This essay is a Foreword I wrote for Paul, Cheryl and Jim’s Handbook of Service Science, Volume 2 published by Springer and coming out later in the year. My thanks go to all 3 of them who helped with the edits of this essay.

Full attribution: Irene C L Ng, (2018), Foreword: Creating Simplicity, in Handbook of Service Science — Volume 2, Editors Paul P. Maglio, Cheryl A. Kieliszewski, James C. Spohrer, Springer

What is simplicity? I ask this question to be provocative of course, but I do mean it seriously. It seems like the world is becoming ever more ‘complex’ — and whenever someone says that, everyone seems to nod in violent agreement. Some great thinkers have given us much wisdom in understanding complexity, such as Senge’s distinction between detail complexity, arising because of the number of variables, and dynamic complexity, arising from the way interactions create subtle causes and effects (Senge, 1990). But is the world really more complex than it used to be? Or is it that our increasing understanding of the world makes it seem more complex?

The reason I start from simplicity is because I don’t think you can call something complex if you can’t define its opposite. You can’t say ‘it’s dark’ until you can explain that ‘dark’ is the absence of light (apologies to physicists that study dark matter). Thus, nothing is just ‘complex’. There is an absence of simplicity. So asking “What is simplicity?” may just help us understand complexity. In a nutshell, simplicity evokes notions of atomic, unadorned, straightforward, or obvious; whereas complexity is considered to be compound, elaborate, difficult, or opaque.

My own quest for a much deeper understanding of service systems and ways to more purposefully intervene, started about ten years ago when I first moved to Cambridge — with three years buyout of my time to do any research I wanted to do — a mentor of mine, Professor Chris Todd from UCL (who has sadly passed away since) visited me. We had tea in a lovely cafe overlooking a great view of Kings College. When he asked me what I would do in those three years, I presented my plans for A (4*) publications. He looked disappointed, and said “Irene, you love research, and you now have time to do it. Why don’t you do something hard.” His comment has stayed with me since, and it has driven the research I choose to do still today.

Hard problems are inconvenient. Yet they are incredibly rewarding, and they can truly make a difference. Working on something that is complex and making it simple, is a hard problem. In our quest to understand complexity, we forget that we should really be trying to understand why there may be a lack of simplicity. The larger question of course, is whether we will take the power to identify the simplicities within a complexity to enable change of a system through re-design and re-engineering, or whether we only wish to observe and manage complexity. Too often, I find researchers choose to be passive, as if their place in the world is merely to describe and to create understanding and insights. I am not diminishing that contribution, but I lament that we do not feel empowered to do more.

Service scientists have a lot on their plate in understanding service in a hyper-connected and complex world of science, technology, humans, cognition, behavioral and social lives. We live in a noun-based world — engineers and scientists focusing on the artifacts, the things, the objects, the structures, and even the systems. Most people, however, create meaning from verbs — eating, seeing, reading, travelling, posting, tweeting, sleeping, running. The combination of the two create institutionalized rules and heuristics from a social angle; tools and data flow from the technological angle. Combining them means the combining of different approaches, methods, cultures, mindsets, skills and training. A look across the room, a meeting of eyes and an instant connection between two people seems too remote in concept compared to the connection between two API end points. Our natural instincts as service researchers investigating cyber-social-physical service systems is to scope it down, ignore one type of phenomenon or change the question so that the harder questions can be put aside. Yet, there are researchers who take the opportunities to try and decipher the simple from the complex.

In thinking about systems, particularly human-centered service systems, there are natural drivers of simplicity that help us navigate the complexity to elicit simplicity. These drivers are conventions that demonstrate themselves as rules-of-thumb (heuristics), repetitive action (procedural memory), norms and rules (institutions), representation (models), limits (boundaries), results or conclusions (outcomes), instruments (tools and technologies), explicit expression (languages), and organized facts (information). In the world of cyber-social-physical service systems, each of these conventions are used to different degrees and in different combinations to gain either an understanding or create an improvement in how services are enabled, measured, delivered, and established within society to improve livelihoods and quality of the planet.

Heuristics. These are ‘practical methods not guaranteed to be optimal or perfect, but sufficient for the immediate goals’ (Simon, 1996). Heuristics are mental shortcuts. They are used when we don’t wish to expend too much cognitive power; when we don’t really want to think too hard. Marketing uses heuristics a lot, so that familiarity with a brand will help you make decisions to buy quickly, without searching for too much information. Common sense, rules of thumb — these are all heuristics. With heuristics, something that may be complex is perceived to be simple.

Muscle memory. As I type these words, procedural memory goes some way toward making us believe in the simplicity of repeated action (Gray & Lindstedt, 2017; Shapiro, 2010). Showing how to brush your teeth ascribes muscle-memorizing action without cognitive effort of description. Sometimes, it is simpler to show someone how to do something, rather than explaining how to do it because when creating a pattern of action, one creates repetition, stability, and yes, heuristics.

Institutions. Institutions are social norms and rules (Ostrom, 2005). They generate recurring behaviors that also reinforce the norm. Eating with chopsticks, driving on one side of the road — these are rules that have been institutionalized. Driving can be complex, but if you understand and believe that the car on the other side of the road will not come over to your side of the road, you won’t panic when you see a car coming toward you. Instead, you are relaxed because your actions are embedded in muscle memory and everyone generally follows the rules, making driving reasonably simple (most of the time).

Models. As Box’s aphorism (1976) goes, “All models are wrong but some are useful”. The map to guide you around the city is probably wrong too, but it is incredibly useful. Often in understanding the world, we try to be as close to reality as possible. If we do that with maps, we will never have useful maps. The simplicity of maps come from having just enough information to guide, and no more. Model making is simplifying to be useful.

Boundaries. The easiest way to force simplicity is to set limits (constraints). Put a man in a cage and his actions become incredibly simple. Widen his freedom to a city and you get more complexity. The point here is not that we should cage people, but that we should understand why and how boundaries matter in complexity. And I don’t mean merely physical boundaries, but also sociological ones like in-groups and out-groups, and economic ones like transaction and payment boundaries. Boundaries drive human behavior and putting them at the right places will change the incentives and the behavior of people within. When data was more expensive, people texted or called. When it became cheaper, they emailed and used WhatsApp. When it became cheaper still, they watched movies. Boundaries can create both simplicity or complexity. Most of all, boundaries define what is possible within a system. How high can you throw a ball? The answer is not derived from how high you have thrown it before, or how good is your throwing skill. The answer is how high is the ceiling. And your behavior? If you know there is a ceiling, you won’t throw as hard. Boundaries can align behaviors, or destroy the workings of a system.

Outcomes. To create simplicity, we can define just one outcome. If the outcome of going to London is just to get to London, it’s relatively simple. If it’s to get to London cheaper than £30, it gets a little more complicated. If it’s to get to London with a group of friends from different parts of the U.K. at 4pm, it gets complex. And when one of them can’t afford to go but others would like him there, it gets political. Complexity often arises when there are multiple stakeholders that want different outcomes.

Tools. The technological answer to human heuristics is physical tooling. The smartphone has created what economists call externalities, side effects, which may be positive or negative. Positive externalities come from better coordination between friends, better tools for productivity and efficiency. Negative externalities come from privacy loss and addiction. Human lives are made simpler with tools. Scheduling meetings is simpler with doodle, coordination is simpler with WhatsApp.

Language. Simplicity is created often with an explicit language, like mathematics or music. Such ‘languages’ have very little ambiguity, which is why mathematics is used for models and music is a representation of the emotions of the creator (Cooke, 1959/1989). Other human languages have modalities, that is, they are like signs and they reflect a status of reality that require interpretation by others. The more words we have for sad or happy, the richer our descriptions become — but also the more complex. Words are performative in that they can be self-fulfilling and using words changes us and changes others. Using words, whether to describe complexity or to create simplicity, immediately creates a description that is value laden.

Information. Information can create simplicity. When I go to London, I know where I am going. A third party observing my movements and predicting where I go next will find my decisions and movements hard to understand and may deem them ‘complex’. For me, even if I deviate along the route, I know why I deviated and what I will do next. My actions are not complex to me at all. Simplicity is therefore a matter of information and perspective. Information and heuristics combine to create templates of behaviors around the goods and services we buy and use. They make the world simple to us, though it may be complex to observers. The question to ask as researchers, is whether a system is truly complex, or whether we simply do not understand it or know it, making a judgment that it is complex. Together with information comes the four types of information drivers of simplicity and complexity. Asymmetry (something I know but you don’t), incomplete (something that is not known now), uncertain (we’re not sure if the information is true or false) and ambiguous (there are two meanings, but we don’t know which one is the right one). Together with assumptions of human rationality, we take a stand on how we view a system. Most economists like to use perfect rationality with symmetric, complete, certain and unambiguous information in their models. The reason for this is to create models that establish the “height of the ceiling”. It is the boundary that helps us understand all human behavior that would lie within and below it. It isn’t important that such a model may not exist. They can serve as a boundary guide. Like stars in our solar system, we may not be able to travel to them, but they are incredibly useful for navigation.

Service science is a discipline of service to humanity. The work captured in this second volume of the Handbook of Service Science embraces the challenge of doing something hard. My hope is that researchers and practitioners in this field continue to take up the baton and meet that challenge through understanding complexity from simplicity, and remain empowered to change systems through re-design and re-engineering. You should do something hard too.

Clap if you think this article have been useful to you!

References

Box, G. E.P. (1976). Science and statistics. Journal of the American Statistical Association, 71(356), 791–799.

Cooke, D. (1959/1989). The language of music. Oxford University Press.

Gray, W. D., & Lindstedt, J. K. (2017). Plateaus, dips, and leaps: Where to look for inventions and discoveries during skilled performance. Cognitive science, 41(7), 1838–1870.

Ostrom, E. (2005) Understanding institutional diversity. Princeton University Press.

Senge, P. (1990). The fifth discipline: The art and science of the learning organization. New York: Currency Doubleday.

Shapiro, L. (2010). Embodied cognition. Routledge.

Simon, H. A. (1996). The sciences of the artificial. MIT press.