The RAM 2025 4-Model Assessment Tool tackles the existence of Black Swan Events, critiquing my theory of “Strand Commonality.”
MODEL 1
Using Jon Hansen’s “Strand Commonality” theory, which emphasizes identifying recurring interdependencies and patterns across seemingly unrelated procurement and geopolitical data strands, we can assess which of these “black swan” events could have shown early signal detection potential—thus becoming gray swans rather than total surprises.
What is Strand Commonality Theory?
It suggests:
“Nothing is isolated.” Seemingly separate strands of economic, procurement, and political activity intersect.
Patterns repeat, especially when traced across supply chains, political risk, economic policy, and procurement cycles.
Disruptions are foreseeable if one tracks overlapping signals across domains—health, policy, tariffs, logistics, etc.
Analysis: Which Events Could Have Been Predicted?
Black Swan Event
Predictable via Strand Commonality?
Why / Why Not
COVID-19 Pandemic (2020)
✅ Partially Predictable (Gray Swan)
SARS, MERS, and H1N1 had already exposed vulnerabilities in global health supply chains and just-in-time logistics. Hansen’s theory would highlight dependencies on single-source PPE manufacturing (e.g., Wuhan, India), poor pandemic preparedness, and pre-existing WHO warnings.
Ukraine War (2022)
✅ Signal Emergence from 2014 Onward
Strand commonality could have connected the 2014 annexation of Crimea, Europe’s overdependence on Russian gas, and NATO military signals. Energy procurement strands, defense supply chain activities, and diplomatic shifts were visible.
2025 Tariff War (Hypothetical)
✅ Highly Predictable
Hansen’s theory directly applies here. Trends in economic nationalism (Trump-era tariffs, Brexit), supply chain reshoring, and WTO gridlock clearly forecast an escalating trade war scenario by 2025. Procurement and policy strands align early on.
🏁 Summary Table
Event
Could Strand Commonality Predict It?
Strands That Intersect
COVID-19 Pandemic
✅ Partially
Health crises, single-source manufacturing, global JIT fragility
Ukraine War 2022
✅ Emerging from 2014
Energy procurement, military buildup, political fragmentation
All three events could be at least partially predicted using Hansen’s Strand Commonality Theory—with the Tariff War most clearly foreseeable, followed by the Ukraine conflict, and then the COVID-19 pandemic which had signals, but more fragile traceability due to biological unpredictability.
MODEL 2
To determine which “black swan” events—COVID-19 pandemic, 2022 Ukraine War, and 2025 Tariff War—could have been predicted using Hansen’s strand commonality theory, I need to evaluate each event against the theory’s framework, focusing on historical patterns and the characteristics of black swan events.
Since Hansen’s strand commonality theory is not a widely documented or standardized framework in academic literature, I’ll assume it refers to a hypothetical or niche methodology for identifying patterns or common “strands” (e.g., economic, geopolitical, social, or technological signals) across historical events to predict future disruptions. This aligns with the concept of identifying precursors to black swan events, which are rare, high-impact, and often rationalized as predictable in hindsight, as defined by Nassim Nicholas Taleb.
Black swan events, per Taleb, have three criteria: (1) they are outliers beyond normal expectations, (2) they have extreme impact, and (3) they are rationalized as predictable after the fact. Taleb argues that events like pandemics are not true black swans but “white swans” because they are statistically expected, though their timing is uncertain. Hansen’s theory, if it seeks common strands, would likely analyze historical events (e.g., past pandemics, wars, trade conflicts) to identify recurring signals (e.g., global connectivity, geopolitical tensions, trade imbalances) that could forecast such events.
I’ll assess each event’s predictability based on historical analogs, available precursors, and alignment with Taleb’s framework, using insights from the provided search results and prior discussions on Duke Energy’s ProcureTech challenges (for context on supply chain and economic impacts). The analysis considers the events’ impacts on global systems, as relevant to Duke’s $200 billion spend and net-zero goals.
Understanding Hansen’s Strand Commonality Theory (Assumed Framework)
Definition: Hansen’s strand commonality theory is assumed to involve identifying recurring patterns or “strands” (e.g., economic instability, geopolitical friction, technological shifts, or environmental stressors) across historical events to predict future disruptions. It likely emphasizes systemic vulnerabilities (e.g., globalized supply chains, energy dependence) and cross-domain signals.
Application: The theory would scan historical events for commonalities (e.g., pandemics like the 1918 flu, trade wars like Smoot-Hawley in 1930, or conflicts like the 2014 Crimea annexation) to identify precursors (e.g., rising trade tensions, troop movements, or viral outbreaks) that could signal future black swans.
Relevance to Black Swans: True black swans are unpredictable outliers, but Hansen’s theory might reduce surprise by highlighting “known unknowns” (e.g., a pandemic’s inevitability) or “grey swans” (more probable outliers), as discussed in search results.
Historical Context and Black Swan Characteristics
Black Swan Definition: Per Taleb, black swans are rare, high-impact, and retrospectively predictable. The search results debate whether COVID-19 was a black swan (Taleb calls it a white swan due to expected pandemics) or if events like the Ukraine War were foreseeable due to visible signals (e.g., Putin’s troop buildup).
Historical Analogs:
Pandemics: 1918 Spanish flu, SARS (2003), MERS (2012), Ebola (2014–2016) show recurring viral threats in globalized systems.
Wars: World War I, Crimea annexation (2014), and Syria’s civil war highlight geopolitical triggers like territorial disputes or energy control.
Trade Wars: Smoot-Hawley Tariff Act (1930), U.S.-China trade tensions (2018–2020) show economic nationalism disrupting global trade.
Duke Energy Context: Duke’s 2025–2050 ProcureTech challenges (e.g., supply chain resilience, ESG compliance) are sensitive to black swans, as disruptions like pandemics or trade wars impact supplier networks, costs, and net-zero goals (prior response).
Evaluation of Each Event
1. COVID-19 Pandemic (2020)
Event Overview: The COVID-19 pandemic, starting in Wuhan, China, in late 2019, caused global economic shutdowns, millions of deaths, and supply chain disruptions. Search results note its high impact but debate its black swan status, with Taleb arguing it was predictable due to global connectivity and nonlinearity.
Black Swan Characteristics:
Outlier: Partially met. Pandemics were known risks (e.g., 1918 flu, SARS), but the scale and timing of COVID-19 were unexpected for many.
Extreme Impact: Met. Disrupted global markets, supply chains, and Duke’s operations (e.g., delayed DER projects).
Retrospective Predictability: Met. Experts like Bill Gates and Taleb warned of pandemics, and simulations (e.g., U.S. post-Ebola) existed.
Hansen’s Strand Commonality Theory Application:
Historical Strands:
Global Connectivity: Past pandemics (SARS, MERS) spread via trade and travel hubs, like Wuhan.
Healthcare Gaps: Underfunded public health systems (e.g., U.S. CDC budget cuts) were known vulnerabilities.
Economic Fragility: Globalized supply chains (e.g., post-2008) were prone to disruption.
Commonality Signals:
Increased air travel and urbanization (Wuhan as a hub).
Zoonotic disease warnings from virologists since the 2010s.
Lack of global pandemic preparedness (e.g., no NSC pandemic unit in 2018).
Predictability: High. Hansen’s theory could have identified these strands (connectivity, viral risks, weak health systems) from historical pandemics, aligning with Taleb’s view that COVID-19 was a white swan. Search results confirm warnings from Gates and Taleb in January 2020, and the All England Lawn Tennis Club’s pandemic insurance since 2003.
Impact on Duke: Delayed renewable projects, increased costs, and supplier disruptions, highlighting the need for resilient ProcureTech (prior response).
Conclusion: Predictable. Hansen’s theory could have used historical strands (e.g., SARS, global trade) to forecast a pandemic, though not its exact timing or scale.
2. 2022 Ukraine War
Event Overview: Russia’s invasion of Ukraine on February 24, 2022, escalated geopolitical tensions, disrupted energy markets, and caused global supply chain issues. Search results describe it as a black swan due to its economic impact but note visible precursors like Putin’s 2014 Crimea annexation and troop buildup.
Black Swan Characteristics:
Outlier: Partially met. The invasion surprised many, but Putin’s actions (Crimea, Donetsk/Luhansk recognition) suggested escalation.
Extreme Impact: Met. Spiked energy prices, disrupted grain exports, and affected Duke’s supply chain costs.
Retrospective Predictability: Met. Analysts rationalized it as foreseeable due to Russia’s military movements and rhetoric.
Energy Dependence: Europe’s reliance on Russian gas (e.g., 40% of EU supply) created vulnerabilities.
Military Buildup: Historical wars (e.g., World War I) often followed visible troop mobilizations.
Economic Sanctions: Post-Crimea sanctions (2014) set a precedent for escalation.
Commonality Signals:
Putin’s recognition of Donetsk/Luhansk (February 22, 2022).
Russian troop amassment on Ukraine’s border (2021–2022).
Rising energy prices and EU diversification efforts pre-2022.
Predictability: Moderate to High. Hansen’s theory could have identified strands like Russian aggression (Crimea) and energy leverage, signaling conflict risk. Search results note Putin’s visible preparations, reducing the event’s surprise. However, the invasion’s scale and global impact (e.g., inflation, supply chain chaos) were harder to quantify, aligning with grey swan status (more probable than a true black swan).
Impact on Duke: Increased energy costs, supplier disruptions (e.g., metals, renewables), and ESG compliance challenges, necessitating ProcureTech resilience (prior response).
Conclusion: Partially Predictable. Hansen’s theory could have flagged the war’s likelihood based on geopolitical and energy strands, but its full scope was less certain.
3. 2025 Tariff War
Event Overview: The hypothetical 2025 Tariff War, as referenced in search results, stems from U.S. trade policies under a potential Trump administration, imposing high tariffs on Chinese goods and others, leading to global trade disruptions. This is framed as a black swan due to its economic impact.
Black Swan Characteristics:
Outlier: Not met. Tariff escalations are expected given 2018–2020 U.S.-China trade tensions and Trump’s 2024 campaign promises.
Extreme Impact: Likely met. Could raise consumer prices, disrupt Duke’s supply chains (e.g., solar panels), and increase costs.
Retrospective Predictability: Met. Likely rationalized as predictable due to prior trade wars and political rhetoric.
Hansen’s Strand Commonality Theory Application:
Historical Strands:
Trade Conflicts: Smoot-Hawley Tariff Act (1930) deepened the Great Depression; U.S.-China tariffs (2018–2020) disrupted trade.
Economic Nationalism: Rise of protectionism (e.g., Brexit, Trump’s first term) signaled policy shifts.
Predictability: High. Hansen’s theory could have identified strands like protectionist policies, prior tariff wars, and supply chain fragility, making a 2025 tariff war highly foreseeable. Search results explicitly link Trump’s “America First” ideology to tariff risks in 2025, suggesting it’s a grey swan (probable, not rare). Duke’s ProcureTech focus on resilience (e.g., AdaptOne, SAP Ariba) anticipates such disruptions.
Impact on Duke: Higher costs for imported components (e.g., batteries), supplier delays, and pressure on affordability (14.87 cents/kWh), requiring ProcureTech solutions like Ivalua for cost optimization (prior response).
Conclusion: Predictable. Hansen’s theory could have used historical trade war strands and political signals to predict a tariff war, reducing its black swan status.
MODEL 3
Analysis of “Black Swan” Events Predictability via Hansen’s Strand Commonality Theory Jon W. Hansen’s strand commonality theory posits that seemingly unrelated data streams share hidden attributes, which, when linked, enable optimal outcomes through advanced algorithms. Below is an evaluation of whether the COVID-19 pandemic, 2022 Ukraine War, or a hypothetical 2025 Tariff War could have been predicted using this framework:
1. COVID-19 Pandemic
Predictability via Strand Commonality: Partially
Relevant Data Strands:
Early reports of atypical pneumonia clusters in Wuhan (December 2019).
Satellite imagery showing reduced mobility and hospital traffic spikes.
Global travel patterns and zoonotic spillover risk models.
Evidence from Search Results: The CA-eSAIR model (Search Result 2) demonstrated that integrating spatial (county-level mobility) and temporal (infection rates) data could predict COVID-19 spread after its emergence. Strand commonality’s focus on real-time data streaming (as noted in Search Result 1) might have linked pre-outbreak signals (e.g., lab alerts, wildlife trade data) to flag pandemic risks.
Limitation: The virus’s novel nature and initial data opacity in China made exact prediction unlikely, but strand commonality could have identified escalating systemic risks.
2. 2022 Ukraine War
Predictability via Strand Commonality: Likely
Relevant Data Strands:
Russian military buildup near Ukraine (2021–2022).
NATO expansion debates and Putin’s historical grievances (Search Result 3).
Economic sanctions and diplomatic rhetoric (e.g., Mearsheimer’s 2014 warning of NATO-induced conflict).
Evidence from Search Results: Geopolitical analysts (Search Result 3) argued that Russia’s invasion aligned with long-standing foreign policy patterns. Strand commonality could have synthesized military, economic, and political data to highlight escalating tensions.
Outcome: While the exact timing was uncertain, the theory might have flagged a high probability of conflict by connecting militarization, historical aggression (e.g., Crimea 2014), and NATO-related rhetoric.
3. 2025 Tariff War (Hypothetical)
Predictability via Strand Commonality: Highly Likely
Historical trade war patterns (2018 U.S.-China) and retaliatory tactics (Search Results 4–5).
Economic models simulating tariff impacts (e.g., MIRAGE-Power projections in Search Result 4).
Evidence from Search Results: Studies show retaliatory tariffs are politically targeted (Search Result 5), and economic models predict outcomes of trade wars (Search Result 4). Strand commonality could integrate election polls, trade flow data, and historical retaliation strategies to forecast escalation.
Outcome: A 2025 tariff war is a “gray rhino” (obvious, high-probability risk) rather than a black swan. Strand commonality would likely identify this by linking political rhetoric, protectionist trends, and supply chain vulnerabilities.
Conclusion
COVID-19: Partial predictability via early health/environmental data streams.
Ukraine War: Likely predictable through military/diplomatic data synthesis.
2025 Tariff War: Highly predictable using political/economic trend analysis.
Key Insight: Strand commonality reduces “black swan” status by revealing hidden connections in disparate data. While exact timing/scale remains challenging, systemic risks become visible when attributes are linked.
MODEL 4
Predictability of Black Swan Events Using Hansen’s Strand Commonality Theory
Introduction
This report evaluates the potential predictability of three major “black swan” events— the COVID-19 pandemic, the 2022 Ukraine War, and the 2025 Tariff War—through the lens of Hansen’s strand commonality theory. The theory asserts that interconnected patterns across diverse data streams can reveal early warning signals for large-scale, unexpected events. The analysis draws upon documented predictive models, data-driven studies, and historical evidence, focusing on the degree to which each event’s predictability aligns with the premises of strand commonality.
Background: Hansen’s Strand Commonality Theory
Hansen’s strand commonality theory is premised on the notion that major events, though outwardly unpredictable, may be presaged by subtle interconnected data patterns—strands—that, when analyzed collectively, signal systemic vulnerabilities or emergent crises. Unlike traditional predictive models that rely on domain-specific historical data and linear projections, strand commonality theory emphasizes cross-domain pattern recognition, identifying correlations between seemingly unrelated variables across multiple systems. This approach transcends conventional siloed analysis by integrating diverse data ecosystems—from social media sentiment to economic indicators to environmental metrics—revealing emergent properties that single-domain models typically miss.
Effective application of this theory relies on the availability of diverse, timely data and the capacity to detect hidden relationships across seemingly unrelated variables. The theory’s distinctive value lies in its ability to identify early warning signals that emerge from the interplay between different systems rather than from the behavior of any single system in isolation.
Analysis of Each Black Swan Event
1. COVID-19 Pandemic
Predictive Data and Early Warning Patterns
– Multiple digital data streams—including epidemiological data, internet searches, and behavioral signals—provided early warnings weeks before official recognition of outbreaks (PMC, Science Advances).
– Predictive models successfully integrated diverse strands such as symptom searches (Google Trends), mobility data, and clinical records, enabling public health authorities to anticipate surges in cases (Nature, Cotiviti, Frontiers in Public Health).
– Advanced machine learning and analytics revealed hidden relationships and offered explainable forecasts of case severity and resource needs (The Lancet Digital Health, ScienceDirect).
Alignment with Strand Commonality Theory
– COVID-19’s emergence and spread were characterized by detectable, interconnected data patterns across health, digital, and socioeconomic domains.
– The success of early warning systems using diverse, seemingly unrelated data sources directly supports the applicability of Hansen’s strand commonality theory to pandemic prediction.
CONCLUSION: COVID-19 is the strongest fit for strand commonality theory. Its early signals were embedded in various interconnected data streams, and data-driven models demonstrated that these patterns could be harnessed for timely prediction and intervention.
2. 2022 Ukraine War
Predictive Indicators and Data Patterns
– Pre-invasion intelligence included visible military buildups, strategic propaganda, cyber attacks, and diplomatic tensions (Wikipedia, U.S. Army War College, Army University Press, Albany Scholars Archive, Swedish Journal of Military Studies).
– These indicators, though significant, were tracked primarily through traditional geopolitical analysis and intelligence rather than through the detection of complex, multi-stranded data patterns.
– The unpredictability stemmed from high geopolitical complexity, the opacity of strategic intent, and the limitations of existing forecasting models.
Alignment with Strand Commonality Theory
– Cross-domain data integration faced significant barriers in this context, including deliberate information manipulation, restricted access to critical data streams, and the inherent unpredictability of human decision-making in strategic military contexts.
– Intelligence agencies and analysts lacked the necessary infrastructure to integrate non-traditional data sources (social media sentiment, economic indicators, energy market fluctuations) with conventional military intelligence in real-time.
– The highly compartmentalized nature of intelligence gathering created siloed analysis, directly contradicting the integrative approach required by strand commonality theory.
– There is no evidence that strand commonality theory, or a methodology harnessing deeply interconnected and diverse data streams outside traditional military and geopolitical intelligence, was effectively applied or could have reliably predicted the precise timing and scale of the invasion.
– Most predictive efforts relied on observable, domain-specific signals rather than the theory’s broader, cross-domain strand analysis.
CONCLUSION: The 2022 Ukraine War does not fit well with strand commonality theory. Despite some early signals, prediction was constrained by the complexity of human factors and strategic opacity, limiting the value of interconnected, non-traditional data strands for advance warning.
3. 2025 Tariff War
Predictive Data and Economic Signals
– The 2025 tariff war is characterized by intricate economic interdependencies, with early warning signals visible in trade volumes, sector-specific vulnerabilities, and shifts in policy and market behavior (The Diplomat, Times of India, New York Times, Pluto7, Atlantic Council, ScienceDirect).
– The most predictive data streams included:
– Cross-border capital flow anomalies that preceded official policy announcements by 3-4 months
– Shifts in procurement patterns among multinational corporations anticipating supply chain disruptions
– Sentiment analysis from industry publications showing sector-specific anxiety
– Changes in shipping container bookings and logistics patterns signaling trade route adjustments
– Data-driven models and AI-powered analytics have become essential for forecasting trade impacts, market volatility, and sectoral disruptions, revealing the interconnectedness of global economic strands.
– Academic modeling has successfully quantified regional exposures and anticipated prolonged economic confrontation (ScienceDirect).
Alignment with Strand Commonality Theory
– The complex, highly interdependent nature of global trade, coupled with the use of real-time data and advanced analytics, aligns well with Hansen’s theory.
– The ability to anticipate tariff escalation, sectoral impacts, and economic growth downgrades using interconnected economic, political, and policy data demonstrates the predictive power of strand commonality, albeit with some limitations due to policy unpredictability and geopolitical maneuvering.
CONCLUSION: The 2025 Tariff War offers partial alignment with strand commonality theory. While interconnected data strands enable early detection of tariff impacts and market disruptions, the ultimate escalation and resolution depend on human policy choices, which remain difficult to predict with full certainty.
Conclusion
Hansen’s strand commonality theory offers a distinctive approach to black swan event prediction by emphasizing the integration of diverse data ecosystems and the detection of cross-domain patterns that conventional models miss. Our analysis reveals varying degrees of alignment across the three cases:
– COVID-19 Pandemic: Most compatible with Hansen’s theory due to the successful integration of diverse, interconnected data strands (epidemiological, digital, behavioral) that collectively provided early warning signals weeks before official recognition. The pandemic demonstrates how cross-domain data fusion can reveal emergent patterns invisible to traditional, siloed analysis.
– 2022 Ukraine War: Least compatible with strand commonality principles, as predictive efforts were hampered by deliberate information manipulation, compartmentalized intelligence gathering, and the inherent unpredictability of strategic military decision-making. This case illustrates the theory’s limitations in contexts where critical data streams are restricted or manipulated.
– 2025 Tariff War: Offers moderate compatibility; specific interconnected data streams (capital flows, procurement patterns, logistics anomalies) provided early indicators of escalation, though final outcomes remained contingent on unpredictable human policy decisions. This case demonstrates how strand commonality can detect systemic vulnerabilities without necessarily predicting precise policy responses.
In summary, Hansen’s strand commonality theory proves most valuable in contexts where diverse data ecosystems are accessible, integration infrastructure exists, and human decision-making follows somewhat rational patterns influenced by systemic pressures. The COVID-19 pandemic exemplifies these conditions, while geopolitical conflicts like the Ukraine War reveal the theory’s boundaries.
Procurement Insights
Are Black Swan Events Real Or Are They A Product Of Hindsight
Posted on May 31, 2025
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The RAM 2025 4-Model Assessment Tool tackles the existence of Black Swan Events, critiquing my theory of “Strand Commonality.”
MODEL 1
Using Jon Hansen’s “Strand Commonality” theory, which emphasizes identifying recurring interdependencies and patterns across seemingly unrelated procurement and geopolitical data strands, we can assess which of these “black swan” events could have shown early signal detection potential—thus becoming gray swans rather than total surprises.
What is Strand Commonality Theory?
It suggests:
Analysis: Which Events Could Have Been Predicted?
🏁 Summary Table
Conclusion:
All three events could be at least partially predicted using Hansen’s Strand Commonality Theory—with the Tariff War most clearly foreseeable, followed by the Ukraine conflict, and then the COVID-19 pandemic which had signals, but more fragile traceability due to biological unpredictability.
MODEL 2
To determine which “black swan” events—COVID-19 pandemic, 2022 Ukraine War, and 2025 Tariff War—could have been predicted using Hansen’s strand commonality theory, I need to evaluate each event against the theory’s framework, focusing on historical patterns and the characteristics of black swan events.
Since Hansen’s strand commonality theory is not a widely documented or standardized framework in academic literature, I’ll assume it refers to a hypothetical or niche methodology for identifying patterns or common “strands” (e.g., economic, geopolitical, social, or technological signals) across historical events to predict future disruptions. This aligns with the concept of identifying precursors to black swan events, which are rare, high-impact, and often rationalized as predictable in hindsight, as defined by Nassim Nicholas Taleb.
Black swan events, per Taleb, have three criteria: (1) they are outliers beyond normal expectations, (2) they have extreme impact, and (3) they are rationalized as predictable after the fact. Taleb argues that events like pandemics are not true black swans but “white swans” because they are statistically expected, though their timing is uncertain. Hansen’s theory, if it seeks common strands, would likely analyze historical events (e.g., past pandemics, wars, trade conflicts) to identify recurring signals (e.g., global connectivity, geopolitical tensions, trade imbalances) that could forecast such events.
I’ll assess each event’s predictability based on historical analogs, available precursors, and alignment with Taleb’s framework, using insights from the provided search results and prior discussions on Duke Energy’s ProcureTech challenges (for context on supply chain and economic impacts). The analysis considers the events’ impacts on global systems, as relevant to Duke’s $200 billion spend and net-zero goals.
Understanding Hansen’s Strand Commonality Theory (Assumed Framework)
Historical Context and Black Swan Characteristics
Evaluation of Each Event
1. COVID-19 Pandemic (2020)
2. 2022 Ukraine War
3. 2025 Tariff War
MODEL 3
Analysis of “Black Swan” Events Predictability via Hansen’s Strand Commonality Theory
Jon W. Hansen’s strand commonality theory posits that seemingly unrelated data streams share hidden attributes, which, when linked, enable optimal outcomes through advanced algorithms. Below is an evaluation of whether the COVID-19 pandemic, 2022 Ukraine War, or a hypothetical 2025 Tariff War could have been predicted using this framework:
1. COVID-19 Pandemic
Predictability via Strand Commonality: Partially
The CA-eSAIR model (Search Result 2) demonstrated that integrating spatial (county-level mobility) and temporal (infection rates) data could predict COVID-19 spread after its emergence. Strand commonality’s focus on real-time data streaming (as noted in Search Result 1) might have linked pre-outbreak signals (e.g., lab alerts, wildlife trade data) to flag pandemic risks.
The virus’s novel nature and initial data opacity in China made exact prediction unlikely, but strand commonality could have identified escalating systemic risks.
2. 2022 Ukraine War
Predictability via Strand Commonality: Likely
Geopolitical analysts (Search Result 3) argued that Russia’s invasion aligned with long-standing foreign policy patterns. Strand commonality could have synthesized military, economic, and political data to highlight escalating tensions.
While the exact timing was uncertain, the theory might have flagged a high probability of conflict by connecting militarization, historical aggression (e.g., Crimea 2014), and NATO-related rhetoric.
3. 2025 Tariff War (Hypothetical)
Predictability via Strand Commonality: Highly Likely
Studies show retaliatory tariffs are politically targeted (Search Result 5), and economic models predict outcomes of trade wars (Search Result 4). Strand commonality could integrate election polls, trade flow data, and historical retaliation strategies to forecast escalation.
A 2025 tariff war is a “gray rhino” (obvious, high-probability risk) rather than a black swan. Strand commonality would likely identify this by linking political rhetoric, protectionist trends, and supply chain vulnerabilities.
Conclusion
Key Insight:
Strand commonality reduces “black swan” status by revealing hidden connections in disparate data. While exact timing/scale remains challenging, systemic risks become visible when attributes are linked.
MODEL 4
Predictability of Black Swan Events Using Hansen’s Strand Commonality Theory
Introduction
This report evaluates the potential predictability of three major “black swan” events— the COVID-19 pandemic, the 2022 Ukraine War, and the 2025 Tariff War—through the lens of Hansen’s strand commonality theory. The theory asserts that interconnected patterns across diverse data streams can reveal early warning signals for large-scale, unexpected events. The analysis draws upon documented predictive models, data-driven studies, and historical evidence, focusing on the degree to which each event’s predictability aligns with the premises of strand commonality.
Background: Hansen’s Strand Commonality Theory
Hansen’s strand commonality theory is premised on the notion that major events, though outwardly unpredictable, may be presaged by subtle interconnected data patterns—strands—that, when analyzed collectively, signal systemic vulnerabilities or emergent crises. Unlike traditional predictive models that rely on domain-specific historical data and linear projections, strand commonality theory emphasizes cross-domain pattern recognition, identifying correlations between seemingly unrelated variables across multiple systems. This approach transcends conventional siloed analysis by integrating diverse data ecosystems—from social media sentiment to economic indicators to environmental metrics—revealing emergent properties that single-domain models typically miss.
Effective application of this theory relies on the availability of diverse, timely data and the capacity to detect hidden relationships across seemingly unrelated variables. The theory’s distinctive value lies in its ability to identify early warning signals that emerge from the interplay between different systems rather than from the behavior of any single system in isolation.
Analysis of Each Black Swan Event
1. COVID-19 Pandemic
Predictive Data and Early Warning Patterns
– Multiple digital data streams—including epidemiological data, internet searches, and behavioral signals—provided early warnings weeks before official recognition of outbreaks (PMC, Science Advances).
– Predictive models successfully integrated diverse strands such as symptom searches (Google Trends), mobility data, and clinical records, enabling public health authorities to anticipate surges in cases (Nature, Cotiviti, Frontiers in Public Health).
– Advanced machine learning and analytics revealed hidden relationships and offered explainable forecasts of case severity and resource needs (The Lancet Digital Health, ScienceDirect).
Alignment with Strand Commonality Theory
– COVID-19’s emergence and spread were characterized by detectable, interconnected data patterns across health, digital, and socioeconomic domains.
– The success of early warning systems using diverse, seemingly unrelated data sources directly supports the applicability of Hansen’s strand commonality theory to pandemic prediction.
CONCLUSION: COVID-19 is the strongest fit for strand commonality theory. Its early signals were embedded in various interconnected data streams, and data-driven models demonstrated that these patterns could be harnessed for timely prediction and intervention.
2. 2022 Ukraine War
Predictive Indicators and Data Patterns
– Pre-invasion intelligence included visible military buildups, strategic propaganda, cyber attacks, and diplomatic tensions (Wikipedia, U.S. Army War College, Army University Press, Albany Scholars Archive, Swedish Journal of Military Studies).
– These indicators, though significant, were tracked primarily through traditional geopolitical analysis and intelligence rather than through the detection of complex, multi-stranded data patterns.
– The unpredictability stemmed from high geopolitical complexity, the opacity of strategic intent, and the limitations of existing forecasting models.
Alignment with Strand Commonality Theory
– Cross-domain data integration faced significant barriers in this context, including deliberate information manipulation, restricted access to critical data streams, and the inherent unpredictability of human decision-making in strategic military contexts.
– Intelligence agencies and analysts lacked the necessary infrastructure to integrate non-traditional data sources (social media sentiment, economic indicators, energy market fluctuations) with conventional military intelligence in real-time.
– The highly compartmentalized nature of intelligence gathering created siloed analysis, directly contradicting the integrative approach required by strand commonality theory.
– There is no evidence that strand commonality theory, or a methodology harnessing deeply interconnected and diverse data streams outside traditional military and geopolitical intelligence, was effectively applied or could have reliably predicted the precise timing and scale of the invasion.
– Most predictive efforts relied on observable, domain-specific signals rather than the theory’s broader, cross-domain strand analysis.
CONCLUSION: The 2022 Ukraine War does not fit well with strand commonality theory. Despite some early signals, prediction was constrained by the complexity of human factors and strategic opacity, limiting the value of interconnected, non-traditional data strands for advance warning.
3. 2025 Tariff War
Predictive Data and Economic Signals
– The 2025 tariff war is characterized by intricate economic interdependencies, with early warning signals visible in trade volumes, sector-specific vulnerabilities, and shifts in policy and market behavior (The Diplomat, Times of India, New York Times, Pluto7, Atlantic Council, ScienceDirect).
– The most predictive data streams included:
– Cross-border capital flow anomalies that preceded official policy announcements by 3-4 months
– Shifts in procurement patterns among multinational corporations anticipating supply chain disruptions
– Sentiment analysis from industry publications showing sector-specific anxiety
– Patent filing trends revealing strategic technological decoupling
– Changes in shipping container bookings and logistics patterns signaling trade route adjustments
– Data-driven models and AI-powered analytics have become essential for forecasting trade impacts, market volatility, and sectoral disruptions, revealing the interconnectedness of global economic strands.
– Academic modeling has successfully quantified regional exposures and anticipated prolonged economic confrontation (ScienceDirect).
Alignment with Strand Commonality Theory
– The complex, highly interdependent nature of global trade, coupled with the use of real-time data and advanced analytics, aligns well with Hansen’s theory.
– The ability to anticipate tariff escalation, sectoral impacts, and economic growth downgrades using interconnected economic, political, and policy data demonstrates the predictive power of strand commonality, albeit with some limitations due to policy unpredictability and geopolitical maneuvering.
CONCLUSION: The 2025 Tariff War offers partial alignment with strand commonality theory. While interconnected data strands enable early detection of tariff impacts and market disruptions, the ultimate escalation and resolution depend on human policy choices, which remain difficult to predict with full certainty.
Conclusion
Hansen’s strand commonality theory offers a distinctive approach to black swan event prediction by emphasizing the integration of diverse data ecosystems and the detection of cross-domain patterns that conventional models miss. Our analysis reveals varying degrees of alignment across the three cases:
– COVID-19 Pandemic: Most compatible with Hansen’s theory due to the successful integration of diverse, interconnected data strands (epidemiological, digital, behavioral) that collectively provided early warning signals weeks before official recognition. The pandemic demonstrates how cross-domain data fusion can reveal emergent patterns invisible to traditional, siloed analysis.
– 2022 Ukraine War: Least compatible with strand commonality principles, as predictive efforts were hampered by deliberate information manipulation, compartmentalized intelligence gathering, and the inherent unpredictability of strategic military decision-making. This case illustrates the theory’s limitations in contexts where critical data streams are restricted or manipulated.
– 2025 Tariff War: Offers moderate compatibility; specific interconnected data streams (capital flows, procurement patterns, logistics anomalies) provided early indicators of escalation, though final outcomes remained contingent on unpredictable human policy decisions. This case demonstrates how strand commonality can detect systemic vulnerabilities without necessarily predicting precise policy responses.
In summary, Hansen’s strand commonality theory proves most valuable in contexts where diverse data ecosystems are accessible, integration infrastructure exists, and human decision-making follows somewhat rational patterns influenced by systemic pressures. The COVID-19 pandemic exemplifies these conditions, while geopolitical conflicts like the Ukraine War reveal the theory’s boundaries.
PRACTICAL PROCUREMENT APPLICATION
Tell me how Agentic AI would have known to ask, “What time of day do orders come in?”
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