Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in project management

Abstract

In project management research, it is acknowledged that two perspectives on project performance must be considered: project efficiency (delivering efficient outputs) and project success (delivering beneficial outcomes). The first perspective is embedded in a deterministic paradigm of project management, while the second appears more naturally connected to the emerging non-deterministic paradigm. Complexity and uncertainty are key constructs frequently associated with the non-deterministic paradigm. This conceptual paper suggests that these two concepts could very well explain and define particularities of both paradigms, and seeks to articulate both perspectives in a contingent model.
First, the constructs of complexity and uncertainty are clarified. Second, the role of project managers' mental models in managerial decision-making is considered. In the third part of this article, we propose a theoretical model suggesting that project managers should consider contingent variables to differentiate managerial conditions of regulation from managerial conditions of emergence.

Full text

Complexity, uncertainty and mental models: From a paradigm of
regulation to a paradigm of emergence in project management
Pierre A. Daniel, Carole Daniel ⁎
Université Côte d'Azur, SKEMA, Avenue Willy Brandt, 59 777 Euralille, France
Received 15 December 2016; received in revised form 3 July 2017; accepted 3 July 2017
Available online xxxx
Abstract
In project management research, it is acknowledged that two perspectives on project performance must be considered: project efficiency
(delivering efficient outputs) and project success (delivering beneficial outcomes). The first perspective is embedded in a deterministic paradigm of
project management, while the second appears more naturally connected to the emerging non-deterministic paradigm. Complexity and uncertainty
are key constructs frequently associated with the non-deterministic paradigm. This conceptual paper suggests that these two concepts could very
well explain and define particularities of both paradigms, and seeks to articulate both perspectives in a contingent model.
First, the constructs of complexity and uncertainty are clarified. Second, the role of project managers' mental models in managerial decision-
making is considered. In the third part of this article, we propose a theoretical model suggesting that project managers should consider contingent
variables to differentiate managerial conditions of regulation from managerial conditions of emergence.
© 2017 Elsevier Ltd, APM and IPMA. All rights reserved.
Keywords: Complexity; Uncertainty; Mental models; Systems; Project Management; Performance
1. Executive summary
It is generally understood that the world is becoming more
and more complex. Project managers are experiencing this
in their daily activities, being faced with a growing number
of complex situations. The project management literature –
particularly in the non-deterministic paradigm –has focused
on this issue of complexity. However, two perspectives –
project management and the management of projects –
co-exist in the project management research community,
as do two paradigms: deterministic and non-deterministic.
This lack of unified theory –as well as the difficulty of
agreeing on a definition of complexity –does not help project
managers understand how to maximize performance in complex
projects.
The research presented here attempts to propose richer
lenses for looking at project management. We suggest that a
better understanding of the construct of complexity, its
associated construct of uncertainty, and the way human beings
predict these through mental models are possible groundings
for a contingent and comprehensive approach.
In this conceptual work, we first investigate the literature
on complexity, highlighting three levels that can be found in
different research works. We then investigate the literature
on uncertainty, which also converges towards three levels of
uncertainty. Finally, we add the notion of mental models as
a means for project managers to understand the situations in
which they find themselves, and gather all the findings in a
conceptual model of project management.
Our study adds to the literature on complexity and un-
certainty in project management by gathering many existing
research works from different sciences. Tables summarizing
these literatures shed light on the possibility of identifying three
different levels of complexity and of uncertainty, which form
the pillars of a contingent project management model.
⁎Corresponding author.
E-mail addresses: pierre.daniel@skema.edu (P.A. Daniel),
carole.daniel@skema.edu (C. Daniel).
www.elsevier.com/locate/ijproman
http://dx.doi.org/10.1016/j.ijproman.2017.07.004
0263-7863/00/© 2017 Elsevier Ltd, APM and IPMA. All rights reserved.
Please cite this article as: P.A. Daniel, C. Daniel, 2017. Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in
project management, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.07.004
Available online at www.sciencedirect.com
ScienceDirect
International Journal of Project Management xx (2017) xxx –xxx
JPMA-02052; No of Pages 14

Overall, our conceptual paper suggests that it is possible to
bridge the existing gap between the two project management
paradigms. One reason for the difficulty of managing complex
project situations lies in the limitations of classic project
management methods. Complex and uncertain projects require
newer methodologies based on understanding: the modelling
approaches. By understanding the levels of complexity and of
uncertainty in a situation, project managers can adapt their
decision-making approach in order to maximize performance.
2. Introduction
Ten years ago, researchers in project management started to
acknowledge the lack of a unified theory of the management of
projects, whether defined in its narrow (‘project management’:
PM) or broad (‘management of projects’: MoP) sense (Smyth
and Morris, 2007). This concern is still raised by the project
management research community, especially in the area of
project performance, where the streams of research on success
and failure do not converge (Padalkar and Gopinath, 2016b).
The growing complexity of projects led to the emergence of a
non-deterministic paradigm (Padalkar and Gopinath, 2016b),
which raised the question of how to generate performance in
complex projects; one major issue was agreeing on a definition
of complexity itself.
The co-existence of the PM and MoP perspectives is a
source of confusion for project managers, who are faced with
a wide variety of project management conditions, and who
cannot really know which project management approach is
better adapted to the complexity of their project. Is there a way
to reconcile these two perspectives? How can project managers
understand which management principles they should adopt,
depending on the managerial decision-making conditions under
which they are working?
The PM perspective is supported by the execution-based
model of the Project Management Institute (PMI), while the
MoP perspective –founded on Peter Morris's research –is
more comprehensive and open to a new definition of project
success (Pinto and Winch, 2016). In his definition of the nature
of project management, Turner makes a real distinction be-
tween the ‘operational’project perspective (which is focused
on the result of the project implementation: the output) and
the ‘strategic’project perspective (focused on the outcome
resulting from the project implementation phase). This dis-
tinction is also found in the project management literature
on success and failure, which differentiates between ‘project
efficiency’(project implementation performance), and ‘project
success’(project benefits performance) (Cooke-Davies, 2002;
Serrador and Turner, 2015a; Turner and Zolin, 2012).
Beyond the two perspectives of PM and MoP, two
paradigms have emerged from surveys on decades of project
management research. The first is the deterministic paradigm,
which is well established (Pinto and Winch, 2016) and is
strongly dominated by operations research. It contributed
significantly to the increase in project management perfor-
mance with phase-project-planning methodologies in the 1960s
(Morris, 2010). The second is the non-deterministic paradigm,
which emerged in the mid-2000s (Padalkar and Gopinath,
2016b), putting a particular emphasis on complexity in projects
(Crawford et al., 2006; Geraldi et al., 2011a; Whitty and
Maylor, 2009). Non-deterministic research employs not only
complexity but also uncertainty (following Turner's broader
definition of project management) as its main lenses, but both
concepts remain ambiguous, preventing this paradigm from
moving forward (Padalkar and Gopinath, 2016a). For instance
the PMI's view on complexity is far removed from that of
complexity theory (Bakhshi et al., 2016).
Although the first paradigm is well established and the
second is attracting much research interest, there is no clear
way for project managers to understand how to position them-
selves in relation to these two paradigms. Complexity can
sometimes be associated with both the deterministic paradigm
(the PMI's view) and the non-deterministic paradigm (the
complexity-theory view), and sometimes it is linked only to the
non-deterministic paradigm. Complexity in projects is regularly
associated with uncertainty, but these two constructs are not
clearly differentiated in order to understand their specific role in
project management theory.
The first contribution of this conceptual paper is to
synthesize various research literatures (systems theory, decision
theory and planning theory) in two tables, which reveal the
contingency nature of complexity and uncertainty. We reveal
not only that both constructs can be categorized in three levels,
but also that each of these three levels suggests a specific
managerial way of addressing situations: algorithmic, sto-
chastic or non-deterministic. General systems theory revealed
that managers interact with projects through decision models
(mental models) to make their managerial decisions. The
second contribution reveals that the prediction capacity of these
decision models defines the level of uncertainty that project
managers have to address, and impacts the level of complexity
of the project as a whole. The third contribution is a contingent
framework of project management, which positions manage-
ment paradigms of regulation and of emergence according to
the level of complexity and uncertainty that project managers
must face. As a consequence, this comprehensive framework
provides new lenses for project managers in order to select the
appropriate management approach.
In Sections 3 and 4 of this paper, the constructs of
complexity and uncertainty will be explored, and the link
between the two will be developed. In these two sections, three
main approaches are revealed: algorithmic, stochastic and
non-deterministic, which can be linked with the constructs of
both complexity and uncertainty, and which are ingrained in
decision theory.
Section 5 sheds light on the fact that mental models are key
in managerial decision theory. Mental models –and, more
specifically, decision models –are characterized by their role in
managerial capacity to predict. Predictability is also a key
concept characterizing complexity and uncertainty.
In Section 6, we propose a theoretical framework for
project management that helps to distinguish the decision and
action conditions of risk versus uncertainty. From a contin-
gency perspective, this conceptual framework reveals systemic
2P.A. Daniel, C. Daniel / International Journal of Project Management xx (2017) xxx–xxx
Please cite this article as: P.A. Daniel, C. Daniel, 2017. Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in
project management, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.07.004

characteristics differentiating and comparing project manage-
ment theories of regulation and of emergence.
3. Towards a clarification of the complexity construct
In the project management community, complexity is not
a clear and unified concept (Padalkar and Gopinath, 2016a;
Vidal and Marle, 2008); rather, complexity takes a variety of
forms (structural complexity, uncertainty, dynamics, pace and
socio-political complexity) and frequently signifies complicat-
edness (Geraldi et al., 2011b). With complicated systems,
outcomes are easily predictable if the starting conditions (the
project's inputs) are known, whereas with complex systems,
outcomes are unpredictable because of continually changing
interactions, even when the starting conditions can be known
(Maylor et al., 2008; Sargut and Mcgrath, 2011). Managers
involved in complex systems must face events that are difficult
to predict or interpret correctly, even retrospectively. These
events influence each other, and produce causality relations
that are not clear for decision-makers (Kauffman, 1993; Rivkin,
2000; Simon, 1969).
Management sciences have borrowed greatly from systems
theory to understand the functioning of projects, and many
authors consider that projects operate as complex systems
(Baccarini, 1996; Williams, 2002). In 1968, Boulding proposed
a‘system of systems’classification to explain how to describe
a system and its behaviour (Boulding, 1968). Each new level
in the hierarchy reveals specific functions and dynamics of a
system, and increases its degree of complexity. Table 1 sum-
marizes this classification.
Boulding's classification of systems uses metaphors at each
level of the hierarchy. As an example, the operational (PM)
perspective would naturally be positioned at the level of the
thermostat, using the metaphor of a cybernetic system, as
evidenced by Shewhart's ‘quasi-cybernetic loop-of-control’
model and Deming's ‘plan–do–check–adjust’theory of control.
This naturally raises the question: which metaphor is most
appropriate for systems from the MoP perspective?
Systems theorists define systems along two dimensions:
their structural functions and their dynamic behaviour. This
suggests that these two dimensions of projects as complex
systems must be taken into account if we are to improve our
capacity to model and manage them. Fig. 1 depicts these two
dimensions.
Most writings on project complexity highlight the two
perspectives of structural complexity and dynamic complexity
(Maylor et al., 2008; Remington and Pollack, 2007; Ribbers
and Schoo, 2002; Xia and Lee, 2005). Both perspectives
establish a natural relationship between project complexity and
managerial capacity of prediction: (1) structural complexity
focuses on interactions producing unexpected effects that
cannot be explained or deduced; and (2) dynamic complexity
focuses on processes that generate unpredictable change in
systems (Floricel et al., 2016). Scientists and practitioners
have highlighted the need to better understand the relationship
between complexity and management, and particularly how
individuals and organizations should act in situations of com-
plexity (Augustine et al., 2005; Austin et al., 2002; Thomas and
Mengel, 2008). Interesting literature reviews based on com-
plexity theory were produced in order to define new directions
for research in organization science in general (Anderson,
1999), and more specifically in project management science
(Cooke-Davies et al., 2007). In the management and organiza-
tion science literature, complexity science (the paradigm of
complexity) is usually contrasted with Newtonian science (the
paradigm of complication), emphasizing the dichotomy and
contradictions between the ‘old mind set’and ‘new thinking’
(Sanders, 1998). The philosophy of sciences addresses the
question of complexity science in a more nuanced way, reveal-
ing its multiple implications for human thinking and rationality,
and providing philosophical and anthropological foundations
for its opposition to Newtonian science based on reductionism,
determinism and objective knowledge (Heylighen et al., 2007).
The epistemological perspective taken by Alhadeff-Jones is
a good example of such a perspective on complexity. In his
article, complexity theory is presented through the perspective
of three generations of complexity theories (Alhadeff-Jones,
2008). Considering complexities rather than complexity is a
fertile opportunity to try to reconcile the various schools
of thought about complexity and project management. This
approach to complexities is rooted in the seminal work of
Weaver on complexity and science (Weaver, 1948). In his
historical perspective on science, Weaver reveals that scientists
have addressed complexity in three specific ways, leading to
three categories of problems: the problems of ‘simplicity’, the
Table 1
Boulding's classification of systems.
Level of the system Name of the system Structural function of the system Dynamic behaviour of the system
‘Framework’Static Static relationships Static equilibrium
‘Clock’Simple dynamic Dynamic relationships Stable equilibrium
‘Thermostat’Cybernetic Transmission and interpretation of information Maintaining a given equilibrium
within limits
‘Cell’Open Exchanges with the environment Self-maintaining equilibrium
‘Plant’Genetic societal Differentiated and mutually dependent parts Equifinal growth
‘Animal’Behavioural Capture of information and transformation of this into a knowledge structure
(an image) of the environment
Teleological behaviour
‘Individual human’Human being Knowledge structure more complex than animal, based on language and symbolism Teleological behaviour
‘Social organization’Social Complex knowledge structure, influenced by communication between systems Teleological behaviour
3P.A. Daniel, C. Daniel / International Journal of Project Management xx (2017) xxx–xxx
Please cite this article as: P.A. Daniel, C. Daniel, 2017. Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in
project management, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.07.004

problems of ‘disorganized complexity’, and the problems of
‘organized complexity’.Table 2 shows that complexity theories
adapted to management science are not unified but rather reveal
that human beings developed three ways of facing complex
situations based on three different scientific assumptions and
technical perspectives. The first two approaches are fundamen-
tally based on a deterministic paradigm, considering that
human beings can simplify the complex reality to control it
through regulation. In contrast, the third approach is rooted in a
non-deterministic paradigm, considering that: (1) human beings
are agents intrinsically subjective and uncertain about their
environment and future; and (2) a global organization emerges
out of local agents' interactions (Heylighen et al., 2007).
Fig. 2 develops Fig. 1 by emphasizing the three levels
of complexity detailed in Table 1 (i.e., three different project
dynamics).
4. Towards a clarification of the uncertainty construct
Complexity and uncertainty are frequently associated in the
project management literature (Sommer et al., 2009; Turner
and Cochrane, 1993; Williams, 1999, 2005)–e.g., in project
typologies (Little, 2005; Shenhar and Dvir, 1996), in the
definition of project performance (Anderson, 1999; Levinthal,
1997; Levinthal and March, 1993; Rivkin, 2000), and in the
selection of managerial tactics for facing uncertainty (Sommer
and Loch, 2004). The imbrication of both concepts is so usual
in this literature that we could wonder whether complexity
and uncertainty are distinct concepts; a theoretical clarification
of the relationship between the two constructs is required
(Padalkar and Gopinath, 2016a). Analysis of the literature
on uncertainty in project management reveals similarities with
the analysis of the literature on complexity: both literatures
are considered non-unified, and each concept is prone to be
confused with the other.
In the previous section, we pointed out that the main issue
with the construct of complexity is the confusion between
complexity and complicatedness. In this section, we acknowl-
edge the confusion between complexity and uncertainty, but
focus our discussion on the confusion between uncertainty and
risk, explaining our rationale for this choice.
In the project management literature, there is a tendency to
confuse the terms ‘uncertainty’and ‘risk’; this means that
uncertainty is treated in the same way as risk, or is ignored
(Perminova et al., 2008). It is dangerous to confuse risk and
uncertainty since doing so tends to focus attention on planning
and operational control, at the expense of strategic issues
(Atkinson et al., 2006). Table 3, building on the work of Daniel
(2010) represents an attempt to synthesize previous research
work on the different levels of risk/uncertainty on which
PRESENT (tn)FUTURE (tn+1)
MANAGEMENT
SUB-SYSTEM
PRODUCTION SUB-
SYSTEM
Structure in tn
Output /
outcome
In tn
Output /
outcome
In tn+1
PRODUCTION SUB-
SYSTEM
Structure in tn+1
Implementing
Fig. 1. Structure and dynamic of project management systems.
4P.A. Daniel, C. Daniel / International Journal of Project Management xx (2017) xxx–xxx
Please cite this article as: P.A. Daniel, C. Daniel, 2017. Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in
project management, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.07.004

managers are placed. Researchers in statistics (Littauer, 1967;
Littauer and Ehrenfeld, 1964), decision theory (Rubinstein,
1975), strategy (Courtney et al., 1997) and project management
(De Meyer et al., 2002; Sanderson, 2012) have identified three
or four levels of risk/uncertainty that they link to managerial
conditions: ‘certainty conditions’,‘risk conditions’and ‘uncer-
tainty conditions’.Table 3 distinguishes between all four levels,
but we have chosen to regroup levels 2 and 3 under one label.
We suggest labelling the three main levels ‘algorithmic’,
‘stochastic’and ‘non-deterministic’. Each of these situations
implies different management modes.
The seminal work of Milton Spencer (Spencer, 1962;
Spencer and Siegelman, 1959) is very helpful when it comes
to pointing out the key question of predictability in this
discussion, focusing on distinguishing risk from uncertainty; it
also builds bridges between the theory of uncertainty, the
theory of decision, and the theory of planning:
Risk may be defined as the quantitative measurement of an
outcome, such as a gain or a loss, in a manner such that the
probability of the outcome can be predicted ….
Uncertainty, like risk, is also prediction-oriented, but unlike
the latter its measurement is not objective and does not assume
perfect knowledge. (Spencer, 1962, p. 197).
Knight restricted the notion of risk to two types of situations:
probabilities and statistics. In both cases, decision-makers were
regarded as able to define objective probabilities within a
known range of future events or results (Knight, 1921). Some
authors have thought that subjective probabilities could also
be a response to ambiguous situations in which leaders found
themselves. But subjective probabilities have their limitations,
because even when the information is available, it can be of
little interest for future results if the conditions of reality change
(Rotheim, 1995); this is typically the case in conditions of
dynamic complexity.
Uncertainty appears when decision-makers cannot consoli-
date past observations to form a subjective probability or
relative frequencies for the future (Davidson, 1991). This
difficult and specific state in which managers cannot know
all the important parameters and the possible results is termed
‘unawareness’or ‘unforeseen contingencies’(Kreps, 1992;
Modica and Rustichini, 1994), ‘unstable non-determination’
(Littauer, 1967), ‘wicked problems’as opposed to ‘tame
problems’(Rittel and Webber, 1973), and ‘unknown unknowns’
by extension of the Knightian concept of ‘known unknowns’
(Wideman, 1992). Business developers often do not properly
forecast market opportunities or the best way to treat them. They
Table 2
Three approaches to complexity in management sciences.
Algorithmic approach Stochastic approach Non-deterministic approach
Weaver (1948) Problems of simplicity: where complex
problems can be reduced to simple issues
thanks to the paradigm of rational mechanics
Problems of disorganized complexity:where
disorder is an integral part of the natural
phenomena (and can be addressed by statistics
and the theory of probability)
Problems of organized complexity: where a
sizeable number of factors are interrelated
into an organic whole (being too complicated
for rational mechanics, and not sufficiently
disordered for statistics)
Cramer (1993),
Mckelvey (2004)
Subcritical complexity: the amount of
information to describe the system is less
complex than the system itself
Newtonian complexity
Fundamental complexity: the minimum
amount of information to describe the system
is equal to the complexity of the system itself
Stochastic complexity
Critical complexity: emergent simple
deterministic structures, with underlying
phenomena made of fundamental complexity
Emergent complexity
Heylighen et al. (2007) Phenomena characterized by order: like
those studied in Newtonian mechanics and
systems science
Phenomena characterized by disorder: like
those investigated by statistical mechanics
and postmodern social science
Neither order nor disorder: situated
somewhere in-between, in the zone that is
commonly called the edge of chaos
Scientific assumptions Mathematical models allow optimization
of the decision and the management of
complex activities composed of very large
number of parameters
Heuristic models improve understanding and
accompany learning in condition of
uncertainty; human decision-making processes
require the mediation of modelling instruments
to learn from apparent disorder
The ‘emergent’nature of unpredictable
activities requires a constant adaptation of
groups of actors that are sources of order
and disorder; emerging processes and
experimentations are management dynamics
generating opportunities and bifurcations
Classic schools of
thought in the
sciences of
complexity
Operational research is interested in
phenomena involving hundreds or thousands
of variables, in order to transform them and
reduce them to linear mathematical formulas
that can be managed by computers
(Beer, 1959; Churchman et al. , 1957)
Cybernetics contributes to the definition of
the concept of ‘feedback’to describe the way
in which a system can follow a predefined
purpose, adapting to its environment
(Wiener, 1948/1961)
Engineering sciences strengthen an
understanding of complexity based on a
quantitative assessment, leading to the
concept of computational complexity
(Ashby, 1957; Knuth, 1968; Marcus, 1977)
Science of systems –with work on the
‘dynamics of systems’(Forrester, 1961),
and on ‘the system approach’(Churchman,
1968)–favours the emergence of techniques
reducing the complexity of a system to the
study of its components and their relationships;
any organized set should be described and
explained through the use of the same
categories (Von Bertalanffy, 1951)
Self-organization theories attempt to define
complexity as emergences produced not only
by the order that constitutes them but also from
the disorder that characterizes the relations
to their internal components (Ashby, 1957;
Atlan, 1972/2006; Von Foerster, 1960, 1974)
Second-order cybernetics favours a definition
of complex systems recognizing the
constructivist nature inherent in their design
(Bateson, 1973; Von Foerster, 1974); the
autopoesis proposes the development of new
representations around concepts such as
adaptation, evolution, self-esteem, autonomy
and emergence (Maturana and Varela, 1992)
5P.A. Daniel, C. Daniel / International Journal of Project Management xx (2017) xxx–xxx
Please cite this article as: P.A. Daniel, C. Daniel, 2017. Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in
project management, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.07.004

are therefore forced to adapt and modify their approach over
time (Drucker, 1985; Mcgrath and Macmillan, 1995); the
information does not exist until the results have produced their
effects (Minsky, 1996). When launching a new company, leaders
often know very little, and are unable to recognize and artic-
ulate the variables and their functional relationships (Schrader
et al., 1993); the unpredictable uncertainties are ‘rampant’
(Bank, 1995).
The uncertainty and complexity constructs appear to be
connected by the concept of predictability, which is rooted in
decision theory. Two interesting parallels appear to exist between
uncertainty theory and the complexity theory: (1) decision-
making conditions of risk (as defined by Spencer) are concep-
tually close to ‘problems of disorganized complexity’(as defined
by Weaver); and (2) decision-making conditions of uncertainty
(as defined by Spencer) are conceptually close to ‘problems
of organized complexity’(as defined by Weaver). Beyond the
parallels revealed in these two sections, a clear distinction can
be made between the complexity and uncertainty constructs:
complexity defines the structure and dynamics of the project as a
system (system of production, and system of management), and
uncertainty defines the decision-making conditions of the system
of management (the manager as a decision-maker). Fig. 3 adds
these decision-making conditions to Fig. 2.
5. Decision theory, mental models and predictability
Classical project management methods/methodologies face
limitations when applied to complex projects. In an empirical
study of project management practices, respondents identified
‘inadequacy for complex projects’(27%) and ‘make it difficult
to model the real world’(15%) as the first two limitations/
drawbacks of such methods (White and Fortune, 2002). While
conventional techniques may be well suited to tackling com-
plicated projects (with large numbers of elements), they are
unsuited to projects subject to high uncertainty (Cicmil et al.,
2006). Complex and uncertain projects require newer method-
ologies that help the project ‘emerge’rather than being fully
pre-planned, and that are based on understanding (model-based
theories) (Williams, 2005). Recent research works illustrated
the relevance of modelling approaches for complex and un-
certain projects (Qazi et al., 2016).
PRESENT (tn)FUTURE (tn+1)
MANAGEMENT
SUB-SYSTEM
PRODUCTION SUB-
SYSTEM
Structure in tn
Output /
outcome
In tn
Output /
outcome
In tn+1
PRODUCTION SUB-
SYSTEM
Structure in tn+1
PROJECT DYNAMIC (tnto tn+1)
1. ALGORITHMIC
2. STOCHASTIC
3. NON-DETERMINISTIC
– stable (simple)
– stable under fixed limits (complicated)
– unstable (complex)
Fig. 2. Three levels of complexity in project management systems.
6P.A. Daniel, C. Daniel / International Journal of Project Management xx (2017) xxx–xxx
Please cite this article as: P.A. Daniel, C. Daniel, 2017. Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in
project management, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.07.004

5.1. Mental models and the question of complexity management
The theory of mental models could be the missing link
between complexity and uncertainty in project systems, as
predictability is a referential concept in complexity theory and
in uncertainty theory. General systems theory applied to man-
agement revealed that managers interact with reality through
representation models to make their management decisions
(Forrester, 1961; Sterman, 2001). The fundamental property of
thought is its ability to predict events (Craik, 1943; Jones et al.,
2011). Mental models are generalizations (or even images) that
influence how we understand –and act in –the world (Senge,
1990). They are constantly adjusted, refined and recreated in
dynamic environments subject to constant change, and they
play an important role in the construction and interpretation of
reality (Chermack, 2003; Ruona and Lynham, 2004). A mental
model is a cognitive structure that allows us to describe, explain
and predict the purpose, form, function and state of a system
(Rouse and Morris, 1986); it establishes causal knowledge
about how the system works (Moray, 1998). Mental models
guide, draw and provide the basis through which individuals
interpret and construct the meaning of life in organizations
(Weick, 1990).
Systems modelling is one tool for dealing with decision in
conditions of uncertainty, because decisions can be tested out
with hypothetical consequences (Morecroft, 1983). If someone
has a ‘small-scale mental model’of an external reality and
of their own possible actions, they are able to define several
alternatives, figure out which one is best, respond to future
situations before they occur, use knowledge from past events to
deal with the present and the future, and react more prudently
and skilfully to what emerges (Craik, 1943). Systems-dynamics
researchers use constructs of mental models in a pragmatic
way, as tools to better understand complex and dynamic
systems, and ultimately to improve their design and use (Doyle
and Ford, 1998; Moray, 2004).
With complex projects, the learning dynamic arises from the
relationship between project leaders and the project systems
they manage. Since models are vehicles for learning about the
world, studying a model makes it possible to discover the
system characteristics that it describes. This cognitive function
of models is well known and has given rise to ‘model-based
reasoning’(Magnani and Nersessian, 2002). Mental models act
as inferential frameworks (Gentner and Gentner, 1983) and
influence decision-making, which takes place through feedback
loops (Forrester, 1961). To learn, we must use the limited
and imperfect feedback that is at our disposal to understand
the effects of our decisions, to adjust them accordingly, and to
align the state of the system with our goals (the simple learning
loop). Thus, we can revise our mental models and redesign
Table 3
Different levels in the conditions of uncertainty of leaders.
ALGORITHMIC approach STOCHASTIC approach NON-DETERMINISTIC approach
Littauer (1967),
Littauer and
Ehrenfeld (1964)
Deterministic certainty
In these situations, an action
leads to a unique consequence
Probabilistic certainty
An action leads to a set of
consequences with known
probabilities of occurrence
Stable uncertainty
In some situations, there is an
even lower degree of knowledge
in relation to the action and its
consequences
Unstable uncertainty
The possible consequences of an
action are unsure, but in addition we
cannot assign probabilities to various
consequences
Rubinstein (1975) The decision under certainty
Actions lead to a defined result
that will definitely occur
Decision under risk
Each state of nature has a known objective probability
Decision under uncertainty
An action can have at least two
consequences, but the probabilities
for the states of nature are unknown
Courtney et al.
(1997)
A clear-enough future
Managers can make an accurate
forecast for the development of
a strategy
Alternative future
The analysis cannot identify what
the outcome will be, but may
establish probabilities there are
different scenarios
A range of future
A range of future potentials can
be identified by a limited number
of key variables, but the end
result can settle anywhere
The real ambiguity
Multiple dimensions of uncertainty
interact to create an environment that
is virtually impossible to predict
De Meyer et al.
(2002)
Variation
Cost, duration and performance
levels vary randomly, but in a
predictable field
Predictable uncertainty
A small number of known factors
will influence the project goal in a
predictable way; the foreseeable
uncertainties are identifiable; the
predictable uncertainty may require
several alternative plans
Unpredictable uncertainty
One or more influencing factors
cannot be predicted; the
unpredictable uncertainty
concerns projects that take place
in a partially known market
Chaos
Unpredictable events invalidate
completely the objectives, the
planning and the project approach;
even the structure of the project plan
is uncertain
Sanderson (2012) Risk of category 1
(probability a priori)
The decision-maker can define
objective probabilities based on
mathematical probabilities
Risk of category 2
(statistical probability)
The decision-maker can define
objective probabilities on the basis
of an empirical sample/statistics
from past data
Uncertainty in category 1
(subjective probability)
The decision-maker lacks data
needed to define a probability
objective; they then define a
subjective probability based on
forecasts built on past experiences
Uncertainty in category 2
(socialized)
The decision-maker faces unknown
future situations; the future is
fundamentally unpredictable –it is
socially constructed and cannot be
linked to the past or the present
7P.A. Daniel, C. Daniel / International Journal of Project Management xx (2017) xxx–xxx
Please cite this article as: P.A. Daniel, C. Daniel, 2017. Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in
project management, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.07.004

the system itself (the double learning loop) (Argyris, 1985;
Sterman, 2000).
5.2. The challenges, uncertainty and complexity of mental
models
Having stressed the key role of mental models in complex
and uncertain projects, it is important to consider two main
difficulties that can disturb the prediction mechanism: cognitive
limitations and socio-organizational issues.
Results from experimental research in the psychology of
decision-making identify significant limitations in the cognitive
abilities of human beings (Gilovich et al., 2002; Kahneman et al.,
1982). Indeed, mental models integrate individuals' biases, such
as beliefs, experiences and values (Ford and Sterman, 1998). The
limitations inherent to human cognition have an impact on how
decision-makers face risk and uncertainty; for instance, decisions
and judgements under conditions of uncertainty are subject
to numerous biases that preclude prediction (Kahneman et al.,
1982) and generate an ‘illusion of control’(Langer, 1975).
Self-regulation theory explains that since individuals are driven
by their internal goals concerning control over their environment,
they try to reassert their control under conditions of chaos,
uncertainty and stress. One way they can deal with their lack
of control is to incorrectly assume control over the situation
(Fenton-O'creevy et al., 2003). Confirmation-bias theory sug-
gests that individuals look for information that corresponds
to their understanding of the world at a given moment. New
information may strengthen existing mental models or be rejected
categorically (Klayman and Ha, 1989).
In complex systems, actions and decisions can amplify
counter performance, notably by the effect of mental represen-
tation, which are counter-intuitive. Effective management is
difficult in a world of high dynamic complexity. Decisions
can create unanticipated adverse effects and lagged conse-
quences over time. Attempts to stabilize the system can in fact
destabilize it (Sterman, 2000); this phenomenon is known
as ‘counter-intuitive behaviour of social systems’(Forrester,
1971). Learning in situations of dynamic complexity is often
very poor (Paich and Sterman, 1993).
Beyond cognitive challenges, mental models must face
social/organizational challenges. The decisions and actions
taken by managers result from many interactions between
various stakeholders. Both at the individual and at the col-
lective levels, facing complexity requires the ability to filter
strategically a vast amount of available information, and to
integrate this into an implicit or explicit prediction model
(Beratan, 2007). The effective functioning of teams requires
the existence of a mental model shared by team members
(Langan-Fox et al., 2000). A shared mental model is the mental
model built within a team, and shared by its members. It
represents the cognition shared among groups of individuals
(Langan-Fox et al., 2001). A team model is the collective
knowledge that team members bring to a specific situation –
i.e., the collective understanding that team members share
about a specific situation, also termed the ‘team situation
PRESENT (tn)FUTURE (tn+1)
MANAGEMENT
SUB-SYSTEM
PRODUCTION SUB-
SYSTEM
Structure in tn
Output /
outcome
In tn
Output /
outcome
In tn+1
PRODUCTION SUB-
SYSTEM
Structure in tn+1
PLANNING ‘conditions’
1. ALGORITHMIC
2. STOCHASTIC
3. NON-DETERMINISTIC –
CONTROLLING ‘conditions’
1. ALGORITHMIC
2. STOCHASTIC
3. NON-DETERMINISTIC
–full predictability
(certainty)
–limited predictability
(risk)
unpredictability
(uncertainty)
–no variation (certainty)
–detect an error in the
‘reality’ (risk)
–detect an error in
the ‘model’ (uncertainty)
Fig. 3. Three levels of uncertainty in project management systems.
8P.A. Daniel, C. Daniel / International Journal of Project Management xx (2017) xxx–xxx
Please cite this article as: P.A. Daniel, C. Daniel, 2017. Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in
project management, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.07.004

model’(Cooke et al., 2000). More recently, research work on
shared mental models (SMMs) proved that higher SMMs in
project teams improved team learning and performance (Yang
et al., 2008), and more specifically improved performance in
project requirement analysis (Xiang et al., 2016).
6. Reconciling deterministic and non-deterministic
paradigms in a new contingent theoretical framework
In this section, we propose a systemic model in an attempt
to integrate the complexity and uncertainty constructs discussed
in Sections 3 and 4, together with the modelling function
discussed in Section 5. We bring these together in a theoretical
framework presenting the three resulting contingent approaches
of project managers' decisions and actions: algorithmic, stochas-
tic and non-deterministic.
6.1. Theoretical model for an integrative perspective of
complexity and uncertainty in project management
The theoretical model of project management presented
in Fig. 4 is an attempt to unify the various approaches of
complexity and uncertainty that were presented in Sections 3
and 4.
In this model, the construct of complexity is depicted in
the project system's dynamic (the black box at the bottom of
Fig. 1), which can be simple (simplified by algorithmic models),
complicated (patterned by stochastic models) or complex
(experimented with through non-deterministic approaches).
This is consistent with the usual categories of systems presented
in the business literature (Sargut and Mcgrath, 2011).
The construct of uncertainty is depicted in the two key
characteristics of the management model: prediction and
detection. For instance, when the project system is simple, the
management system can design a model able to predict the
project dynamic with certainty (model prediction) and reveal
no gap between the prediction of the model and the output
delivered by the production system (model detection).
Through a single systemic model, we differentiate two para-
digms of project management: regulation (deterministic), based
on a planning–implementing–controlling cycle (number 1 in the
triangle of arrows in Fig. 4); and emergence (non-deterministic),
based on a modelling–experimenting–learning cycle (number 2
in the triangle of arrows).
PRESENT (tn)
MODEL ‘DETECTION’
1. ALGORITHMIC
2. STOCHASTIC
3. NON-DETERMINISTIC
PROJECT SYSTEM’s ‘DYNAMIC (tnto tn+1)’
1. ALGORITHMIC
2. STOCHASTIC
3. NON-DETERMINISTIC
FUTURE (tn+1)
MODEL ‘PREDICTION’
1. ALGORITHMIC
2. STOCHASTIC
3. NON-DETERMINISTIC –
1 & 2. PLANNING*
3. MODELING** 1 & 2 CONTROLLING*
3. LEARNING**
MANAGEMENT SUB-SYSTEM
Modelling capability
MODEL
3. EXPERIMENTING**
1 & 2. IMPLEMENTING*
* PM PARADIGM
OF REGULATION
** PM PARADIGM
OF EMERGENCE
PRODUCTION SUB-SYSTEM
Structure in tn
Output /
outcome
In tn
REALITY
Output /
outcome
In tn+1
REALITY
PRODUCTION SUB-SYSTEM
Structure in tn+1
–no variation
(certainty)
–detect an error
in the ‘reality’ (risk)
–detect
an error in the ‘model’
(uncertainty)
–stable (simple)
–stable under fixed limits (complicated)
–unstable (complex)
–full
predictability (certainty)
–limited
predictability (risk)
unpredictability (uncertainty)
Fig. 4. The theoretical model of project management.
9P.A. Daniel, C. Daniel / International Journal of Project Management xx (2017) xxx–xxx
Please cite this article as: P.A. Daniel, C. Daniel, 2017. Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in
project management, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.07.004

In Fig. 4, white boxes represent the project system, com-
posed of two sub-systems interacting together, both contribut-
ing to generating project complexity:
(1) A production sub-system represents the project-
implementation function, delivering project outputs and
outcomes. It is defined by its structure, outcomes and
dynamics (revealing the potential changes in the project
structure and outcomes over time, the dynamic from tn
to tn + 1).
(2) A management sub-system represents the project-
management function focused on achieving project
performance. It interacts with the production sub-system
over time through a modelling function based on a
capability to predict the production sub-system behav-
iours, as well as a capability to detect a gap between the
reality produced by the production sub-system and the
prediction of the model.
The interactions between the production sub-system and the
management sub-system constitute a dynamic process compris-
ing feedback loops, the management system that impacts the
dynamic of the production system, and the production system
that impacts the modelling capability of the management
system (Sterman, 2001).
In our model, the uncertainty construct is a characteristic of
the management sub-system; it defines the decision-making
conditions of project managers interacting with the production
sub-system. It is a fundamental characteristic of the modelling
capability of the management sub-system.
In contrast, the complexity construct is a characteristic of
the project system, including the management sub-system, the
production sub-system and their interactions. Complexity is a
characteristic of the production sub-system (multiple interac-
tions with the environment, and changing conditions from
the environment), amplified by the interactions between the
production sub-system and the management sub-system through
positive feedback loops, and by the cognitive limitations of
mental models.
6.2. The deterministic paradigm of regulation
The project management literature has clearly presented the
difference between a classical project management paradigm
and a model-based theory paradigm (Williams, 2005). The
latter challenges three characteristics of the former: (1) a heavy
emphasis on planning; (2) the influence of the cybernetic-
control model; and (3) a low sensitivity to environmental
influences. The classic planning-and-control paradigm is fully
influenced by cybernetics, a systemic theory of regulation
based on: (1) the capacity of the agents in the management
system to predict the behaviour of the production system;
and (2) the deterministic or statistical stability of the production
system (Littauer, 1967). Cybernetics deals with all forms of
behaviour insofar as they are regular, determinate or reproduc-
ible (Ashby, 1957). The real strength of cybernetics is in its
scientific application by Shewhart (through the quasi-cybernetic
loop-of-control model) in management science, and more
specifically in operations research (Shewhart, 1931). The famous
‘plan–do–check–adjust’theory of operational control, popular-
ized as the Deming wheel, is a perfect example of the planning-
and-control paradigm proposed by cybernetics (Deming, 1986).
‘A phenomenon will be said to be in control when, through
the use of past experience, we can predict, at least within
limits, how the phenomenon may be expected to vary in the
future’(Shewhart, 1931). Consequently, in the planning-and-
control paradigm, the behaviour of the production sub-system
(identified as a phenomenon) is characterized by stability
(subjectonlytovariation),andthemanagement sub-system
can predict the behaviour of the production sub-system
through its repetition (past experiences). Thus the manage-
ment sub-system plays the role of a regulator that must model
what it regulates, modelling being a necessary part of regu-
lation (Conant and Ashby, 1970). Finally, following the words
of Ashby, ‘Cyberneticsoffersthehope of providing effective
methods for the study, and control, of systems that are intrin-
sically extremely complex’(Ashby, 1957).
In our model, the deterministic paradigm of regulation
corresponds to this classic project-management paradigm of
planning and control –i.e., to the deterministic paradigm of
risk.
6.3. The non-deterministic paradigm of emergence
When it comes to unique events (changes that have never
happened before) or so-called discontinuities (e.g., technolog-
ical innovations, price increases, evolution of consumers'
attitudes, and legislative decisions), forecasts become virtually
impossible (Makridakis and Hibon, 1979). In these contexts,
the retrospective approach often fails (Pant and Starbuck,
1990). The understanding of unique events is delicate, as
their modelling is often impossible to build (Makridakis and
Wheelwright, 1981). System-dynamics theory reveals that
sometimes the project behaviour is non-intuitive, leading to
non-linear behaviour, being difficult for the human brain to
predict and understand intuitively (Sterman, 1989). Project-
management researchers have used the systems theories char-
acterized by unpredictability and instability (such as chaos
theory, dissipative structures, and complex adaptive systems)
to identify the theoretical aspects that should be analysed in
project-management science: non-linearity, emergence, insta-
bility and radical unpredictability (Cooke-Davies et al., 2007).
While the epistemology of cybernetics is very influential in
the paradigm of regulation, the epistemology of second-order
cybernetics is key to understanding the paradigm of emergence.
The main thesis of second-order cybernetics is that human
beings, as observers, are also cybernetic systems (Von Foerster,
1974). Their knowledge is a subjective construction, not an
objective reflection of reality, which means that the emphasis
should shift from the apparently objective systems around us to
the cognitive and social processes by which we construct our
subjective models of those systems (Heylighen et al., 2007).
The concept of emergence has never been analysed clearly
in the project management literature, yet it is central to the
10 P.A. Daniel, C. Daniel / International Journal of Project Management xx (2017) xxx–xxx
Please cite this article as: P.A. Daniel, C. Daniel, 2017. Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in
project management, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.07.004

concept of the non-deterministic paradigm, just as the concept
of regulation is central to the deterministic paradigm. In the
deterministic paradigm, regulation is based on the modelling
capacity of the management sub-system to produce a good
model of the production sub-system (named the ‘isomorphic’
or ‘homomorphic’model) (Conant and Ashby, 1970). This
high-quality model enables us to regulate the production
sub-system, which means maintaining its stability. In the
non-deterministic paradigm, emergence is the result of the
incapacity of the management sub-system to produce a good
model of the production sub-system, as the production
sub-system itself is unstable over time. Therefore, the project
management ‘model’is not a ‘plan’able to create stability;
it is a model able to generate both stability and instability in
the production sub-system. The result of such an unstable
interaction is the emergence of unexpected outputs and out-
comes in the project. In the non-deterministic paradigm of
emergence, as in model-based theories, the imperfect model is a
management artefact enabling decision-makers to learn from
reality through feedback loops; by so doing, they improve the
quality of their model, and consequently the quality of their
management decisions.
7. Discussion and conclusion
The systemic model presented in this paper shows the
contingency nature of project management systems where
degrees of uncertainty and of complexity are embedded,
uncertainty being a characteristic of the management sub-
system, and complexity being a characteristic of the project-
management system, combining the management and produc-
tion sub-systems. In this theoretical model of project manage-
ment, the paradigm of regulation is clearly related to the
deterministic paradigm of project management (Padalkar and
Gopinath, 2016b) that one can associate with the operational-
project perspective (Turner et al., 2010). Consequently, project
managers applying the PMI's execution-based model should
verify the conditions of stability of the project's production
systems (inputs, outputs and outcomes). Without such require-
ments, the planning–implementing–controlling paradigm of
regulation is inappropriate, because of management models
that are unable to predict –or unable to detect –an error in
the production systems. In the theoretical model, the paradigm
of emergence is related to the non-deterministic paradigm
of project management. It emphasizes a project management
theory based on modelling–experimenting–learning processes,
built on imperfect management models. We believe that
the paradigm of emergence would be fruitful to improve the
strategic project perspective (Turner et al., 2010).
Recently, Serrador and Turner asked an interesting question
about the ‘quantity’of planning that is required in a project
(Serrador and Turner, 2015b). The legitimacy of planning
projects is questioned in dynamic environments if activities
cannot be foreseen, or if planning leads to false expectations
(Andersen, 1996; Collyer et al., 2010; Collyer and Warren,
2009). The theoretical model presented in this paper sug-
gests that this question could be interestingly supplemented
by another: what is the nature of the planning that is
required? The rationale of the paradigm of regulation is
questioned in the sense that in the uncertainty paradigm,
there is no further reference to a ‘good’model or plan.
While the project plan (or schedule) is a good model of
project reality in the risk paradigm, it is an imperfect model
in the uncertainty paradigm.
Organizational improvisation theory is an example of
management practices that are faced with imperfect plans,
even in structured contexts such as projects (Moorman and
Miner, 1998, 2001). Improvisation in organizations is a man-
agerial capacity to explore unexpected opportunities and to
neutralize unpredicted threats (Cunha et al., 1999, 2003). The
perspective of complex responsive processes of relating is
another attempt to transfer elements of complex adaptive
systems theory to organizations and complexity management,
through an emphasis on the interactions among people, based
on acts of communication, power relations, and interplay
between people's choices (Stacey, 2007). Systems-modelling
approaches, such as systems dynamics, are based on building
better models of the complexity of the project (the production
sub-system) to improve the learning process of decision-
making (the management sub-system) (Diehl and Sterman,
1995; Sterman, 1992, 2001). However, no attention has been
paid to how project decision models (project plans, work
breakdown structures, product breakdown structures etc.) are
built, adapted and applied in the project-management loop. One
of the basic assumptions of the paradigm of emergence is that
imperfect decision models impact the production sub-system
and create emergent outcomes. But we know very little about
how this emergent dynamic of production systems interacts
with management systems. Management under ‘unforeseeable-
uncertainty’theories promotes management processes based on
‘selectionism’and ‘trial-and-error learning’(Loch et al., 2001,
2008). A preliminary step consists of breaking down the project
into sub-projects under conditions of ‘foreseeable uncertainty’
and ‘unforeseeable uncertainty’, in order to contingently adapt
the management process (routine execution, or novel strategic
project) (Lenfle, 2011; Lenfle and Loch, 2010). But the
literature is poor when it comes to present management
models able to reveal/analyse the conditions of predictability
and control of the management sub-system towards the
production sub-system. Project-management tools and tech-
niques mainly focus on descriptions of the production sub-
system, whereas project performance is a consequence of
the unstable and emergent interactions between the production
sub-system and the management sub-system. More than
describing the static conditions of predictability and control,
project management scholars and professionals need to know
more about the evolution of this interaction over time.
Studying the dynamic of the emergent interaction between
the production sub-system and the management sub-system
is central to understanding how process performance and
success are created, as it cannot be controlled in conditions
of unforeseeable uncertainty. The next challenge of project
management science should be to generate a theory of
emergence, just as a theory of regulation.
11P.A. Daniel, C. Daniel / International Journal of Project Management xx (2017) xxx–xxx
Please cite this article as: P.A. Daniel, C. Daniel, 2017. Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in
project management, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.07.004

Conflict of interest
The authors declare no conflicts of interest.
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