Based on the provided sources, specifically the “Risk Case Study” regarding Environmental Consistency Confidence by Professor Michael Mainelli [220–247] and the various works of Dave Snowden, here is an analysis of how Snowden’s ideas bring insight and resolution to the challenges presented in the case study.
1. Diagnosing the Ontology: Distinguishing “Complicated” from “Complex”
The core tension in the case study is the attempt to apply a “scientific” and “statistical” paradigm to financial risk management. Mainelli’s approach assumes that “if you can predict incidents and losses with some degree of confidence, then you have some ability to manage your risks”[1]. This relies on the discovery of correlations between inputs (environment/activity) and outputs (losses)[2].
**Snowden’s Insight:**Snowden’s Cynefin framework challenges the universality of this assumption by distinguishing between Ordered (Complicated) and Un-ordered (Complex) domains.
• The Complicated Domain: Mainelli’s statistical approach (PKRI-LI) operates well here. Snowden acknowledges that in the Complicated domain, cause and effect are discoverable through analysis and expert investigation[3],[4]. Where the system is stable and relationships repeat, statistical modeling is valid[5].
• The Complex Domain: However, Snowden argues that human systems (like financial markets) are often Complex, where cause and effect are only coherent in retrospect and do not repeat[6]. In these domains, relying on forecasting and predictive models is dangerous because agents (traders, markets) adapt and change the system dynamics[7],[8].
**Resolution:**Snowden’s ideas resolve the limitation acknowledged by Mainelli regarding “fat tails” and “black swans”[9]. Instead of treating these as annoying statistical outliers, Cynefin classifies them as Complex or Chaotic phenomena. The resolution lies in bounded applicability: Use Mainelli’s statistical methods for the Complicated aspects of risk (high frequency, low impact), but abandon them for the Complex aspects (low frequency, high impact), where they provide false confidence[10],[11].
2. Addressing Goodhart’s Law and Data Reliability
The case study highlights a critical failure point: “garbage in, garbage out” and the human tendency to game the system. Mainelli cites Goodhart’s Law: “when a measure becomes a target, it ceases to be a good measure,” illustrating this with an IT department redefining “downtime” to skew results[12].
**Snowden’s Insight:**Snowden reinforces this, noting that explicit, outcome-based targets in complex systems create perverse incentives[13]. He argues that traditional data collection lacks context and is filtered through the biases of those collecting it[14].
**Resolution:**Snowden proposes replacing or augmenting algorithmic data collection with Human Sensor Networks[15],[16].
• Distributed Ethnography: Instead of relying solely on defined metrics (which can be gamed), an organization should engage the workforce to provide continuous “micro-narratives” about their environment[17].
• Weak Signal Detection: Because humans are better at detecting nuances and “weak signals” than algorithms[18],[19], a network of human sensors can detect the cultural shifts that precede a risk event (e.g., a shift in attitude toward rule-compliance) long before it manifests in the statistical data[20],[21]. This moves the organization from reactive “prediction” to “anticipatory awareness”[22].
3. Moving from Prediction to Resilience
The case study focuses on “prediction” and “costing risk” (e.g., Value-at-Risk models) to set capital aside[23]. Mainelli admits that while this has merit, it does not provide a core management process for the “unknown-unknowns”[24].
**Snowden’s Insight:**Snowden argues that in complex systems, we must shift from Robustness (trying to resist failure through better prediction) to Resilience (survival and fast recovery from inevitable failure)[25].
• The Problem of Prediction: Focusing on prediction in a complex system is wasteful because the system is “dispositional,” not causal[6]. We can map the disposition of the system (how likely it is to fail), but we cannot predict when[26].
• Safe-to-Fail: Rather than designing “fail-safe” systems (which inevitably catastrophically fail when the context changes), leaders should run “safe-to-fail” experiments[27],[28].
**Resolution:**For the risk case study, this implies a strategic shift. While the bank should continue using Mainelli’s statistics for regulatory capital (Robustness), operational management should focus on monitoring vector measures (direction and speed of travel) rather than fixed targets[29],[30]. If the “energy cost” of virtue (safe trading) is made lower than the energy cost of sin (risky trading), the system naturally evolves toward safety without requiring perfect prediction[31].
4. Summary of Application
Applying Snowden’s concepts to the Mainelli case study transforms it from a purely analytical exercise into a holistic risk strategy:
| Risk Case Study Concept[1] | Snowden/Cynefin Critique/Resolution |
|---|---|
| Goal: Predict incidents/losses. | Critique: Prediction is impossible in Complexity; leads to complacency[54]. Resolution: Shift to Anticipatory Awareness and Resilience[22]. |
| Method: Statistical Correlation (PKRI). | Critique: Confuses correlation with causation; valid only in Ordered domains[6]. Resolution: Use for Complicated risks; use Narrative/Sensing for Complex risks[5]. |
| Data: Historical data & KPIs. | Critique: Subject to gaming (Goodhart’s Law) and lacks context[55]. Resolution: Augment with Human Sensor Networks for real-time contextual feedback[16]. |
| Failures: “Black Swans” / Fat Tails. | Critique: Treating these as outliers ignores the Pareto nature of reality[10]. Resolution: Treat high-impact events as the norm in complexity; manage for Early Detection and Fast Recovery[56]. |
In conclusion, Snowden’s ideas resolve the “garbage in, garbage out” and “black swan” issues identified in the case study by re-framing them not as failures of data, but as failures of ontology[57]. By recognizing that financial risk involves complex human systems, the bank can stop trying to “calculate” the incalculable and start “sensing” the emerging patterns of risk before they manifest as losses[17],[58].
References
[1] Risk-case-study.md [2] Risk-case-study.md [3] Snowden & Boone HBR Leaders Framework.pdf [4] Snowden - Complex acts of knowing - Paradox and descriptive self-awareness.pdf [5] Snowden - Good fences make good neighbors 2011.pdf [6] Snowden - Anticipatory Models for Counter-Terrorism.pdf [7] Snowden - Naturalising Knowledge Management.pdf [8] Snowden - Naturalising Knowledge Management.pdf [9] Risk-case-study.md [10] Snowden - Good fences make good neighbors 2011.pdf [11] Snowden - Good fences make good neighbors 2011.pdf [12] Risk-case-study.md [13] When dave snowden tells you your models are lying to you you listen.pdf [14] Snowden - Naturalising Knowledge Management.pdf [15] Dave-Snowden-IEA14.pdf [16] EU Managing Complexity (and chaos) in Times of Crisis.pdf [17] EU Managing Complexity (and chaos) in Times of Crisis.pdf [18] Rettig.Snowden-ComplexityTheoryAndNarrativeResearch_notes.pdf [19] Snowden - Perspectives Around Emergent Connectivity Sensemakimg and Asymmetric Threat Managmenet 2006.pdf [20] EU Managing Complexity (and chaos) in Times of Crisis.pdf [21] Snowden - As through the glass darkly a complex systems approach to futures.pdf [22] Snowden 2011 - Naturalising sense-making.pdf [23] Risk-case-study.md [24] Risk-case-study.md [25] Snowden - Good fences make good neighbors 2011.pdf [26] Snowden - Good fences make good neighbors 2011.pdf [27] Snowden - Good fences make good neighbors 2011.pdf [28] Snowden - Good fences make good neighbors 2011.pdf [29] EU Managing Complexity (and chaos) in Times of Crisis.pdf [30] Snowden - As through the glass darkly a complex systems approach to futures.pdf [31] Snowden - As through the glass darkly a complex systems approach to futures.pdf [54] Snowden & Boone HBR Leaders Framework.pdf [55] When dave snowden tells you your models are lying to you you listen.pdf [56] Snowden - Good fences make good neighbors 2011.pdf [57] 044_ Making Sense of Human Systems an interview with Professor Dave Snowden.pdf [58] Snowden - Anticipatory Models for Counter-Terrorism.pdf