5 Domains for a sense of place.

This article discusses five different decision-making domains, each with its unique characteristics and approaches:

1. Clear Domain:

In this domain, everything is well-understood and follows established rules or best practices. Situations are stable with clear cause-and-effect relationships. Decision-making involves a “sense–categorize–respond” approach. However, there’s a caution against oversimplifying, as this can lead to complacency.

2. Complicated Domain:

Complex fields with uncertainties, where expertise is crucial. Multiple correct approaches can be taken. The recommended decision-making process is “sense–analyze–respond.” Professionals like engineers, surgeons, and AI systems excel in handling complicated problems.

3. Complex Domain:

This domain deals with “unknown unknowns,” making cause and effect hard to predict. There are no one-size-fits-all solutions, and leaders need to experiment and learn from experience. The common approach is “probe–sense–respond.” Examples include complex insurance cases and unpredictable systems like battlefields and markets.

4. Chaotic Domain:

Understanding cause and effect in real-time is extremely challenging. Immediate action is essential, and the “act–sense–respond” approach is used. Leaders must restore order, identify stability, and transition to complexity. Top-down communication is crucial in chaotic situations. Examples include the September 11 attacks and crises requiring quick, decisive actions.

Considerations about Chaos:

Unclear cause-and-effect relationships. Urgent need for immediate action.

5. Constraints in Decision-Making:

Constraints play a crucial role in decision-making, with different types operating in various domains. Fixed constraints stipulate actions must be done in a specific way and order and apply in the clear domain. Governing constraints are more flexible, acting like rules or policies, and apply in the complicated domain. Enabling constraints, which function in the complex domain, allow a system to operate without full control. These concepts are related to the theory of constraints, highlighting the alignment between constraint types and Cynefin’s domains.

Explanations and examples below

Clear

In the “clear domain,” everything is well-understood and follows established rules or best practices. It’s a stable situation with a clear cause-and-effect relationship. To make decisions in this domain, you use a simple “sense–categorize–respond” approach: gather facts, categorize the issue, and then follow the established rules or best practices. For instance, in the context of loan payment processing, an employee identifies a problem, categorizes it, and responds by following the loan terms.

This domain includes areas with well-defined rules, like legal structures and proven practices. It’s a realm of rational decision-making where you find the right rule and apply it.

However, it’s essential to be cautious about trying to fit every situation into this domain by oversimplifying or ignoring new perspectives. Complacency and sticking to past practices can lead to a shift into chaos. So, it’s recommended for leaders to encourage open communication, even anonymously, to prevent complacency and welcome diverse viewpoints.

About The Clear Domain

Known knowns: Well-understood situations with established rules and best practices. Stability: The situation is stable and predictable. Clear cause-and-effect: Actions have predictable outcomes.

Sense:

Explanation: In the first phase, individuals or teams gather relevant facts, information, and data related to the situation. This step involves observing and collecting data to ensure a comprehensive understanding of the issue at hand. Examples: Quality Control: In manufacturing, quality control teams sense the performance of a product by measuring its specifications, inspecting its features, and monitoring its consistency to ensure it meets predefined standards. Customer Service: When handling customer inquiries, service representatives sense the customer’s issue by actively listening, asking clarifying questions, and gathering details to fully understand the problem.

Categorize:

Explanation: After gathering information, the next step is to categorize or classify the issue. This involves identifying the nature of the problem, its specific attributes, and the relevant categories or types it falls into. Categorization helps structure and organize the data for more effective decision-making. Examples: E-commerce: When customers report issues with online orders, the customer support team categorizes the problems into groups such as shipping delays, product defects, or payment-related issues for efficient handling. Project Management: Project managers categorize project risks into different types, such as technical, financial, or scheduling risks, to address each category with appropriate risk mitigation strategies.

Respond:

Explanation: Once the issue is well-sensed and categorized, the final phase involves responding to the situation. This response typically follows established rules, best practices, or predefined procedures that are relevant to the specific category of the issue. It’s about applying the right solutions based on the understanding gained in the previous steps. Examples: Healthcare: In a clinical setting, when a patient’s vital signs and symptoms are sensed and categorized, healthcare professionals respond by applying clinical guidelines or treatment protocols that align with the patient’s diagnosis or condition. Financial Compliance: In the financial industry, compliance officers respond to potential violations by applying regulatory guidelines, policies, and procedures that correspond to the specific category of the compliance issue identified.

Complicated

In a complex field, there are many uncertainties, and understanding cause and effect requires expertise. There can be multiple correct approaches. The suggested approach is “sense–analyze–respond”: gather facts, analyze them, and apply best practices. This kind of decision-making is typical for professionals like engineers, surgeons, and AI systems, which excel in handling complicated problems, such as chess, by exploring various move sequences with precision.

Expertise is essential to comprehend cause and effect in this context. Multiple valid approaches can be applied to tackle the complexities. The recommended decision-making approach is “sense–analyze–respond,” involving: Gathering facts and information. Analyzing the collected data. Applying the most suitable best practices. This decision-making process is commonly associated with professionals like engineers, surgeons, and AI systems. AI, such as Deep Blue, excels in handling complex problems by meticulously exploring various sequences of moves, like in the game of chess.

Sense:

Explanation: In the initial phase, individuals or teams gather relevant facts, information, and data related to the situation. This step involves observation, data collection, and information gathering to ensure a comprehensive understanding of the complex issue. Examples: Cybersecurity: In response to a potential security breach, IT security experts sense the situation by monitoring network logs, analyzing unusual system activities, and examining incoming data to identify potential threats. Market Analysis: In financial markets, traders sense market conditions by observing price movements, collecting economic data, and monitoring news reports to gain a better understanding of market trends.

Analyze:

Explanation: After gathering information, the next step is to analyze the data and information systematically. This involves examining and processing the collected data to identify patterns, relationships, and potential causal factors. Expertise plays a significant role in this phase. Examples: Medical Diagnosis: When a patient presents with a complex set of symptoms, doctors analyze medical records, conduct tests, and use their expertise to identify potential diagnoses based on patterns and medical knowledge. Engineering Design: In the field of engineering, professionals analyze the performance of a complex structure or system by conducting simulations, stress tests, and data analysis to identify weaknesses and areas for improvement.

Respond:

Explanation: Once the data is analyzed and patterns are identified, the final phase involves responding to the situation. This response typically involves applying the most suitable best practices, approaches, or strategies based on the analysis and expertise. Examples: Aerospace Engineering: When analyzing the performance of an aircraft, engineers respond by making design modifications, altering maintenance schedules, or implementing improvements in line with the analysis results and industry best practices. Crisis Management: In a corporate crisis, senior executives analyze the crisis’s root causes and potential impacts and then respond by following established crisis management procedures and communication strategies based on the analysis.

Complex

The complex domain is where we face “unknown unknowns” – things we can’t predict. Here, it’s tough to figure out cause and effect in real-time, and there are no clear, one-size-fits-all solutions. To navigate this, leaders have to experiment cautiously and learn from what works and what doesn’t. This approach is often called “probe–sense–respond.”

A good example is dealing with complex insurance cases, where human underwriters need to carefully analyze each situation. Another way to think of it is like spreading out a jigsaw puzzle to see the whole picture.

This complexity isn’t just about insurance; it’s also found in places like battlefields, markets, ecosystems, and corporate cultures. These systems are hard to understand by breaking them down into pieces because our actions can have unpredictable effects. So, in the complex domain, leaders need to be adaptable and learn from their experiences.

The Complex Domain

Dealing with “unknown unknowns” Uncertainty in determining cause and effect in real-time Lack of clear, straightforward solutions

Probe:

Explanation: In this step, you take a small, safe-to-fail action or experiment to gain insights and gather information about the complex issue. These actions are like probes into the unknown, designed to help you understand the problem better. Examples: Software Development: When faced with a complex software bug that you can’t immediately identify, you might decide to try a specific code change to see if it resolves the issue. This action is a probe into the problem’s root cause. Marketing: In a complex market where customer behavior is unpredictable, you might run a small-scale ad campaign with different messaging to gauge how customers respond. This experiment can provide insights into what works.

Sense:

Explanation: After implementing the probe, it’s crucial to closely monitor and observe the effects of your actions. This step involves collecting data, observing patterns, and understanding the system’s responses to your probes. Examples: Healthcare: In a hospital, the administration introduces a new process to improve patient care. They closely monitor patient outcomes, staff satisfaction, and the efficiency of the process, looking for patterns and trends to assess the impact of the change. E-commerce: An online retailer modifies its website’s layout and tracks user behavior, click-through rates, and conversion rates to sense how the changes are affecting customer interactions and sales.

Respond:

Explanation: Based on the insights and patterns gathered during the sensing phase, you make informed decisions about how to address the complex issue. Your response may involve adapting your approach, fine-tuning your actions, or even trying entirely new probes to navigate the complexity effectively. Examples: Education: A school implements a new teaching method in response to data indicating that students are struggling with certain subjects. The response may involve modifying the curriculum, offering additional support, or providing teacher training to better address student needs. Manufacturing: A factory senses that a particular production line is experiencing frequent disruptions. In response, the operations team reassigns workers, updates equipment, and introduces a new maintenance schedule to improve production efficiency.

Navigating Complexity

Emphasis on conducting safe-to-fail experiments The process of “probe–sense–respond” The need for adaptability and learning from experience

Challenging Systems

Identification of battlefields, markets, ecosystems, and corporate cultures as complex systems

The limitation of a reductionist approach in understanding complex systems

The unpredictable nature of how actions can influence these systems

Chaotic

In the chaotic domain, understanding cause and effect is extremely challenging. It’s a situation where things are so confusing that waiting for a thought-out response isn’t practical. Instead, immediate action is essential. Managers follow an “act–sense–respond” approach: they take action to bring some order, then assess where stability can be found, and finally respond to transform chaos into complexity.

For leaders in chaotic situations, the first priority is to restore order. They must then identify where stability exists and where it’s lacking. Afterward, they work on transitioning from chaos to complexity, where emerging patterns can help prevent future crises and reveal new opportunities. Communication in a direct and top-down manner is crucial in chaotic situations, as there’s no time to seek input.

Examples of chaotic situations include the September 11 attacks and crises where quick, decisive actions are needed, such as a firefighter’s instinctive decision to turn a certain way or a trader’s quick response to suspicious stock news.

The 1993 Brown’s Chicken massacre in Palatine, Illinois, is cited as a scenario where local police had to navigate various domains. The deputy police chief had to act swiftly to address the initial chaos, maintain day-to-day operations (simple), consult with experts (complicated), and ensure community confidence in the following weeks (complex).

Act:

Explanation: In this initial phase, the focus is on taking swift and decisive action to regain control and stabilize the chaotic situation. The key is to act immediately, even if the action taken is not entirely well-informed. It’s about addressing the most urgent issues to restore order. Examples: Emergency Response: In the event of a natural disaster, like a hurricane, emergency responders, such as firefighters and paramedics, act immediately to save lives and protect people, even without a full understanding of the disaster’s scale. Cybersecurity: In the case of a sudden and severe cyberattack on a company’s network, the IT security team may isolate affected systems and initiate emergency response protocols to contain the breach.

Sense:

Explanation: After taking initial actions to restore stability, the next step is to closely monitor the situation. It involves observing, collecting data, and assessing the effects of the actions taken during the “Act” phase. The goal is to identify any patterns or emerging trends. Examples: Public Health Crisis: During the early stages of a disease outbreak, healthcare professionals continuously collect data on the spread of the disease, analyze patient outcomes, and study infection patterns to understand how the outbreak is evolving. Financial Markets: In response to a sudden stock market crash, financial analysts and traders closely monitor trading activities, examine market trends, and analyze price movements to sense any potential recovery patterns or triggers.

Respond:

Explanation: In the final phase, decisions and actions are based on the observations and insights gathered during the “Sense” phase. The response may involve adjusting the initial actions, fine-tuning the approach, or even introducing new strategies to move from chaos to a more controlled and predictable state. Examples: Disaster Recovery: After the immediate response to a natural disaster, emergency management teams may reevaluate their actions, allocate resources more efficiently, and adapt their strategies based on the evolving needs and conditions of the affected area. Manufacturing: When a factory experiences sudden equipment failures and production interruptions, the operations team may respond by reorganizing the production schedule, conducting maintenance, and optimizing processes to prevent further disruptions.

From the “Clear Domain,” where established rules govern decision-making, to the “Complex Domain,” where experimentation and adaptability are key, and the “Chaotic Domain,” demanding swift actions to restore order this article is designed to provide you a framework to understand how to approach a problem understanding these domains and their corresponding decision-making frameworks equips individuals and organizations with the tools to navigate the dynamic landscape of problem-solving effectively and make well-informed decisions in a wide range of contexts.

References Pelrine, Joseph (March 2011). “On Understanding Software Agility: A Social Complexity Point Of View”. Emergence: Complexity & Organization. link