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Complex Interaction LARS-ERIK JANLERT Umea˚ University and ERIK STOLTERMAN Indiana University

8 An almost explosive growth of complexity puts pressure on people in their everyday doings. Digital artifacts and systems are at the core of this development. How should we handle complexity aspects when designing new interactive devices and systems? In this article we begin an analysis of interaction complexity. We portray different views of complexity; we explore not only negative aspects of complexity, but also positive, making a case for the existence of benign complexity. We argue that complex interaction is not necessarily bad, but designers need a deeper understanding of interaction complexity and need to treat it in a more intentional and thoughtful way. We examine interaction complexity as it relates to different loci of complexity: internal, external, and mediated complexity. Our purpose with these analytical exercises is to pave the way for design that is informed by a more focused and precise understanding of interaction complexity. Categories and Subject Descriptors: D.2.2 [Software Engineering]: Design Tools and Techniques—User interfaces; H.5.2 [Information Interfaces and Presentation]: User Interfaces— Graphical user interfaces (GUIs) General Terms: Design Additional Key Words and Phrases: Interaction complexity, interface design, design approach, design theory, product design, benign complexity ACM Reference Format: Janlert, L. E. and Stolterman, E. 2010. Complex interaction. ACM Trans. Comput.-Hum. Interact. 17, 2, Article 8 (May 2010), 32 pages. DOI = 10.1145/1746259.1746262 http://doi.acm.org/10.1145/1746259.1746262

1. INTRODUCTION Modern information technology tends to increase the complexity of artifacts, whether they are small, personal devices or huge systems like industrial plants Authors’ addresses: L. E. Janlert, Department of Computing Science, Umea˚ University, 901 87 ˚ Sweden; email: [email protected]; E. Stolterman, School of Informatics and Computing, Indiana Umea, University, 919 E. 10th St., Bloomington, IN 47408; email: [email protected]. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or [email protected].  C 2010 ACM 1073-0516/2010/05-ART8 $10.00 DOI 10.1145/1746259.1746262 http://doi.acm.org/10.1145/1746259.1746262 ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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or traffic control systems. Limitless complexity is something the new digital technology can deliver that older technologies could not. A contributing factor is the increased connectivity. What used to be small, isolated artifacts are getting aggregated and integrated into larger artifacts by modern communication and sensor technology [Greenfield 2006; Norman 2007; McCollough 2005]. Larger, more far-reaching systems can evolve freely without “natural” constraints. An almost explosive growth of complexity puts pressure on people in their everyday doings. At the same time as the new technology gives us the power to increase our control of the world, we experience that we lose control due to increased complexity [Norman 2007; Castells 2000]. The design of digital artifacts is at the core of this dilemma, and every design, small or large, contributes. How should we relate to complexity when we design new devices and systems? Should we avoid complexity, hide it, confine it, or refine it and try to live with it? While focusing on complexity in our study of interaction design, we do not mean to dismiss other aspects of interaction as being less important. The total experience of interacting with a digital artifact depends on a complex combination of conditions, having to do with functionality, performance, appearance, and more. Complexity is just one aspect, but, we believe, an aspect of fundamental importance in interaction studies and interaction design, which still has not got quite the careful attention and consideration it deserves. The problem of complex interaction has been known and addressed since the early days of computing. It has over time and under various guises become a recurring topic of concern within the field of human-computer interaction (HCI) [Carroll 2003; Rogers 2004]. HCI research concerned with complex interaction has seen it as a design problem [Norman and Draper 1986; Preece et al. 2002] having to do with “unfriendly” interfaces and difficulties of use that create irritation and resistance among users. The problem has been addressed by the development of usability approaches, in most cases with a focus on how to test artifacts [Petersen et al. 2002]. Another line of research has been the mental model approach focusing on the way a user thinks about and experiences digital artifacts [Fischer 2001]. Yet another approach is the study of artifacts as carriers of cues and affordances [Norman 1988], or character traits and characters [Janlert and Stolterman 1997], which has led to ways of thinking about how information about use can be signaled to or recognized by the user through the design of the artifact. The need to address complexity in design is also addressed in numerous more innovative design attempts [Hengeveld et al. 2007; Landay et al. 2005; Griffeth 1996] and in some focused workshops [Erickson and Thomas 1997; Microsoft Research 2005]. These approaches have been valuable and have in many cases resulted in successful design methods for dealing with complex interaction. There is still a need, we believe, for another take on complexity that is more analytical, aiming at understanding the nature and roots of complexity in interaction. This is our attempt. In this article we thus begin an analysis of the notion of interaction complexity. We portray different views of complexity in artifacts, we try to explore not only negative aspects associated with complexity, but also positive ones. ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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We argue that complex interaction is not necessarily bad, but that in designing digital artifacts we need to have a better understanding of artifact complexity as a whole, and how that relates to interaction complexity. We start with a discussion of the notion of artifact complexity and how designers try to handle complexity by different design strategies. We argue that the common strategies are sometimes insufficient, and sometimes even harmful. As a proposed corrective we introduce the notion of benign complexity. We explore the lure of complexity, making the case that complexity in many situations offer desirable interaction qualities. We go on to examine interaction complexity in a wider perspective that relates it to other loci of complexity: internal, external, and mediated complexity. We begin to explore these locations and their interrelations, as well as the relation between control and complexity, by way of examples, making our way to a better understanding of how to analyze and explain interaction complexity issues in design. These analytical exercises can be viewed as preparation for design that is informed by a more focused and precise understanding of interaction complexity, which becomes extra important for future, highly complex interactive systems and artifacts. 2. DEALING WITH COMPLEXITY Complexity is desirable when it serves the purpose of improving security, economy, sustainability, power, range, flexibility, situatedness, and subtlety of function. The downside is the increasing demands on the people handling and living with these complex artifacts. Users experience information overload, decision overload, communication overload—and often have a very deficient view of their systems, they do not really know what is going on, what will happen next, what they should do, what the effect will be, what this output, behavior, or signal means. As users of complex digital artifacts we probably now and then experience confusion, stress, failure to meet expectations; occasionally panic and disaster. Obviously, the advantages complex artifacts and systems can bring are often so great that they overshadow the inconveniences and accidents their complexity may give rise to in our interactions with them. Complex interaction has thus typically been presented as a problem, an undesirable side effect of a superior technical solution, to be solved by interaction designers. Consequently, designers have been hard at work trying to make complexity disappear from the user’s view, sometimes perhaps too hard. The general attitude is reflected in language and actions. For instance, taking “user friendliness” as a goal, designs that demand anything out of the ordinary from the user are strongly discouraged. Designers tend to act as if they believe simplicity to be axiomatically good and assume that the user will be unable to interact with anything but the simplest tool. In some theoretical work this view has been problematized and complicated by the introduction of notions such as usefulness, utility, and worth [diSessa 1986; Cockton 2009]. Recently, we have seen some new approaches advocating simplicity that are more reflective [Maeda 2006]. Still, the general attitude has not changed: complexity is commonly seen as intrinsically an evil ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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that is admissible only when it is necessary to achieve some correspondingly greater good in terms of function or quality of function [Barnum 2001]. One of the fundamental questions when discussing any property or quality of technological artifacts or systems is “where the property resides.” In our case, the question becomes: is complexity fundamentally a property of a technology, a design, or maybe the situated use? This raises profound philosophical issues. The field of interaction research has lately moved towards an understanding where properties and qualities of interactive artifacts are seen as relational and situated, depending on the user’s intentions and needs, and to a lesser degree as intrinsic qualities of the artifacts themselves [Bodker 2006; McCarty and Wright 2004; Forlizzi and Battarbee 2004]. In our analysis of interaction complexity we are using an artifact approach as developed in the field of philosophy of technology [Mitcham 1994; Verbeek 2005; Borgmann 1984; Latour 1999]. Such an approach is based on the assumption that artifacts can be analyzed and described as things in themselves and not solely as relational objects. When artifacts are primarily seen as relational, the description and interpretation of their qualities is defined as an emerging quality depending on who is using the artifact. However, in our attempt to better understand interaction complexity and to be able to answer questions such as what complexity is, where it resides, and how it can be manipulated and moved, we need and have chosen an artifact approach. With a focus on artifacts, it is possible and productive to view artifacts as objects with properties, as carriers of qualities, that actually “do things” [Verbeek 2005; Borgmann 1984]. Such an approach is not suitable for all forms of analysis and for all kinds of purposes, but in our investigations we will analyze and examine artifacts as if their complexity is primarily a property of their intrinsic constitution. Other approaches might lead to fundamentally different analyses of complex interaction. From a social constructivist perspective of technology [Bijker et al. 1987] and a basis in the reflective practice and context of the design process [Sch¨on 1983; Wakkary 2005], for instance, the analysis would be grounded in the way designers and users experience and make meaning of their artifacts. The choice we have made is not a dismissal of other approaches. We do believe, however, that an artifact approach is worth exploring and can lead to insights regarding complexity and interaction that other perspectives cannot deliver. An artifact approach lends itself to analytical studies, that is, studies where artifacts are examined in detail based on some theoretical or conceptual framework. The purpose of an analytical examination is usually to experiment with and further refine definitions of concepts and to develop a theoretical framework. This is also our approach. We will present a conceptual framework for interaction complexity analysis that we consequently will experiment with through examinations of artifacts that in different ways manifest complexity of interactions. A benefit of an analytical artifact approach in examining complexity is that it coincides with the way a designer thinks about the artifact to be designed. To a designer, the artifact is the object that will be shaped and given certain properties and qualities. These properties and qualities are for the designer ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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objective and intrinsic to the designed artifact. Our approach is therefore both design and artifact oriented and our method is primarily analytical. Another important aspect of interaction complexity is the social use of artifacts. Many interesting variations in strategies for dealing with complexity and complex artifacts have to do with how designers, multiple users, and other stakeholders interact with each other with the purpose to handle artifact complexity through collaborative efforts. Support from communities of practice (colleagues, friends, etc.), apprenticeships, documentation, online help (chats, forums, etc.), and help desks are just a few of the many strategies for dealing with complexity that come into play once we include a variety of user roles and collaboration. These collaborative and social strategies of dealing with interaction complexity are in most cases not part of the artifact or system in question: they are support systems built around the artifact. In our studies of interaction complexity we have decided to keep our focus on interaction complexity and strategies for handling it directly related to the artifact itself—without implying that the social approach is unimportant; it is just another, and complementary angle. 2.1 Common Strategies We have mentioned some of the more common approaches in interaction design that over the years have been developed with the purpose of supporting design of usable digital artifacts. While recognizing the importance of these approaches, we believe it is possible to distinguish a number of higher-level strategies for dealing specifically with complexity. These strategies are not necessarily being used explicitly or intentionally even though they might be seen as tacit assumptions underlying some of the more explicit and well-developed approaches. They are not conceptually or theoretically developed; they rather have the character of rules of thumb, simple guidelines, or best practices. We have, for the purpose of this article, identified five such strategies for dealing with complex interaction: (1) (2) (3) (4) (5)

eliminate unnecessary complexity make it simple by sacrificing (quality of) function hide complexity confine complexity dilute complexity

Strategy 1 (eliminate unnecessary complexity) is to identify and eliminate unnecessary complexity without compromising functionality, that is, complexity that does not need to be there to produce the same results. On the whole, this would seem to be a universally commendable approach as long as the gain is worth the effort—some possible, functionally lossless, simplifications may be easy and inexpensive to identify and implement, whereas others may be arbitrarily hard to find or even to know whether they exist. Although easiest to formulate in terms of redesign, this strategy often takes constructive forms: advice and principles that guide the design work so that unproductive complexity should not arise in the first place. ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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Strategy 2 (make it simple by sacrificing (quality of) function) is to enforce simplicity, no matter what. Then you also forsake the advantages complexity may bring, so it is less a strategy than a retreat or denial. Once the advantages of a more complex design have been clarified, it would be a rare event, indeed, if considerations of interaction complexity would lead to functional redesign that meant giving up the promised advances of function. If it is deemed improbable that people will be able to handle the complexity at the level required for efficient use, the strategy is more likely to be one of the following. This strategy still leaves room for artifacts that are simple but clumsy and inefficient: there will always be a place for them, but they are unimportant in this discussion. Strategy 3 (hide complexity) is to put the complexities behind a cover of simplicity, apparent or real, depending on how you view it. This is the most common strategy of all for interface design. If the initial motivation to put a cover over the internal workings of an artifact was to protect it from the environment (users included), and to protect the environment (users included) from the hazards of the inner mechanism, the cover has since developed into a very deliberate instrument for information hiding, operation guiding, and esthetic expression. This has sometimes been used to distract attention from deficiencies of function or give a misleading impression of functional qualities. By restricting the possible interactions, control is affected. Specifically, automation directly removes interaction and control at the same time, and typically increases the overall artifact complexity while decreasing the complexity of interaction. These are quite complex issues that will be further discussed below. Strategy 4 (confine complexity) is to concentrate and confine complexity to well defined and clearly delimited parts, with the intention that ordinary users should only have to deal with these complexities on certain, rare occasions, if at all, or that only special people, experts, should have to deal with them. It is to some extent a variant of strategy 3, but we mention it specifically because it has become particularly common in digital artifacts: “difficult” controls are for instance put behind a little lid, or under a menu choice labeled “advanced,” or something similar. This sends the message “Use at your peril” to the user, partly as a warning, partly as an invitation. Strategy 5 (dilute complexity) is to spread and divide complexity over a wider area of interaction with several users (copresent or not) operating a spatially extended artifact, so that no single user has to face the system in its total complexity; it is an approach of distribution and decentralization. As with the hiding and confining strategies, the diluting strategy has important implications for control, attenuating and relocating it, to which we will return below. The strategy presupposes that the artifact is or can be distributed in space, which makes it apt for the latest, well-connected digital artifacts and distributed systems. 2.2 Limitations of Existing Strategies These strategies are, in varying degrees, helpful, but it would be wrong to say that the problem of complex interaction has been finally solved or is under ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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control. While the success of a certain strategy in each particular case obviously depends very much on the skill and inventiveness of the particular designer, strategies not being more than general indications of direction, even the best designs regularly fall short of canceling out complex interaction. Strategy 1, seeking and destroying unproductive complexity, is fine as far as it goes, but arguably complexity will still remain if we want to keep those desired qualities of function that require complexity, which is also why Strategy 2, to forbid complexity, is counterproductive. Strategy 3, hiding complexity in various ways, pretending it is not there, and Strategy 4, confining complexity to restricted areas of complexity hazards, may ameliorate the problem considerably, but ultimately they fail because of a fundamental conflict between control, quality of function and simplicity of interaction (that may not even be resolvable by giving up control).1 Strategy 5, portioning out complexity in more digestible pieces among several users, can ameliorate the problem, but obviously it suffers from the same limitations as Strategy 3 and 4. These discussed strategies all rest on the assumption that complexity is bad and a problem. In this article we explore the idea that complexity is not necessarily bad and therefore we argue for adding a strategy of a different kind, complementary to the already listed: (6) shape complexity into a benign form that humans can comfortably deal with. This article does not do more than begin to explore how such a strategy can be operationalized or implemented; our purpose is rather to make the case for such a strategy as a necessary complement to the ones already listed. 3. THE LURE OF COMPLEXITY The reason we want to introduce this new “conciliatory” strategy is that we do not think it is possible for people in general to enjoy the benefits of complexity without ever having to deal with complexity. Since complex technical systems and artifacts can bring many advantages, and there are no signs that people in general are willing to give up their current way of life just in order to decrease complexity, most likely they will have to live with it. We will leave digital artifacts and human-computer interaction for a moment, to reflect on our everyday experiences of interacting with our environment, natural and artificial. The point is to remind ourselves that complexity, and especially humans interacting with complexity, is considered to be valuable in many contexts outside human-computer interaction. The fact is that we have been living with complexity for a long while—long before the advent of modern information technology, long before industrialism even—and often enjoyed it. Complexity is not just a necessary evil and not just instrumentally good. Given the right circumstances, encounters with the very complexity of some systems and behaviors can give us fullness, entertainment, aesthetic and sublime experiences, spur and develop our abilities 1 Another

way of putting it is to say that in order to deliver the promised function or quality of function, complexity has to find its way to the outside somehow, where it may still continue to haunt us. ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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and ambitions, and give depth to our experiences and understandings [Nelson 2007; Csikszentmihalyi 1990]. Rather than being a universal human ideal, simplicity is often disapproved and derided in our everyday lives. Being simple can provoke condescension and even contempt. On the other hand, humans seem to seek and enjoy certain experiences of complexity. Sometimes complexity may be understood as richness, generally found to be a positive and wanted quality. The experience of being in a forest with its overwhelming profusion of different life forms and natural structures is seen as richer than being in the controlled and simplified park. The simpler an environment is, the easier it is to understand and handle, but at the same time it lacks the richness and stimulation that we appreciate and enjoy. (Gardner et al. [2002] discuss similar aspects when examining what constitutes “good work.”) Many natural phenomena, such as a river or the weather, are in our daily lives not experienced as problematically complex, but when we want to control them or understand them in a scientific sense we need extremely complex tools, systems, and explanations. These natural phenomena are of course extremely complex, even though humans live with them without feeling intimidated by their complexity. There is something intriguing about complexity. It constitutes a challenge, something we can explore and experience, something we can attempt to learn and attempt to master, and something that we know can send us off into new and unpredicted directions, something that promises adventure. We can gladly spend a lot of time and energy trying to figure out the mechanisms behind a complex behavior or system. We can even be prepared to devote our life to the complexity of something such as wine or music. One aspect of the challenge of complexity is its entertainment value. Games, for instance, must not be too simple, or else they will be boring. It is the complexity of chess, football, and Warcraft that make them entertaining. This aspect of complexity as a balance between challenge and achievement and as a source of enjoyment has been famously developed with the concept of “flow” by Csikszentmihalyi [1990]. Then we have not mentioned the aesthetic aspect. The beauty of football or chess is very much in the complexity and the mastery of complexity. To be sure, not all art is complex on the face of it. For instance, minimalist music is deliberately made simple compared to traditional classical music. Still, there is complexity just behind the surface: the closer you listen, the more complexity is revealed. Eisner [1998] writes, “This ability to make finegrained discriminations among complex and subtle qualities is an instance of what I have called connoisseurship. Connoisseurship is the art of appreciation.” So you could claim that the apparent simplicity forces the listener (viewer) to supply the sought-for complexity. Things may be experienced as simple and complex at the same time. For instance, we know that a seemingly simple phenomenon often has a complex explanation and we can contemplate and appreciate both aspects simultaneously; a single drop of water hits the smooth surface of a pond, momentarily producing a small pinnacle of water with a tiny droplet hovering above and a more long-lived pattern of spreading, concentric circles on the surface. In these situations, complexity might be interpreted as depth: complexity reveals ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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the depth of value and meaning of something. Provided they take an interest, people do not shun such manifestations of complexity; they may be awed, but intrigued and attracted just the same. In all these cases, a reward of digging into complexity is the increased understanding and the pleasure of having learnt something new. The positive values are not only in the complex object; they transfer to the beholder [Hillman 1989]. You become more complex in the same positive sense: fuller, more mature, more capable, deeper. We see these examples of complex interactions outside the field of humancomputer interaction as indicating that there is a role for complexity, for benign complexity, not only as something that is possible to endure but maybe even something that humans require and need in their environment to be able to experience it as rich and rewarding. Before we introduce our framework on complex interaction, we will briefly examine some human-computer interaction research sources and how they relate to the notions of complexity and interaction. 4. INTERACTION DESIGN RESEARCH AND COMPLEXITY Recent examinations of contemporary human-computer interaction theory [Carroll 2003; Rogers 2004] show the breadth and diversity of the field, but we cannot find any approach that takes the perspective presented here. It is of course possible to find a number of theories that in some part and to some extent address our concern. These attempts can be grouped into three or four more general approaches according to how they focus on different aspects of complexity. User interface design continues to be the most influential and common approach in interaction design practice, which is perhaps not very surprising, considering its closeness to physical and material design. These theories address how to design the interface structurally, functionally and aesthetically [Norman 1988; Nielsen 1989, 1999; Shneiderman 1997]. Although interaction is the purpose and the proof of success, the design process is focused on the interface, which means that with regard to complexity the attention is on complexity as part of the artifact’s surface. In most of these writings complexity is not addressed specifically or exclusively, but the consequences of having complex (or confusing) interfaces and artifacts is seen as a major problem. Interaction design can be seen as a more recent alternative to interface design, or as a development. The concept has quickly become the standard for referring to design with special regard to usage and users of digital artifacts [Preece et al. 2002; L¨owgren and Stolterman 2004; Pirhonen et al. 2005; Buxton 2007; Kolko 2007; Moggridge 2007]. With interaction in focus, in so far as the latest theorizing in human-computer interaction has some bearing on complexity, it will naturally be on the complexity of the interaction itself. There are also approaches with a focus on the user’s mental model [Craik 1943; Johnson-Laird 1983; Gentner and Stevens 1983] of the inner mechanism of an artifact—rather than just the purpose and the handling—and by implication the complexity of those models and mechanisms [Young 1981; diSessa ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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1982]. Some of the concepts from these theories are present in the everyday language of designers, for whom mental models are a way of imagining the mind and thoughts of users. They are however more oriented towards understanding the user and the user’s actions, and less towards modeling the inner mechanism of the artifact. We believe it would be a good idea to go back to the study of artifact-oriented mental models, reflect on the possible match or mismatch between mental model and actual, internal mechanism, and consider the implications for complexity. Other theories have a strong emphasis on the context, like situated action, activity theory, distributed cognition [Carroll 2003; Rogers 2004; Beyer and Holtzblatt 1998; Kaptelinin et al. 2007; Hutchins 1995], which means that they bring what we will call mediated complexity into the picture. These contextual approaches seem to match the newest paradigms of digital artifact use—ubiquitous computing and mobility—particularly well. These theories, although not about interaction per se, highlight the importance of the distribution of tasks, resources and functions in the design of a system. It is possible to interpret them as having something to say about resolving or dissolving complexity by organizational strategies, that is, (re)distributing tasks and functions among artifacts, people, behaviors, etc. From an engineering point of view, complex artifacts require developed techniques to handle the complexity during the process of inventing, designing and putting together the artifact. This is of paramount importance in software engineering, which has to deal with the most complex artifacts. Various techniques of structuring, systematizing, modularizing, involving extensive use of hierarchies and abstractions, have been developed and are indeed essential for keeping complexity in check in the artifact as a whole; see Agre [1997] for an historical account of abstraction in computing science. These techniques are, it can be argued, cognitively necessary to enable engineers and designers to develop reasoned and rationally justifiable constructions and designs [Janlert 2008]. Not surprisingly, the complexity management techniques of software engineering, developed in computing science, have also influenced interaction design, before this notion even existed: by providing the underlying structure of the artifact to be interacted with, and also by directly applying the same techniques on interface and interaction. Our understanding of the human-computer interaction research field is that even though complexity is in parts and to different degrees addressed, directly or indirectly, in many of today’s approaches and theories, it is not done from a carefully considered view of the nature of complexity, and even less from its potentially positive properties, its different forms, locations, and configurations. We see this as an opportunity for the field to move in such a direction and we are confident that such a move would open up for knowledge and insights of theoretical as well as practical value. 5. LOCI OF COMPLEXITY Increasing the complexity of an artifact does not necessarily mean that interacting with it gets more complicated. A car is an example of an artifact that has increased its internal, structural and functional complexity over a long ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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time, while its interaction complexity has stayed roughly the same, and even dropped somewhat in the last couple of decades with the introduction of digital technology. On the other hand, a simpler interface does not necessarily mean that interaction with the artifact becomes simpler. Writing a text message on a mobile phone is at this time typically done using the numerical keyboard of the phone, which is obviously not simpler than using the more complex, full qwerty keyboard. Examples and considerations such as these have motivated our attempt to approach complexity in interaction in a more precise, descriptive and analytical way. Complexity is indeed a puzzling issue, but instead of leaving the problems to usability testing or referring to them as a matter of user experience, we propose to develop analytical tools that can be used to understand the anatomy of complexity quantitatively as well as qualitatively in order to deal with it more proactively. We will start with the quantitative aspect, considering not just the amount of complexity but also how it is distributed over a few distinct loci that are important from the design point of view as well as the use point of view. Design activities focused on one locus will usually have repercussions on other loci, and we want to understand such possible tradeoffs better. It will inevitably lead us into discussions of control and automation, and what role the individual user plays. We will also consider the qualitative aspect of complexity. We believe that quantitatively equally complex artifacts can differ in important ways in the qualities of the interaction. In particular, they can offer interaction that is more or less benign, meaning satisfactory from a use point of view in a sense that needs further elaboration. We have already indicated that complexity can be good in various ways, and we want to understand what quality of complexity that may make the difference. In developing these concepts and analytical tools, we are committed to a quest for objectivity: our assumption is that complexity can be approached as an objective property of the artifact. That means that it will be important and possible to distinguish complexity from, let us call it, difficulty: the ease or trouble that a particular user has in dealing with the artifact, which is variable and at least partly subjective, dependent on the particular individual. The following is our proposed conceptual framework for interaction complexity analysis. 5.1 Internal and External Complexity Let us begin by making distinctions between internal complexity, external complexity, interaction complexity, and mediated complexity of an artifact—all with respect to the purpose it has been designed for, which we will refer to as the purpose of the artifact. Obviously, users also have a purpose with using the artifact; the user’s purpose may be more or less consonant with the artifact’s purpose, but it is not what we primarily refer to in this article.2 These are not 2 Also,

the purpose of an artifact may well be wrongheaded and bad in all sorts of ways, including serious failure to match any real user’s purpose—but that is not a problem we are tackling in this article. ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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different measures or types of complexity; they represent a rough division into the main loci where complexity is present in varying degrees, manifesting itself in various ways. Internal complexity is the complexity of the internal workings of the artifact. The internal complexity is related to the range or number of different possible internal, functionally important states. Often, internal complexity will be reflected in a corresponding structural complexity, so that a visual inspection of the inside may give useful cues of the degree of internal complexity; for example, a tangle of cogwheels or electrical wiring will give the impression of considerable internal complexity. Obviously, the internals of the artifact are normally hidden from the user, so internal complexity remains a rather peripheral notion from the user’s point of view. External complexity is the complexity of the artifact’s interface with the outside world and the user. Often it is possible to make a distinction between the interface to the user and the interface to the rest of the environment, each with its own external complexity; our primary focus will obviously be on the complexity presented to the user. Similarly, the external complexity is related to the range or number of different possible operationally and functionally important states in the interface. Again, a brief look at the interface may give useful indications of the degree of external complexity; for example, by the number of buttons and displays. Although inspections of structural complexity can give an idea of internal and external complexity, these impressions are not very reliable: the apparent complexity may be lower as well as higher than the real complexity. The apparent complexity is subjective and can vary depending on the user’s knowledge and skills, the cultural setting, the situation of use, etc. The real complexity, in contrast, is objective and fixed. A hammer is an example of an artifact with little internal and external complexity, while the proverbial VCR typically has relatively high internal and external complexity. High external complexity hints at high internal complexity: it is natural to assume that the external complexity is high because it is necessary in order to be able to control a complex internal mechanism. This may be true for the VCR but is not always the case: for example, a manual telephone exchange is internally less complex than externally; as with the hammer there simply does not seem to be much of an “inside.” A refrigerator exemplifies the combination of low external complexity with relatively high internal complexity. In these complexity definitions we are trying to keep reasonably close to the intuitive, everyday notions, while making distinctions and specifications that make the definitions more objective and more precise, and thus more useful as analytical tools. We need to proceed with some caution, however, so as not to prematurely make overly precise definition: the definitions are designed to allow for more precise versions further on. At this stage we believe that elaborations along the line of algorithmic complexity would be suitable, roughly meaning that complexity should be measured in terms of the size of the shortest possible descriptions; but these emendations will have to await the result of further studies of applying the current definitions. ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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5.2 Interaction Complexity Interaction complexity is the complexity of the relation between input and output, between what the outside world and the user does and what the artifact does. As with external complexity, it is often possible to make a distinction between the interactions with the user and the interactions with the rest of the environment, each with its own interaction complexity; obviously, our primary focus will be on the complexity of the user–artifact interaction. Since interaction is a dynamic relation between two (or more) parts or parties, interaction complexity necessarily depends on a relation rather than a property and is sensitive to the development over time: patterns of interrelated state changes. That makes interaction a locus of complexity more difficult to come to grips with than internal and external complexity, and the degree of complexity in interaction with an artifact even less predictable from a cursory inspection of the artifact. In fact, interaction complexity appears as an emergent quality with no obvious relation to internal and external complexity. For example, internal and external complexity can be quite low, and yet the interaction complexity high: a violin is a highly complex artifact from the point of view of using it, even though structurally it does not strike us as particularly complex.3 This should come as no great surprise: from the theory of computation it is known that computational units with very simple internal mechanisms and very simple interfaces can produce arbitrarily complex interactions and behavior. It takes several years of practice to become a good violin player. Interestingly, when you have become an accomplished violin player, you will find it quite easy to play what you once found exceedingly difficult. Learning can make complex interaction seem easy: the complexity is still there, and you may be aware of it, but it doesn’t bother you. This is an example of what we mean with benign complexity: it can be handled with a certain ease, even if, as in this case, the ease does not come at all easily. To motivate going through this long period of learning, the quality of the outcome must be considered worth the effort. This has been discussed as the production paradox by Carroll and Rosson [1987]. That the purpose of the artifact is an important (but implicit) factor in the complexity definitions is particularly clear in the case of interaction complexity. If the purpose were just to produce a sound, then it would be very simple to use a violin. But the purpose of the violin as a designed artifact is to produce (well sounding) music. If driving a car seems a moderately complex task, it is largely because the basic purpose of an ordinary car is transportation. Change the purpose to drive on two wheels, and the interaction gets more complex. In our analysis of complex interaction, we always take the designer’s purpose with the design as our starting point.

3 We may note, however, that the production complexity, the complexity of the production procedure, is high. That the production process is more complex than the structural complexity of the product seems to be common, not least in industrial production. A car factory, for example, is more complex than the cars it produces.

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5.3 Mediated Complexity To be able to understand interaction complexity in context, we need to bring in yet another locus of complexity that we will call mediated complexity: complexity that is located not in the interior or the exterior of the artifact but in the environment, channeled through the artifact and impacting interaction complexity. The role played by the environment in complex behavior has been analyzed by, among others, Simon [1996] and Pylyshyn [1981]. The score plays an important role in the complexity of playing the violin. It is one thing to play Bach’s Air in D Major; playing the Paganini variations is of an entirely different order of complexity. For artifacts that channel the complexity of (some part of) the environment, impacting on the interaction complexity in this variable manner, we can view the interaction complexity as a function, and the complexity analysis should aim to find out which function. This complexity function is, of course, still to be considered a property of the artifact. Skilled use of simple tools, like in carving wood figures, is another example of complex interaction in which the inner and outer complexity of the tool has little to do with the complexity of interaction, but the material, the wood, and the changes it is going through have very much to do with it; and this is indeed how skilled tool users think. Transparency is a commonly professed theoretical ideal for tool-like digital artifacts: the user should be able to interact with the focal object as if the intervening artifact were not there. Since you can obviously not carve wood with your bare hands, transparency seems to require that the artifact becomes part of you in the sense of the phenomenological school of philosophy (see Sokolowski [2000], which has been explored in HCI by, e.g., Ehn [1988]; Winograd and Flores [1986]; Croon [2006]). The design of the artifact remains equally important for the interaction complexity, of course. Sometimes, however, users locate what is in fact mediated complexity to the inside of the artifact. The following is an example related by Wegner [1997]. Two users are, unaware of each other, simultaneously using a computer application that looks like a chess-playing program. Actually, the program simply copies the moves of one player to the other player, and vice versa. In practice they are playing chess with each other, but because of the setup they have the impression that they are playing against a rather decent chess program (as good as their actual opponent, obviously). The players naturally locate the complexity to the computer application itself since they have no way of distinguishing between the internal complexity and the complexity that unbeknown to them is mediated from the other player. 5.4 Distributions of Complexity Given these definitions of internal, external, mediated and interaction complexity we get a range of conceivable complexity distributions, complexity profiles, or complexity allocations; for example, the complexity profile in the woodcarving example might look like this: low internal, low external, high mediated and high interaction complexity. A large variety of different distributions are conceivable (all of which may not be feasible), with different properties, possibilities and difficulties. Let us consider a few examples. ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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There is the case where all loci have a low degree of complexity. A fork, for instance, is relatively simple in all respects.4 But, again, it depends on purpose: you may decide to become a fork virtuoso, whatever that means, complicating its use and the interaction; compare with how writing goes from middling complexity to high complexity in calligraphy, virtuoso writing. Or, you might use the fork on a piece of food that is difficult to handle, which means that the mediated complexity and the interaction complexity increase. A church organ might be an example of an artifact that is complex internally and externally as well as in the interaction. It seems as if no one is seriously trying to simplify the interaction, probably because any attempt to do so would mean loss of desired qualities of the outcome; you would end up with a quite different kind of artifact. Generally, interaction can often be simplified while preserving basic functionality at the cost of increased internal complexity, by some measure of automation. Qualities may be lost and qualities may be gained in the transition. Clearly, some measure of control also shifts locus: from the user towards the artifact. Taking the design move from the ordinary piano to the self-playing piano as an historical example, it is easy to point out lost qualities like less nuanced play and the non-uniformity of different performances; naturally, any qualities added by the executant in a performance on an ordinary piano, qualities related to the real-time context. There are also qualities gained: the self-playing piano can play faster, use chords that span any number of notes, have polyphony in any number of voices, and rhythms whose complexity defy human execution.5 It is to be expected that an internally relatively complex artifact has a proportionally much simpler interface, and this should be true in particular for digital artifacts, which can be considered to be internally complex by default. The relative complexity or complexity quotient between external and internal complexity may tell us something important about the artifact. Given a certain class of artifacts, the external complexity of a specific artifact can exceed or fall below the average. Excessive external complexity could for example result from having a separate button for each possible action, as in the TV remote control in Figure 1: a button for each channel. This could lead to simple as well as complex interaction. It might be simple to interact since every action is simply represented by a distinct physical location (one button per possibility), but on the other hand interaction might be complicated by navigation and orientation problems in the physical interface.6 For many well-established kinds of artifacts there will be a certain level of expected and accepted external complexity, which is de facto considered normal, having to do with factors such as the artifact’s complexity of function, how 4 Interestingly,

its design history is not equally simple; see Petroski [1992]. qualities have been creatively exploited by the composer Conlon Nancarrow. 6 In the remote control of Figure 1 the different channel buttons are ordered sequentially by channel number, from left to right, top to bottom, making it very simple to locate the desired button. Imagine, however, that the channel numbers were randomly distributed over the physical buttons: it would make finding the right button a more complex subtask of the channel selection operation. 5 These

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Fig. 1. Complex interface, simple interaction. Pultius TV remote control (concept) from Art. Lebedev Studio.

Fig. 2. Simple interface, complex interaction. Telegraph key.

highly that function is valued, the estimated internal complexity, the expected degree of proficiency required by the user in relation to those factors, the degree of control exercised by the user, and to no small part also to what has been customary. It would be problematic to introduce a vacuum cleaner, say, that were twice as complex to use as an ordinary, even if it could be motivated by a much more complex internal mechanism that would deliver a much improved performance. Simpler interfaces do not necessarily mean simpler interactions. When external complexity is reduced beyond some limit (without changing functionality or shifting locus of control), increased interaction complexity is likely to result. There can hardly be a simpler interface for inputting alphanumeric data than a telegraph key, but tapping Morse code (sequences of carefully timed key presses and releases for each character) is more complex than typing on an ordinary and rather complex keyboard (see Figure 2). A common purpose of minimizing external complexity at the expense of interaction complexity is to give an impression of simplicity, a misleading impression since it is usually at the expense of interaction complexity (in other words the “hiding” strategy is in operation), which is sometimes hard to distinguish from purely aesthetic motives, a cleaner design. For instance, the remote control in Figure 3 has a clean and simple look with only two buttons and one scroll wheel, that is, low external complexity. The remote has a built-in motionsensing capability that translates the user’s hand movement into on-screen cursor movements. While the external complexity is reduced in comparison with ordinary remote controls (at the cost of increased internal complexity), the interaction is not obvious since it is almost “hidden” in a visually less intuitive interface. A more pressing reason for minimal interfaces is that digital technology excels in producing complex artifacts that are physically small. There is little ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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Fig. 3. Modern minimalist interface. TV remote control (concept) from Hillcrest Labs.

room for external complexity in a mobile phone or an MP3 player. Some of the ways the complexity deficit can be handled we have had to learn quite recently: buttons with multiple functions, buttons that do something different if you press them longer, or double-click, or press them in combinations and sequences that you need to memorize. New sensor technology also paves the way for invisible, or rather vanishing, interfaces that react to, for example, squeezing, shaking, bending, pointing, tilting, or gesturing in the air, with a small device (as in Figure 3). Little or nothing of that complexity meets the eye or any other sense before the user is already engaged in actual interaction. Modern interfaces seem to be in the process of interactionalizing, that is, complexity is moved from interface to interaction. This may lead to users that are intrigued or confused, or both. In examples like these, we perceive how designers and makers are engaged in moving or transforming complexity, for various reasons and purposes. These moves seem to be constrained by trade-offs and proportionalities. We recognize, for example: (1) a trade-off between external complexity and internal complexity7 — increased internal complexity may require increased external complexity to enable the user to handle added (quality of) functionality (given a certain degree of user control); (2) a trade-off between external complexity and interaction complexity— decreased external complexity may lead to increased interaction complexity (as exemplified above); (3) a trade-off between interaction complexity and internal complexity— relating to control and automation (to be discussed further in the next subsection).

7 Increased internal complexity can generally be considered a good when used to improve functional quality—more is better; whereas increased external complexity and interaction complexity are usually considered undesirable—less is better. Of course, in this article we are making a case for a more nuanced view on the desirability of low interaction complexity.

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The conditions for and consequences of different distributions of complexity in different artifacts offer an interesting field of exploration. All kinds of artifacts are amenable to complexity profile analysis, but digital artifacts are of particular interest because of their considerable complexity and great designability. We do not claim that the recognition and consideration of the distribution of complexity in itself will solve many problems, but it may help avoid some of them, and we are convinced that the field of interaction design would benefit from such investigations. We believe such an awareness is useful for design purposes since it can inform and help develop the compositional judgment crucial to any designer, that is, the ability to make decisions on how to compose the whole in relation to details in a design [Nelson and Stolterman 2003; Krippendorff 2006]. In this case, a designer would use her compositional judgment in the design and distribution of complexity over the artifact’s loci in order to achieve desired artifact qualities. We see the notion of the distribution of complexity as playing a role similar to the notion of visceral, behavioral and reflective levels of design as developed by Norman [2004]. Both conceptual frameworks can be used by designers as analytical tools or as tools for inspiration and creativity, without prescribing the design process or outcome. With regard to digital artifacts specifically, we think that the conditions for locating, deploying and relating to complexity are somewhat different from artifacts in general, with important implications for design. Digital technology is the technology of complexity par excellence; digital artifacts are by default internally complex. The external complexity is less dependent on the actual mechanisms of the artifact, and the interaction complexity is less dependent on the actual functions of the artifact—and more in the hands of the designer. We can choose to design the interface in almost any fashion; the material restrictions and limitations with non-digital artifacts do not apply to the same degree. This also means that digital artifacts are more designable when it comes to interaction complexity; overall, there are many more degrees of freedom in digital design. Understanding of complexity in all its forms therefore becomes more important, even crucial, in the design of digital artifacts. 5.5 Complexity and Control A discussion of complexity, its amount and distribution over different loci, must also takeup the issue of control, and its amount and location into account. For example, in considering that decreased external complexity may result in increased interaction complexity (see trade-off 2): this is based on the assumption that the user keeps the same degree of control. If we simply remove some nonredundant controls (i.e., controls that enable operations not possible to reproduce with the remaining controls) without doing anything to compensate, interaction complexity will not increase, but obviously some control (and maybe also functionality) will be lost. Control is an important additional dimension that we need to have in mind when making complexity analyses. The role of control is particularly obvious in one of the most common ways of hiding complexity, namely automation: complex interaction is possible to ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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simplify by removing parts or details of the (explicit) interaction. In the extreme case, interaction is eliminated altogether. For the purpose of this article, automation is taken to be the process of making an artifact more autonomous, that is, less dependent on user control, while as far as possible retaining its function and functional qualities. This usually entails increased internal complexity and decreased external complexity, with respect to the user interface (whereas the complexity of the world interface is more likely to rise). Automation often means leaving control at a higher level to the user, who provides the overall purpose, while the artifact tries to do the rest. A problem can be that purpose sometimes is difficult to define or articulate except at lower levels of expression. Alternatively, automation might aim for the higher levels, leaving to the users rather to assist in the implementation. This is not what we think of when speaking of automation today but it was normal in the time of Ford and Taylor, as well as in the early days of electronic data processing. Control given up to automation at one level may actually increase control at a higher level. As an illustration, modern fighter planes are inherently very unstable; they need new corrective steering inputs several times per second in order not to drop from the sky. Computers take care of that, giving the pilot the impression of handling a completely stable airplane.8 In fact, it is humanly impossible to fly the plane without this low-level automation; it would crash immediately. Generally, the cognitive effort saved in not having to control a lower level can be spent on improved, more sophisticated, control at a higher level. There is a parallel in human automatization: in learning to drive a car or play the violin, the initial struggle with low-level control is relieved by better focus on medium-level control when the low-level control has become automatic, and so on with higher levels. When the student of the violin has automated the basic control of pitch, bow position, bow movement, and bow pressure, more effort can be focused on tonal quality, phrasing, melody, etc. The same development and shift in focus can be seen with advanced digital artifacts like a sophisticated editing tool for images or music. In automation, the information that the artifact no longer gets as explicit input from a user must be compensated for or provided in some other fashion. There are two main approaches, which can be used independently or in combination. The first is to try to draw the same or equivalent information from the environment that the user has been relying on in manually steering the artifact. The second approach is to try to figure out what the user, were she still steering the artifact, would do. The first approach assumes that the purpose is quite clear and stable, that there are technologies and methods for extracting the required environmental information, and, of course, that there are workable methods for achieving the purpose given the information. These methods could be similar to the user’s methods, or they could be rather different. When the purpose is more variable and less clear, this approach will not be sufficient, and recourse might be taken to the second approach: trying to interpret the user’s behavior in various ways, 8 The point of having an unstable plane is that it reacts faster and makes sharper maneuvers possible.

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including inadvertent signs, and predict the purpose, or at least likely user expectations. Needless to say, if the user no longer has access to the information required to steer, or no longer takes an interest in that kind of information, then trying to interpret the user will be of little avail. In the context of control and automation we also need to seriously consider the interaction between the artifact and the rest of its environment: objects, physical circumstances, and other people to be considered not as “users” but rather as bystanders. Far from being a special case, however, we find that in many modern digital artifacts the user interface is only a minor part of a total world interface that includes an array of physical sensors and actuators as well as a range of digital communication channels. For instance, an artifact with built-in GPS technology does not have to ask the user where she is; instead the artifact communicates through the world interface to find that out. The less the artifact interacts explicitly with the user, the more it will have to interact directly with the world or rely on other ways of getting information from the user. Various forms of implicit interaction [Schmidt 2000] are likely to become common as a way to save users from a proliferation of trivial or redundant interaction tasks. From the point of view of many modern artifacts a human user is just one resource of information among others. 6. QUALITIES OF COMPLEXITY Let us turn to the qualitative aspect of complexity. We consider the quantitative analysis of complexity to be basic but still leaving some of the most important and interesting aspects of complexity unaccounted for. We believe that qualitatively equally complex artifacts with comparable complexity distributions, can differ in qualities of their use, such as how easy they are to use, how enjoyable, challenging, beautiful, stressful, and so on, not just on account of their differences in purpose, application area, content, etc., but because of differences in the shape or form (for lack of better terms) of their complexity. 6.1 The User and the Artifact One may perhaps have begun to wonder, at this point, if not also be the complexity of the user should added to make the list of basic loci complete. We will explain our reasons for keeping the user at some distance from our complexity analyses. Our ambition is to inject a measure of objectivity (in the sense of the Gegenstandlichkeit ¨ that Goethe strived for9 ) or “object orientation” into the discussion of the infinitely complex relations between users, artifacts and contexts that threaten to overwhelm and paralyze the designer. So we are trying to attribute complexity to the artifact as far as possible as a property of the artifact, given its purpose, rather than as a relation between object, user, context, purpose, history, and whatever more that is thought necessary. We want to attempt 9 “[Goethe]

passionately demands that artistic invention and design takes the object as its point of departure, not the subject. In this he sees . . . the decisive difference between the poet and the dilettante: ‘The dilettante never describes the object, only his feelings before the object. He shuns ´ the object’s character.”’ [Georg Lukacs, 1947; our translation].

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to explain without making complexity a relative concept the fact that some persons can handle an artifact with ease while others have great difficulty. Therefore, we shall assume that complexity is an objective attribute, but the means different users have at their disposal to handle that complexity varies (and usually these means can be acquired by a person who does not have them at a cost). This may not be the whole truth, but it is certainly an approach that can make the designer’s task seem less impossible. In particular, we argue that interaction complexity, too, is to be viewed as a property of the artifact. It is the complexity of the interplay between inputs and outputs required to achieve the purpose with the artifact. Most users may find an artifact with high interaction complexity difficult to use, and an artifact with low interaction complexity easy to use, but we need to understand the role of the individual user and how that makes the relation between complexity and difficulty less straightforward. From an outside observer’s perspective interaction involves (at least) two parts or parties.10 Symmetry may seem to demand that we take an equal interest in the other part, the human users, and their internal and external complexities (the interactional complexity should be the same since it is the same interaction). Changes in the complexities of the artifact may have to be compensated by internal (cognitive) changes in the user and changes in how the user’s external resources (motor and sensor capabilities) are deployed. Extending complexity analysis to include users would quickly take us into deep waters, however: users are very complex, much more complex than any artifact that they will ever use, and vary little in complexity, relatively speaking; most of that complexity is hidden, not directly accessible for study; users change dynamically and are extremely sensitive and adaptive to changed conditions. Since we are ultimately studying interaction with artifacts from the point of view of designing artifacts, our primary focus is on understanding artifacts (designing users, if that is a meaningful notion, is a task of an entirely different order), and we believe it is possible to make headway in the study of artifact complexity while bracketing off the complexities of the user (for the time being). Of course, at some point particular user differences must yet be taken into account by the designer. As we have noted, artifact complexities depend on purpose. The basic, designed purpose of the artifact is embedded in the complexity definitions; personal purposes may refine, extend, or build upon the basic purpose without disrupting the basic complexity analysis but when radically different purposes not consonant with the designed purpose are introduced (like using a computer as a makeshift doorstop), the complexity analysis obviously has to be revised to make sense. This means that whenever there is a need for it, from a design point of view, more specialized complexity analyses can and should be made, 10 We

are observing human–artifact interaction from the outside, normally taking the human to be the primary point of view in the observed scene. It is also possible to switch perspective and study the user (and the rest of the environment) from the point of view of the artifact, which may potentially give some added insights useful in artifact design. ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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for example, investigating interaction complexities in different situations or modalities. 6.2 Complexity and Difficulty It seems almost trivially true that when interaction complexity is low, the artifact will be easy to handle. We should take care not to make the wrong conclusion that high interaction complexity implies that the artifact must always be hard to operate. Examples to the contrary abound. Driving a car is difficult for a complete beginner and certainly not a trivially simple task, but easy when you become experienced. If you have become an expert at playing the violin, you do not find playing particularly difficult. With training, what is very hard in the beginning may become comparatively easy—while the complexity remains. Certainly, there are also issues of motivation and talent: is it considered worthwhile to make the effort required to reach the level of skill that would make it easy? The level of difficulty (for a particular person, or for persons at a particular level of expertise) itself should not be confused with the difficulty relative to the value or utility of the result. If the reward is high, users can be willing to handle great difficulties without complaining; if the utility is low, users tend to be more conscious and critical of troubles [Carroll and Rosson 1987; Csikszentmihalyi 1990]. The subjective difficulty can differ from the objective difficulty. The task of learning to use an artifact skillfully has been a topic of study for various theories of learning in relation to professional competence [Dreyfus and Dreyfus 1986; Eraut 1994; Sch¨on 1983]. These theories and insights have influenced work in human-computer interaction inspired by concepts such as incremental learning and zone of proximal development [Kaptelinin 1996] that emphasize the importance of designing for a learning curve with appropriately spaced learning goals at increasing levels of (objective) difficulty. Once a certain level has become relatively easy, it is time to step up the level of difficulty, until eventually the learner has become a skilled user of the artifact. A very ambitious artifact user, a virtuoso or a virtuoso in the making, will continue to seek new challenges beyond the fundamental mastering of the artifact. When a certain level of performance has become easy enough, new and higher goals for performance are set, sometimes new also in the sense of creative. Difficulty can in certain cases be reduced or removed without doing anything to the artifact and its complexities, and without extended training. The difficulty can prove to be in a certain mindset or lack of knowledge of the user, which means that given the right piece of information the trouble can suddenly disappear. For instance, with the remote control in Figure 3, the user might have serious problems with the interaction since there are no obvious external buttons or input devices to control the cursor. The user might press the few visible input buttons with no result, while experiencing that the cursor moves. This state of confusion can continue until someone mentions to the user that the remote has motion-sensing capability that is used for input, and that it is hand movements that control the cursor. At that moment the interaction ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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instantly goes from apparently complex and difficult, to easy and controllable. Imagine another example, a game where the player is using arrow buttons (up, down, left, right) to control a cursor through a simple maze: each button press moves the cursor to the next square in that direction (if it is possible). To make this seem complex, introduce a delay so that each button press will effectuate and display the effect of the previous command, not the current. To the unknowing player it will appear difficult, even confusing, but given the hint to think and act one step ahead all the time, the problems disappear. We do not think, however, that just any case of complex interaction can be made easy by training or by sudden insight. Again, we believe it has to do with the form of the complexity. 6.3 Benign Complexity There is more information available at our fingertips during a walk in the woods than in any computer system, yet people find a walk among trees relaxing and computers frustrating. Machines that fit the human environment, instead of forcing humans to enter theirs, will make using a computer as refreshing as taking a walk in the woods. [Weiser 1991]

Nature is complex, but its complexity can be experienced as good. As we discussed earlier, in many situations complexity is actually enjoyed and sought after. Could we design our artifacts so that their interaction complexity could be high, but at the same time benign? Can our interactions with digital artifacts become a walk in the woods? Benign interaction with a complex artifact is interaction that is satisfactory for the user in a certain sense. As a first approximation we may try to identify benign complexity with complex interaction that is or in principle can become comparatively easy or seemingly effortless (without any changes to the artifact). Considering the diverse view of everyday complexity we presented in Section 4, it would seem too simplistic to stop at this first approximation relating only to ease of use, however. What about aspects of interaction such as being enjoyable, challenging, beautiful, rewarding, and so on? These may be distinct qualities but they are not completely independent either; for instance, when it is too easy to do something it becomes less challenging. Perhaps easy has to be qualified, or alternatively, perhaps there are several, not always compatible flavors of benign. By studying examples of complex interaction with artifacts (digital or nondigital) that are experienced by their (skilled) users as having such qualities, we may hope to gain understanding of the kind of design choices that makes for benign complexity. If benign complexity is, as we have suggested, basically a matter of the shape of complexity, then we should seek for fundamental characteristics of good interaction as such, disregarding the purpose of the artifact and interaction, disregarding the content and application area. In the search of such characteristics, one important source of inspiration as well as test of viability is human–human interaction. Humans generally have a readiness or even urge to respond to others’ actions and to act so as to elicit responses from others, often also anticipating and forestalling them: this is an elementary human ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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trait that drives interaction [Mead 1934; Asplund 1987; Israel 1988] in the entire gamut of human activity, irrespective of content and purpose. We expect human–artifact interaction to be under the sway of basically the same elementary behavioral patterns, the social responsivity of human beings. Indeed, Asplund, who introduced the term, has flying a paper kite, and driving a car, among his examples.11 From that point of view we can venture to say something about what characterizes good interaction: it should be an interesting, dialectic, lively interplay. In the field of artificial life, Wolfram [1984] has classified cellular automata into four groups according to their overall behavior: Class I consists of automata that eventually get stuck in a fixed state; like crystals. Class II consists of automata that end up in a periodically repeating sequence of states; like clockwork. Class III consists of automata that evolve to chaotic, aperiodic patterns; like a gas. Class IV consists of a kind of automata that neither get stuck, fall into strictly repetitive patterns nor develop into random behavior. This is the class with the most interesting performances, displaying abstractly lifelike behavior.12 If this is taken as a high-level characteristic of life, we suggest that a minimal requirement of good interaction is that it has the same lifelike characteristic; good interaction is like life. Good interaction should not be characterized by too much passivity and lack of response; it takes (at least) two to interact, and if what you do has no effect, if the other part(y) seems dead, unmoved by your actions, you quickly lose interest. Good interaction should not be characterized by too predictable patterns of interchange; if the interaction becomes too repetitive, too mechanical, it soon becomes boring, and you lose interest. Good interaction should not be characterized by too unpredictable behavior that will result in blind clashes of actions rather than interactions; this is like the first case in that none of your actions affects the other part(y), but unlike it in that you may have to guard yourself and your activities from the erratic outbursts of the other; rather than bored you become irritated, confused and stressed. Good interaction should, we believe, balance or oscillate precariously on the fine edge between too much chaos and too much order, too little response and too little independence, too safe and too risky, too boring and too exciting (some of these interaction qualities have been examined in Gaver et al. [2009] and L¨owgren and Stolterman [2004)]. Humans, we believe (with at least some support from social psychology), are interaction-seeking creatures. Furthermore, if there is a choice between engaging in some good interaction and in some not so good interaction, humans will generally prefer to devote their time to the good one, other things being equal. If we have some understanding of what constitutes good interaction then we can relate that to benign complexity. It might be that benign complexity is in 11 Asplund

[1987]. It is very unfortunate that practically the entire oeuvre of this excellent Swedish theoretical social psychologist is still not available in English. 12 Significantly, this delicate and conflicting combination of stability and variability may also serve as a platform for general computation. One rather well known example is Conway’s Life game, which belongs to Class IV and supports universal Turing machines. ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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many cases required to establish good interaction. Without a certain amount of complexity it is not possible to have the required interaction richness to avoid “passivity and lack of response” or too “predictable patterns.” This is the reason why too simple interactions do not provide the necessary preconditions for advanced or virtuosic interactions in which users can fully explore and exploit their skills and competences to reach a desired outcome or experience. The richness existing in benign complexity opens up for user control and for advanced use. This means that when benign complexity is absent, potentiality for virtuosity is reduced. 6.4 Complex Interaction: From Automation to Virtuosity Let us take a closer look at the tension between control and complexity and how that may affect qualitative aspects of complexity. We will do that in a quick sweep along the control dimension, spanning automation to virtuosity. Modern cars depend heavily on digital technology for most of their function and operation; the car has become one of the most common digital artifacts, and we have picked the braking system for some illustrative snapshots. In a really old car, the driver’s pressure on the brake pedal is mechanically transformed to a pressure on the brake discs of the wheels, causing friction that works to bring down the speed of rotation of the wheel. The exact effect on the movement of the car depends on its speed and momentum, the friction between the tires and the road surface, etc., but generally the effect is to slow down the vehicle, which of course is exactly the designed purpose of the braking system (normally coinciding with the driver’s personal purpose). As usual in the real world, there are many complicating factors. Full braking effect may require considerable muscle power; the friction in the brakes produced by a certain pedal pressure may vary if the brakes are wet, dirty, or overheated; the coefficient of friction between a tire and the road surface can vary much and change suddenly; the driver may need to brake and steer at the same time if the road turns or there is an obstacle; too much power on the brakes, depending also on the angle between the wheels and the car’s direction of movement, causes tires to slip rather than roll, making steering impossible and reducing deceleration; deceleration should preferably not be physically uncomfortable but be smooth and moderate, not create hazards from vehicles close behind; and so on. Consider now the interaction. The driver applies the brakes, that is, exerts muscular force on the brake pedal to slow down the rotation of the wheels. The feedback, the information picked up by the driver, includes the pressure on the foot and the tension in the muscles, which serve to give immediate confirmation that the input is the intended amount of pressure; the visual information of the car’s trajectory through the landscape; felt acceleration forces; mechanical vibrations and noise from the brakes and from the tires and the road. The driver needs to have a sense of the road surface condition: is it slippery? is it wet? is that some sort of gravel that rolls easily? is that wheel rolling or gliding? is that wheel in contact with the asphalt or is there snow slosh in between? In bad conditions the interaction required to quickly slow down the car becomes ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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quite complex, despite the external simplicity of the brakes. We know that an ordinary driver’s performance will be far from optimal. Now consider how this can be automated: First, power brakes to amplify the pressure on the pedal. This step turns the user input into a matter of information rather than of force. Second, an improvement in the brake system so that a certain pressure on the pedal translates into a certain frictional force on the brake discs regardless of dirt, water, heat, or whatever disturbance. This gives the driver direct control of the braking power applied to the wheels (rather than the pressure of the braking pads). Third, a feedback system so that the brake of a wheel that stops to spin and gets locked is momentarily released to let it spin again, retaining close to maximum braking effect, that is, an antilock braking system or ABS. We are now well under way in transforming the car into a digital artifact. With a more autonomous braking system the interdependence of steering and braking loosens up, and the driver no longer has to have a precise sense of or pay attention to the materials and conditions on the road and the momentary states of the wheels. The braking system takes care of the braking in the sense that the driver directly controls the desired rate of deceleration, more or less, while the braking system tries hard to deliver. Of course, this is not always possible if the road is in a bad or rapidly fluctuating condition. Next, consider a heavily computerized system that continuously measures speeds, accelerations, and momentums of vehicle and wheels, senses the road condition in front of each wheel, the vehicle’s position on the road, its distance and speed in relation to other vehicles, etc., to calculate and effectuate the optimal braking performance. Now, the driver’s braking action have become an even more hedged intention or expression of a desire: “I would like to decelerate at this rate—given that it is possible, safe and convenient to do that at this time.” The driver is on the verge of becoming a passenger expressing personal opinions about how the driving should be conducted. If we continue to press further in the direction of autonomy we will eventually end up in some completely automatic vehicle that will have its own plan for where and how we should go, using its inputs to set and reach its goals. The design choices with regard to control and complexity are very much the same with more traditional digital artifacts like a word processor. At the low end of automation we have the very simple text editor that allows (and compels) the user to work in close connection with every single character. This provides a sense of control and the word processor is maybe more felt as an instrument of precision than anything else. At the high end, where the driver has almost become a passenger, we will find highly advanced autonomous word processors that can guess what word we want or wanted to type, amend our bad grammar and aesthetically structure and present the text material. The user is on the way of becoming not more than a source of input. With a simple click, the form and looks of the document can change completely, which may give the author an eerie feeling of “who wrote that?” The surprise is not only on the user side. We are all familiar with the situation where the word processor tries to predict a user’s purpose but fails, causing extra work and frustration for the user. ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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6.5 Virtuoso Interaction Going in the opposite direction, towards higher degree of user control and more complex interaction, it is interesting to note that the “violin case”, that is, the combination of low external complexity with very high interaction complexity that seems to invite virtuosity, has few parallels in human-computer interaction. Part of the explanation could be the up till now typically strongly discretized input and output and strict turn taking of digital artifacts, quite far from the analogue flow of violin playing. Discrete input combinations related to desired output combinations in a complex manner is exemplified by some computer games, which open for the possibility of virtuoso performances; similarly, computer hackers can handle keyboard and computer in dazzling performances of quickly finding and solving software problems. A difference is that the interaction is overtly digital and the external complexity considerable: it is a “combinatory” virtuosity rather than the “smooth” virtuosity of top-class violin playing. The difference between analog and digital interaction, however, has over time been blurred; first with the advent of graphical user interfaces (GUI) and pointing devices, then with new tracking, sensing and presentation techniques partly deriving from virtual-reality technology, and with tangible user interfaces (TUI) where physical objects are used in analog mode to interact with the digital artifact. The stage is perhaps set for new applications and forms of interactions that would be more like violin playing, and we might already have artifacts like these around, for instance, in the form of games that have a simple interface but require sophisticated interaction skills, for instance, the Wii game consol with its controls. It is possible to see such games as artifacts with potential benign complexity. There are of course also many examples of benign complexity where external complexity is high. For instance, in highly advanced editing software (for music, film, or modeling) the external complexity can be extremely high and still invite virtuosity. The command of such external complexity can be expressed in amazing performances of interactions. However, it is clear that the benign quality depends on good design. Two seemingly similar editing software products may have superficially similar external complexity and yet one has the quality of inviting virtuosic use while the other will continue to be awkward, difficult, and in the way of efficient interactions even after learning and extensive use, hence lacks the property of benign complexity. We believe that virtuosity as a manner of interaction should be reconsidered and revisited in interaction design. There may be situations and technology use that would benefit from such a perspective. The history of virtuosity is rich, ranges over many fields, and might provide us with new insights into interaction design possibilities with contemporary technology. There are of course arguments against a move towards virtuosity in the field of interaction design. One obstacle to virtuosic interaction with digital artifacts may turn out to be their short lives and violently rapid development: the violin has been around for hundreds of years with hardly noticeable changes, giving users ample opportunities to develop a culture of virtuosic use. Many of the ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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new digital artifacts, in contrast, have a market life of less than five years before they disappear or are superseded by completely different designs, often building on new and different technologies. Another factor working against virtuosity could be that while virtuosity might be a sometimes desired manner of interaction, such extreme interaction is not typically what designers and users look for. In much everyday artifact use, users care for little more than very basic performance—and there usually is a conflict between the requirements of high-level performers and low-level performers that prefer low interaction complexity. Taken together, our examples in Sections 5 and 6 demonstrate the importance of the design of complexity distribution in digital artifacts. The examples also indicate that there is no predefined complexity distribution that is inherently best. At the same time every particular complexity distribution have consequences for what the interaction will look like. For designers it is crucial to understand what kind of interaction they are trying to establish when they design and distribute complexity in their artifacts. 7. BENEFITS OF A FRAMEWORK FOR COMPLEX INTERACTION The rationale behind our examination of complex interaction is that complex artifacts and complex interactions are a growing phenomenon in our society and a challenge to interaction designers. It also rests on a belief that an examination and understanding of complex interaction can lead to new design opportunities and products that would be experienced as more supportive and usable by their users. A closer look at complex interaction gives some insights into the nature of complexity and interaction. First of all, the realization that complex interaction does not necessarily mean bad or even difficult interaction is important. The notion of benign complexity highlights the fact that there are positive aspects of complexity that also apply to interactive artifacts. This in turn leads to the realization that interaction design principles can be more diverse and inclusive, not necessarily dominated by principles advocating simplicity and ease of use. Our examination of complex interaction has convinced us that there is a need for a theoretical framework providing the necessary concepts and definitions that would support designers in their analysis, understanding, and design of interactive artifacts. We anticipate that there are at least three benefits from having a framework that includes appropriate concepts for describing and discerning forms of interaction complexity: it can be used for predictive purposes, analytic purposes, and theoretical purposes. Even though we do not believe that there is much predictive power in the framework as presented here, we believe that the framework can inform and challenge traditional thinking about interaction design, and by doing that, it can support and help designers to explore and develop new forms of interaction. Maybe the most practical benefit of a framework like this lies in its analytical power. The framework, with its notions of complexity loci, can be used in ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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careful examinations and critical studies of existing interactive artifacts. Such studies can in themselves serve several purposes, but the most obvious is that it leads to an increased understanding of complex interactions and to a developed sensibility of what might be considered good distributions of complexity in artifacts in particular situations. The theoretical benefit is potentially great. The framework provides a conceptual descriptive language that can be used to examine and describe concrete instances of interactive artifacts. We can see several possible and needed theoretical developments based on the framework. There is a need to further and more empirically test and explore the framework and its fundamental elements. It is also possible to anticipate a development where the framework is further detailed; for instance, the notions of complexity and loci can be expanded with additional concepts, and even though we have addressed notions such as simple, easy, difficult, learning, in relation to the framework, more work needs to be done. But, even in the state of the framework as presented here, it provides a foundation and opens up for further examinations of complexity and interaction in a structured and analytical way. In addition to these advantages of having a developed framework for complex interaction, this work opens up for new research based on a deeper understanding of complex interaction. For instance, we can see opportunities in developing design strategies, practical as well as theoretical, based on this framework. It is for instance possible to see design strategies based on different ideas and ideals on how to handle and distribute complexity, such as, incremental design versus compositional design, and other strategies that relate to the idea of transferring the issue of complexity to the user via tailorable interactions. Taken together the framework and the language it provides for the examination and description of complex interactions can influence the field of HCI in the way it approaches complexity. 8. CONCLUSION Our society and technology will continue to pull and push us towards increasing complexity; digital technology in particular accelerates this development. Complex interaction is a challenge and especially so when it comes to digital artifacts and systems, in our professional lives as well as our private lives. Even if complexity in many situations is causing us problems, we conclude that complexity is not always something to avoid. Complexity can sometimes be seen as a resource. It provides us with experiences and ways of interacting with our environments we would not want to be without. But, since it is a potent resource it needs to be administered carefully and thoughtfully. Our overall conclusion is that there is no way we can escape the task of designing for and with complexity. We have to face the issue of complexity. We have argued that there is value in distinguishing between internal, external, mediated, and interaction complexity, and that a deeper understanding of these different locations of complexity and their interrelations can be ACM Transactions on Computer-Human Interaction, Vol. 17, No. 2, Article 8, Publication date: May 2010.

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