Exploring the Relationship Between Knowledge and Fiction (Part 5: How Knowledge Drives Plot)

This is the fifth post in the "Knowledge and Fiction" Series.

Part 5 - How Knowledge Drives Plot: Withholding, Revealing, and Structuring Information in Fiction

Introduction: Knowledge as a Driving Force in Storytelling 

In fiction, knowledge is more than just a backdrop. It can be the engine that drives the entire plot. Some of the most compelling stories revolve around the acquisition, control, or revelation of knowledge. A secret long buried, a piece of forbidden wisdom, or a discovery that changes everything. These elements shape the stakes of a narrative and influence characters' choices in profound ways. 

Think of The Da Vinci Code, where the protagonist races to uncover hidden historical knowledge. Or Dune, where those who control spice production control the universe. In Jurassic Park, genetic knowledge makes the impossible possible, with unanticipated consequences. 

In my own novel-in-progress, I’m grappling with what knowledge is most critical to the story. Right now, I’m exploring multiple threads. But eventually, the plot will dictate what is essential and what fades into the background. 

So how do writers decide what knowledge should be central? And how does the way we reveal (or withhold) knowledge shape the reader’s experience? 

Defining "Critical Knowledge" in Fiction 

What do we mean by “critical knowledge” in a story? It’s the knowledge that: 

• Drives conflict and resolution. 
• Separates those who have power from those who don’t. 
• Forces characters to make choices they wouldn’t otherwise make. 
• Is either hidden, misunderstood, or in the wrong hands. 

Many great novels are built around this kind of knowledge: 

• Magic in Harry Potter – who can access it, how it’s controlled, and the dangers of lost or forbidden spells. 
• Religious knowledge in The Name of the Rose – a battle over books, literacy, and secrecy. 
• Genetic knowledge in Jurassic Park – knowledge that both enables and threatens life itself. 

In each case, knowledge is not just background, it’s a force that moves the plot forward. 

So how do I determine the critical knowledge in my own novel? The key question is: What’s at stake? 

• If this knowledge is lost, what happens? 
• If the wrong person gets it, does it change the course of history? 
• If it is misunderstood, could it lead to catastrophe? 

Right now, I’m in the phase of exploration, pulling together ideas and research that have fascinated me for years. But as I move further into drafting, I’ll need to get sharper about what’s truly necessary versus what’s just interesting to me. 

The Narrative Flow of Knowledge: When and How to Reveal It 

Revealing knowledge in fiction is an art. Writers use a variety of techniques to manipulate what the reader knows and when. Some common approaches include: 

• The Slow Reveal – Used in mystery and detective fiction, where the protagonist (and reader) piece together the truth over time. (Sherlock Holmes, Gone Girl) 
• The Omniscient Reader – The reader knows more than the characters, leading to dramatic irony. (Romeo and Juliet, The Handmaid’s Tale) 
• The Unreliable Narrator – Knowledge is distorted, requiring the reader to question what is true. (Fight Club, Life of Pi) 

The way knowledge unfolds in a novel is just as important as the knowledge itself. If you dump too much information at once, the reader disengages. If you withhold too much, they may feel lost. 

The Teaching Parallel: How We Build Knowledge for Readers 

I keep coming back to the idea that storytelling is a lot like teaching. A teacher can’t just dump a complex concept onto students without first understanding what they already know. Similarly, a novel needs to scaffold knowledge, introducing pieces at the right moment so that the reader can follow, learn, and eventually reach an "aha!" moment. 

Many science fiction and fantasy books do this exceptionally well. Consider The Three-Body Problem, which gradually layers complex physics concepts without overwhelming the reader. Or The Handmaid’s Tale, where the reader slowly pieces together the dystopian society’s rules through the protagonist’s fragmented recollections. 

For my own novel, this means thinking about not just what knowledge is essential, but how and when to reveal it. If the world is too complex upfront, it might alienate readers. But if I introduce knowledge too late, it could feel like a forced twist. 

Knowledge in My Own Novel: Challenges and Choices 

This is where theory meets reality. 

Unlike my previous fiction projects, where I had to research entirely new areas (like synesthesia for a past novel), this time I’m pulling from decades of personal research and professional experience. That should, in theory, make things easier. But in practice, it means I have too many ideas, and I need to start cutting. 

Some questions I’m working through: 

• What knowledge is essential to the conflict? 
• How do I balance speculative elements with realism? 
• Should knowledge be a puzzle for the reader, or should they always be one step ahead of the characters? 

This phase of writing is about experimenting—trying different knowledge structures to see what feels natural for the story. 

Expanding the Role of Knowledge in Fiction 

Beyond just structuring my own novel, I’m thinking about the bigger picture: how different types of knowledge shape different kinds of stories. 

Some possibilities: 

• Knowledge as power – Characters fight to control it (1984, The Hunger Games). 
• Knowledge as burden – A character knows something they wish they didn’t (Oppenheimer and the atomic bomb, Frankenstein). 
• Knowledge as survival – A character must acquire knowledge to stay alive (The Martian, Station Eleven). 

Then there’s the question of narrative structure—should knowledge be delivered in a straightforward chronological story? Or would alternative structures, like epistolary storytelling (Dracula), found documents (House of Leaves), or multiple POVs better serve the themes? 

These are open questions I’m still exploring. 

Closing Thoughts: Beyond the Last Page 

One thing I know for sure: I want my novel to leave the reader with something lasting,  It could be a question, a shift in perspective, or even a lingering sense of uncertainty. 

Some of my favorite books did exactly that. So as I refine my own approach, I’ll be thinking about not just how knowledge functions in my novel, but how it might change the reader’s own knowledge beyond the book itself. 

For now, I’m still in the process of discovery. But that’s what makes writing—and knowledge itself—so fascinating. 

What’s Next? 

As I continue writing, I’ll be reflecting more on: 

• How knowledge structures influence world-building 
• The ethics of knowledge control in fiction 
• Memory, truth, and unreliable narrators 

The next post in the series will explore World Building and Personal Knowledge Management.  Writing a novel isn’t just about creativity.  It’s also about managing vast amounts of knowledge, figuring out how best to organize it, keep research notes, manage manuscript editing, versioning, etc.  

Can Knowledge Management Be the Key to Navigating Federal Workforce Reductions?

My first degree was in Political Science, so I understand that almost everything ultimately ties back to decision-making, values, and politics. Knowledge Management (KM) is no exception. While often described as neutral, KM is, in reality, a tool in the hands of people who have priorities and values to uphold. They will therefore use KM—if they value it at all—to support their priorities.

When discussions arise about reducing the federal workforce, they often center on eliminating waste and redefining the government’s role. While these debates are deeply political, I have aimed to provide a neutral analysis, focusing on how KM plays a vital role in ensuring that institutional knowledge is preserved, efficiency is maximized, and transitions—whether through downsizing or restructuring—are executed without jeopardizing essential government functions. My intent is not to judge ongoing changes but to explore how KM can be leveraged in a shifting landscape.

At the same time, I acknowledge that my perspective is shaped by my career in Knowledge Management. My experience leads me to view KM as a critical enabler of efficiency and continuity. Others may see different priorities, and I welcome discussion on how KM aligns—or does not align—with different viewpoints on government workforce reductions.

KM as a Tool for Efficiency, Not Bureaucracy

A key argument for workforce reduction is inefficiency—too many employees performing redundant or unnecessary tasks. However, KM can enhance efficiency without workforce reductions by:

• Streamlining operations through better information-sharing and collaboration.
• Preventing rework by ensuring employees access lessons learned from past initiatives.
• Implementing AI-powered knowledge retrieval, reducing time spent searching for information.

A well-managed KM system ensures that knowledge flows seamlessly across agencies, reducing unnecessary duplication and allowing government employees to work smarter, not harder.

Preventing Unintended Consequences of Workforce Cuts

While downsizing may reduce costs on paper, it can also create new inefficiencies if critical knowledge is lost. KM mitigates these risks by:

• Preserving institutional memory so agencies don’t lose expertise that takes years to develop.
• Easing onboarding for new or remaining employees, preventing gaps in service delivery.
• Ensuring knowledge continuity for long-term projects, preventing disruption when experienced employees depart.

Without a strong KM strategy, workforce reductions can lead to costly mistakes, inefficiencies, and delays that offset potential savings.

Aligning Knowledge Retention with Changing Government Roles

If the scope of government changes, knowledge must transition accordingly. Whether services are being privatized, decentralized, or restructured, KM helps by:

• Capturing critical knowledge before employees exit.
• Facilitating knowledge transfer between agencies and external partners.
• Ensuring accessible archives of regulations, policies, and historical data for future use.

Additionally, one of the persistent challenges in the federal government is the reliance on antiquated electronic systems that are not well connected. Many agencies continue to use legacy databases and software that are incompatible with modern knowledge-sharing tools. This fragmentation further exacerbates knowledge loss when employees leave, as critical information is often siloed in inaccessible or outdated systems. Addressing this issue requires investments in modernized KM platforms that integrate across agencies, ensuring knowledge remains accessible and actionable even during workforce transitions.

KM doesn’t dictate the size or role of government; it ensures that any transition is managed intelligently and without unnecessary disruption.

Lessons from NASA: Knowledge Management in Workforce Reduction

Having worked with NASA’s KM program at the Goddard Space Flight Center for nearly a decade – as a contractor rather than a civil servant--, I am more familiar with this real world example. I was privileged to attend and present at the January 2011 Knowledge Forum dedicated to Shuttle Lessons learned and to work on case studies documenting some of NASA’s most tragic events. When NASA retired the Space Shuttle program in 2011, thousands of employees, many with specialized knowledge, left the agency. To mitigate knowledge loss, NASA implemented several KM initiatives:

• Knowledge Capture Initiatives: Video interviews, wikis, and structured documentation preserved insights from departing employees.
• Lessons Learned Databases: Institutional knowledge was centralized for use in future space missions.
• Knowledge-Sharing Networks: Retired experts were engaged as consultants to provide continuity for emerging projects.

These KM strategies—while not perfect— helped sustain institutional memory, ensuring that knowledge critical to future missions, including Artemis and commercial spaceflight partnerships, remained accessible despite significant workforce reductions.

As a side note, I came to work for NASA’s KM from a completely different industry, international development, an industry I rejoined later and which is today being destroyed (at least in the US)– sorry, losing neutrality here!

The Role of AI in Workforce Optimization—But AI Alone Is Not Enough

Artificial intelligence (AI) is often touted as a solution to inefficiency in government operations, from automating processes to assisting with decision-making. While AI can enhance KM by improving searchability, generating insights, and automating routine tasks, AI without a strong KM foundation is unlikely to succeed. AI systems rely on structured, well-organized data and knowledge repositories. Without KM ensuring that information is curated, contextualized, and up-to-date, AI risks amplifying errors, reinforcing biases, or failing to deliver meaningful insights.

To effectively integrate AI into government KM strategies, agencies should:

• Ensure high-quality, structured data that AI can access and process accurately.
• Develop AI models that support knowledge retrieval, rather than replacing human expertise.
• Implement ethical AI practices to minimize misinformation and bias.
• Use AI to enhance knowledge-sharing through automation and intelligent recommendations, rather than merely as a cost-cutting tool.

Thus, while AI can support workforce optimization, KM remains the backbone that ensures knowledge is captured, organized, and made actionable. Agencies looking to modernize must invest not only in AI but also in robust KM strategies that ensure AI tools work effectively and ethically.

Contrasting Republican and Democratic Perspectives on KM

The main issue KM professionals encounter isn't related to a Republican/Democrat divide but rather about whether leadership will understand and support KM, especially when KM is often on the chopping block in challenging budget contexts.  Nevertheless, let's try this chain of thoughts.  Both perspectives recognize that losing knowledge is costly, but they might differ on how KM should be applied to government workforce reductions. The key challenge is finding KM strategies that work regardless of political shifts—ensuring government remains effective whether agencies grow, shrink, or transform.

Republican Perspective: KM as a Tool for Efficiency and Lean Government

Republicans generally advocate for a smaller, more efficient government with reduced federal oversight and streamlined operations. From this perspective, KM should be used to:

• Eliminate redundancy and ensure that government functions remain lean and agile.

• Facilitate outsourcing and privatization, ensuring that knowledge transfers smoothly to contractors or state agencies when functions are moved outside the federal workforce.

• Leverage AI and automation to reduce reliance on human-driven processes and decrease operational costs.

• Minimize knowledge retention costs, focusing KM efforts on essential knowledge that directly supports the most critical government functions.

Under this model, KM plays a supportive role in enabling workforce reductions, ensuring that knowledge gaps do not disrupt government services while aligning with broader goals of downsizing and fiscal responsibility.

Democratic Perspective: KM as a Safeguard for Institutional Knowledge and Public Services

Democrats tend to emphasize the stability and continuity of government services, arguing that institutional knowledge is a public asset that should be preserved. From this perspective, KM should be used to:

• Protect knowledge continuity to prevent disruptions in public services caused by workforce reductions.

• Invest in workforce upskilling and retraining, ensuring that government employees can transition into new roles rather than being replaced by external contractors or automation.

• Increase transparency and knowledge equity, ensuring that public access to government knowledge remains robust even when agencies downsize.

• Strengthen cross-agency collaboration, using KM to prevent knowledge silos and ensure institutional expertise remains available across different branches of government.

Under this model, KM is seen as a critical safeguard that ensures government effectiveness and accountability despite workforce reductions.

Bridging the Divide: Common Ground in KM Approaches

While the two perspectives differ in how they approach workforce reductions, they share some common ground in KM applications:

• Risk Mitigation: Both recognize the need to prevent critical knowledge loss that could harm national security, economic stability, or essential public services.

• Data-Driven Decision Making: Regardless of political stance, effective KM strategies rely on data and AI-enhanced insights to guide workforce changes.

• Improved Operational Efficiency: Both perspectives agree that government inefficiencies should be addressed, whether through workforce optimization, better collaboration, or smarter knowledge-sharing systems.

Ultimately, KM must be adaptable to different political priorities, ensuring that it supports workforce transitions in ways that align with broader governance objectives.

Both perspectives recognize that losing knowledge is costly, but they differ on whether KM should support a leaner government (Republican view) or protect institutional continuity (Democratic view). The key challenge is finding KM strategies that work regardless of political shifts—ensuring government remains effective whether agencies grow, shrink, or transform.

A Values-Driven KM Approach to Workforce Decisions

KM may be neutral as a set of tools and frameworks, but its operationalization is not neutral—it reflects the priorities of those who wish to use it. Whether an agency is downsizing, restructuring, or shifting responsibilities, KM provides the framework to:

• Identify and retain essential knowledge.
• Ensure smooth transitions for employees and services.
• Minimize disruptions to government functions.

This is an initial set of ideas, written from the perspective of someone who has spent a career in KM. While my bias toward the importance of KM is clear, I invite discussion on these perspectives and alternative viewpoints.

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There is a literature on KM in the public sector but it tends to assume that politics don't impact public service institutions that much.  This may need revisiting.

For additional light reading:

* "Navigating the Political Landscape: Insights on Knowledge Management and Progressive Politics," by Keith Markovich, January 30, 2025. An interesting take which leans towards advice for Personal Knowledge Management (PKM), which I find to be extremely relevant in today's challenging information environment. 

* "The incoming US administration:  transition, decision-making, and the value of Knowledge Management," by Bill Kaplan, December 4, 2020. Obviously about the previous administration's transition, but advocating "unemotional, evidence-based, analytical, understanding history and lessons learned."  I'm afraid all that gets thrown out the window.