Managing complexity and chaos in times of crisis

From: jimruttshow8596

This article explores key concepts and practical approaches to managing complex and chaotic situations, drawing heavily from the work of Dave Snowden, creator of the Cynefin framework and SenseMaker. It is centered around insights from the field guide, “Managing Complexity and Chaos in Times of Crisis: A Field Guide for Decision Makers” [02:05:00].

Introduction to the Cynefin Framework

The Cynefin framework is a complexity and form framework that posits three types of systems: ordered, complex, and chaotic [02:44:00]. It uses the metaphor of solid, liquid, and gas to describe phase shifts between these states, introducing the concept of a “triple point” [02:55:00]. In Cynefin, this triple point is called the “apparatic domain,” referring to a question that can only be answered by thinking differently about the problem [03:09:00].

Complex vs. Complicated Systems

A core distinction in complexity science is between complex and complicated systems [03:41:00].

• Complicated: Derives from the Latin “to unfold” [03:53:00]. Something complicated can be unfolded and folded, remaining the same [04:01:00]. In a complicated system, components can typically be taken apart and reassembled because the logic is implicit in the statics of the design [04:59:00]. Examples include a lawnmower motor or a Boeing 777 [05:34:00].
• Complex: Originates from the Greek “entangled” [03:55:00]. An entangled system is constantly shifting and changing [04:06:00]. If a complex system is taken apart and put back together, it won’t work the same way because much of the information is in its dynamics [05:08:00]. Examples include a human cell or an economy [05:20:00]. A useful metaphor is “bramble bushes in a thicket,” where everything is entangled, and there will always be unintended consequences [04:32:00].

Causality in Complex Systems

In complex adaptive systems, there is no linear material causality [06:07:00]. A complex system has dispositionality and is modulated, but it lacks causality in a meaningful sense [06:13:00]. Even in deterministic complex systems, they can pass through “deterministic chaos,” where cause and effect are theoretically possible but practically unmappable [06:31:00]. The properties of the whole are always different from the properties of the part, as seen in superconductors [06:47:00]. The discovery of emergence is crucial when working with complexity [07:13:00].

Apparatic Domain and Aporia

The term “aporia” (or “apparatic moment”) refers to a question that can only be answered by thinking differently about the problem [03:09:00]. It’s the realization that conventional approaches (“business as usual”) will not work [00:56:47]. In a crisis, people need to be pushed into this state of aporia before they can move forward [00:56:40]. This can be achieved through physical, linguistic, or aesthetic interventions that make it impossible for people to rely on past ways of thinking [00:56:25].

Constraints

Complexity thinking emphasizes the role of constraints.

• Enabling vs. Governing Constraints: An ordered system is generally governed, while a complex system is connected [07:57:00]. Complexity thinking defines systems by connections rather than boundaries [08:04:00].
• Managing Constraints: While you cannot control the output of a complex system, you can influence emergence by managing constraints [08:35:00]. The strategy is to start by understanding the current state and what actions are possible next [08:43:00]. This is likened to the “Frozen 2” strategy of “do the next right thing” [08:48:00].
• Mapping Constraints: Knowing which constraints are in play allows for better management [09:34:00]. A metaphor for managing constraints is a series of magnets influencing cast iron discs [09:41:00]. The more modulators (constraints and constructors) that can be controlled with real-time feedback, the more influence one has over the system [10:24:00].

Characteristics of Complex Systems

• Open Systems: Complex systems are generally open or semi-permeable, interacting with an outside environment [11:19:00]. This distinguishes complexity thinking from traditional systems thinking, which often deals with closed systems with fixed boundaries [12:28:00].
• Short-Term Teleology and Top-Down Causality: In human systems, there can be short-term teleological cause, where agents generate energy in unexpected ways [12:17:00]. This links to the idea of assemblages forming strange attractors [13:16:16]. Top-down causality in human complex systems is effective when focused on changing:
  1. Boundary conditions [14:31:00]
  2. Catalysts for attractors [14:34:00]
  3. Allocation of energy [14:36:00]

Responding to Crisis

Crises, such as the COVID-19 pandemic, demonstrate the non-stationary nature of complex systems [20:10:00]. Post-COVID, there’s a wider recognition that the future cannot be anticipated, necessitating systems that can handle “unknowable unknowns” [21:26:00].

Key actions to take in a crisis:

1. Build Informal Networks: In a crisis, people rely on informal networks for decision-making rather than formal systems [28:49:00]. Rapidly building these networks across silos, using methods like “entangled trios,” clears channels for rapid knowledge flow [29:25:00].
2. Map Knowledge at the Right Granularity: Knowledge should be stored at a level of granularity that allows for repurposing for novel situations, a concept known as acceptation [30:00:00]. The “right level of granularity” means breaking down information until there’s agreement on its placement [32:08:00]. This enables rapid response to lead indicators, crucial in uncertain times [24:01:00].
3. Set Draconian Constraints: In a crisis, leaders must act decisively and quickly to stabilize the situation [34:28:00]. The goal is not to solve the problem immediately but to stabilize it sufficiently to create more options for others [34:37:00]. This involves making “option-increasing decisions” rather than merely decisive ones [35:07:07].

Practical Tools and Approaches

Comprehensive Journaling (Gamba)

Comprehensive journaling, known as “Gamba” (from the Japanese manufacturing concept of “gemba” or “go to the actual place”), is crucial [39:53:00].

• Replacing Reporting: It replaces traditional reporting by having individuals continuously keep field notes, which saves time and provides richer, real-time data [40:29:00].
• Network for Questions: Journaling creates a network of individuals who can be queried in real time [40:46:00].
• Identifying Weak Signals: It can capture “microaggressions” or early warnings of fraud, triggering anticipatory alerts based on patterns in encrypted, anonymous reports [41:31:00]. This generates training data for AI to trigger alerts [42:00:00].
• Granularity and Adaptability: This approach provides a continuous stream of small, granular insights from diverse perspectives [43:10:00]. In a non-stationary crisis, this enables “lessons learning” rather than relying on “lessons learned,” which can be distorted by memory [44:08:00].
• Empowering the Periphery: Large Language Models (LLMs) and embedded vector spaces can be used for automated aggregation, summarization, and clustering of journaling data, potentially empowering the periphery of an organization [44:38:00].

Creating Specialized Crews

In a crisis, specialized crews, rather than individuals, perform critical functions based on roles and role interaction:

• Continuity Crew: A senior executive in a crisis needs a deputy to manage business as usual, allowing the executive to focus on crisis management [48:53:00].
• Journaling Crew: Composed of individuals (e.g., junior staff, students) trained to capture real-time narratives at the point of learning [54:41:00]. They provide knowledge at the right level of abstraction because they are not experts interpreting events [55:06:00].
• Devil’s Advocate Crew: Provides critical challenge to dominant viewpoints.
• Optimal Group Sizes: Military models show that group sizes of three to five are effective for active decision-making, while groups of less than 50 (like extended families or platoons) maintain coherence without excessive formal structure [51:14:00]. Trios are particularly effective for bringing together people from different silos, as they tend to compromise rather than entrench their silo’s view [52:05:00].

Strategic Interventions in Complexity

Instead of traditional strategy that defines future goals and attempts to close the gap, complexity-based strategy focuses on:

• Mapping the Present: The first step is to map the current state of the organization and identify what is possible next [10:06:00].
• Micro-Scenarios and Narrative Topographies: The entire workforce can gather micro-scenarios to create “narrative topographies” or “fitness landscapes” [11:09:00]. These indicate where an organization can go next, rather than where it ideally wants to be [11:18:00].
• Vector Theory of Change: This approach uses narrative landscapes to guide actions that create “more stories like these and fewer stories like those” [11:32:00].
• Fractal Representations: The same source data can be aggregated to different levels of competence, allowing everyone from a president to a school principal to see relevant actions for their context [11:47:00]. This enables numerous small, parallel changes, leading to more learning and greater system steering capability [11:42:00].

Maintaining Cadence and Control

As a crisis stabilizes, it’s vital to maintain “cadence” (rhythm) rather than just “velocity” (speed) [01:05:26]. This means keeping up the pace of working at the right level of granularity and continuing to use sensor networks, informal networks, and distributed knowledge [01:05:54].

Future Directions: Applying Constructor Theory

Snowden’s work is moving into applying Constructor Theory, a physics theory by David Deutsch that focuses on enumerating what is impossible rather than the rules of what is doable [01:16:51].

• Esterhazy Mapping: This method maps constraints onto a grid based on the energy cost of change versus the time to change [01:18:25].
  • Counterfactual Line: Anything beyond this line is too expensive or takes too long to change within the system [01:18:41].
  • Liminal Line: Identifies things the system cannot change but another entity could [01:18:57].
  • Vulnerability/High Variability: The bottom left quadrant identifies areas easily changed.
• Dispositional Management: This approach involves clustering irreducible constraints and then determining micro-actions to either increase or lower the energy cost or time for specific changes, shifting the system’s dispositionality to make desired outcomes more probable [01:19:17].
• Catalysis: This aligns with the concept of catalysis in chemistry, where actions reduce the activation energy for desirable transformations [01:23:31]. In social systems, a “Constructor” can change in the act of construction, and counterfactuals relate to how people feel as much as physical reality [01:22:07]. This provides a non-controversial mapping of the operational space before political considerations [01:24:41].