce our uncertainty of a situation. They might actually increase it. Obligations quickly lead to conflicts because they span a region of our world about which we certainly have incomplete information.
Imagine two parents and a child. Mum and Dad impose their speech patterns on their innocent progeny as follows. Mum, who is American, tells the child, “You say tomaetoe,” while English Dad says, “I say tomahtoe.” Mum and Dad might not even be aware that they are telling the child different things, unless they actually promise to communicate and agree on a standard. So there is a conflict of interest.
But the situation is even worse than that. Because the source of intent is not the child, there is nothing the child can do to resolve the conflict; the problem lies outside of her domain of control: in Mum and Dad. Obligations actually increase our uncertainty about how the child will behave towards another agent.
The solution is to invoke the autonomy of all agents. Neither the child nor the Mum or Dad have to obey any commands or obligations. They are free to reject these, and choose or otherwise make up their own minds. Indeed, when we switch to that viewpoint, the child has to promise Mum or Dad what she intends to say. In fact, she is now in possession of the information and the control, and can promise to say one thing to Mum and another to Dad without any conflict at all.
Isn’t That Quasi-Science?
Scientists (with the possible exception of certain social scientists) and engineers will bristle uncomfortably at the idea of mixing something so human, like a promise or intention, with something that seems objectively measurable, like outcomes in the real world. We are taught to exorcize all reference to humanity in natural science to make it as objective as possible. Part of the reason for this is that we have forgotten a lot of the philosophy of science that got us to the present day, so that we now believe that natural science is in some sense “objective” (more than just impartial).
In my book, In Search of Certainty (O’Reilly), I describe how the very hardest natural sciences have forced science to confront the issues of observer relativity (or what we might call subjective issues), in an unexpected twist of fate. As a physicist myself, it took me a while to accept that human issues really have to be represented in any study of technology, and that we can even do this without descending into talk about feelings, or moral outrage over privileged class systems, and so on.
The idea that a promise is more fundamental than a command or an obligation is not difficult to understand. It has to do with simple physics: promises are local, whereas obligations are distributed (nonlocal).
The goal of Promise Theory is to take change (dynamics) and intent (semantics) and combine these into a simple engineering methodology that recognizes the limitations of working with incomplete information. Who has access to what information?
When we describe some behaviour, what expectations do we have that it will persist over time and space? Is it a one-off change, like an explosion, or a lasting equilibrium, like a peace treaty?
Is Promise Theory Really a Theory?
Like any scientific method, Promise Theory is not a solution to anything specific; it is a language of information to describe and discuss cooperative behaviour among different agents or actors. If you operate within the framework of its assumptions and idioms, it will help you frame assumptions and find possible solutions to problems where distributed information is involved.
Promise Theory is, if you like, an approach to modelling cooperative systems that allows you to ask: “How sure can we be that this will work?” and “At what rate?” You can begin to answer such questions only if some basic prerequisites can be promised by an agency involved in the possibly collaborative promise to “make it work.”
Promise Theory is also a kind of atomic theory. It encourages us to break problems down into a table of elements (basic promises), from which any substantial outcome can be put together like a chemistry of intentions (once an intention about self is made public, it becomes a promise). SOA is an example of a promise-oriented model, based on web services and APIs, because it defines autonomous services (agents) with interfaces (APIs), each of which keeps well-documented promises.
The principles behind Promise Theory exist to maintain generality and to ensure that as few assumptions as possible are needed to predict an outcome. They also take care of the idea that every agent’s worldview is incomplete (i.e., there are different viewpoints), limited by what different parties can see and know.
What is unusual about Promise Theory, compared to other scientific models, is that it models human intentions, whether they are expressed directly by a human or through a technological proxy, and it does this in a way that is completely impersonal. By combining Promise Theory with game theoretic models, we can also see how cooperation can ultimately have an economic explanation (sometimes referred to as bounded rationality). Why should I keep my promises? What will I get out of it?
The Main Concepts
We will refer to the following key concepts repeatedly:
This is the subject of some kind of possible outcome. It is something that can be interpreted to have significance in a particular context. Any agent (person, object, or machine) can harbour intentions. An intention might be something like “be red” for a light, or “win the race” for a sports person.
When an intention is publicly declared to an audience (called its scope) it then becomes a promise. Thus, a promise is a stated intention. In this book, I’ll only talk about what are called promises of the first kind, which means promises about oneself. Another way of saying this is that we make a rule: no agent may make a promise on behalf of any other (see Figure 1-2).
This is an attempt to induce cooperation in another agent (i.e., to implant an intention). It is complementary to the idea of a promise. Degrees of imposition include hints, advice, suggestions, requests, commands, and so on (see Figure 1-3).
There are other levels of interaction between agents. One could, for example speak of an attempt to force an agent to comply with an imposition, which might be termed an attack; however, we shall not discuss this further as it leads to discussions of morality, which we aim to avoid as far as possible.
Promises are more common than impositions and hence take precedence as the primary focus. Impositions generally work in a system of preexisting promises. Moreover, promises can often be posited to replace impositions with equivalent voluntary behaviours.
How Much Certainty Do You Need?
Promise Theory is still an area of research, so we shouldn’t imagine it has an answer to everything. Moreover, it synthesizes ideas also discussed in other theories, like graph theory and relativity, so we should not imagine it is something completely new. It starts with a minimal set of assumptions, and then goes on to describe the combined effect of all the individual promises, from the viewpoint of the different parts of the whole, to form a network of cooperation. If you would like to understand it more deeply, I encourage you to study it in a more formal, mathematical language.
The word promise is one that seems familiar, and invites certain associations. In Promise Theory,5 it has a specific and clear meaning. Others have taken the word for technical usage, too: futures and promises are discussed in concurrent programming. These also take the common word and attribute specialized meaning to it. We need to be careful not to project too many of our own imaginings into the specialized meanings.
As you read this book, you will find that Promise Theory says a lot of things that seem obvious. This is a good thing. After all, a theory that does not predict the obvious would not be a very good theory. Then there will be other conclusions that stretch your mind to think in unfamiliar ways, perhaps cutting through cultural prejudices to see more clearly. You might be disappointed that there there are no stunning revelations, or you might be wowed by things you have never realized before. It all depends on where you start your thought process. Whatever your experience, I hope this book will offer some insights about formulating and designing systems for cooperation.
A Quick User Guide
- Identify the key players (agents of intent)
The first step in modelling is to identify the agencies that play roles within the scope of the problem you are addressing. An agent is any part of a system that can intend or promise something independently, even by proxy. Some agents will be people, others will be computers, policy documents, and so on—anything that can document intent regardless of its original source.
To get this part of the modelling right, we need to be careful not to confuse intentions with actions or messages. Actions may or may not be necessary to fulfill intentions. Maybe inaction is necessary!
Also, you shouldn’t be squeamish about attributing promises to blunt instruments. We might be talking about the parts of a clock, or even an HTTP request, as agencies within a system. The motivations that play a role in making a bigger picture are not necessarily played out by humans in the end game.6
To be independent, an agent only needs to think differently or have a different perspective, access to different information, and so on. This is about the separation of concerns. If we want agents that reason differently to work together, they need to promise to behave in a mutually beneficial way. These agents can be humans (as in the business-IT bridge) or computers (as in a multitier server queue).
- Deal with the uncertainties and obstacles
How likely is it that the agent will be able to keep the promise? In the real world there is no absolute certainty, so forget about that right now! Dealing with uncertainty is what science is really for, so roll up your sleeves and prepare to engineer your promises to make the best of what you have to work with. There are techniques for this.
The bottom line is that promises might or might not be kept (for a hundred different reasons). After all, they are only intentions, not irresistible forces.
Machines and people alike can break down and fail to keep a promise, so we need to model this. Each promise will have some kind of likelihood (perhaps even a formal probability) associated with it, based on our trust or belief in its future behaviour.7
Agents only keep promises about their own behaviour, however. If we try to make promises on others’ behalf, they will most likely be rejected, impossible to implement, or the other agent might not even know about them. So it is a pull or use what’s promised model of the world rather than a push or try to impose on others model. It assumes that agents only bend to external imposition if they want to (i.e., control cannot be pushed by force). That means we have to look more realistically upon illusions like forcible military command structures, and see them as cases where there is actually a consensus to voluntarily follow orders—even when these sometimes fail.
- From requirements to promises (top-down to bottom-up)
Promise Theory focuses attention on the active agents for two reasons: first, because these are the ones that know most about their own ability to keep promises. Second, because the active agents are the atomic building blocks that can be added easily into any larger act of cooperation. Requirements get imposed top-down. Promises are kept bottom-up.
This is analogous to the insight made by atomic theory. Think of chemistry and the table of atomic elements. No one can invent a new element by imposing a requirement. Imagine designing a plane that requires a metal with twice the strength of steel but half the weight of aluminium. You can try writing to Santa Claus to get it for Christmas, but the laws of physics sort of get in the way. We can dream of things that are simply not possible, but if we look at what the world promises and try to build within that, instead of dreaming arbitrarily, we will make real progress. From the promised chemistry of the basic elements, we can build combinations of elements with new material properties, just by understanding how the individual types of atoms with their different properties (i.e., promises to behave) combine.
This is a bottom-up strategy. When you work from the top down, your whole viewpoint is nonlocal, or distributed. You are not thinking clearly about where information is located, and you might make assumptions that you have no right to make; for example, you might effectively make promises on behalf of agents you don’t control.
On the other hand, when you work from the bottom up, you have no choice but to know where things are because you will need to document every assumption with an explicit promise. Thus, a promise approach forces a discipline.
Isn’t this just an awkward way of talking about requirements? Not really. It is the opposite. A requirement is an obligation from a place of high-level generalization onto a place of more expert execution. There is an immediate information gap or disconnect between requirer and requiree. The important information about the likely outcome is at the wrong end of that gap. From a promise viewpoint, you force yourself to think from the point of execution and place yourself in the role of keeping the promise, confronting all the issues as they appear. It is much harder to make unwarranted assumptions when you do this.
Thinking in promises also makes you think about contingency plans. What if your first assumptions fail?
The promise position is an extreme position, one that you might object to on some grounds of convention. It is because it is an extreme position that it is useful. If we assume this, we can reconstruct any other shade of compliance with outside influence by documenting it as a promise. But once we’ve opened the door to doubt, there is no going back. That’s why this is the only rational choice for building a theory that has any predictive power.
The goal in Promise Theory is thus to ensure that agents cooperate by making all the promises necessary to collectively succeed. A magical on-looker, with access to all the information, would be able to say that an entire cooperative operation could be seen as if it were a single organism making a single promise. How we coax the agents to make promises depends on what kinds of agents they are. If they are human, economic incentives are the generic answer. If the agents are programmable, then they need to be programmed to keep the promises. We call this voluntary cooperation. For humans, the economics are social, professional, and economic.
Is this crazy? Why not just force everyone to comply, like clockwork? Because that makes no sense. Even a computer follows instructions only because it was constructed voluntarily to do so. If we change that promise by pulling out its input wires, it no longer does. And, as for humans, cooperation is voluntary in the sense that it cannot be forced by an external agent without actually attacking the system to compromise its independence.
- Deal with conflicts of intent
If all agents shared the same intentions, there would not be much need for promises. Everyone would get along and sing in perfect harmony, working towards a common purpose. The fact that the initial state of a system has unknown intentions and distributed information means that we have to set up things like agreements, where agents promise to behave in a certain way. This is what we call orchestration.
But what if promises in different locations affect a third party unwittingly? This happens quite a lot, as an emergent effect. In obligation theories (requirements, laws, and distributed permission models), the possibility for conflict is very high. Promise Theory is rather good at resolving conflicts because an agent can only conflict with itself, hence all the information to resolve the conflicts is located in the same place.
Just Make It Happen
Promise Theory seems upside down to some people. They want to think in terms of obligations. A should do B, C must do D, and so on. But apart from riling a human being’s sense of dignity, that approach quickly leads to provable inconsistencies. The problem is that the source of any obligation (the obliger) is external to the agent that is being obliged. Thus if the agent is either unable or unwilling to cooperate (perhaps because it never received a message), the problem cannot be resolved without solving another distributed cooperation problem to figure out what went wrong! And so on, ad nauseum. (One begins to see the fallacy of trusting centralized push models and separate monitoring systems.)
Promise Theory assumes that an agent can only make promises about its own behaviour (because that is all it is in control of), and this cuts through the issues surrounding distribution of information, ensuring that both the information and resources needed to resolve any problem are local and available to the agent. In this way, an agent can autonomously repair a promise by taking responsibility. This is the meaning of agency.
Test yourself on your ability to think in terms of promises instead of desires, requirements, needs, and so on. Spend a whole day thinking about the promises made by people, places, processes, and things:
To whom are the promises made?
In what form are the promises made?
Do they depend on something else to help them keep their promise?
How do these things try to keep their promises?
How do you assess their success?
Don’t miss anything: from the bed you get out of (did it give you back pain?), to your morning exercise regimen (will it reduce fat?), the food you eat (is it fresh, tasty?), the people you work with (what are their roles?), the city you live in, all the way up to the toothbrush you end your day with.
If you can’t see any promises, try asking yourself: what is the intended function? What is my relationship with these things? What value do I see in them? Finally, what additional interpretations do you add, which are not part of the promises around you? Does money mean lifestyle, recreation, savings for a rainy day?
At the end of your day, you will have a better understanding of what we call semantics and intentionality in the world, and you will be ready to apply that thinking to all kinds of situations.
1 There have been many theories of promises in the past, but here we refer to my work with collaborators. I described a more formal or mathematical account of Promise Theory in Promise Theory: Principles and Applications.
2 Perhaps the most important thing about Promise Theory is that it drives people to the most terrible puns, without realizing that those puns say something involuntarily insightful, too.
3 You can always twist the usual meanings of command and promise to contradict these points, so we agree to use limited meanings that correspond to normal usage. In other words, this is not about the words, but about the ideas they usually represent. You can command yourself to empty the trash every Wednesday. Or you can promise someone that you are going to do a somersault right now. But it is hard to command yourself to do something persistently.
4 When I first proposed the concept in 2004, it was met with the response: this is just Service-Oriented Architecture (SOA). Although SOA is about promises, Promise Theory goes far beyond SOA’s scope and goals.
5 Promise Theory, in the sense of this book, refers to a specific theory that emerged from my work around distributed computing. There are other theories about the meaning of promises in philosophy, which I promise to politely ignore throughout this book.
6 All intentions originate with human observers if we trace them back far enough. But many day-to-day objects can be vehicles of those intentions, and therefore act as proxies. A cup is just a piece of ceramic; its intent to act as a cup is something a designer (acting on behalf of a user) decided. From a modelling perspective, that chain of provenance is not usually important, so we simply attach the promise to the inanimate cup. Now that it exists, that is the promise it makes to potential users.
7 The simplistic two-state model of faults, where manufacturers like to talk of all their 9s, the expressions Mean Time Before Failure (MTBF) and Mean Time To Repair (MTTR) are coined. These are probabilistic measures, so they have to be multiplied by the number of instances we have. In today’s massive-scale environments, what used to be a small chance of failure or MTBF gets amplified into a large one. To counter this, we need speedy repair if we are going to keep our promises.