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FUTURES

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Futures 40 (2008) 586–595

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Design as a social process: A complex systems perspective

Katerina Alexioua,�, Theodore Zamenopoulosa,b

aDepartment of Design and Innovation, The Open University, Milton Keynes MK7 6AA, UKbCentre for Advanced Spatial Analysis (CASA), Bartlett School of Graduate Studies, University College London,

1-19 Torrington Place, London WC1E 7HB, UK

Available online 21 November 2007

Abstract

Design is increasingly perceived as a social process, but despite a wide exposition of the concepts of cooperation or

collaboration, a coherent account of the important dimensions of design as a social multi-agent process is still missing. It is

argued that a generic, abstract concept is required to be able to capture the social distributed character of design, without

making a-priori assumptions about the kinds of interdependencies that exist among agents (in terms of goals, knowledge or

roles), or about their dispositions towards cooperation or conflict. The paper suggests that the most appropriate concept

for this purpose is the concept of coordination. A model of coordination as a distributed learning control is thus presented

as a vehicle for focussing on the important dimensions and qualities of coordination. Motivated by this discussion, the

paper subsequently delves further into the issue of sociality by considering the question of the micro–macro link extensively

discussed in sociology. In particular, the paper shows how coordination can offer a resolution of the question in the

context of design, and discusses the effects of such an approach for our understanding of the relationship between

complexity and design.

r 2007 Elsevier Ltd. All rights reserved.

1. Introduction

In recent years there has been a growing interest in studying and supporting design as a social process. Thiscomes from the recognition that design cannot be anymore considered as the activity of an isolated mind, thedesigner. As Domeshek et al. [1] note: ‘‘The lone design genius, if not mythical or completely extinct, is surelyon the endangered species list. Nowadays, significant design projects require teams of designers coordinatingtheir varied expertise to arrive at effective design solutions’’ (p. 143). But it is not only the coordination ofdifferent experts that is instrumental in design. Design involves the coordination of knowledge, resources,goals and requirements that comes from other equally important agents, like clients, stakeholders and users.Indeed, design, in all its manifestations (web and graphic design, product design, architectural and urbandesign, organisational design, policy design, etc.), is increasingly becoming an activity that involves multipleagents with different needs, knowledge and goals. To the human agents that take part in the process oneshould add the computational agents—computational tools in general—which inform and support designdecision-making. But design is an intrinsically social process in another important respect. Design products

e front matter r 2007 Elsevier Ltd. All rights reserved.

tures.2007.11.001

ing author. Tel.: +44 1908 655960.

ess: a.k.alexiou@open.ac.uk (K. Alexiou).

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(from buildings to policies) are motivated by and realised within a social environment. The agents (individualsor social structures) that constitute the context or environment of design, provide both the requirements andthe final evaluations of the design products; so they should be considered as essential parts of it.

In this paper, we are going to explore how we may deepen our understanding of design as a social process byadopting a complex systems perspective. The main assumption is that design is a capacity derived from thedistribution and organisation of complex socio-technical systems, and that appropriate abstractions areneeded in order to capture and represent this capacity. This new type of understanding is important both formodelling design processes and problems and for developing appropriate tools to support design reasoningand decision-making.

The paper is organised as follows: Section 2 presents the problem around formulating a comprehensiveunderstanding of multi-agent design and introduces the notion of coordination as an appropriate abstractionfor addressing this problem. Section 3 discusses a model of coordination based on a distributed learningcontrol as a vehicle for focussing on the important dimensions and qualities. Section 4 delves further into theissue of how multi-agent design can be perceived as a social process by offering an exposition of the issue ofthe macro–micro link. The paper ends with a short conclusion and some general remarks about therelationship between design and complexity.

2. Understanding and defining multi-agent design as a coordination problem

What are the characteristics and principles of multi-agent design? There is an extensive literature oncollaboration and cooperation in design but these concepts take different forms in different design domains.For example, in urban planning the concept of collaboration is more tightly linked to issues of participation,collective choice and consensus, while in computer-aided design, the focus is on supporting collaborativework, communication, and management of information. Additionally, the concepts of cooperation andcollaboration take different forms according to the particular design setting; for example, differentassumptions are made when the design process involves multidisciplinary collaboration, collaboration amongthe experts coming from the same field, or collaboration between experts and non-experts. Furthermore, therequirements of collaboration vary according to the objective of the application (for example, to supportcommunication, to encourage participation, to support problem solving, to support conflict resolution or tosupport creativity).

It becomes apparent from this discussion that a deeper understanding of design as a distributed, multi-agentprocess is necessary. In particular, a generic, abstract concept is required to be able to capture the socialdistributed character of design, without making a-priori commitments about the kinds of interdependenciesthat exist among the agents (in terms of goals, knowledge or roles), or about their dispositions towardscooperation or conflict. The paper suggests that the most appropriate concept for this purpose is the conceptof coordination.

2.1. A short introduction to coordination

Coordination problems arise due to the fact that complex processes and decisions involve bringing togetherand organising many different people, components or knowledge, which are linked to each other in variousways. A very helpful and comprehensive definition of coordination is given by Malone and Crowston [2] as,‘‘the act of managing interdependencies between activities to achieve a goal’’ (p. 361). The definitionrecognises interdependency as a key to understanding coordination. There is indeed a variety of differentinterdependencies present in a complex task, including those arising due to shared resources and tasks, timeconstraints, or task–subtask relationships.

The problem of managing interdependencies is common in all design domains; examples includeestablishing translatable relationships between different representations (for instance between structural andarchitectural drawings), synchronising the exchange of information, ordering activities in engineering andmanufacturing processes, establishing roles and delegation structures in organisations and many others.

Defined in this way the concept of coordination encompasses notions of conflict and cooperation and can bebroadly applied to a variety of goal-oriented activities. However, the subject of investigation here is not a

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general area of management, but the activity of design in specific. So there is a need for understandingcoordination not only as management, but also in relation to questions of exploration and generation ofalternative designs, particularly novel or creative design solutions. Consequently, we have here the dualproblem of using the concept of coordination in a way that is appropriate for design, and defining (distributed)design as a coordination problem.

The concept of coordination has a long history and has been studied in many different fields and from manydifferent perspectives. For example, in game theory, coordination is linked to competitive behaviour; incontrast to cooperative games where the focus is on optimisation of joint profits, coordination games focus onsituations with multiple Nash Equilibria where the players need to select the appropriate action to avoidreaching a mutually inferior outcome. As another example, in collective choice, coordination is more linkedto the idea of aggregating or combining alternatives to form a single choice, and involves evaluation andordering of goals and preferences to ensure resolution of conflict and collective agreement. In any case,adaptation and learning are not only considered as important tools for enhancing the individual ability ofagents to derive appropriate strategies and solutions, but also to achieve a common level of understanding andagreement.

The concept of coordination is also very important in the field of distributed artificial intelligence (DAI) andmulti-agent systems (MAS). The general standpoint in this case is that individual (artificial) agents havelimited knowledge and resources, information processing abilities and viewpoints, and thus need to engage insome kind of collective activity or interaction to improve their problem-solving and goal-attainmentcapabilities. The term distribution here is quintessential; it suggests the existence of incomplete and dispersedinformation, interdependency of actions and decisions, and lack of global control mechanisms or rules todictate global behaviour. Coordination therefore becomes instrumental and might refer to effective ordering,synchronisation, planning, and adaptation of actions, decisions and goals [3]. It is interesting to note here thatfrom the inception of the field there has been a division between approaches focussed on the development ofappropriate ontologies, rules and shared conventions as a basis of coordination, and approaches focussed onlocal-agent interactions and the idea of building coordinated group behaviour ‘‘from the bottom up’’ (for anoverview and pointers to relevant literature see for example [4–6]). The latter (often termed ‘‘reactive’’approaches) have strong links with research in an artificial life, as well as behaviour-based robotics (see forexample [7]). The reactive approaches have grown to be more dominant in recent years, and together withdynamical approaches to the emergence of coherent global behaviour [8–10] have become seamlesslyincorporated in the core curriculum of complex-systems science.

2.2. Coordination as an abstraction of multi-agent design

Let us now turn to the literature in design studies which is concerned with theories, models and methods ofdesign. Design is generally characterised as a purposeful decision-making activity that moves from a perceivedneed or a desire for change towards a new state (a design solution, or a description of it) that satisfies this needor desire [11–14]. Traditionally, design has been perceived as a kind of generative and explorative process thatseeks to define appropriate solutions to a design problem or goal. By the term design solution, we generallyrefer to a structure intended to satisfy a function, or more generally an artefact, a design specification or aplan. In contemporary approaches of design, the exploration and articulation of goals alongside theexploration and generation of solutions, is considered to be an essential part of designing—particularly acreative design. This approach is often labelled co-evolution [15,16].

If we look at distributed design in particular we have a situation where the different agents express diverseand conflicting goals—so that the identification and creation of goals become a necessary part of the collectivedesign process. From the brief review above, we saw that coordination is both a problem that arises due to thedistribution of knowledge, resources and goals, and a process of working within a complex network ofinterdependencies, conflicts and constraints, to arrive at a state of equilibrium or mutually satisfying solution.We can thus start understanding coordination as a process of generating and reconciling distributed goals, butalso generating and reconciling alternative solutions. This view of coordination, not only extends theunderstanding of coordination beyond management to include creative design, but it also places creativedesign (or co-evolutionary design) in a distributed, social setting.

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2.3. The dimensions of multi-agent design as coordination

Bringing all these points together, we can identify some crucial dimensions of multi-agent design using theabstraction of coordination.

�

Firstly, distributed design tasks require knowledge that is spread among local agents. Coordination thusinvolves synthesising or constructing the knowledge necessary for the collective task. In this sense, learningis seen as an important instrument not only for enhancing the individual ability of agents to derive designsolutions, but also for creating shared knowledge about the design task and its constraints. � Secondly, in a multi-agent design, decisions are driven from individual goals and requirements, and are

taken at a local level without any external centralised source of control. In line with the view coming fromcomplex systems science, coordination can be seen as a distributed control process, which leads to anemergence of design solutions.

� Finally, in distributed design decision-making, the definition of problem and solution spaces also becomes

a collective undertaking. Coordination signifies the need for a parallel exploration, generation andreformulation of problem and solution spaces.

3. A model of coordination based on distributed learning control

We have experimented with computational models and simulations in order to express and explore theabove ideas in greater depth. For the experiments, the concept of coordination is used assuming that adistributed design task involves a number of agents acting on a common world, while trying to achieve theirindividual visions about this world. This common world constitutes both the subject and the medium of agentinteraction.

In one of these models coordination is formalised as distributed learning control. We will not present heredetails of the computational aspects, but we will discuss the model at a conceptual level (for a more detailedpresentation of the model see [17]). In this model each agent effectively embodies a control process thatgenerates control actions or decisions to meet time-variant individual targets, despite endogenousuncertainties and exogenous disturbances coming from the actions of other agents. Importantly, this controlprocess is informed by learning. Each agent learns from the reactions of other agents and developsexpectations about the world. Expectations not only convey knowledge about the effects of design decisionsand the performance of the designed artefact, but also convey knowledge about the relationship between thegoals and design solutions. Agents use this knowledge to discover actions that can steer the overall system

DesignGeneration

Actual World

Modelling/Learning

Expectations

ControlGeneration

ReferenceModel +-

-+

Reference Goals /Desires E

E

Disturbance d

Fig. 1. A model of coordination as distributed learning control.

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towards individual goals and avoid conflicts with other agents. This knowledge is also crucially used toformulate new goals for the design process, and to motivate the creation of new design solutions (see Fig. 1 fora diagrammatic illustration). The evaluation of appropriate design problems and solutions is achieved throughmonitoring the discrepancy (denoted by E in the figure) between expectations and actual states of the world, aswell as between desired and actual states. Through this distributed process of learning and reciprocal control,the problem space and the solution space can be said to co-evolve together. Note that disturbances are notonly considered as a source of conflict here, but also as a source of variation and hence an opportunity fornovelty for individual agents.

In this model, the distribution of the design process in different agents, that hold individual knowledge andtargets, implies the possibility for global phenomena to emerge through the interaction and self-adaptation ofagents at the local scale. Coordinated solutions can therefore be thought to emerge out of the distributedlearning control process. A typical view of emergence in MAS defines it as a new pattern or behaviour that isformed at the macro-level from the interaction of agents at the local level. In MAS, emergence is usuallydesirable when it leads to behaviours that can achieve a certain task or goal without the existence of a centralmechanism directing the activities of agents towards this end. In this sense, we can consider that coordinationis exactly the desirable emergent effect of the activities of the distributed agents.

The overall conception of coordination, based on this model of distributed learning control, is consistentwith a view of design whereby distributed agents act on a world by constraining and enabling each other.Design artefacts are the product of the complex interaction of agents acting within this world. This conveysthe idea that design is a fundamentally social process. What seems to be missing from this framework is a cleardescription of the relationship between the micro-level of individual agents and the macro-level where thesecome together into a coherent, coordinated whole. This is a crucial question for understanding the socialaspect of design and deserves special attention.

4. Focussing on sociality

In this section, we will explore in greater depth the question of how design can be understood both as anenaction of individual goals and desires that construct socially recognised artefacts and as an object of socialenaction, produced and constrained by social structures. Understanding and defining how the macro-level ofsociety is causally related to the micro-level of individual agents is an age-old question in sociology, sociologyof knowledge and social psychology, but has also recently been raised in the domains of DAI, MAS and socialsimulation. In fact, the question of resolving or reconciling micro and macro, agency and structure, is at thevery heart of sociological theory as it refers to the problem of explaining social reality and its origins. Afterreviewing some central approaches and arguments in these fields, we will return to an analysis of the model ofdistributed learning control particularly with regard to this question.

4.1. The micro– macro link in sociology and MAS

The term micro–macro link reflects a dichotomy between approaches that ascribe primacy on individualaction (typically represented by rational economic theory) and approaches that ascribe primacy on socialorder and collective structures (typically represented by structuralism). The problem is not only to explain theontological status of social structures, where they come from, and how they are sustained, but also howindividuals take part in the process, and whether and how they shape or are shaped by the society.The investigation is fundamentally linked to an understanding of emergence as a way to explain theinterdependence between the two.

Alexander and Giesen [18] distinguish five major approaches to the question that correspond to differentpositions about the nature of agency (rational versus interpretive) and the source of social patterns (individualversus collective) (see Fig. 2). More analytically, approaches (1) and (2) both take an individualistic view, butthe first stresses the rational and objective character of action, while the second puts an emphasis on thesubjective psychological factors and processes of interpretation. On the other hand, approaches (3)–(5) take acollectivist position but have different attitudes towards the role and status of individual agents. At one end,approach (5) considers that social control shapes and constraints action, and denies any subjective perception

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(1)

(5)

InterpretiveRational

Col

lect

ive

Indi

vidu

al

Nature of action

Sour

ce o

f so

cial

ord

er

(3)

(4)

(2)

Fig. 2. Approaches to the micro–macro link in sociology (inspired by Alexander and Giesen [18]).

K. Alexiou, T. Zamenopoulos / Futures 40 (2008) 586–595 591

of order, while approach (4) accepts subjectivity and purports that the socialised individuals reproduce societyby translating the existing structure into the micro-realm. Approach (3), which actually rests somewhere in-between of all the other approaches, gives analytical autonomy to the micro-level by recognising that thesocialised individual re-creates a society during the process of reproducing it. The various contributions to thesynthesis of the macro with the micro usually build on distinct combinations of these approaches.

Finding a link between the micro, and the macro, or a middle ground between agency and structure,signifies a need on one hand to explain the stability of societal structures despite the individual action orviolation, and on the other hand to explain variability, creativity and innovation. Likewise, it is important toaccount for the existence of material and structural constraints and influences, as well as for purposeful actionand the individual experience of reality. For a more complete view on the subject one may consult thecollections of articles in [19–22]. Here we will concentrate on two theories. The first, coming from thesociological tradition, is Giddens’ theory of structuration [23], which has become the centre of muchsociological discourse. The second is the Castelfranchi’s theory of social functions [24], which comes from theperspective of cognitive science.

In the case of Giddens’ theory of structuration, the focus of investigation is neither on the individualexperience nor on the society as a totality, but human social practices which are (self-)reproduced in space andtime. Central to this theory is an understanding of the ‘‘duality of structure’’. Structure is seen both as apattern of social practices and as a ‘‘virtual order’’ of rules and transformative relations. So the structureconsists of a set of rules and resources, better perceived as ‘‘structural properties’’ that constrain and at thesame time enable social activities. Giddens uses a ‘‘stratification model’’ to establish agents as knowledgeableactors in this framework (Fig. 3). The agents reflexively monitor the actions and aspects of the contexts withinwhich they move; they have a ‘‘theoretical understanding’’ of the grounds of their activity, and embody apotential for action (motivation). Through these processes the agents reproduce the conditions that make theirpractices possible and continuous in space and time. So the relation between agent and structure is also dual:‘‘the structural properties of social systems are both medium and outcome of the practices they recursivelyorganize’’ (p. 25). This is a key to understanding the stability of structures. However, Giddens also argues thatwhile agents reproduce the rules and resources on which their activities are based, agents are also alwaysbounded in their capacities. The unintended consequences of actions play an important role in bounding, orconditioning, further activities and thus constitute an important source of structure variation.

The structuration theory of Giddens is useful for understanding how the macro (society) can have causalpowers over the individual agents without it being an external absolute form of control or constraint; but hisaccount of the micro-part is somehow limited to concepts of re-production or re-construction. In other words,the causal effects of intentional action do not receive considerable attention.

It is interesting to contrast this situation with the situation of research in DAI and MAS. The sameproblem about linking micro and macro seems to have developed from a completely opposite direction.Starting from a very strong tradition of exploring emergence by adopting the bottom-up approach of micro-sociology and focussing exclusively on agency and individual (local) action, a considerable literature hasdeveloped which seeks to incorporate mechanisms and explanations for downward causation—see [25–28]. Itis because of the traditional focus in MAS on the micro that the concept of downward causation becomes soimportant.

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Reflexive monitoring of action

Rationalisation of action

Motivation of action

Unacknowledgedconditionsof action

Fig. 3. Gidden’s stratification model of action.

Beliefs

Goals Action+

+

Intendedeffects

Unintendedeffects

Functionalunintendedeffects

CONTEXT

Fig. 4. Castelfranchi’s model of social action.

K. Alexiou, T. Zamenopoulos / Futures 40 (2008) 586–595592

Castelfranchi [24] in particular, suggests that to reconcile micro- with macro-teleology, it is necessary todevelop theories of cognitive agents (in terms of goals of beliefs) alongside models of an emergent functionality(in terms of unintended mechanisms). In his view, an emergent macro-structure, i.e. social order, existsobjectively when it has a causal effect. An even stronger conception requires that a macro-structure reproducesbecause of these effects. Castelfranchi essentially frames the question of linking micro and macro as a questionof defining and explaining social function: how can emergent effects reproduce through an intentional action,but ‘‘independent of the agent understanding and pursuing these effects’’? (p. 20). The model illustrated inFig. 4 explicates how certain unintended effects of intentional action are reproduced because they reinforce thebeliefs and goals that caused them.

His view is similar to Giddens, in proposing that unintended consequences of actions play an important rolein conditioning (here selecting or reinforcing) further activities. But Castelfranchi additionally dwells on theaspects of individual cognition and action. He further argues that the appropriate mechanism for linkingintentional action with playing social functions is some form of ‘‘learning without understanding’’. Hediscusses three different mechanisms through which such form of learning can be achieved. The first isreinforcement learning, where the beliefs and goals of agents are reinforced (either the beliefs and goals areconfirmed, or the association between beliefs, goals, and the context within which they operate, isstrengthened); the second is restating, where the contextual conditions that lead to the action are sustained orreproduced; and the third is emotional reinforcement, where possible goals are ‘pre-selected’ because of thelearned emotional reactions to anticipated future scenarios.

Castelfranchi’s treatment, which builds upon evolutionary conceptions of function, offers a way to study therelationship between the agents and the collective macroscopic reality. Not only does it allow us to account forthe circular causality between micro and macro, but also for the independence of one from the other. This isthe reason why he is particularly focussed on the question of how cognitive intentional agents may participatein the reproduction of social order without being aware of their doing so. He distinguishes two ways in whichagents may be playing a social role: the convergent way (functionality impinges on the intended effect) and thedivergent way (functionality is derived from the side-effects of intended action). Nevertheless, in the modelillustrated in the figure, only the second type of behaviour is incorporated. Moreover, while he recognises theimportance of relating and distinguishing intentionality and function (he considers this to be ‘‘the hardproblem’’), he does not show in his model how a social order may be created/reproduced not only through

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functional unintended effects, but also through deliberate creation/reproduction (i.e. the relation betweenintended effects and functional unintended effects apropos the creation of social order is not discussed).

Coming from a cognitive science background, Castelfranchi is understandably focussed on issues ofindividual cognition and action, and is primarily concerned with the development of (formal) models ofindividual and social cognitive agents. His work is part of an ongoing research and debates about thenecessary characteristics and capabilities of social agents, especially in the view of implementing themcomputationally.

Having explored the two theories, we can now turn to the model of coordination presented above anddiscuss how the micro–macro link is incorporated.

4.2. The micro– macro link from the perspective of distributed learning control

The model proposed in the previous section had as its basis the idea that multi-agent design coordinationcan be modelled as a distributed learning control. The action of change performed by each agent is in essence acontrol action that tries to guide the world towards a state that corresponds to individual goals and beliefs aswell as individual and global constraints. Beliefs are created and re-created through observation and learningof action-effect associations and provide the stock for discovering appropriate control actions. Goals are alsoformulated and re-formulated on the basis of knowledge about the world (Reference Model). The world is asynthesis of the different control actions bound together under resource limitations and other globalconstraints. To allow comparisons with the ideas discussed above the model is represented in Fig. 5.

There are some noteworthy similarities and differences between this model and the one proposed byCastelfranchi. The two models have in common the idea that individual action, which is guided by goals andbeliefs, is the basis for the creation of the macro-level, while the macro-level becomes autonomous asunintended effects feedback into individuals through a learning process. The coordination model however, bydefinition, focuses on the interrelationship between multiple agents and explicitly considers this to be a causalfactor for the creation of macro-level structures. So, in contrast to Castelfranchi we have here an explicitrepresentation of the world as a field where convergent and conflicting actions are manifested. So agentsinteract through this world and use knowledge about it to guide their future action. The model of coordinationconsiders that both intended and unintended (collective) effects contribute functionally to the creation of themicro–macro dynamics. From the point of view of an individual agent, the perceived distance (or error)between the intended and unintended effects not only motivates action, but is also used as a metric of thelimitations and constraints over that action. In reality, agents are bounded in two ways: one is related to thelimitations of available resources, laws and other external constraints, and the other is related to the ability ofan agent to learn, interpret or internalise the external world and therefore form expectations and predictionsabout it.

It is essential here to clarify what is meant by a social structure in the context of this work. There are manyconceptions of what the macro is in analytical terms. The coordination model is focussed on (distributed)

World Model(Beliefs)

Reference(Goals)

ControlAction

World

Intendedeffects

Unintendedeffects

‘error’

ControlActions(Disturbance)

Feedback: Monitoring, Interpretation, Constraint

Resourceconstraints

Fig. 5. Micro–macro link in the model of coordination as distributed learning control.

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design decision-making and is therefore primarily concerned with the processes that take part in the formationof the design space (problem and solution spaces) and the characteristics of this space. In this sense, the studyof coordination is the study of how the different dimensions of the problem and solution space are createdand organised. The model particularly addresses the question of how distributed goals, requirements andknowledge about a design solution (micro-level) are synthesised into an actual collective configuration(macro-level). Clearly, the way by which agents interact and get grouped together or differentiated (in terms ofgoals and beliefs), is reflected in the organisation of the design space at the macro-level. So micro and macrodo not exactly correspond to the problem and solution space, but rather to their expression in an individualand a global scale, respectively.

5. Conclusions and discussion

The view of coordination offered in this paper aspires to convey a clear sense of multi-agent design as asocial process by offering a framework for understanding the interplay between the micro-level of individualgoals, needs and decisions and the macro-level where these are expressed giving rise to a complex net ofinterdependencies, constraints and opportunities for creativity. This type of understanding is important bothfor modelling design processes and problems and for developing appropriate tools to support design reasoningand decision-making in the future.

It is also important for understanding the role design plays within a complex world. Rather thanconsidering that design is either a powerless local activity doomed to fail because of the complexity of theworld, or an absolute kind of control originated from the top to constrain local action, the approach impliesthat design is a capacity derived from the complexity of distributed systems. In this sense, design and complexityare not opposing but are in fact linked at a fundamental level. Seeing design through the lens of coordinationsuggests focussing on understanding the organisation and the causal processes developed between micro andmacro-level entities.

An interesting issue for further investigation is how a more sophisticated view of the micro–macro link indesign can be elaborated, that does not solely focus on the two opposite poles (micro and macro) but considersthe micro–macro linkage as a multi-level process. Such a development would be useful for betterunderstanding the processes and effects of organisation and the role of meso-level agents, like for instanceinstitutions. The issue is also related to an effort of breaking free from a naıve conception of the micro assimple and the macro as complex. Knorr–Cetina [29] tells us that this dichotomy is inappropriate from asociological perspective, and reminds us that complexity is not only a matter of size, but it also relates to ourchoice of the unit of analysis (and its variety), as well as the knowledge of the observer, i.e. the knowledgeavailable for the analysis of a particular phenomenon.

Acknowledgement

Thanks to Elena Besussi for some early discussions about the agency-structure debate and design.

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