2024 | Buch

Navigating Uncertainty Using Foresight Intelligence

A Guidebook for Scoping Scenario Options in Cyber and Beyond

verfasst von: Bruce Garvey, Adam D. M. Svendsen

Buchreihe : Management for Professionals

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Inhaltsverzeichnis

Über dieses Buch

Dieses Buch vereint eine Reihe unterschiedlicher Erkenntnisse zu aktuellen und sich abzeichnenden geschäftlichen Belangen, als die Autoren eine Reihe von 12 Weißbüchern des Analytic Research Consortium (ARC) entwickelten. Er präsentiert mehrere, unterschiedlich konfigurierte Szenarien, wobei er das Cyber als Beispiel heranzieht; den Einsatz und die weitere Optimierung schätzender / probabilistischer Sprache; die Vermittlung analytischer Erkenntnisse und anderer Erkenntnisse über "(Un-) Gewissheit" an die Entscheidungsträger und die Risikominderung. Außerdem werden die sich rasch entwickelnden Gen-KI-Systeme und -Technologien von heute detailliert bewertet, z. B. diejenigen, die OpenAI's ChatGPT und Googles Bard / Gemini zugrunde liegen. Dazu gehören ihr jeweiliger Wert in Bezug auf Szenarienentwicklung und andere unternehmensrelevante Methoden, Werkzeuge und Techniken, wie z. "Red Teaming". Die behandelten Themen werden anhand der Multi-Methoden von "Intelligence Engineering" (IE) und "Strategic Options Analysis" (SOA) bewertet. In der zweiten Hälfte des Buches wird ein alternativer Prozess der Szenarioplanung vorgestellt, einschließlich des Einsatzes neuer Computersoftware und KI-Tools. Zusätzlich zur Gen-KI identifizieren wir, dass die sich herausbildende Disziplin der Kausalen KI für Vorausschauungs- und Szenarioaktivitäten besser funktionieren könnte. Das Buch ist eine wertvolle Lektüre für eine vielfältige Leserschaft aus dem öffentlichen und privaten Sektor, die Regierung, Militär, Strafverfolgung, Bildung, Industrie, Handel, Einzelhandel und Unternehmen aller Größenordnungen umfasst. Auch Studenten an Business Schools und hochrangige Entscheidungsträger, darunter Politiker, Militärkommandeure und C-Suite-Führungskräfte in verschiedenen Bereichen, werden davon profitieren.

Mit KI übersetzt

Inhaltsverzeichnis

Frontmatter

ARC White Paper Insights: Differently Configured Cyber Scenarios

Frontmatter

Chapter 1. Generating Cyber Intelligence (CYBINT) Scenarios and Solutions to Address Uncertainty for Decision-Advantage: Using Intelligence Engineering and Strategic Options Analysis

Abstract

This chapter presents in detail how the multi-methodologies of Intelligence Engineering (IE) and then Strategic Options Analysis (SOA) can be combined, offering the development of a number of scenarios and solutions to address uncertainty and generate decision-advantage in cyber contexts.

The Federation-/System-of-Systems factors & indicators of PESTLE+ (Political, Economic, Social, Technological, Legal/Legislative and Environmental + Time) are all drawn upon as variables, enabling the capture of ‘key actors’, ‘forces/factors of change’ and ‘possible change over time’.

Recorded by IE Matrices/Maps, this work enables the establishment of a ‘Problem Space’, which can be transformed into a ‘Solution Space’ following SOA’s pair-wise analysis that identifies consistencies and/or inconsistencies between the different variable options that arise. Thereby, the potential number of scenarios/solutions is majorly reduced making the initial Problem Space much more manageable.

Comparison can then be made between the fixed reference point of an ‘anchor scenario/solution’ and any other scenario/solution options that might be possible, helping identify any ‘outlier’ that otherwise might not have occurred to participants (an ‘innovative outlier’). Guiding probabilities (or likelihood of occurrence) can be ascertained and then communicated using ‘estimative language’.

Ultimately, several different end-users are catered for during the course of the work undertaken here, firmly adhering to the well-established STARC intelligence criteria of delivering Specific, Timely, Accurate, Relevant and Clear results that are subsequently ready for their further consideration with substantial foresight.

Bruce Garvey, Adam D. M. Svendsen

Chapter 2. A Macro Cyber Scenario Case Study Using Intelligence Engineering and Strategic Options Analysis Methods

Abstract

This chapter is a continuation of and from the previous one. Whereas that chapter looked at the topic from a more granular and detailed (micro case study) angle, we now look at a broader contextual approach, addressing a number of early stage strategic identifiers using the PESTLE+ (including Time) framework. This allows a review of cyber issues from a more macro-overview and generalised perspective, using just the basic PESTLE+ to provide more general insights.

The chapter also presents the ‘what if?’ analysis work that was conducted following on from the identification of the contextual PESTLE + Time-based Problem Space. Following pair-wise reduction, the Problem Space is reduced to a much smaller set of viable options, forming the Solution Space.

The resulting options are then explored, including a description of an ‘anchor’ (or most likely configuration-based) scenario. Alternative outlier/weak signal options, majorly different from the ‘anchor’ can be identified giving the analyst a broader range of possible outcomes—such outcomes having both ‘positive’ and ‘negative’ connotations.

Once these viable solutions from a much larger Problem to Solution Space have been compiled, then the translation of such configurations into descriptive narratives is rendered. That work also allows for the use of ‘estimative/probabilistic language’ relating to their likelihood or possibility/probability of occurrence, offering for the further refining of these solutions for the decision-maker and other relevant end-users.

Bruce Garvey, Adam D. M. Svendsen

Chapter 3. Examining the Landscape of Unauthorised Cyber Access (with Reference to POSTnote #684)

Abstract

A recent publication in the UK Parliament’s Office of Science and Technology (POST)’s established ‘POSTnote’ series, titled: States’ use of cyber operations (No. 684, October 2022), examined hostile state-backed cyber activities (‘POST is an office of both [UK] Houses of Parliament charged with providing independent and balanced analysis of policy issues that have a basis in science and technology’—for more, see via: https://post.parliament.uk/). The POSTnote also evaluated how and why states use cyber operations against other nations and the threats posed to the UK, as well as scoping both UK and international mitigation approaches (POST (27 October 2022)).

This chapter uses the above POSTnote publication as a basis for developing a landscape of threats resulting from unauthorised access via cyberspace. The introductory paragraph of the POSTnote states specifically that:

States are increasingly engaging in cyber operations to support their strategic aims. This POSTnote considers hostile state-backed cyber activities. It looks at how and why states use cyber operations against other nations and the threats posed to the UK. It also consider mitigations, both internationally and in the UK (Ibid., p. 1).

This chapter aims to establish a ‘Problem Space’ (PS), which reflects the main variables and their respective ‘states’ and ‘conditions’ as identified by the POSTnote #684 document. The work has been undertaken as a demonstrator and proof-of-concept exercise for the purposes of helping analysts and decision-makers explore an array of strategic options that require their deployment in order to address the problem of unauthorised cyber access.

Bruce Garvey, Adam D. M. Svendsen

Chapter 4. Intelligence Engineering-Led Set-up of Generic Strategic Options Analysis Problem to Solution Spaces: Cyber Example Demonstration

Abstract

Building on insights from previous chapters, we now re-iterate the importance of taking a system-of-systems-based ‘factors’ and ‘indicators’-orientated Intelligence Engineering (IE) approach towards mapping uncertainty for its improved navigation and for decision-making purposes. That last IE approach and methodology, essentially conveyed in the form of a series of framing lenses, assists in the establishment of a generic Problem Space—in the case of the exercise under-examination in this current chapter, focused on the challenge of negotiating widely cast cyber concerns.

That IE work then allows for the Strategic Options Analysis (SOA) approach and methodology to be advanced, thereby enabling transformation of the generic Problem Space via Pair-wise Analysis (PWA) evaluations into a Solution Space. This offers a series of viable scenario options for their further exploration and consideration by end-users, such as those participants ranging from analysts to more C-Suite-located decision to policy-makers. During the course of this chapter, both expected—‘anchor’ scenario—and less-expected outcomes—such as presented in the form of an ‘outlier’ scenario—are developed, alongside another additional scenario example being provided for further illustration.

The Problem Space presents a generic focus on cyber. During conducting the PWA, the different factors and indicators forming the variables and sub-variables, as well as their respective conditions and states, are judged as being substantially ‘possible’ amongst the range of their assessed compatibilities (consistencies) and incompatibilities (inconsistencies). Therefore, much openness and flexibility is maintained during the formulation of the Solution Space(s), ensuring that adequate possibilities are covered during the scoping of uncertainties that could be at least potentially encountered and then experienced. Radars are deliberately configured as being not too exclusive in their range, ensuring that adequate inclusiveness is covered overall and is sufficiently taken into account for subsequent onwards communication.

Bruce Garvey, Adam D. M. Svendsen

ARC White Paper Insights: The Implications of Using Estimative/Probabilistic Language in Scenario Development

Frontmatter

Chapter 5. More than Semantics? Communication of (Un)certainty via ‘Estimative/Probabilistic Language’

Abstract

This chapter examines how (un)certainty can be communicated to ‘end-users’, such as to strategic-level policy- and decision-makers. The domain of ‘Estimative’ or ‘Probabilistic Language’ offers most promising routes forward when employed with appropriately placed caveats relating to its deployment.

After defining ‘uncertainty’ and eight different interpretative versions, the ‘Ambiguity Problem’ is next engaged head on by this chapter. Qualitative and quantitative areas relating to ‘Estimative’ or ‘Probabilistic Language’ are then raised for consideration, before looking at ways pertaining as to how (un)certainty is specified. Here, the ‘issue of synonyms’, ‘words to ignore’, ‘attempts at quantification’ to ‘standardisation’ considerations feature much more centrally.

Examples drawn upon include those from the US Intelligence Community, considering the US ‘father of intelligence analysis’ Sherman Kent’s historic addressing of the ‘Estimative’ or ‘Probabilistic Language’ areas being focused on in this chapter; extending to presenting more contemporary publicly available US National Intelligence Council authored National Intelligence Estimates (NIEs), and how their insights are communicated to top US political leaders in the White House and Congress.

Key conclusions and takeaways note that while there might be much ‘fuzziness’ surrounding ‘uncertainty’, greater clarity can be realised and then more effectively communicated on to end-users via using suitably weighted ‘Estimative’ or ‘Probabilistic Language’.

In the next chapter, Chap. 6, different estimative language phrases will be tested together via conducting a Strategic Options Analysis related pair-wise analysis exercise, ascertaining which phrases are compatible or not—thereby offering some potential greater clarity in the communication of (un)certainty, as well as demonstrating how ‘Estimative’ or ‘Probabilistic Language’ can perhaps be better deployed.

Bruce Garvey, Adam D. M. Svendsen

Chapter 6. Estimative/Probabilistic Language: Part II—Expanding the Range of Scenario Options

Abstract

Following directly on from the previous chapter, Chap. 5, titled: More than Semantics? Communication of (Un)certainty via ‘Estimative/Probabilistic Language’, this current chapter highlights several different estimative or probabilistic language phrases, which are tested together. This is done via conducting a Strategic Options Analysis (SOA)-related pair-wise analysis (PWA) exercise.

Adopting the path introduced in Chap. 5 means that we can then ascertain which estimative/probabilistic language phrases are compatible or not—thereby offering some potential greater clarity in the communication of (un)certainty both to and for the benefit of end-users. The work undertaken here also demonstrates how ‘Estimative’ or ‘Probabilistic Language’ can perhaps be better deployed for decision-making purposes.

At its very least, the work introduced here provides several further discussion points deserving of their further consideration as a guide relating to how estimative/probabilistic language conventions can potentially be further optimised in the future.

Bruce Garvey, Adam D. M. Svendsen

Chapter 7. Scoping ‘Digital Twins’ in Intelligence & Strategic Foresight Projects

Abstract

In this chapter, we examine the area of ‘Digital Twins’. Recently breaking much more substantially on to the overall business scene and being increasingly adopted by a wide range of organisations particularly in the early 2020s, a ‘Digital Twin’ (defined more fully, below) essentially enables a digital ‘mirror’ to be created and then run in parallel alongside a ‘real’ or ‘physical’ world product and/or process to system (For further insight here, see the definitions that feature throughout this chapter; see also Violino (2023)).

Bruce Garvey, Adam D. M. Svendsen

ARC White Paper Insights: The Efficacy of Using Generative AI Datasets in Accelerating the IE/SOA Processes and of Broadening Objective Inputs into such Processes

Frontmatter

Chapter 8. Generative-AI Pilot for Problem Spaces: Can ChatGPT Help Develop Scenarios?

Abstract

Building on our findings presented in our previous chapters, the purpose of the chapters in PART III, starting with this chapter, Chap. 8, is to investigate the development of Problem Spaces via a Generative-Artificial Intelligence (AI) ‘Pilot’ study. The main research question posed, here, is: ‘Can ChatGPT help develop Scenarios, such as through helping generate the Problem Spaces?’

The problématique is subsequently broken down into a series of different deep-dive sections, thereby yielding further insights into the research work undertaken. Before delving into the main content of this chapter, some background relating to scenarios and Problem Spaces is first presented to help orientate readers as to the AI-focused discussion that follows.

Many of the Generative-AI technologies referenced, such as notably ChatGPT, are very new and therefore continue to be subject to much rapid development and other associated changes (See, for example, as discussed in Loukides (2023)). In such circumstances, usual caveats apply as to much being in a condition of substantial flux and several of the insights presented can be expected to morph in their detail over time.

Bruce Garvey, Adam D. M. Svendsen

Chapter 9. An Outline for an Interrogative/Prompt Library to Help Improve Output Quality from Generative-AI Datasets

Abstract

This chapter provides insight into an outline for what is termed a proposed ‘Interrogative/Prompt Library’ (IPL) designed to help optimise the quality of output when engaging with Generative-AI (Gen-AI) datasets, such as, most notably, the recently rapidly developing ChatGPT.

We begin with a recap of the authors’ findings from earlier chapters. Next, the importance of questioning Large Language Model (LLM) datasets so that they can be better understood is covered, before investigating ‘interrogatives’ (involving who, why, what, when, where, how, etc. questions) and scoping their role in analytical search processes, including fundamentals relating to how interrogative questions are structured.

Following on from the above work are ‘Phase 1’ suggestions towards building an ‘Interrogative Library Typology’, before delving more specifically into the area and activities of ‘Prompt Engineering’. The chapter then examines the development and maintenance of an Interrogative/Prompt Library, in the form of presenting a second phase. That work includes insight into the ‘Interrogative Prompt Library Engine’ that underpins the above work.

A number of overall Conclusions and Key Takeaways are then tabled, noting especially the guidance value acquired from engaging with the activities discussed throughout the chapter. Thereby, end-users are increasingly better armed for engaging with Gen-AI datasets helping ensure that they best reduce the risks of, amongst others, falling into ‘Garbage In, Garbage Out’ (GIGO) traps.

Finally, we end with a ‘call for action!’ for further research and development relating to what is tabled in the chapter paving the way for further collaboration. Appendices are also included to provide further reference detail.

Bruce Garvey, Adam D. M. Svendsen

Chapter 10. Prompt-Engineering Testing ChatGPT4 and Bard for Assessing Generative-AI Efficacy to Support Decision-Making

Abstract

In this chapter, we examine what the Generative-AI (Gen-AI) systems of OpenAI’s ChatGPT4 and Google’s Bard (from 2024, re-named Gemini) can offer during each stage of the Strategic Options Analysis (SOA) process.

Using a prompt-engineering approach, the work in this chapter has been conducted through running a series of parallel tests of ChatGPT4 and Bard at each stage of the SOA process, resulting in a number of outputs and findings that are presented alongside one another for ready comparison purposes.

Beginning with the rationale for and development of a ‘focus question’, the Gen-AI systems are subsequently tasked on that basis following on from a version conducted manually. The chapter moves through the testing procedure, before delving into depth during the course of each stage of the SOA Process Sequence. The differences in ChatGPT4 and Bard outputs are displayed one after another in a highly comparative manner. They soon demonstrated their strengths and weaknesses, including as their outputs varied over time, such as during the two consecutive days in early June 2023 when the Gen-AI tests were conducted and run in parallel.

Offering some preliminary conclusions and takeaways, in the section focused on Current Prompting Advice, answers are tabled as to the key question asked: Is Gen-AI/ChatGPT better than a manual process? Responses in this section set the scene for the presentation of some overall conclusions and takeaways in the form of both specific and more general insights.

Ultimately, this area continues to be one to watch closely, recalling that the clue is in the name of ‘artificial intelligence’. It is always a requirement to further verify the Gen-AI outputs alongside both ‘human’ and ‘real’ intelligence. In addition, users should properly assess sources, whether they and their province are kept ‘classified’ for a whole slew of legitimate confidentiality reasons, relating to security, privacy, intentions and methods-used requirements.

Bruce Garvey, Adam D. M. Svendsen

Chapter 11. Can Generative-AI (ChatGPT and Bard) Be Used as Red Team Avatars in Developing Foresight Scenarios?

Abstract

This chapter examines the question of whether the Generative-AI (Gen-AI) systems of OpenAI’s ChatGPT and Google’s Bard (from 2024, Google Gemini) have value as ‘Red Team Avatars’ when developing foresight focused scenarios. After some initial explanation, both exploratory scenario and more dystopian science fiction (sci-fi)-style scenarios are drawn upon to provide illumination.

As demonstrated in turn, many limitations—even more profound restrictions—were encountered during the course of the exercises conducted for this chapter. Overall, results could be argued to be somewhat disappointing, in that, as found also in previous chapters, for the Gen-AI systems to be most effective at Red Teaming they required substantial efforts in the area of prompt engineering. The efforts quickly became more resource costly, for example, in terms of the time taken to task the Gen-AI effectively, than when compared to the value that could be elicited by using them.

When not actually denied, reference points soon become more lost in overall background ‘noise’ than realised as extractable ‘signals’. Once more, the findings here remind that properly verified ‘real’ and ‘human’ intelligence has greatest use and value when it comes to sophisticated activities, such as those of and required by Red Teaming, and to other similarly advanced analytical and assessment or estimation activities.

In highly differing circumstances, Gen-AI might be able to assist at best, but it cannot compensate or replace. This conclusion is particularly acute in safety and security terms. End-users and other stakeholders should take close and continuing note.

Bruce Garvey, Adam D. M. Svendsen

Part IV

Frontmatter

Chapter 12. Realising Foresight Intelligence (FORINT): Advancing an Intelligence-Derived Foresight Framework

Abstract

This chapter provides a background overview introduction to Strategic Foresight, further distinguishing it from other closely related, yet also discernibly different, Forecasting and Futures Studies activities when it comes to varying situations and conditions of uncertainty. Showing where scenario development Strategic Options Analysis (SOA) can be applied, the chapter continues by elaborating further on the benefits and value of engaging with foresight activities for a wide range of different stakeholders and across many different business processes, as well as fostering a greater foresight mindset. The contextual background for foresight activities is covered next, including problem-definition work and how conditions of complexity can be navigated. Those last insights are conveyed before examining the questions of sources of data and intelligence, thereby introducing a series of Data Intelligence (DATINT) considerations taken into account in Foresight relevant contexts. These DATINT concerns have an immediate bearing on the wider area of Foresight Intelligence (FORINT) and its conduct, highlighting that those considerations being kept in mind offer substantial help when guiding future activities.

Bruce Garvey, Adam D. M. Svendsen

Chapter 13. The Role of the Scenario and Its Re-assessment

Abstract

This chapter further interrogates what scenarios are, as well as further parsing the role and value of engaging in scenario planning processes. Alongside detailing strengths and advantages, a number of observed current scenario planning process weaknesses and limitations are charted, notably the frequent adoption of 2×2 matrix approaches, which often generate merely 4 different scenarios overall. A major contention this chapter raises pivots around the key question of: Are detailed scenarios worth the effort, if reduced to just 4? It further asks: Is adopting that approach to scenarios an oversimplification and extended abstraction, which fails to adequately capture the complexities involved at their fullest and dynamically enough? Also a greater role for continuously updating data and intelligence input into overall scenario planning processes is equally advocated throughout, again underscoring the importance of Foresight Intelligence (FORINT) and its conduct for constantly upgrading responses and solutions.

Bruce Garvey, Adam D. M. Svendsen

Developing Foresight Intelligence (FORINT): Presenting a Multi-Phase Framework for Intelligence-Derived Scenario Options

Frontmatter

Chapter 14. Advancing a New Methodological Process

Abstract

The methodological process being proposed in this chapter aims to comprehensively and substantially address the issue of scenario complexity. In our proposed model, we have identified some 18 key variables necessary to explore in-depth the characteristics of a ‘typical’ scenario with its inherent complexities being adequately captured. Interpreting foresight as a series of scenarios and where each scenario is seen through a different lens leads us to address the issue as a problem. However, the objective of the foresight process is not so much to solve the problem—which, indeed may not be possible, due to its complexities, interconnectivities and uncertainties, aka its ‘wickedness’—but to understand the structure of the challenge confronted so that the effects of the problem can be, at best, mitigated.

Bruce Garvey, Adam D. M. Svendsen

Chapter 15. Process Implications: Current Software Enhancements, Including Increasing Levels of AI

Abstract

Due to all of the observed limitations encountered thus far throughout the development of a more sophisticated scenario planning process, the necessity arose to explore how other software enhancements, plus the deployment of AI technologies, could help speed up and make more manageable the overall methodology. In Chaps. 8–11 found above in the first half of this book, we explored how Generative-AI (Gen-AI) might help accelerate Strategic Options Analysis (SOA) and scenario planning processes, with very mixed results being found. However, such is the pace of change and development in the broader AI domain that we felt that further research was required to see how and, more crucially, where and when AI could contribute to overcoming, via mitigation, concerns about the complexity of advancing a more enhanced scenario methodology, as found in the second half of this book. This chapter now engages those concerns through sharing the results of a project conducted in close collaboration with a team of students based at Bayes Business School in London.

Bruce Garvey, Adam D. M. Svendsen

Backmatter

Titel: Navigating Uncertainty Using Foresight Intelligence
verfasst von: Bruce Garvey
Adam D. M. Svendsen
Verlag: Springer Nature Switzerland
Electronic ISBN: 978-3-031-66115-0
Print ISBN: 978-3-031-66114-3
DOI: https://doi.org/10.1007/978-3-031-66115-0