Towards Real-time Static and Dynamic Profiling of Organisational

Complexity

Kon Shing Kenneth Chung

Complex Systems Research Group, Project Management Program, The University of Sydney, NSW 2006, Sydney, Australia

Keywords: Social Network Analysis, Complexity, Organisational Performance, Operational Performance.

Abstract: In this position paper, I argue that although the definition and quantifiable metric for organisational

complexity may still be controversial, it is possible to capture structural aspects of complexity in both static

and dynamic forms. Based on Kannampallil’s theoretical framework for computing complexity, it is

proposed here that complexity, in an aggregate sense, can be evaluated in terms of (i) the number of

components (NoC) there are within a socio-technical organisation and (ii) the degree of interrelatedness

(DoI) between these components. Given these variables, it is then possible to characterise complexity in

terms of simple, complicated, relatively complex and complex profiles. These profiles serve as useful

toolkits for indicating the complexity level a team, a department or the entire organisation is at for useful

interventions or insights to be made. Adapting the ideas of Pentland, I also argue that with technological

advances in Information Systems, organisations are now able to capture relational or social network data

with relative ease, to construct useful network and complexity maps of individuals, teams and organisations

in real time.

1 INTRODUCTION

Wherever coordination of tasks and resources are

involved, there almost always exists an element of

complexity. The degree to which this complexity

varies depends on a number of factors, e.g. the

intellectual cognitive load required to complete the

task, the experience of the person doing it, the

number of entities (e.g. machines, people) required

to coordinate them, etc. In organisations,

decomposition of structure, tasks and responsibility

is usually required to ensure efficient and effective

completion of tasks to achieve organisational goals.

In projects, meticulous coordination is required for

tasks, resources, scheduled and cost so that the

project can be completed within quality, time and

budget. Although the colloquial meaning of

complexity is often accepted as being “not simple”

or “more than complicated”, complexity is

understood in different ways, not only in different

fields, but has also different connotations within the

same field (Mitchell, 2009).

According to Manson (2001), research in the

science of complexity may be categorised broadly as

either of the three: (i) “Algorithmic complexity” –

which deals with deriving complexity of a system by

appraising the difficulty ascribed to describing

system characteristics by using mathematical

complexity theory and information theory; (ii)

“Deterministic complexity” – which stipulates, using

chaos theory and catastrophe theory, that the entire

system may become de-stabilised or inactive due to

the interaction of certain few key variables; and (iii)

“Aggregate complexity” – which posits that

complexity can be understood by observing how

individual agents interact and work in concert with

each other in the system to create complex

behaviour. In this paper, I focus on aggregate

complexity because I consider the organisation as a

larger system that comprise smaller sub-systems

such as social, technological and group-level. I

contend that the former two streams of complexity

study do not adequately suit organisational systems.

For instance, interactions between knowledge

workers and organisational entities (e.g. computer

systems) are diverse, rich and experiential.

Therefore, information theoretic measures, which

generally identify complexity as the simplest

computational algorithm that can reproduce system

behaviour (e.g. Shannon’s entropy measure

(Shannon, 1948)), are over simplified. Deterministic

complexity is also marred by several limitations,

466

Chung K..

Towards Real-time Static and Dynamic Proﬁling of Organisational Complexity.

DOI: 10.5220/0004984404660471

In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 466-471

ISBN: 978-989-758-028-4

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

particularly in its applicability to social phenomena

(Mitchell, 2009).

In the following sections, I discuss the definition

of complexity used in this paper, a proposed

framework for computing aggregate complexity and

how it is possible for organistions to capture and

profile it in real-time.

2 EXAMINING AGGREGATE

COMPLEXITY

For the purpose of this study, I define complexity in

terms of one of the most salient concepts postulated

by aggregate complexity – the interrelatedness of

components of a system (Kannampallil et al., 2011).

According to Kannampallil et al. (2011), complexity

of a system is relative in the sense that complexity is

a function of the number of components (NoC) and

the degree of interrelatedness (DoI) within the

system. This definition is in congruence with others

in the field (Manson, 2001; Bar-Yam, 2006;

Johnson, 2007; Mitchell, 2009). In other words, as

both variables increase, so does complexity of the

system. It is also important to note that while

increasing the number of components may make the

system “complicated”, it is the degree of

interrelatedness, or in other words the unique

relationships (both manifest and latent) that makes

the system “complex”. As a consequence, the

interrelatedness of system components results in

properties that characterise complex systems (Bar-

Yam, 2006), these properties being non-

decomposability (that systems cannot be understood

by focusing on components in isolation), emergence

(where unexpected behaviour arises as a result of

component interactions), nonlinear behaviour

(characterised as non-predictability and non-

proportionality of behaviour) and self-organisation

(where individual actors take on different structural

positions so the system can be maintained).

Accordingly, by combining ranges of extremes for

both variables, there can be four conditions

(although not postulated in a prescriptive or

exhaustive manner) to characterise the range of

complexity as in shown in Figure 1.

Firstly, there are simple systems with few

components and low interrelatedness (1), whereby

the system along with its behaviour is easily

predictable, understood, managed and described. For

instance, an individual accountant who runs his own

practice by himself may only have few components,

such as patients, notes and computer, and relations

Figure 1: Range of Complexity (Kannampallil et al.,

2011).

(interaction with computer, customer and

stationery). The accountant is considered to be in a

very simple system. Secondly, systems with many

components and low interrelatedness (2) are also in

many cases, quite predictable to a certain extent

because of the low interrelatedness. For instance, a

receptionist in a firm who handles many phone

requests and relies only on the computer booking

system. Thirdly, relatively complex systems have

few components but a high degree of

interrelatedness (3). Such systems can be studied as

a “whole” because of its few components but high

level of interrelatedness – e.g. section of an

emergency hospital department where members are

few but the interactions are quite diverse. Finally,

complex systems are systems exhibiting high degree

of interrelatedness and many components (4), e.g.

multiple employees from varying organisational

units attending to multiple victims in a disaster-

struck area.

In light of the framework proposed, one cannot

deny the importance of context. According to

Herbert Simon (1996), “one cannot study the

complexity of a system without specifying the

content of complexity”. Therefore, while context is

important, Simon also argues that a complex system

may be decomposed wherever possible, into smaller

functional components, characterized by the

interrelatedness between them. Thus, while the

number of components is easily computable, the

question remains as to what constitutes

“interrelatedness” precisely.

Drawing on closing remarks from Kannampallil

et al. (2011), “…complex systems can typically be

considered in terms of functionally smaller

components and the relations between them, based

on theoretical, rational, and practical

considerations….There often is a structure in the

relationships that exist between care providers,

Degree of

Low

High

Few Many

Number of Components

Simple

Complicated

Relatively

Complex

TowardsReal-timeStaticandDynamicProfilingofOrganisationalComplexity

467

artifacts, and patients….As such, it is possible to

characterize it as a network of actors, where (at a

high level of decomposition) the nodes are actors (or

artifacts) and the edges are their relationships.”

Although no single operational definition of the

construct, interrelatedness, is offered, I argue that

there are two salient measures in network science, in

social networks analysis particularly, that might help

develop an operational definition of the construct.

Firstly, interrelatedness connotes a meaning of

cohesiveness and integration. That is, given a system

which can be represented in the form of a network,

what is the current number of connections, as

opposed to the maximum possible. In social network

parlance, this is specifically referred to as the density

of a given network (i.e. ratio of existing ties to the

theoretical maximum) (Wasserman et al., 1994). The

second important measure that taps into aspects of

interrelatedness is inclusiveness, which refers to the

number connected actors within the network. In

other words, it is the total number of entities or

actors or nodes minus the number of isolated ones

(Scott, 2000). So if we consider a social network of

10 actors, with 5 isolated actors, inclusiveness would

be 5. However, in order to allow for standardization

and comparison across several networks (similar to

the density measure), it is useful to express

inclusiveness as a proportion of the total number of

actors within the network. Therefore, using the

example above, inclusiveness expressed as a

proportion of the entire network would be 0.5, with

the range being 0 to 1. Therefore, while

inclusiveness represents the connectedness of

individual actors within a network, density captures

the extent to which the connections are current as

compared to the latent. So while inclusiveness is a

measure based at the actor level, density is about the

extent to which the actors are connected and is

situated at the tie level. The notion of inclusiveness

is a useful indicator of social network membership

as well group dynamics (Mitchell et al., 1980; Pfeil

et al., 2009) and can thus be used in conjunction

with the density measure as a proxy for

interrelatedness. The following section describes

how complexity profiles can be constructed by using

these measures.

3 COMPLEXITY PROFILES – SO

WHAT?

Consider a knowledge-intensive organisation such as

a hospital emergency department. It comprises

doctors, specialists, nurses, managers, and other

hospital staff members. In effect, this can be

considered as a social system. The hospital also

cannot function without its technology such as

computers, specialist equipment, beds and so on. We

term these artefacts as being part of the the

technological system. Therefore, this healthcare

socio-technical system (which can be represented as

a‘network’), the doctors, patients, specialists and

nurses are treated as ‘components’ of the network.

Artifacts, such as beds, healthcare technologies, used

by the patient or by the medical professionals within

the patient’s network, are also deemed as

components of the network.

If we use the mean value of the ‘number of

components’ and the mean value of the ‘degree of

interrelatedness’ as points of segregation on the x

and y axis of the framework respectively, the range

of complexity can thus be categorized into ‘simple’,

‘complicated’, ‘relatively complex’ or ‘complex’

clusters or profiles. These profiles can then be

associated with a myriad of dependent constructs or

variables such as the coordination of care of the

hospital, patient waiting times, length of patient

queues, which are in a sense aspects of operational

performance and indirectly, organizational

performance. When sufficient historical data is then

collected, one may use the data to fit to whatever

model one is interested in observing or testing.

With the notion of this conceptual modelling

crystallised, applying the same type of modelling to

other domains and disciplines become only a matter

of what phenomena one is interested in studying. For

instance, one may be interested to understand the

aggregate complexity level one’s project team is at.

In the context of Information Technology (IT)

development projects, although there are a myriad of

well-structured project management processes and

frameworks such as Extreme Programming,

PRINCE II methodologies and so on, complexity at

an aggregate level is hardly captured or examined.

At the minuscule level, task complexity may be

measurable; for instance, COCOMO II and Lines of

Code techniques allow for one to establish just how

complex a software program is. Another example

would be the number of dependencies a task has to

and from other tasks within a project plan. In

Critical Chain Project Management, resource

dependencies are also accounted for along with the

normal constraints of quality, time and cost. While

current tools and methodologies are fairly efficient

in capturing such complexity, it does not account for

it holistically. Therefore, a model that accounts for

human-level, organisational-level, group-level and

ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems

468

technological-level factors is needed. The following

example shows how aggregate complexity can be

captured at both a micro (e.g. individual level) and

macro (e.g. organisational) level.

Micro Level: At the individual-level, one can

construct complexity profiles of social-professional

networks of knowledge-intensive workers, that can

be used to associate with individual performance or

decision making (Chung et al., 2013). Taking the

example of a general practitioner as a knowledge-

intensive worker, one can ask him or her to list a

finite (e.g. up to 15) number of contacts who are

important to her in the provision of care. One can

also ask her to elicit the relationship amongst the

contacts she provided, thus completing the entire

socio-professional network (see Figure 2). Once this

is done, mean values of the distribution of number of

contacts (i.e. components of the network) and the

mean values of the distribution of density and/or

inclusiveness of connections (i.e. degree of

interrelatedness) can be derived to define cut-points

for the complexity profiles. These profiles can then

be associated with social and professional outcomes

such as performance, coordination and decision-

making. That is, patterns of performance or

decision-making for various profiles can be

compared (e.g. simple vs. complex) for further

insights, useful for intervention mechanisms.

Figure 2: Example network map of knowledge intensive

worker (ego’s network indicated in bottom left green

colour).

Macro Level: If one wants to understand aggregate

complexity at an organisational level, it is also

possible to account for interdependencies beyond the

individual by accounting for interdependencies

between individuals, departmental units and

organisational units and so on, at specific points in

time. Reverting back to the example where one

wants to understand how such macro-level

complexity may be used to indicate or even provide

a sense of prediction about its impact of overall

organisational or operational performance, I

consider a hospital emergency department (ED), to

illustrate. Here, patients, doctors, human resources

and even artifacts, such as beds, healthcare

technologies, used by the patient or by the medical

professionals, within the boundaries of the ED, can

be deemed as components of the network. Therefore,

in this case, each tie would depict a form of

connection, be it an interaction between the

computer and the nurse, or a communication that

took place between the doctor and the patient, or the

utilization of the bed by the patient. In general, one

may treat these relations as simply

“interdependencies”. One can then start obtaining a

distribution of NoC and DoI variables at various

points in time. Once this is obtained, complexity

profile cut-points can then be obtained from the

distribution and complexity profiles can be obtained

and individual cases can be plotted against these

profiles (Figure 3).

Figure 3: Example Plots in Complexity Profiles.

4 DYNAMIC COMPLEXITY

Much of the description of how aggregate

complexity can be captured detailed above pertains

to static states. In other words, it is analogous to

taking snapshot of the number of components and

interdependencies amongst them within a social or

organisational system at any point in time. Similar to

how movies are essentially multiple frames of

snapshots put together, I argue here that dynamic

complexity is simply capturing various snapshots of

the interactions occurring within the system at

various points in time.

Professor Alex Pentland’s (2012) from MIT

TowardsReal-timeStaticandDynamicProfilingofOrganisationalComplexity

469

Media Labs pioneered the use of wireless tags that

captures relational information as well as body

language, including tone, volume and pitch, from the

communicator. The tags, whose size is similar to

those of name cards can be worn like any ordinary

ID card, are unobtrusive in nature. It ubiquitously

captures the ‘when’, ‘who’, ‘whom’ and ‘how’ of

the communication but not the ‘what’. In other

words, it does not capture content. Therefore, at any

point in time, it is possible for the communication

pattern of individuals to be captured. Furthermore,

with the use of Radio Frequency ID tags also

available these days, it is possible for these tags to

be used to capture relational data, particularly when

individuals deal with non human resources such as

computers, machines, and so on. Pentland used the

patterns of communication captured to associate

with individual and team success. In reality, the

association can be made with other social

phenomenon such as creativity, coordination, etc.

In a similar manner, reverting back to the

example of the hospital ED, it becomes possible for

us to understand how organisational complexity

associates with operational performance such as

patient queues and waiting times. Here, one would

capture the organisational complexity of the ED as a

whole, having these tags in place in both human and

non-human resources. This enables us to capture all

relations and interdependencies at various points in

time. It is also important that at these points in time,

data relating to the dependent variables - patient

queues and waiting times, for instance, should also

be recorded. To illustrate, the relational snapshots

can be taken at every 3 hours in a 24 period, yielding

8 data points. If one does this for a week, there

would be 56 data points and for two weeks, 112 data

points. A distribution of the NoC and DoI can then

be computed, and the mean values for each of these

variables can serve as the relative cutpoints for the

complexity profiles to be obtained. In this manner,

one can compare which organisational complexity

states perform better (e.g. when at the ‘simple’

profile or at the ‘complicated’ profile) in terms of

operational performance.

5 CONCLUSIONS

Complexity is still a controversial topic, one that is

multi-faceted in epistemological stance, in definition

and in oeprationalisation. In general, literature in

complexity studies can be categorised in to

deterministic, algorithmic and aggregate complexity.

In this position paper, I focus particularly on

aggregate complexity and argue that it is possible to

capture structural aspects of complexity in both

static and dynamic forms. Based on Kannampallil’s

theoretical framework for computing complexity, it

is proposed here that complexity, in an aggregate

sense, can be evaluated in terms of (i) the number of

components (NoC) there are within a socio-technical

organisation and (ii) the degree of interrelatedness

(DoI) between these components.

Given these variables, it is then possible to

characterise complexity in terms of simple,

complicated, relatively complex and complex

profiles. These profiles serve as useful toolkits for

indicating the complexity level a team, a department

or the entire organisation is at for useful

interventions or insights to be made. Adapting the

ideas of Pentland, I also argue that with

technological advances in Information Systems,

organisations are now able to capture relational or

social network data with relative ease, to construct

useful network and complexity maps of individuals,

teams and organisations in real time.

REFERENCES

Bar-Yam, Y. (2006). Improving the Effectiveness of

Health Care and Public Health: A Multiscale Complex

Systems Analysis. American Journal of Public Health,

96, 459-466.

Chung, K. S. K., Young, J., & White, K. (2013, 25 - 28

August). Towards a Network-enabled Complexity

Profile for Examining Responsibility for Decision-

making by Healthcare Professionals. Paper presented

at the International Symposium on Network Enabled

Health Informatics, Biomedicine and Bioinformatics,

Niagara Falls, Canada.

Johnson, N. (2007). Simply Complexity. Oxford: Oneworld

Publications.

Kannampallil, T. G., Schauer, G. F., Cohen, T., & Patel,

V. L. (2011). Considering Complexity in Healthcare

Systems. Journal of Biomedical Informatics, 44(6),

943-947.

Manson, S. M. (2001). Simplifying Complexity: A

Review of Complexity Theory. Geoforum, 32(3), 405-

414.

Mitchell, M. (2009). Complexity: A Guided Tour. New

York: Oxford University Press.

Mitchell, R. E., & Trickett, E. J. (1980). Task Force

Report: Social Networks as Mediators of Social

Support. An Analysis of the Effects and Determinants

of Social Networks. Community Mental Health

Journal, 16(1), 27-44.

Pentland, A. S. (2012). The New Science of Building

Great Teams. Harvard Business Review, 90(4), 60-70.

Pfeil, U., & Zaphiris, P. (2009). Investigating Social

Network Patterns within an Empathic Online

ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems

470

Community for Older People. Computers in Human

Behavior, 25(5), 1139-1155.

Scott, J. (2000). Social Network Analysis: A Handbook.

London: SAGE Publications.

Shannon, C. (1948). A Mathematical Theory of

Communication. The Bell System Technical Journal,

27, 379-423.

Simon, H. A. (1996). The Sciences of the Artificial.

Cambridge (MA): MIT Press.

Wasserman, S., & Faust, K. (1994). Social Network

Analysis: Methods and Applications. New York:

Cambridge University Press.

TowardsReal-timeStaticandDynamicProfilingofOrganisationalComplexity

471