Public Sector Leadership in Assessing and Addressing Risk

Peter C. Young, Simon Grima, Rebecca E. Dalli Gonzi

• Publisher: Emerald Publishing Limited
• Year: 2022
• Print ISBN:
  9781801179478
• eBook ISBN:
  9781801179485
• Topic: Business
• Subtopic: Financial Risk Management
• Index: Business

Public Sector Leadership in Assessing and Addressing Risk explores risk management in practice, taking a specific focus on the identification of risks in the European public sector while contextualising its Eurocentric analysis within a global setting.

The volume lays important groundwork for understanding the main philosophical premises of risk management. Navigating the text's philosophical underpinnings such as 'Risk Management is a misnomer', the editors provide deep insight into global, strategic, and operational risk management that will prove invaluable for any practitioner.

Providing high quality academic research, ESFIRM provides a platform for authors to explore, analyse and discuss current and new financial models and theories, and engage with innovative research on an international scale.

Section One

Context

Chapter One

Our Complex World

Peter C. Young and Simon Grima

Abstract

Ours is a complex world. On these five words will be built a foundation for an alternative way of framing our thinking about risk management. Complexity means many things, but a key feature is that outcomes cannot be predicted with certainty. In the best cases, opportunities arise to analyse and develop some understanding of the uncertainty within a complex system, and in the most fortunate of such circumstances it is possible to anticipate specific outcomes with some degree of accuracy. The authors call such circumstances risks – that is, measurable uncertainties. Complexity, however, consists mainly of interconnected uncertainties and unknown/unknowable possible outcomes or effects. And, of course, complex systems can include humans whose (in)ability to perceive and interpret such environments makes things – well – more complex.

This book ultimately will focus on how the authors construct a way to lead and manage in this environment, but first it is critical that the terminology and description of this world be given some precision. Therefore, Chapter One begins with an introduction to the idea of complexity, including some mention of the principles and concepts that inform our understanding of it. In turn, this discussion introduces uncertainty. Risk, as a category of uncertainty is discussed and the implications of its measurability are presented, which leads to a discussion of human perception and behaviour under conditions of uncertainty. Attention is then drawn to the unknown and the unknowable, and to emergent phenomena. Since the focus of this book is on public sector risk management, the chapter concludes with a brief discussion of the idea of public risk.

Keywords: Risk management; public sector; leadership; uncertainty; insurance; ERM

Principles and Concepts

Complexity

Many management and leadership scholars have been attracted to Complexity Theory, which originated in numerous fields of scholarship in the natural sciences (Dooley, 1997). An introduction to complexity will be a useful starting point in this book, although it is important to recognise that the translation from natural science to management science creates some wrinkles that will need to be explored later. Nevertheless, a basic definition might be:

Complexity is a characterisation or condition of the behaviour of a system, model, or other context – as a whole or within its constituent parts – all behaviours (interactions) guided by localized rules with no higher instruction evident. (Johnson, 2001, p. 19)

A very critical implication is that complexity can be studied and described even though the outcomes of the system, as far as can be known, are unpredictable. This makes the ability to quantify and assess threats, opportunities, and likely outcomes a challenging – and frustrating – task (Plsek, Lindberg, & Zimmerman, 2005). Systems theory, an affiliated concept, frames the study of complex behavioural patterns among interdependent agents in a social system whose purpose, structure, and functions are influenced by environmental forces (affecting parts of or an entire system). These forces and responses can appear chaotic and counterintuitive. They are certainly primarily nonlinear, since the observed effects of the individual parts cannot be easily aggregated into a cohesive whole – which is why the ability to predict escapes us. Chaos theory, also a related concept, among other things, considers dynamic systems that seem to occasionally reach disordered states, and identifies patterns between linked effects and feedback loops where a small change in one state of the system can cause large effects in a later state (Gleick, 1998). Climate change is an example of such system dynamics.

Complex systems can create spontaneous order – often referred to as self-organisation – formed by interactions between local parts, or agents, in an otherwise disordered context. Self-organisation is not the result of organisation-wide controls, but rather the actions by/among individual agents responding to various stimuli. Organisational multi-agent systems reveal a form of a self-organised system composed of autonomous interacting intelligent agents that can resolve emergent problems through negotiated solutions based on updated (often supported by digital technology) information (Mařík et al., 2002).

As a further extension, a complex adaptive system (CAS) gives shape to a dynamic network of interactive agents where the agglomerated, or collective, effect of their combined behaviours can be adaptive and innovative. That is, together the individual responses mould and reshape the system so it becomes more compatible with its environment.

Research on complexity has brought some important perspectives to management science, including the idea of organisations as CAS (Weaver, 1948). As with complexity itself, there are numerous variations of the CAS concept, but the major attraction for management scholars is the belief that CAS can provide remedies for the limitations of traditional linear and deterministic strategy modelling (Dooley, 1997). What does all these mean for thinking about risk management? Some influences are obvious, but others only reveal themselves on closer examination. A summary of the key insights would be the following:

• Adaptation and innovation are the two central responses that agents employ to address challenges (challenges including, of course, risks, uncertainties, the unknown, and emergent phenomena).
• The fundamental operational feature of CAS organisations is that all agents (employees, managers, etc.) have unfettered ability to work with all other agents in order to assess and address threats or opportunities. These interactions may be seen as unconfined individual actions but might also be conceived as operating within organisational systems.
• CAS feature local rules (called schema) that guide individual agent’s judgement, decision rules, and behaviour. By use of the term ‘local rules’ it is suggested that the system is not hierarchical, nor does it operate in a centralised fashion. Everyone has a role in adapting to new conditions, innovating when necessary, and otherwise monitoring for challenges to the system.
• Leaders in CAS have different roles. Two management scholars recently observed that leaders in CAS are expected to … ‘Facilitate … (b)oundary spanning, organizing and implementing aligned actions, promoting cross-functional training, joint planning and decision-making, deploying resources across units to foster interconnectivity’ (Uhl-Bien & Arena, 2018). This is sometimes called complexity leadership.
• It may sometimes be said that CAS are ‘naturally regulated’, by factors like gravity and biological constraints. In organisational systems, questions abound as to what structures may need to be built to acknowledge artificiality. In other words, naturally regulated CAS are regulated by natural laws, while organisations have to recognise that the ‘regulations’ that bind them must be imposed and managed by humans.

The concept of Complexity Theory introduces a set of ideas that will shape the content of future chapters, as well as changes in risk management thinking. But perhaps the additional point to be made here is that our world is made up of many complex systems, and these systems represent ‘units of measurement’ that provide some degree of meaning. However, these systems ALL may be interconnected in various ways. And so here we must come to terms with – but ultimately learn to live with – the realisation that Everything is Connected, a phrase that will be repeated throughout the book. Within limits, we can understand specific components of a complex system or systems but it remains fully beyond our ken to clearly understand the interconnectivity of all systems to one another.

This is a humbling matter, and indeed humility is suggested as an essential quality for risk managers and leaders later in the book.

Uncertainty

If complexity can be imagined as the description of the functioning of a system or an environment, elements or components of that system would include certainties (at least in theory), uncertainties, and phenomena that cannot be known or anticipated. Additionally, humans may be present so their capabilities, perceptions, and behaviours would be components of that complex system/environment. In adding the observer to the list we discover that there is more than one way of thinking about uncertainty.

Arguably uncertainty exists independently of observation (some theorists would not agree). That is, whether or not humans are present to observe an uncertain situation, that uncertainty could be said to exist. A tree will or will not fall in the forest whether or not we are present to observe it. When it will fall or not fall is what might be called an objective uncertainty – that uncertainty will resolve itself with or without an observer present. It might be said for convenience, it is objectively determined.

Then add the observer. There is a school of thought that uncertainty exists (only?) in the mind of the observer. For example, Webster’s New Collegiate Dictionary defines uncertainty as ‘doubt about our ability to know’, and thus it might be present at any time, in any situation – even if the imagined situation was objectively certain. If we consider uncertainty as the result of doubting our ability to know, we could claim that doubt only occurs when an observer is present. The degree of doubt would vary and be influenced by things such as availability and understandability of information, the capacity of the observer to interpret and apply that information, and a variety of other human factors (prior experience, culture, and even genetics). Michael L. Smith developed a simple and useful representation of the varying degrees of uncertainty. See Fig. 1.1 (Williams, Smith, & Young, 1998).

Professor Michael L. Smith of the Ohio State University has developed an important conceptualisation of uncertainty. He argues that, like risk, uncertainty exists in varying degrees, and he has proposed a scheme for classifying levels of uncertainty. His ‘Certainty–Uncertainty Continuum’ appears as follows:

In addition to these levels, there is an implicit sliding scale of – let us call it – measurability, where a step from Level 3 towards certainty marks an increasing ability to understand the characteristics of an uncertainty. As indicated in Fig. 1.1, this understanding arises primarily from our ability to assess the probability of certain outcomes – as well as the value of the possible outcomes themselves. This growing ability to understand is what ‘moves’ uncertainties into the category of risks. But, to conclude here, uncertainty can be thought of as an objective phenomenon (things will either happen or not happen whether we are present or not), but uncertainty is also a state of mind. More broadly, the debate rages as to whether uncertainty is either or both – we will not trouble ourselves further here.

Fig. 1.1. Smith’s Four Levels of Uncertainty. Source: Authors’ compilation adapted from Williams et al. (1998).

Risk

Risk, by definit...

Table of contents

• Cover
• Title
• Section One. Context
• Section Two. Assessment and Analysis
• Section Three. Tools, Applications, Responses
• Section Four. Leading and Managing in an Uncertain World
• Index