by Philip Boxer Bsc MBA PhD^[1]

Open systems and their 1st-order behavioral closures
Individuals identified with an enterprise, the formal behavioral model of which is thought to be deterministic, believe that the enterprise can dictate responses to all events, which are believed to be completely enclosed within its boundary.  This formal behavioral model is denoted by the transitions between its sets of input and output symbols and is referred to as a first-order system. Such a system-of-interest is said to be over-determined if its structure over-determines its behavior in the sense of rendering it deterministic. Such a system-of-interest is a closed first-order system with a first-order structure.

Conversely, a non-deterministic formal model of behavior signifies an under-determined system-of-interest, the behavior of which is uncertain because there is more than one outcome possible from any given set of input conditions.  A first-order system that cannot be assumed to have all its inputs within its boundary is an open first-order system because we cannot know that its first-order behavioral closure is deterministic.  An open system is therefore one for which the first-order closure of its behavioral model is non-deterministic.

Sentient organisation and 2nd order behavioral closure
Consider now the elements of a system-of-interest with a first-order behavioral closure whose degree of non-determinism may be modified by an alteration to its behavior through choices exercised over the transitions available at each of its states, exercised through control of the behavior of its elements.  Any agency that so modifies the system-of-interest’s behavior must be outside the system-of-interest, and may be said to be controlling it. If the behavioral model of this controlling behavior is itself deterministic, then such behavior may be treated as further elements of an expanded system-of-interest through processes of mechanization. If the model of controlling behavior is not deterministic, however, then this agency may usefully be referred to the way the enterprise is organised, defining the forms of novel emergence that are possible with respect to the components of the system-of-interest with its first-order behavioral closure^[2]. The relation of 2nd order organisation to 1st order structure is a stratified relation.

The characteristic of such 2nd order organisation is that it is sentient^[3]. However, we may redefine the system-of-interest so as to include the elements (aka people!) of its sentient organisation that are the sources of controlling behaviors, defining a second-order system-of-interest referred to as a socio-technical system. The boundary of a second-order system-of-interest whose behavioral closure can be made deterministic is its perimeter.

Each deterministic second-order closure that may be constructed by the exercise of sentient organisation may be considered to be a point in a model space, just as each state of a first-order system is a point in its state space. Each possible modification by its sentient organisation of the structure of the first-order system-of-interest that changes the nature of the second-order deterministic closure is a transition in that model space. The behavior of the enterprise may therefore follow a set of possible trajectories through model space which together comprise its repertoire of possible deterministic second-order behavioral closures. While some changes to sentient organisation may reduce the variety of these possible trajectories of the enterprise through model space, this is unlikely to be the case given that the elements of a sentient organisation are people, the controlling behaviors of whom are not going to be (‘reliably’) deterministic.

Sovereignty and 3rd order behavioral closure
To remove this non-determinism in the second-order behavioral closures, a further form of agency, a governance process, will be needed to restrict the set of trajectories through model space.  The power to impose such a third-order closure through a governance process is the power of sovereignty over the 1st order structure and 2nd order sentient organisation of an enterprise. We may again re-define the system-of-interest to include the elements of the governance process (again, people), referring to it as a third-order system.  If this governance process is able to make the 2nd order behavioral closure deterministic, then it defines the perimeter of the enterprise.  This allows the enterprise to be defined as a system-of-interest in which its governance processes have the power to impose third-order behavioral closure, i.e. single trajectories through model space. We may say that the enterprise can be identified with a 2nd order system-of-interest, but its behaviors are realized through the way sovereignty is exercised over that 2nd order system.

What is frequently referred to as an ‘open system’ therefore, when used to refer to the way an enterprise is organised, is a third-order system, the behavioral closure imposed by which is deterministic.^[4] Continuing with the example of Bob Martin, used in distinguishing emergence from hierarchy, clearly there was a need for 2nd order organisation to manage the 1st order systems of production.  But the maintenance of 3rd order sovereignty over all this is apparent in its continuing status as a family firm:

In 1938 Bob Martin’s opened a showpiece factory in Southport – it was to play an important role in providing vital medical supplies for British soldiers [during the Second World War] as well as in maintaining the growth of the company. Robert Martin continued to run the company after 1948 and maintained an active involvement up until his death in 1979. His son, now Sir Bruce Martin QC, took up the reins of the business in his turn and Bob Martin has remained a privately owned, family firm to this day.

Distinguishing edge from perimeter
If the enterprise must organise its behaviors differently as a function of different kinds of client-customer relationship, then its governance processes must surrender sovereignty to the particular relationship to some extent.  The relation across its perimeter will therefore be different for each different kind of client-customer relationship, becoming an edge. An enterprise may thus have many edges to the extent that there need to be many such ways in which it must surrender sovereignty in its interactions with its client-customers, the most interesting of which involves network formation within a larger ecosystem^[5] – interesting because sustaining such surrendering of sovereignty demands asymmetric leadership.

The person and the enterprise
A difference between the person and the enterprise, therefore, is

• the relationship of a person’s identification to their singular embodiment as a person, whereas with an enterprise, its 1st, 2nd and 3rd order systems provide multiple forms of support for identification by persons.
• a person takes up a role in the life of an enterprise, whereas with an enterprise, it takes up a role in the lives of its client-customers.^[6]

Notes
[1] This posting is based on a joint paper with Bernie Cohen on “Modeling and the Modeler”, July 2007.
[2] Novel because if the relation is one of weak emergence, then by increasing the resolution of the state space, then the behavioral model of the 2nd order system may be rendered deterministic. This is what is done to the business models of enterprises through the effects of digitalisation with its attendant de-layering of sentient organisation.
[3] Miller, E. J. and A. K. Rice (1967). Systems of Organization: The Control of Task and Sentient Boundaries. London, Tavistock.
[4] The biological systems from Maturana derived his distinction between structure- function-organisation are 2nd order systems with deterministic 2nd order closures of their behavior. See Lacan and Maturana: Constructivist origins for a 3⁰ Cybernetics in Communication and Cognition (1992) Vol 25. Number 1 pp73-100.  For some of the issues facing ‘open systems’ thinking, see Leading organisations without boundaries: ‘quantum’ organisation and the work of making meaning.
[5] The formation of such networks around their social objects involves new forms of stratification aka novel forms of emergence demanding new governance processes that are incommensurable with the sovereignty of the existing enterprise as a whole. This gives rise to the challenges requiring a double alignment of ‘know-how’.
[6] Note that the implications of the word ‘role’ here are different, in the former use referring to an individual, in the latter a whole system-of-interest. In dilemmas as drivers of change I argue that this latter perspective requires a systemic view of the enterprise.

by Philip Boxer BSc MBA PhD

Some years ago, I published a paper titled: The stratification of cause: when does the desire of the leader become the leadership of desire”^[1]. The paper’s aim was to understand an enterprise not so much in terms of its business model(s), as in terms of its being a response to an undecidability, experienced by its client-customers, in relation to which it created value.  The ‘desire of the leader’ had to give way to the ‘leadership of desire’ under conditions of environmental turbulence in which a highly connected environment gave rise to tempos of demand that quickly rendered any static business model(s) obsolete. The demands of client-customers had to be responded to one-by-one in how the enterprise created value.  This had to be a process of continuous innovation responding to the demands as the demands arose in their contexts-of-use.^[2]

A persistent problem facing the way we think about how this ‘leadership of desire’ translates into the way an enterprise is organised is the confusion of hierarchy with emergence. The originating innovation (aka novel forms of emergence) of a new way of creating value became ‘fixed’ in the form of a business model subject to hierarchical accountability established by the originator of the business model, in order that it might be repeated.^[3] Under conditions of environmental turbulence, however, such hierarchies are insufficiently dynamic.  The need for continuous innovation therefore requires that hierarchy be distinguished from emergence in order that the nature of originating innovations be grasped more effectively.^[4,5]

The confusion of levels of hierarchy with emergent strata hides the fact that descriptions in terms of ‘levels’ are unable to account for the emergence of strata either as a natural phenomenon or as an artifact of the process of observation.^[6] The emergence of strata can be defined, however, without invoking the prior concept of levels.  It does this through the use of the concepts of spatial and temporal scope, spatial and temporal resolution and state.^[7]

Scope, Resolution and State
Implicit in the prediction of the properties of a system by a modeler is that modeler’s interest in that system as a system-of-interest. This system-of-interest is defined by boundaries derived from the properties of the system that are of interest. The modeler may provide an interpretation that maps symbols in the modeler’s model to observable phenomena in the world, but the boundaries around the modeler’s system-of-interest identify its scope, whether as it is modeled or as it is observed.  Saying of a model that “it is able to predict the properties” of a system-of-interest, is to say that the modeler considers the model to provide an adequately explanatory account of the system-of-interest to the extent that causal effects in the system-of-interest are interpretations of inferences in the model. In the following example, the properties that Bob Martin wanted of his conditioning powder led him to define the system-of-interest that could produce the powder: ^[8]

In his greenhouse in 1892, a 25 year old man called Bob Martin invented a conditioning powder for dogs. Whilst working for a local veterinary practice Bob Martin had become increasingly interested in basic healthcare for ordinary pet dogs. He constantly pestered his new employers with questions and visited local mining districts to talk about canine ills. Miners would show him their simple remedies and inspired him to conduct his own experiments. The new conditioning powder was intended to supplement the poor diet endured by many dogs at the time and to ensure that every pet could be kept in peak condition…

Spatial scope is defined by a spatial boundary. Spatial is used in the broadest sense of the word to include conceptual and formal, as well as physical spaces, provided the system has a physical manifestation (spatial refers to the set of components, in contrast to temporal, which refers to the dynamics of those components). The spatial scope of a system representation is the set of components within the boundary between the associated system and its environment. If an observer shifts from representing the system to representing a component, such that the component is now the system-of-interest, the scope of observation has narrowed. Conversely, when the scope is increased to include components that were previously part of the environment, the scope has broadened. There is also a temporal dimension to scope, temporal scope defining the set of moments of time over which the system is represented. In Jaques’ terms, therefore, a broader spatial scope of a system-of-interest defines a greater span-of-complexity, while a broader temporal scope defines a longer timespan-of-discretion.

Spatial resolution is defined by the spatial distinctions made in describing the representation of a system-of-interest. In comparing two alternative system representations, if a fine (high) and a coarse (low) resolution representation have the same scope, the fine resolution can distinguish a greater number of possibilities.^[9]  Once the resolution is set, this determines the ‘size’ of the components that comprise the system-of-interest. There is also the temporal resolution of a representation, defining the duration of a moment in time, where longer moments represent coarser (lower) resolutions.

The State of a system is the information that distinguishes between alternative system representations at some spatial resolution and moment in time. A macrostate M and microstate μ denote states with two different resolutions and scopes, with macro-to-micro relations such that the macrostate has either a coarser resolution or a broader scope, or both. A property of a system-of-interest, then, is defined as emergent if (and only if) it is present in a macrostate and not in a microstate. This leads to distinguishing novel emergence from weak emergence.

Novel and weak emergence
A property of a system-of-interest is a novel emergent property if (and only if) it is present in a macrostate but not present in any microstate, where the microstates differ from the macrostate only in scope. In contrast, a property is weakly emergent if (and only if) it is present in a macrostate but not present in any microstate, and this macrostate differs from the microstate only in resolution.^[10] A weakly emergent property is a limitation of the observer, therefore, and not a property of the system-of-interest.  There was novel emergence in the way manufacturing processes and supply chains were organised to enable Bob Martin to produce the conditioning powder – under no circumstances could the powder be the predictable outcome of the constitutent parts of those processes and chains.
This macrostate/microstate distinction allows us to define a minimal macrostate M* with respect to an emergent property, if the emergent property is present in M*, and is not present in any microstate μ with the same resolution and narrower scope (i.e. in any proper subset of the components of M*).  In the case of the conditioning powder, this would have been the irreducible core of the way the product had been defined. We can now return to the definition of the boundary of the system-of-interest:

1. A system-of-interest is defined by the set of properties that characterise and identify that system.
2. For each property, the minimal macrostate is identified, which associates that property with a particular scope.
3. The system boundary is defined as the set union of the scope of each property.
4. The resolution must be at least as fine as the highest resolution minimal macrostate.

This definition removes the effects of weak emergence in distinguishing systems-of-interest and their component systems.  In these terms, therefore, while the behavior of a system-of-interest exercising control relationships over the behavior of its component systems exhibit weak emergence (i.e. its components have an is-a-part-of relationship to the system-of-interest), the behavior of a system-of-interest with a stratified relationship to its component systems exhibits novel emergence (i.e. its components have an is-used-by relationship to the system-of-interest).^[11]

In the case of Bob Martin’s powder, with its novel emergent properties, we can assume that he patented the means of its production (or kept it secret).  As the demand for the product expanded, however, and production was scaled up, while its production would have become standardised and routinised, its quality would have remained dependent on the way the enterprise was organised in order to generate novel emergent properties from its component parts:

… The Bob Martin range quickly expanded from the original conditioning powders to remedies and preventative healthcare products for a wide range of canine and feline ills. In 1938 Bob Martin’s opened a showpiece factory in Southport.

Distinguishing stratification and levels of hierarchy
In order to describe stratified relationships, we also need the parallel definition of a maximal microstate μ*, which is maximal with respect to an emergent property in M* if the emergent property is not present in μ* and is present in any μ with broader scope and the same resolution.  Stratified relationships between systems-of-interest and their component systems can be described in terms of the relationship between matrix 1 of maximal microstates and matrix 1b of minimal macrostates in the following set of matrices (in which all of the states are described at the same level of resolution):^[12]

This stratified relationship between systems-of-interest and their component systems is based on a structural distinction between their representations.  This differs from a hierarchical relationship between systems-of-interest, levels of which are distinguished by the observer solely in terms of the modeler’s perspective and interpretation, independent of any structural distinction.^[7,13]

Defining the first asymmetry – the technology is not the product
Remembering that the states in the above matrices are all defined at the same resolution, the importance in separating out matrix 1 from 1b is therefore because it distinguishes novel emergent behaviors of any given (composite) system-of-interest that “cannot be localized to any single component of the composite system but instead produce effects that arise from the cumulative action and interactions of many independently acting components.”^[14] A novel emergence would be a ‘product’, the nature of which cannot be reduced to the ‘technology’ of its component systems. The first asymmetric dilemma, therefore is the fact that “the technology does not define the product”.^[15] The following hints at this transition from a focus on organising the technological means of production to a focus on the business of selling the product, leading up to the opening of the showpiece factory in 1938:

His son, Robert, was born in 1901 and at the age of 20 followed his father into the family firm. Over the years he gradually took over the business side and was responsible for Bob Martin’s innovative advertising campaigns from the 1930’s onwards.

Notes
[1] Published in Psychanalytische Perspektieven, 1998. 32(33): p. 137-159. An earlier form of this argument was presented as a paper at the 8th International Conference on Systems Research, Informatics and Cybernetics in Baden-Baden on August 14th-18th 1996, sponsored by the International Institute for Advanced Studies in Systems Research and Cybernetics and the Society for Applied Systems Research.
[2] Failure to do this through processes of maladaptation was what gave rise to vortical environments. See Must we fall into the vortex?
[3] This is to be seen in The ideologies of Architecture, and the difficulties encountered in the impact of (inappropriate) governance approaches on system-of-system environments, as well as in the north-south bias in leadership qualities and the difficulties of leading organisations without boundaries.  This confusion is compounded by the work of Elliot Jaques in his work on hierarchy, which nevertheless makes a distinction between levels and pseudo-levels in arguing the necessary presence of 7 levels in all enterprises.  These levels are argued for in the differing nature of timespans of discretion/span or complexity.  I begin to disentangle Jaques’ levels from hierarchy in timespan of discretion and the double alignment of know-how. The double alignment is because there needs to be alignment not only to the founding assumptions of the enterprise, but also to its edges where it meets the demands of client-customers.
[4] This is an issue facing not only current ways of understanding the organisation of an enterprise in terms of what is happening to boundaries, authority and containment (the boundaries, authority, role and task (BART paradigm), but also to the way we understand authority itself.
[5] An originating innovation is what lies at the heart of a ‘network intervention‘, itself a response to a something lacking made present as a social object.
[6] Based on extracts from Alex Ryan’s paper “Emergence is coupled to scope, not level”, September 2006.
[7] Implicated also are the concepts of the observer’s perspective and interpretation that influence the representation of a system by an observer. Perspective is that through which some information at a particular resolution is hidden e.g. the state of internal organs to the naked eye; and interpretation allows for there to be multiple valid interpretions, not only in the sense of optical illusions, but also in the sense of there being multiple ways in which the representation of a system by an observer is itself structured by the observer-as-a-structuring-system – Miller, J.-A. (2009 [1968]). “Action of the Structure.” The Symptom 10(Spring). We will return to this in a later posting.
[8] This example is taken from Bob Martin History.
[9] A closely related concept is scale, which is a transformation by multiplication. The connection is that as a property is scaled up (multiplied) within a system, it can be detected at coarser resolutions. The distinction (which is rarely made) is that scale is independent of how the system is represented, whereas resolution is an attribute of the representation (scale is ontological, but resolution is epistemological).
[10] In essence, weak emergence assumes that the properties of a macrostate can be simulated on the basis of modeling the behaviors of its component systems if enough could be known about their behaviors and interactions. In effect, a presumption of weak emergence is reductionist. See Mark A. Bedau’s (1997) ‘Weak Emergence’ in J. Tomberlin, ed., Philosophical Perspectives: Mind, Causation, and World, Vol 11, Malden:MA, Blackwell. pp375-399.
[11] In evaluating platform architectures within ecosystems (modeling the supplier’s relation to indirect value), novel emergence was identified with structural holes of three kinds (corresponding to the three asymmetries) in the relations between component systems with their maximal microstates.  These are described in this posting, in stratifying relations of novel emergence subject to supply-side sovereignty, and in surrendering sovereignty – business platforms and K-type propositions. What this use of novel emergence adds, however, is a fourth asymmetry between the effects ladder and its associated demand situation and a something more that always escapes this formulation of an organisation of demand. This fourth asymmetry might be expressed as “what we demand is never what we desire”. This fourth asymmetry is between the effects ladder and its associated demand situation, which define a ‘pseudo value articulation’, and the embodied subject’s actual experience of value and of value deficit aka desire.
[12] Note that in Matrix 0, some of the states are considered ‘inputs’, while others are ‘outputs’.  This becomes significant when further layers of stratification are considered.
[13] For an earlier treatment of this issue in terms of the three asymmetries, see why is a stratification not a universal hierarchy?.
[14] See Fisher, D. S. (2006). An Emergent Perspective on Interoperation in Systems of Systems. http://www.sei.cmu.edu/publications/documents/06.reports/06tr003.html, SEI Technical Report CMU/SEI-2006-TR-003. Key here in the stratified relationship is the absence of dominant (vertical) control relationships over the component systems, allowing (horizontal) cause-and-effect relationships between component systems to become dominant in the generation of properties.The characteristics of these distinctions between different kinds of system are summarized in the following:
For more on the distinction between simple-complicated-complex-chaotic, see the drivers of organisational scope.
[15] This is the first of the three asymmetries.

by Philip Boxer PhD

What follows is the abstract and presentation given as an invited talk at the School of Systems & Enterprises, Stevens Institute of Technology:

The challenge
Organisations driven to avoid losses and improve gains must ultimately achieve new levels of value for their customers if they are to survive in the long run. This in turn means transforming the way they create value. As the competitive pursuit of value moves them further and further away from products towards services ^[1], value becomes increasingly specific to the customer’s context-of-use, and dependent on the organisation’s capacity to learn from those contexts ^[2]. The complexity involved in delivering this value is organised, and not just emergent from amongst the interactions between multiple organisations and stakeholders ^{[3, 4]}. To organise complexity, the forms of agency demanded of actors present them with unprecedented challenges, not only in defining relevant relationships between systems and environments, but also in defining the architectures organising the complexity ^{[5, 6]}.

If we compare the approach to value creation in healthcare to that in manufacturing, we find the focus of effort moves beyond managing the supply-side complexity of supply chains ^[7]. The clinician has to manage the demand-side complexity of aligning services to the patient’s condition within a healthcare system that is a complex adaptive system with no overseer ^[8]. How is complexity to be ‘organised’ within such an environment? What forms of agency does this ‘organising’ demand of actors? And what approach to creating value does this imply?

A closer examination of the assumptions making healthcare delivery different to manufacturing has revealed eight differences ^[9]. From these eight (in single quotes below), two main challenges can be drawn:

1. In considering ‘the expectations of customers’, there always remains an unknowable aspect of the customer’s need. It is experienced by the customer as a value deficit, and only becomes apparent over time as new forms of demand. The tempo at which these new forms of demand emerge is much faster in healthcare than the tempo at which manufacturing has classically designed new ways of delivering value.

2. Bridging between the supplier’s design tempo and the customer’s demand tempo are the clinicians’ processes of alignment. The tempo of these processes link suppliers’ products and services together in ways that challenge the traditional uncoupling of supply from demand. They entangle the way any individual supplier creates value with others’ ways of creating value. This leads to the emergence of complex adaptive behaviours by the larger system because of the circular paths of causation they set up. This entanglement puts in jeopardy the classical supplier’s expectations concerning their ability to be certain with respect to ‘their knowledge of their future’, ‘the traceability between their performance and the result for the customer’, ‘the longevity of the production process’, ‘the ability to buffer the production process against variability in levels of demand’, ‘the connection between cost of production and revenue from the customer’, ‘the variability in their work processes’, and ‘the costs of production’.

How are these challenges to be taken up within a service environment such as healthcare?

Responding to the challenge
The presentation reports on research into the way suppliers use platform architectures to capture indirect value within business ecosystems ^[10]. Examples are used to illustrate how the concepts of value deficit and entanglement lead to a different approach to understanding the role of a supplier within an ecosystem. This difference is based on considering the relationships that suppliers have to indirect forms of demand, and the organisational processes by which suppliers’ products and services can be aligned with those of other suppliers to meet those demands. These indirect forms of demand render customers’ demands multi-sided ^[11], and reflect indirect forms of value.

The costs associated with these indirect forms of value include the costs of aligning suppliers’ products and services to the customer’s demand, and fall ultimately on the customer. Driven by their value deficits, the accelerating tempo at which customers make demands increases these costs of aligning products and services. The opportunity created for the supplier by multi-sided demands therefore comes from capturing some part of the economies in the costs-of-alignment that it can create for the customer. This in turn means that the supplier must adopt a platform architecture capable of capturing indirect value ^{[12, 13]}.

The reported research uses a framework that (i) describes the variety of indirect demands, (ii) the organisation of the alignment processes, and (iii) the agility of the supporting business platforms, where agility is defined as the variety of indirect demands a platform can support at a given tempo. This framework is ‘triply-articulated’ because of the need to articulate relationships among three types of sub-model: (i) the organisations of value implicit in indirect customers’ demands, (ii) the social entities and supporting systems managing the supply and alignment of products and services, and (iii) the socio-technical systems generating these products and services. The framework enables the derivation of a layered analysis of the risks to which the capture of indirect value exposes a supplier, and provides the basis for an economic valuation of changes in the agility of platform architectures.

Implications of the research
The presentation discusses the nature of the complexity that makes this way of thinking about the relationships between suppliers and customers ‘non-classical’. Thus entanglement means moving from a one-sided to a multi-sided understanding of markets, which changes the unit of analysis from the supplier to the ecosystem with which the supplier is interacting. Analysing market behaviours in a way that is driven by a tempo of demand organised by customers’ value deficits means that there are many different local environments within which market behaviours are expected to be aligned.

A quantum metaphor will be used to cast light on what makes this way of thinking ‘non-classical’. The varieties of simultaneous behaviours which the business platform must be able to support are a superposed set of states. Each customer’s local environment collapses a singular local state from this platform that need not be correlated with states experienced in other customers’ environments. This collapse takes place through the local coherence created by alignment processes organised by shared meaning established within the customer’s local environment.

Two implications can be drawn from this way of thinking: first, agile platforms have to be engineered to support this level of variety in simultaneous complex behaviours; and second, forms of agency have to be developed within an organisation through which many forms of simultaneous local coherence may be created and sustained cost-effectively at its edges.

References
1. Prahalad, C.K. and V. Ramaswamy, The Future of Competition: Co-Creating Unique Value with Customers. 2004, Boston: Harvard Business School Press.
2. Rouse, W.B., A Theory of Enterprise Transformation. Systems Engineering, 2005. 8(4): p. 279-295.
3. Carlson, J.M. and J. Doyle. Complexity and robustness. in Proceedings of the National Academy of Science. 2002.
4. Doyle, J.C., et al., The “robust yet fragile” nature of the Internet. Proc natl Acad Sci USA 102(41), 2005: p. 14497-14502.
5. Rouse, W.B., Complex Engineered, Organizational and Natural Systems. Systems Engineering, 2007. 10(3): p. 260-271.
6. Barandiaran, X., E. Di Paolo, and M. Rohde, Defining Agency: individuality, normativity, asymmetry and spatio-temporality in action. Journal of Adaptive Behavior (Rohde, M. & Ikegami, T, (Eds) Special Issue on Agency), 2009: p. 1-13. This reference points to the singular and non-rational nature of the agent (“non-rati0nal” in the sense of not being governed by ‘rules’) .
7. Porter, M.E. and M.R. Kramer, Creating Shared Value: How to reinvent capitalism – and unleash a wave of innovation and growth. Harvard Business Review, 2011(January-February).
8. Rouse, W.B., Health Care as a Complex Adaptive System: Implications for Design and Management. The Bridge, 2008. 38(1): p. 17-25.
9. Rouse, W.B., et al. Models of Complex Enterprise Networks. in Second International Symposium on Engineering Systems. 2009. MIT CAmbridge, Massachusetts.
10. Boxer, P.J., Evaluating Platform Architectures within Ecosystems: modeling the relation to indirect value, in School of Engineering and Information Sciences. 2012, Middlesex University.
11. Evans, D.S., Some Empirical Aspects of Multi-Sided Platform Industries. Review of Network Economics, 2003. 2(3).
12. Gawer, A. and M.A. Cusumano, Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation. 2002, Boston: Harvard Business School Press.
13. Evans, D.S., A. Hagiu, and R. Schmalensee, Invisible Engines: How Software Platforms Drive Innovation and Transform Industries. 2006, Cambridge: MIT.

Philip Boxer and Richard Veryard will be speaking at the Unicom Enterprise Architecture Forum in London on Thursday September 29th.

Summary

Philip’s presentation on Slideshare
Supporting Social Complexity in Collaborative Enterprises

Richard’s presentation on Slideshare
Next Generation Enterprise Architecture (EA)

by Philip Boxer^[1]

An ecosystem is a community of managerially and operationally independent organizations interacting with each other and with their environment. Software-intensive ecosystems—ecosystems in which the behaviors of the participating organizations are themselves dependent on software because of the intensive use they make of it—are an increasingly important social, financial, and political force in the world. We find examples of software-intensive ecosystems in industries concerned with such things as transport, healthcare, defense, government, and communications. These software-intensive ecosystems are different from traditional “closed-world” software systems that can be analyzed independently of the contexts in which they are embedded; and their emergent properties cannot be predicted by the designers of the software systems on which they depend.  A number of key drivers underlie this change, challenging the former “closed-world” perspective on software engineering. Amongst these are the tempo at which the ecosystems are themselves expected to evolve, the ubiquity and criticality of the software on which they depend, and the entanglement not only between software systems and the way they are used by people, but also between interoperating software systems that are themselves managerially and operationally independent of each other ^[2]. To understand the behaviors of such ecosystems, the analysis of their architectures must not separate the software systems from the organizational contexts-of-use that depend on them. This is the case even where the interactions between the organizations within the ecosystem primarily concern the use of software itself, such as is to be found in the Microsoft and iPhone ecosystems ^[3].

The core-periphery distinction
The Metropolis Model ^[4] provides a starting point for understanding how software systems are constructed, maintained, and operated. At the heart of this model is the distinction between the core and the periphery, which has been often noted as a key architectural construct in complex software systems ^[5]. The core-periphery architectural pattern provides the maximum opportunity for developers at the periphery (and the producers and consumers—sometimes called prosumers—of content) to embed the behavior of a system into their own contexts, enabling their own activities or those of eventual customers and end-users within a larger ecosystem. Examples of cores include the Linux kernel, the Android platform, Facebook’s application platform, the Apache core, iPhone’s iOS platform, Hadoop Common, and so forth. Each of these “cores” provides an abstraction upon which the functionality of the actual customer/end-user-facing application (’App’) is built. The core itself provides little or no end-user value, but rather provides the architectural foundation upon which others build value. The core is typically relatively small, compared with the size of the periphery. For example the (iOS) core of the iPhone enables application developers and users to develop Apps independently that can in turn by taken up within a wide variety of contexts; and making GPS location data accessible to users and application developers on the iPhone platform has spawned a wide variety of location-based services.

This core-periphery distinction can be applied to the way modularity and its associated inter-dependencies are defined for a software system. For modules in the core, tight coupling between them creates mutual dependencies. In the periphery coupling is much looser, creating a measure of independence in the way modules can be used. The strength of the core-periphery pattern is therefore the relatively few constraints at the periphery on how modules may be used and combined, and how their use may be modified. Consequently a core-periphery structure is less over-determining of its use at the periphery than it is at the core, enabling the necessary dependencies defined by the core to be combined with chosen dependencies created by developers in their Apps.

Direct scenarios and direct interactions
Apps are developed to deliver their functionality within a direct scenario in which they are used. These direct scenarios, in addition to describing the direct interaction of some end-user with the App, also describe a quality attribute of the system (a latency goal, for example) within the context defined by the scenario. The achievement of these quality-attribute-related responses brings benefit to the stakeholders in the behavior of the App. For example, a correct response might be of great value to an end-user stakeholder if it arrives predictably within 100 ms., of moderate value if it arrives predictably within 1 second, and of little value if it arrives unpredictably or if it predictably arrives in 10 seconds.

This is depicted in the above figure, where there is one core and three different direct scenarios represented at the periphery. Each of these direct scenarios connects some end-user input—a stimulus—to an output. Consider, for example, the connecting lines for each scenario: α₁β₁, α₂β₂ and α₃β₃. These might represent different direct scenarios in which end-users are making voice-over-IP (VoIP) connections, for example conferencing, or saying hello to a colleague on the other side of the world, or giving a seminar. The end-users within each direct scenario might be employing different App software, but in the end they are using and sharing some resources at the core—name servers, protocol translators, routers, satellite links, IP stacks, fiber-optic cables, etc. Furthermore, the stakeholder perspective on each of these scenarios could be that the VoIP call would only be of value if latency and jitter were both kept within specified ranges.
Direct scenarios can be used to trace through the core-periphery interactions when trying to identify, measure, and understand the benefits radiating out to the end-users at the perimeter on the basis of their direct uses Apps. Scenarios such as these are necessary to analyze a shared core infrastructure, illuminating such things as contention for resources. Consider, for example, understanding the load on an operating system core when multiple processes are operating and competing for shared resources. On a larger scale, cloud computing platforms such as Amazon’s EC2 support large numbers of simultaneous users, each of which could request thousands of server instances. And social computing platforms such as Facebook have over 250 million active users each day, each of whom will consume core resources, potentially interacting through applications that are built on top of Facebook’s core application platform.

Indirect scenarios and indirect interactions
In the larger ecosystem within which Apps are being used, the under-determining effects of core-periphery systems enable Apps to be used by end-users independently of each other, but they also enable indirect interactions between Apps and end-users. The indirect scenarios associated with these indirect interactions are not necessarily related to their use of any one App, and may actually involve multiple core-periphery systems that are operationally and managerially independent of each other. For example, Facebook’s location features allow users and service providers to interact with each other in ways that are only made possible by their use of a smartphone. This potentially creates a much larger and more complex environment defined by interactions that are indirect (from the perspective of the core software systems). For example, the actors within scenarios 1, 2 & 3 may belong to different research institutions, but are all involved in a single research collaboration for which a number of core systems become a key enabling element. This research collaboration is shown in the figure below as an indirect scenario linking indirect interactions between particular direct uses of Apps to the ultimate end-use. The figure below represents this by adding an extra ‘App use’ layer to the onion:

A new challenge arises therefore for the analysis of architecture: what is the impact of these indirect interactions on the architecture of the operationally and managerially independent core-periphery systems (for example VoIP, Screen-sharing, etc.)? For example, consider the indirect effects that might arise where the end-users in the research collaboration are combining the use of VoIP, screen-sharing, and file sharing systems. These indirect interactions represent architectural challenges. For example, many users of the VoIP systems will be competing for resources, such as computation and bandwidth, affecting latency. Or the developers of those Apps may have chosen a common protocol affecting the development time of individual systems. Or charging schemes may affect an end-user’s choice of combined systems to employ (for example as a result of differential charging schemes for relatively time-sensitive internet-traffic such as VoIP packets, and relatively non-time-sensitive traffic, such as file sharing). Such considerations are of real concern to core providers because these indirect interactions can collectively have a huge effect on the performance and behavior of the core. As an example, Google Maps imposes resource restrictions on the services that it provides to other organizations, in an effort to moderate worst-case resource usage.

The multi-sided matrix
Consider the multi-sided matrix in the figure below, in which the sets of columns correspond to sets of direct scenarios associated with direct use of an App (for example particular uses of VoIP or screen-sharing), and in which each set of columns is operationally and managerially independent of the others. The rows correspond to the indirect interactions between multiple Apps supporting, each row corresponding to a particular indirect scenario (for example a research collaboration, or a marketing project). From the perspective of the App developers in the periphery, each row represents indirect interactions between Apps that creating indirect effects within the social collaborations that they are supporting. A one-sided approach to App development will only examine the mix of direct scenarios represented by a column. But each the indirect scenarios create multi-sided demands on the direct scenarios depending on how the App is expected to interact with other Apps:

Thus decisions made affecting the capabilities of core-periphery systems will also impact on the way they can participate in the indirect interactions chosen by stakeholders. The variety and scale of these indirect interactions arising between stakeholders within indirect scenarios will determine the emergent qualities of the larger ecosystem in which these interactions are taking place. Multiple X’s in a row reflect social collaborations that also present technical challenge: how will the Apps interoperate, and how will collaborations manage that interoperation within their larger contexts?

Platform scope
Returning to the example above, each column for VoIP stream, screen-sharing, and file-sharing session must have direct scenarios defining their direct scope across the rows. But since the rows themselves represent indirect scenarios, the dependencies and alignment processes between the direct scenarios along a row must also be considered, each of which presents an interoperability requirement and a potential contention for shared resources between operationally and managerially independent systems supporting the direct scenarios. Thus an end-user in the Research Collaboration in the first row above may wish to capture the content of a screen-sharing session and share that with other end-users. Such an indirect scenario imposes an interoperability requirement between the screen-sharing and file-sharing systems. A platform that enables this to be accomplished may be said to have a platform scope defined by the number of direct interactions it can enable to interoperate, shown below as a separate set of columns.

Analysing the technical *and* the social
The presence and impact of indirect interactions is an essential characteristic of ecosystems arising from the entanglement of the technological core-periphery systems with an environment of social systems. To understand and analyze these ecosystems, we therefore need to be able to characterize a statistically representative variety of indirect interactions between many independent actors across independent core-periphery systems. But if we just analyze the direct impact of the αβ paths of each direct scenario on the core-periphery architecture of a system in any given column—the traditional architectural analysis—we will not understand the collective impact of the indirect interactions within indirect scenarios across many core-periphery systems caused by the social collaborations taking place within the larger socio-technical ecosystem.

We will thus not be in a position to analyze the potential emergent effects and behaviors arising from the way the core systems are used within this larger context. It follows that we will also be unable to define the architectural characteristics of the supporting platforms needed.

Notes
[1] With special thanks to Rick Kazman for his contributions to an earlier draft.
[2] Coyle, L., et al., Guest Editor’s Introduction: Evolving Critical Systems. Computer, 2010. 43(5): p. 28-33.
[3] Jansen, S., A. Finkelstein, and S. Brinkkemper. A Sense of Community: A Research Agenda for Software Ecosystems. in 31st International Conference on Software Engineering (New Ideas and Emerging Results Track). 2009. Vancouver, Canada: IEEE CS Press.
[4] Kazman, R. and H.-M. Chen, The Metropolis Model: A New Logic for the Development of Crowdsourced Systems. Communications of the ACM Vol 52 No 7, 2009: p. 76-84.
[5] MacCormack, J., C. Baldwin, and J. Rusnak, The Architecture of Complex Systems: Do Core-Periphery Structures Dominate? MIT Sloan School of Management, 2010. Working Paper 4770-10.

by Philip Boxer

This presentation was given in collaboration with Suzanne Garcia at the Fifth SEI Architecture Technology User Network Conference on Architecture at all Scales, May 4-7 in Pittsburgh.

Software architects are increasingly being asked to address how their architectural representations relate not only to those of systems (of systems) engineers, but also to the views commonly found in DODAF (Department of Defense Architecture Framework) or other enterprise architecture frameworks. In many cases, these requests made to software architects are part of trying to understand how one software system is likely to interoperate with others that are either inside or outside of the enterprise. Understanding some of the limitations of the Zachman framework and DODAF 2.0 in understanding both software architectures and interoperability in complex systems of systems should make it easier for software architects to place their architectures in relation to these other common frameworks. This presentation describes proposed modifications to the Zachman framework that are required to account for the needs for cross-enterprise collaboration and for accommodating new user needs at a rapid pace. The presentation also highlights a set of modeling elements that are commonly found in multi-enterprise situations. These modeling elements are illustrated in reference to DODAF 2.0 entities to emphasize what is currently missing. The presentation concludes with an example from a modeling approach that addresses these gaps, which is used at the SEI to describe not only the social and technical aspects of systems (including software systems), but also their relationship to the changing demands (especially user needs) placed upon them.

by Philip Boxer

This paper was presented in collaboration with Suzanne Garcia at the 3rd Annual IEEE Systems Conference in Vancouver March 23-26 with the following abstract:

An enterprise architecture is an accepted, widely used means for an organization to capture the relationship of its business operations to the systems and data that support them. Increasingly, enterprises are participating in complex system-of-systems contexts in order to meet changing customer demands that require them to collaborate with other enterprises in new and innovative ways. For a complex system-of-systems context, a shortcoming of enterprise architecture is that it presumes a single enterprise or a single, ultimate source of control.

This paper explores an approach to reasoning about distributed collaboration in the complex system-of-systems, multi-enterprise context, in which this single, ultimate source of control does not exist. It outlines the ways in which the long-used Zachman Framework for enterprise architecture would need to be modified to account for multi-enterprise collaboration and decentralized governance. It proposes a concept of stratification to meet this need and puts forward the main characteristics of the methods needed to model the stratified relationships of complex systems-of-systems to their contexts-of-use.

by Richard Veryard
A 40-minute podcast Philip and I recorded with Ron Jacobs in December has been released on Microsoft Channel Nine.