=Paper=
{{Paper
|id=Vol-2558/short8
|storemode=property
|title=Toward the Application of Anticipatory Thinking in Support of Risk Identification
|pdfUrl=https://ceur-ws.org/Vol-2558/short8.pdf
|volume=Vol-2558
|authors=Michael Geden,Randall Spain,Jing Feng,Andy Smith,Richard Wagner,James Lester
|dblpUrl=https://dblp.org/rec/conf/aaaifs/GedenSFSWL19
}}
==Toward the Application of Anticipatory Thinking in Support of Risk Identification==
https://ceur-ws.org/Vol-2558/short8.pdf
Toward the Application of Anticipatory Thinking
in Support of Risk Identification
Michael Geden, Jing Feng, Randall Spain, Andy Smith, Richard Wagner, James Lester
North Carolina State University, Raleigh, USA
{mageden, jfeng2, rdspain, pmsmith4, rbwagner, lester}@ncsu.edu
Abstract an individual imagines alternative futures and is a critical
Risk management is a critical process for organizations to component for successfully navigating complex
manage and navigate environments that are uncertain, circumstances (Anderson 2011; Hines and Bishop 2006).
complex, and dynamic. The first step of the risk management The extrapolation component of anticipatory thinking
process is risk identification, which has the goal of (Klein et al. 2007) is the process of anticipating alternative
identifying a diverse space of specific and relevant potential futures based on the current situation, and directly ties into
risks. Despite the central role of risk identification in the risk the objectives of risk identification.
management process, limited work has investigated cognitive
This paper presents an analysis of linkages between the
processes in risk management. This paper conceptualizes risk
identification as a type of anticipatory thinking—the process mechanisms and processes of risk identification and
by which we imagine alternative states of the world. It anticipatory thinking to support a deeper understanding of
explores how three anticipatory thinking metrics (novelty, assessing risk identification. It considers how metrics
specificity, diversity) can be used to assess risk identification. devised for the assessment of anticipatory thinking can be
used to measure the quality of risk identification.
Introduction
Related Work
Risk management has increasingly become a required
process for organizations, within both public and private
Risk Identification
sectors, that are attempting to navigate uncertain, complex,
and dynamic environments (Baird, Skromme, and Thomas Risk management is a widely used technique in
1986; Hood and Rothstein 2000). It is employed across as management, engineering, finance, defense, and public
diverse topics as information security (Gerber and Solms health, to determine the allocation of resources in order to
2005), product development (Chin et al. 2009), construction monitor and minimize the impact of unfortunate events and
(Chileshe and Boadua 2012), and water supply (Ameyaw maximize the potential of opportunities (Hubbard 2009). It
and Chan 2015). The first step of risk management is risk is a cyclic process that involves identification, evaluation,
identification, which plays a critical role in the success of and prioritization of risks. Among these, risk identification
any risk management process. Unidentified risks can pose is the first step of the process and often introduces a
major threats to an organization (Australia & New Zealand bottleneck for the success of following steps due to the vast
Standards 2004; Greene & Trieschmann 1984), and even problem space (Department of Defense 2017). During risk
specialists have cognitive biases and can experience identification, an analyst employs detailed knowledge and
miscalculations due to failure of anticipating all possible systematic methods to generate a set of risks and their
factors (Freudenburg 1998). Despite the fundamental role of impacts, which are sometimes accompanied by other
risk identification in the risk management process, there is a features of the identified risks such as vulnerability, speed
paucity of research on how analysts effectively engage in of situation development, potential gain from taking the risk,
risk identification, what cognitive processes are involved, and others depending on the context. The risks could be
and how it can be assessed. threats, opportunities, or uncertainties in general. All the
The exploratory nature of risk identification is similar to information is gathered for subsequent qualitative and
that of anticipatory thinking, which is the process by which quantitative analyses.
Copyright © 2020 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
Two qualitative risk assessment methods, bow-tie them through uncertain conditions, and the goals of the
analysis and risk classification charts, are of particular analyst influence where the uncertainty is mapped out.
relevance in the application of anticipatory thinking. A bow-
tie analysis aims to identify the causes and preventive
measures of a particular risk (Hancock 2016), making links
among risk, impact, and cause (which could be the impact
of another risk). This method, wherein an analyst generates
risk-impact pairs, shares commonalities with the
anticipatory thinking methodology. A risk classification
chart is a grid plot of impact against likelihood for each
particular risk. It is created to quantify the diversity of
identified risks and their impacts.
Despite the challenges of risk identification, research has
shown several promising methods that can improve risk
identification performance. For example, there is evidence
that risk identification is a trainable skill and that part of this
skill may be domain general as experience within a domain
is not sufficient to fully support superior performance
(Maytorena et al. 2007). In addition, based on observations
by risk and project practitioners, assembling a panel of
Figure 1. Three forms of anticipatory thinking with arrows
individuals with relevant but diverse backgrounds can yield representing the temporal direction in which the individual is
better risk identification outcomes (Emmons et al. 2018). anticipating.
These methods generally align with ways that may support
divergent and anticipatory thinking. Divergent thinking is central to anticipatory thinking.
Individuals with strong divergent thinking skills are
Anticipatory Thinking hypothesized to be able to generate creative ideas by
Anticipating how situations may evolve into the future is a exploring many possible solutions. Strong divergent
significantly challenging task, yet this form of anticipatory thinking skills may be particularly useful during the
thinking plays a central role in strategic decision-making generative phase of anticipatory thinking wherein
and risk identification activities in areas such as military individuals anticipate potential futures and generate
planning, business planning, and medicine, where indicators tied to those events. In fact, recent research shows
individuals must generate ideas about the conditions under strong correlations between performance on anticipatory
which events occur, identify second and third-order effects, thinking activities and divergent thinking skills (Geden et al.
and develop explicit potential alternatives to a given 2019).
scenario in order to avoid tactical or strategic surprise. Anticipatory thinking is essential for effective risk
Anticipatory thinking relies on many connected cognitive identification. Prior to assessing and weighing risks,
components including attention, memory, executive organizations and individuals must identify high and low-
function, situational awareness, and domain expertise likelihood events and determine the level of risk associated
(Koziol, Budding, and Chidekel 2012; Mullally and with each event. Identifying vulnerabilities and risks
Maguire 2014). Each of these components serves an requires individuals to think across time and identify causal
important role in perceiving the status, attributes, and links between events, causes, and consequences. For
dynamics of relevant elements in the environment and instance, if a risk has been realized, then a risk management
projecting how these elements could lead to different future team may need to engage in retrospective branching to
states. identify indicators that led to the risk. Conversely, if a team
Anticipatory thinking can take three distinct forms: is engaging in a strategic risk identification exercise to
prospective branching, backcasting, and retrospective reduce vulnerability, then team members will need to
branching (Figure 1). Prospective branching involves engage in prospective branching to identify leading
anticipating future system states and identifying indicators indicators and causal dependencies of future scenarios.
that may lead to these system states. Backcasting involves Geden et al. (2019) developed an anticipatory thinking
examining a particular future system state and thinking back assessment that may be relevant for assessing risk
in time to identify warnings and indicators that lead to its identification skills. The assessment presents respondents
occurrence. Retrospective branching is the identification of with a future-oriented prompt (e.g., “The impact of smart
possible unknown past system states and their paths towards home technologies on older adults in 10 years”), and asks
the present one. All forms of anticipatory thinking focus on them to generate as many pairs of potential future events
the mapping of alternative system states and paths towards (uncertainties) and their subsequent consequences (impacts)
as they can within a short ten-minute window.
The format of the assessment uses a similar dyadic pairing
form that risk identification can take (i.e., cause → risk; risk
→ consequences). Individuals are able to generate and reuse
multiple impacts and uncertainties to generate a list of novel
and specific outcomes tied to the scenarios (Table 1). This
simple methodology allows for significant flexibility while
also assessing the extrapolation component of anticipatory
thinking. Individuals’ anticipatory thinking performance is
assessed using three metrics that aim to capture the novelty
and uniqueness of each response, the level of diversity
across responses, and the level of detail in the description of
the responses.
Table 1: Example responses on anticipatory thinking task with the
prompt: “Nutritiously and sustainably feeding 8.5 billion people
in 10 years” (Geden et al., 2019; adapted from World Economic
Forum, 2017). Uncertainty and impact responses in the same line Figure 2: Example plot of identified risks using likelihood and
forms a dyadic pair. impact ratings. Shapes and colors represent different categories of
risks.
Uncertainty Impact Novelty
Novelty is an AT metric that describes the level of
More international trade Resource efficient food
uniqueness of a given response. Ideally, this would be
production
assessed relative to other responses for a given prompt,
Rapid adoption of new Increased preference for though practically it can often only be assessed relative to a
food technology vegetarianism portion of all generated responses. In divergent thinking
research, novelty is also sometimes referred to as originality
Increased preference for Lower resource (Guilford 1967).
vegetarianism requirements for An important goal of the risk identification process is to
production identify risks that may be unexpected so that proper
monitoring or identification of risks can take place. Novelty
is an important metric for this goal, as it can provide a
measure of how similar identified risks are, and demonstrate
Translating Anticipatory Thinking Metrics that the ideation process has not shifted toward premature
convergent thinking and evaluation.
Traditional risk identification metrics typically focus on
assigning each risk with characteristics such as likelihood Specificity
and impact ratings. These numeric ratings are then used to While novelty/uniqueness are important characteristics of a
produce rankings or visualizations, such as heat maps or response, even the most creative response is not useful if it
scatterplots, to categorize the most important risks for is not clearly elaborated and appropriate to the problem.
further analyses (Figure 2). Risk plots can provide Specificity attempts to capture this by rating how clearly a
information about which risk categories are not being given response is described.
sufficiently explored and regions of unexplored risk space This metric relates to risk identification, as a risk needs to
(e.g., high impact / low likelihood). These metrics, while be clearly described in order to enable a proper evaluation
informative, miss out on the actual quality of the ideas being of its likelihood and impact, as well as how it relates to other
generated, providing a limited view into the quality of the risks. Experts in a given domain may score higher on
risk identification process. specificity due to extensive knowledge in the given context
The AT metrics complement this process, as they can be compared to novices. In a practical context, a low level of
used to assess the quality of individual risks, and provide a specificity across responses could lead to difficulties later in
more complete picture of the set of risks identified. Overall, the risk assessment process, when trying to determine
three AT metrics were identified that related to risk mitigation and monitoring strategies or more directly
identification (Geden et al. 2019). They are meant to broadly quantify the severity of potential impacts.
investigate the depth of the ideas generated and the breadth
across the search space that individuals explored.
Diversity As part of Build’s risk identification process, they
categorize risks according to Al-Bahar & Crandall’s (1990)
Diversity seeks to measure how well a set of responses taxonomy: financial and economic, design, political and
covers the breadth of the problem space. For AT, this was environmental, construction related, physical, and acts of
measured by looking at how many different categories a god. After continuing on with the risk identification process,
participant generated a response for. This metric helps to they decide to review their identified risks to see if they have
contextualize the quantity of submissions generated, while reached a reasonable stopping point. They note that
also helping to identify areas of the problem space that may according to the diversity metric there is one risk category
have not been fully explored in the ideation process. which they have not identified any risks, and another
For application to risk identification, a key challenge is category for which they have only identified one risk. They
identifying categories for a particular domain. While decide to flesh out these risk categories before finishing the
individual organizations or domains may have their own risk identification process in order to improve the breadth of
categorization structure, there are also more generalizable considered risks.
paradigms such as PESTLE (Political, Economic, Social, This example, while simplified, illustrates how the
Technological, Legal, Environmental) or PMESII (Military, anticipatory thinking metrics could be applied toward real
Infrastructure, Information Systems). General circumstances employing risk identification. In a real risk
categorization schemes can be used across domains assessment exercise, many risks would be identified and the
(Tchankova 2002) without requiring a labor-intensive novelty, specificity, and diversity of the generated risks
grounded theory approach at the cost of specificity. This would be evaluated.
metric is especially important, as the risk identification
process has been shown to be susceptible to cognitive biases
(Emmons et al. 2018), and analysts often will allocate too Limitations of Anticipatory Thinking Metrics
much attention to a particular category of risk while
overlooking another (Letens, Nuffel, Heene, and Leysen, The AT metrics described here (i.e., novelty, specificity,
2008). diversity) have several limitations. First, they are resource
intensive to calculate as they are hand coded, which limits
their scalability. A second limitation is that it is not clear
Example Application of Anticipatory how to calculate a single score for each analyst based on the
Thinking Metrics response level metrics. One potential method is to take the
mean of the top n responses, which unlike the total mean,
To illustrate the application of anticipatory thinking metrics, would not punish for analysts who create many low/medium
consider an example of risk identification in an industry that quality responses. However, this does not account for
regularly employs risk management: construction. The overlooking key risks, such as environmental impact of a
construction industry is an inherently dynamic, risky, and nuclear meltdown. Ideally, individual metrics should
unpredictable field with risks able to detrimentally impact account for both the presence and absence of relevant risks,
the productivity, quality, and budget of a construction but it is currently unclear how to create a composite that
project (Maytorena, Winc, and Kiely, 2007). For this provides this more holistic perspective.
example, we will take the perspective of a construction
company, which we will refer to as Build, working on a site
in the northern panhandle of Texas. Conclusion
As part of Build’s typical risk identification process, the
Risk identification is the critical first step in risk
company considers environmental risks such as fires or flash
management. However, current understanding of the
flooding. One employee notes the increased risk of
cognitive processes underlying risk identification is limited.
earthquakes due to fracking (Magnani et al. 2017) in the
There appears to be a strong relationship between
northern panhandle and suggests that earthquakes should be
anticipatory thinking and risk identification, and
added to the list of environmental risks, even though they
anticipatory thinking metrics originally developed for
historically have been atypical for the region. The novelty
anticipatory thinking hold promise for assessing the quality
metric would identify this suggestion as being new and
of a risk assessment. These metrics may serve as powerful
creative, and due to its lack of previous consideration worth
research tools to develop an empirical understanding of the
further exploration.
cognitive process of risk identification.
This risk sparks a conversation about liability and safety
regulations involving earthquakes, and whether Build would
be at fault for any accidents due to insufficient design for Future Work
environmental factors. The specificity metric would identify
this precise new liability risk as being useful, as there is Future studies should be conducted to evaluate the
enough detail for further exploration as opposed to a vaguely psychometric validity of these metrics within the domain of
identified risk, such as “legal liability”. risk identification beyond the construct validity detailed
here. Another promising direction for future work is Hancock, B. 2016. The Bow-Tie Analysis: A Multipurpose ERM
improving the generalizability of the proposed metrics by Tool. Available at: https://erm.ncsu.edu/library/article/the-bow-
developing natural language processing models to support tie-analysis-a-multipurpose-erm-tool
the automatic assessment of identified risks. Additionally, Hines, A., & Bishop, P. J. 2006. Thinking about the future:
Guidelines for strategic foresight. Washington, DC: Social
an important extension of this work is to use these metrics Technologies.
to investigate how the quality of risk identification can
Hood, C & Rothstein, H. 2000, Business Risk Management in
impact the downstream phases of risk management, such as
Government: Pitfalls and Possibilities. CARR Discussion Paper
risk assessment, planning, and system resilience. No. 0. http://dx.doi.org/10.2139/ssrn.471221
Hubbard, D. W. 2009. The Failure of Risk Management: Why It’s
Acknowledgement Broken and How to Fix It. Hoboken, NJ: John Wiley & Sons, Inc.
Klein, G., Snowden, D., & Pin, C. L. 2007. Anticipatory thinking,
This work was supported by the North Carolina State Proceedings of the Eighth International NDM Conference, Pacific
University Laboratory for Analytic Sciences. Grove, CA, June 2007
Letens, G., Van Nuffel, L., Heene, A., & Leysen, J. 2008. Towards
a balanced approach in risk identification. Engineering
References Management Journal 20(3):3-9.
Al-Bahar, J. F., & Crandall, K. C. 1990. Systematic risk Magnani, M. B., Blanpied, M. L., DeShon, H. R., & Hornbach, M.
management approach for construction projects. Journal of J. 2017. Discriminating between natural versus induced seismicity
Construction Engineering and Management 116(3):533-546. from long-term deformation history of intraplate faults. Science
Anderson, B. 2011. Population and affective perception: Advances, 3(11), e1701593.
Biopolitics and anticipatory action in US counterinsurgency Maytorena, E., Winch, G. M., Freeman, J., & Kiely, T. 2007) The
doctrine. Antipode 43(2):205-236. influence of experience and information search styles on project
Ameyaw, E. E., & Chan, A. P. 2015. Risk ranking and analysis in risk identification performance. IEEE Transactions on
PPP water supply infrastructure projects: an international survey of Engineering Management 54(2):315-326.
industry experts. Facilities 33(7/8):428-453. McLennan, J., Elliot, G., & Holgate, A. 2009. Anticipatory
Australia and New Zealand Standards 2004. Risk Management - thinking and managing complex tasks: Wildfire fighting safety and
Principles and Guidelines. effectiveness. In Proceedings of the Industrial & Organisational
Psychology Conference, 90-95.
Baird, I. S., & Thomas, H. 1985. Toward a contingency model of
strategic risk taking. Academy of Management Review 10(2):230- Tchankova, L. 2002. Risk identification–basic stage in risk
243. management. Environmental Management and Health 13(3):290-
297.
Chileshe, N., & Boadua Yirenkyi-Fianko, A. 2012. An evaluation
of risk factors impacting construction projects in Ghana. Journal of World Economic Forum (2017). Shaping the Future of Global
Engineering, Design and Technology 10(3):306-329. Food Systems: A Scenarios Analysis.
Chin, K. S., Tang, D. W., Yang, J. B., Wong, S. Y., & Wang, H.
2009. Assessing new product development project risk by
Bayesian network with a systematic probability generation
methodology. Expert Systems with Applications 36(6):9879-9890.
Department of Defense 2017. Department of Defense risk, issue,
and opportunity management guide for defense acquisition
programs. Office of the Deputy Assistant Secretary of Defense for
Systems Engineering.Washington, DC: Author.
Emmons, D. L., Mazzuchi, T. A., Sarkani, S., & Larsen, C. E.
2018. Mitigating cognitive biases in risk identification:
Practitioner checklist for the aerospace sector. Defense Acquisition
Research Journal 25(1):52-93.
Freudenburg, W. R. 1988. Perceived risk, real risk: Social science
and the art of probabilistic risk assessment. Science 242(4875):44-
49.
Geden, M., Smith, A., Campbell, J., Spain, R., Amos-Binks, A.,
Mott, B., Feng, J., & Lester, J. 2019. Construction and Validation
of an Anticipatory Thinking Assessment.
https://doi.org/10.31234/osf.io/9reby
Gerber, M., & Von Solms, R. 2005. Management of risk in the
information age. Computers & Security 24(1):16-30.
Guilford, J. P. 1967. Creativity: Yesterday, today and tomorrow.
The Journal of Creative Behavior 1(1):3-14.