Harnessing the collision of four ways of knowing
By Adrian Wolfberg. Originally published on the Integration and Implementation Insights blog.
How can solving today’s most complex challenges reckon with four fundamentally different ways of knowing? How can the collision of their distinct epistemic strengths and blind spots be harnessed for innovation in threat assessment and decision-making on complex problems?
Let me unpack these four ways of knowing and how they shape, support, and sometimes undermine each other. Here, I use the example of climate security intelligence, but the insights and lessons are likely to apply to a wider range of complex societal and environmental issues. The four ways of knowing are:
- Scientific knowledge from the physical sciences
- Scientific knowledge from the social sciences
- Judgment under uncertainty by knowledge-producing professionals
- Practical decision-making by practitioners who are senior executives.
Using climate security intelligence as the example, the figure below illustrates the divide between science as knowledge, highlighted by the physical and social sciences, and experience as knowledge, highlighted by professional intelligence analysts and practitioners who are senior executives.
1. Scientific Knowledge from the Physical Sciences
Physical scientists—such as climatologists, hydrologists, oceanographers, atmospheric physicists—work in a domain driven by rigorous empiricism, model-based projection, and a demand for precision. Their methods are transparent and peer-reviewed. For these scientists, knowledge is not just testable—it is cumulative, built incrementally through the logic of replication and controlled uncertainty.
As illustrated in the figure above, these scientists operate primarily on the left-hand side of the knowledge production spectrum: science as knowledge. Their insights, while indispensable, rarely grapple with the political or social meaning of those insights. Notably, while the science of climate change is increasingly robust, its implications for security are often probabilistic and indirect.
2. Scientific Knowledge from the Social Sciences
Social scientists explore how societies respond or fail to respond to stress associated with complex problems. For example, political scientists analyze regime stability; economists forecast financial disruption; anthropologists and sociologists trace cultural resilience and migration. Their methods are more interpretive, often operating through qualitative analysis, comparative case studies, and scenario building.
Yet their orientation toward explanation over prediction introduces a different kind of rigor, one that values context, complexity, and contingency. Still, many knowledge-producing professionals and policymaker practitioners may find the assumptions of the social sciences elusive or even contradictory, particularly when causality is difficult to establish. Here again, the above figure reveals the social sciences as essential but sometimes epistemically siloed.
3. Judgment Under Uncertainty by Knowledge-Producing Professionals
The third way of knowing comes from knowledge-producing professionals. In relation to climate security intelligence, they can come from national security, defense, or even the commercial sector. These professionals are experts in drawing conclusions under conditions of ambiguity, limited data, and high stakes. They produce judgments rather than certainties. Their craft is grounded in inference, analytic tradecraft, and structured methods that often prioritize decision-relevance over academic rigor. Their knowledge generally aims to be “good enough” to support action.
The following figure illustrates just how deep the cultural divide can be between academic and professional organizations, with the latter again focusing on professionals from climate security intelligence. The former prioritizes openness, debate, and long-term knowledge production. The latter thrives on compartmentalization, classification, and the logic of immediate utility. These divergent cultural norms create friction at the boundary of collaboration.
4. Practical Decision-Making by Practitioners who are Senior Executives
The final way of knowing by decision-making practitioners is perhaps the most grounded and the most political. It is the realm of mayors, chief executive officers, generals, agency heads, and elected officials. These decision-makers must act, often under pressure and with incomplete knowledge. For them, decisions are not abstractions but commitments with real-world consequences. Their judgments are shaped not only by facts and forecasts but by power, values, constituencies, and risk calculus.
I describe this as the “practical knowing” of those embedded in social, political, and economic institutions. Their decisions shape the futures that science models, that social science explains, and that intelligence warns about.
Why These Four Ways Clash—and Must Be Integrated
These four ways of knowing are not just epistemological differences; they are institutional, cultural, and cognitive distinctions that define how people see the world and act within it. They create knowledge boundaries that are hard to cross.
The first figure above offers a visual taxonomy of the transdisciplinary participants needed to bridge these boundaries. It distinguishes not only domains (science versus experience) but also institutional affiliations from public to private, for-profit to non-profit, and indigenous communities to government authorities.
What’s missing in most efforts to integrate these perspectives is a shared mental model of how each contributes to the whole. Integration is not consensus. It is about coherence without uniformity. That means allowing each way of knowing to retain its integrity while contributing to a shared understanding of risk, threat, and opportunity.
A Tool for Integration: The Knowledge-Producing Professional Mindset, Reimagined
An important tool for bringing these ways of knowing together is to reimagine the role of the knowledge-producing professional in this mix, in this case viewing the intelligence community more broadly. This requires adaptation by this community.
Intelligence, like other knowledge generation efforts, when viewed as a platform for knowledge integration, has the potential to be more than a collector of data and/or secrets. It can be a bridge-builder across epistemic divides. But this requires a shift in mindset from control to collaboration, from secrecy to transparency where possible, and from rigid analytic frames to adaptive synthesis. In this vision, intelligence is not just a producer of classified reports. It is a co-creator of transdisciplinary insight.
The Path Ahead
Tackling complex problems requires invention of a new capability, one that thrives on difference, invites discomfort, and makes use of multiple logics of inquiry. This requires hybrid roles, flexible institutions, and leaders who can recognize the value of judgment, evidence, experience, and politics, all at once. As described above, one contribution to achieving this is through reimagining the professional mindset.
What other options can you see? How else can the collision of these four ways of knowing be reconceptualized from being a problem to be solved to a resource to be cultivated?
To find out more:
Wolfberg, A. (2025). Climate security intelligence: From knowledge transfer to co-creation. Springer: Cham, Switzerland. (Online) (DOI): https://doi.org/10.1007/978-3-031-86259-5
Use of Generative Artificial Intelligence (AI) Statement: Generative artificial intelligence was used as a copyediting tool to improve grammar and spelling in the development of this i2Insights contribution. (For i2Insights policy on generative artificial intelligence please see https://i2insights.org/contributing-to-i2insights/guidelines-for-authors/#artificial-intelligence.)
Biography:
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Adrian Wolfberg PhD is currently the founder and president of Organizational Insight Consulting LLC and an independent scholar in Silver Spring, Maryland, USA. His research interests include human versus generative artificial cognition, knowledge transfer, decision-making, effects of information overload and ambiguity on knowledge production, organizational learning, creativity, organizational and temporal boundaries, and boundary crossing. |
Article source: Harnessing the collision of four ways of knowing. Republished by permission.
Header image source: Created by Bruce Boyes with Microsoft Designer Image Creator.
