Systems Design for Projects: Analyzing Complexity and Governance

This report delves into the critical aspects of systems design for projects, emphasizing the study of complexity and its implications. It explores the consequences of ignoring complexity, such as accidents caused by tight coupling and unnecessary coupling, and highlights the importance of recognizing adaptive systems. The report examines the application of systems techniques in addressing real-world problems, like acquiring land for a new facility and combating student plagiarism through autonomous systems. It advocates for the use of influence as a key method for managing interactions within autonomous entities and concludes by describing how a project manager exercises governance by creating a vision, supporting open communication, being authentic, and taking risks. The paper provides insights into the benefits of autonomous systems and adaptability in addressing complex challenges.

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SYSTEMS DESIGN FOR PROJECTS
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Introduction
This paper will discuss the reasons for studying complexity, and the consequences of not
recognizing complexity. In addition, the paper provides comments on the use of systems
techniques to addressing problems such as acquiring a large tract of land for a new facility and
approach on how to deal with student plagiarism. The paper also recommends the choice of
methods to understand the issue of complexity and finally describes how a project managers
exercise governance.
The reason for studying complexity when a reductionist methodology offers a simpler alternative
is because complexity is not a model but a program in the field of science that research the
manner in which features interact in a system to generate general configurations (Kerr, 2014).
Similarly, studying these patterns will enable individuals to understand the manner in which they
result in the relating features to change. Therefore, complexity is concerned with the creation of
structures and how the development of these structures influence the objects that form them.
Allen, Maguire and McKelvey (2011) defines complexity system as many interacting parts that
are controlled by certain forces that correlate in terms of behavior at a given time contingently to
the state of others.
We study complexity because it is an adaptive system that consists of several features. Some of
these features include: several heterogeneous agents. Each heterogeneous agent is tasked with
making decisions regarding how it work. The most significant measurement is that these
decisions will advance with time (Lessard, Sakhrani, and Miller, 2014). Second, these agents
interact with each other and through these interaction it results in another aspect referred to by
scientists as emergence. It is not possible to comprehend the entire system by observing distinct
parts. These representatives makes independent resolutions on how to act and then interact with

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each other to result in the emergence that create the general system. A combination of the three
aspects when put together are greater than each distinct parts.
If complexity is not acknowledged it has consequences that can cause accidents for example tight
coupling. In multifaceted systems in which evolving effects are predominant, there is a
propensity in the direction of high risk of calamities. In this essence an accidents is any occasion
that is not intended resulting in the damage of the subsystems or even the entire system. When an
accident occurs it abruptly stops the intended output from taking place. Accidents happen not
because of natural consequences of the complexity but due to the attempts to manage the
complexity (Gupta, and Anish, 2009). Accidents in multifaceted systems happen because of
unexpected interactions failures happening in the system. When a system is more tightly
coupled, the more possibility of experiencing failures that influence each agents leading to
greater risks of accidents.
In general tight coupling is principle that is applicable for all systems of systems. When
components of a system are tightly coupled, it results to their actions as well as interactions
being constrained. Consequently, this results in higher probability of failure occurring. Tight
coupling minimizes the existent chances for evolution and adaptation. Therefore, any
unnecessary coupling will negatively influence the feasible solutions by causing delays,
heightening costs and excessive consumption of resources (Boisot, and McKelvey, 2011). On the
same note, the emergent of unnecessary coupling has been found to easily prevent all feasible
solutions. Certainly, in regard to accidents, tight coupling is challenging because it work to
prevent system of systems from realizing its objectives. It is unfortunate that actions undertaken
to minimize risks in system of systems traditional originate from traditional software as well as
system engineering approaches that does not factor in emergent effects. As a result, these actions

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like imposing additional synchronization and increasing reporting requirements further tighten
coupling. While these actions at times have short-term advantages in a system of systems, these
actions tighten coupling increasing risk (De Florio, Bakhouya, Coronato, and Di Marzo, 2013).
Thus, the tightening of coupling further destabilize the possibility of general success.
Unnecessary coupling is another example. Coupling is the extent to which elements of a system
rely on each other. Indeed, the interdependency as well as coupling are vital for cooperation and
fulfilment purposes hence cannot be completely averted. However, coupling often is associated
with undesirable features in the presence of modifications since it prevents changes from being
confined into one node. In addition to that, coupling serves to retard emergent effects.
In the event of acquiring a large tract of land for a new facility, the facility will have to use
autonomous entities. These entities have the capacity to perform independent actions, make
independent decisions and provide self-direction. The large tract of land require an autonomous
entity since it cannot be influenced or regulated by external forces (Gupta, and Anish, 2009). In
this cases, influence is the mechanism that allow entities to interact with each other in a manner
that results in changes to the physical, emotional and informational state of the other. Even
though influence has both negative and positive aspects, regardless of the influence it is not
inherent (Hazy, and Ashley, 2011). The influence rely on the perspective of the observer.
By definition an individual is said to be autonomous. For example, criminal law is an external
force but it do not control the behavior of any individual. In most cases, the impact of the law in
connection with other social powers are often enough to make sure they abide by it. Since
autonomous entities have the capacity to make independent decisions and actions, they at times
use the independence particularly in cases where there are many local inconsistent rewards
(Andriani, and McKelvey, 2009). There is point when one can assure the independent

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accomplishment of an independent entity, which means that an autonomous entity cannot be
overpowered but can only be subjective in their actions and decisions. Therefore, since persons
are independent entities, they are able to involve in agreements like regulations, laws, contractual
requirements, they will apply these autonomy to lead the large tract of land of the new facility.
The advances in computing technology, which allows for controls sections of mechanical
machines being replaced software operating on general computing services will address student
plagiarism. This will be made possible by turning the mechanical machines into software-
intensive system as well as into autonomous system in some cases. The autonomous system has
the ability to make independent actions and decisions with regard to the system that it is part.
The autonomous aspect provides significant benefits where every component can be planned,
tested, executed and evolved autonomously regardless of the system used. Equally, the
unconventionality declines the volume of information that components ought to have about one
another and streamlines the entire system (Ireland, 2018). On the same note, because of the
objectivity of autonomous the elements increases the possibility of the system being used by
several systems of systems. Since student plagiarism is a growing issue that keep on increasing it
needs a scalable and adaptable systems of systems. The adaptability of systems makes it possible
to modify the systems roles as well as functionality of their elements, network structure, and
quality of service.
In my point of view, influence is the most suitable method of my choice. The reason being that
influence does not force anybody to abide by any laws or agreements. However, what makes
people to abide by these laws and regulations are the benefits or consequences that are associated
with their actions (Richardson, 2005). Influence acts as a fundamental mechanism for all
connections amongst autonomous objects as they cannot regulate each other. In this sense,

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autonomous have the capacity to realise goals which are not local to themselves alone by
extending their influence through cooperative interactions with others.
In conclusion, the paper discuss the way a project manager exercise governance in a project. A
project manager exercises governance in a number of ways such as creating a vision, supporting
open communication with the project team being authentic and acting by taking risks. The most
vital aspect of governance for a project manager is to create a vision (McKelvey, and Andriani,
2010). The vision sheds light to the team on how they should participate in the project. Indeed,
when the team knows where they are going it is the primary quality needed to bring them
onboard.

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Reference List
Richardson, K.A. ed., 2005. Managing organizational complexity: Philosophy, theory and
application. IAP.
Kerr, F., 2014. Creating and leading adaptive organisations: the nature and practice of
emergent logic (Doctoral dissertation).
Lessard, D., Sakhrani, V. and Miller, R., 2014. House of Project Complexity—understanding
complexity in large infrastructure projects. Engineering project organization journal, 4(4),
pp.170-192.
Ireland, V., 2018. Is the need for evolvable, adaptable and self monitoring complex engineering
changing the traditional engineering process?. In Systems Evaluation Test and Evaluation
Conference 2018: Unlocking the Future Through Systems Engineering: SETE 2018 (p. 251).
Engineers Australia.
Andriani, P. and McKelvey, B., 2009. Using Scale-free theory from complexity science to better
manage risk. Risk Management: An International Journal, 11(4).
Gupta, A. and Anish, S., 2009. Insights from complexity theory: understanding organizations
better. IIMB Management Review, 21(3).
Hazy, J.K. and Ashley, A., 2011. Unfolding the future: Bifurcation in organizing form and
emergence in social systems. Emergence: Complexity & Organization, 13(3), pp.57-79.
De Florio, V., Bakhouya, M., Coronato, A. and Di Marzo, G., 2013. Models and concepts for
socio‐technical complex systems: towards fractal social organizations. Systems Research and
Behavioral Science, 30(6), pp.750-772.

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McKelvey, B. and Andriani, P., 2010. Avoiding extreme risk before it occurs: A complexity
science approach to incubation. Risk Management, 12(1), pp.54-82.
Boisot, M. and McKelvey, B., 2011. Complexity and organization-environment relations:
Revisiting Ashby’s law of requisite variety. The Sage handbook of complexity and management,
pp.279-298.