Understanding and Measuring Resilience: Addressing the Discrepancy Between Theory and Practice

Concetti Chiave

While resilience is widely understood as a system's capacity to cope with disruptions, its assessment often focuses on event-specific processes or outcomes, leading to a dissonance between theory and practice.

Sintesi

Bibliographic Information:

Halekotte, L., Mentges, A., & Lichte, D. (2024). Do we practice what we preach? The dissonance between resilience understanding and measurement. [Insert Journal Name Upon Publication].

Research Objective:

This article examines the discrepancy between the prevailing understanding of resilience as a capacity and the common practice of assessing it based on event-specific processes or outcomes. The authors argue for a shift towards assessing resilience as a capacity to better reflect its true meaning and enhance resilience management.

Methodology:

The authors conduct a critical review of existing literature on resilience, analyzing different definitions, assessment approaches, and management implications. They introduce the concept of a "resilience backbone" to illustrate the relationships between resilience properties, capacities, processes, and outcomes.

Key Findings:

• The majority of resilience definitions conceptualize resilience as a capacity.
• However, most assessment approaches focus on analyzing single performance curves, which reflect event-specific resilience processes rather than overall capacity.
• This discrepancy can lead to an incomplete understanding of resilience and hinder effective resilience management.

Main Conclusions:

The authors advocate for understanding and assessing resilience as a capacity to enable continuous monitoring, proactive management, and a more comprehensive evaluation of a system's ability to handle diverse disruptions. They propose using multiple performance curves and property-based approaches to gain a more holistic view of resilience capacities.

Significance:

This article highlights a crucial gap between theory and practice in resilience assessment, urging researchers and practitioners to adopt methods that align with the true meaning of resilience as a capacity.

Limitations and Future Research:

The article primarily focuses on conceptual aspects of resilience assessment. Future research could explore specific methodologies for effectively measuring resilience capacities in various domains.

Statistiche

Citazioni

"What is largely missing, however, is a thorough discussion of the understanding of resilience on a much more general level: Do we think that resilience is an ability a system can acquire, a process a system can conduct or a measure of a system’s performance under pressure?"
"We suggest that there are three main resilience understandings. First, resilience is often understood as a capacity... Second, resilience can also be understood as the reactive process which follows the disruption of a system... Third, resilience can be seen as an outcome or measure of the outcome, e.g., a speed, rate, or degree..."
"We argue here that resilience should best be seen as a capacity – a perspective that is also expressed in the majority of scientific publications and policy documents."

Approfondimenti chiave tratti da

Do we practice what we preach? The dissonance between resilience understanding and measurement

by Lukas Haleko... alle arxiv.org 10-11-2024

https://arxiv.org/pdf/2410.07253.pdf

Domande più approfondite

How can the concept of resilience as a capacity be translated into practical tools and metrics for assessing complex systems like critical infrastructures?

Assessing resilience as a capacity in complex systems like critical infrastructures requires a shift from focusing on single-event outcomes to understanding the system's inherent abilities to handle a range of disruptions. Here's how this can be translated into practical tools and metrics:
1. Moving Beyond Single Performance Curves:

Ensemble Modeling: Instead of relying on a single performance curve, employ ensemble modeling techniques. This involves simulating the system's response to a diverse set of disruptive events, varying in type, magnitude, location, and timing.
Multi-Metric Assessment: Develop and utilize a suite of metrics that capture different facets of resilience, going beyond traditional metrics like recovery time. Examples include:

Redundancy: Quantifying the availability of backup components or systems.
Diversity: Measuring the variety of resources, suppliers, or response options.
Modularity: Assessing the degree to which the system can be isolated or reconfigured to contain failures.
Adaptive Capacity: Evaluating the system's ability to learn from past events and adjust its responses accordingly.
2. Integrating Property-Based Assessments:

Resilience-Focused System Reviews: Conduct structured reviews of the infrastructure, focusing on identifying and evaluating resilience-building properties. This should involve:

Expert Elicitation: Gathering insights from engineers, operators, and stakeholders familiar with the system's vulnerabilities and strengths.
Benchmarking: Comparing the infrastructure's design and operational practices against best practices and industry standards.

Dynamic System Modeling: Develop dynamic models that simulate the interplay of various system components and their influence on resilience capacities under different disruption scenarios.
3. Emphasizing Continuous Monitoring and Evaluation:

Real-Time Data Integration: Integrate real-time data from sensors, monitoring systems, and operational databases to track key resilience indicators and detect early warning signs of potential vulnerabilities.
Resilience Dashboards: Develop interactive dashboards that visualize key resilience metrics, trends, and potential vulnerabilities, enabling informed decision-making and proactive resilience management.
4. Practical Example:
Consider an electrical grid. Assessing its resilience as a capacity would involve:

Simulating its response to various disruptions like extreme weather events, cyberattacks, or cascading failures.
Evaluating not just the recovery time but also the impact on different customer groups, the effectiveness of redundancy measures, and the grid's ability to adapt its operating strategies.
Continuously monitoring grid parameters, weather forecasts, and cybersecurity threats to proactively adjust operations and resource allocation.
By adopting these practical tools and metrics, we can move beyond a narrow view of resilience as a post-event outcome and develop a more comprehensive understanding of a system's inherent capacity to withstand and adapt to disruptions.

Could focusing solely on resilience as a capacity lead to neglecting the importance of effective event-specific response mechanisms and post-disruption recovery processes?

Yes, focusing solely on resilience as a capacity could potentially lead to an overemphasis on building inherent resilience into systems while neglecting the critical aspects of event-specific response mechanisms and post-disruption recovery processes.
Here's why:

Capacity Doesn't Guarantee Effective Response: While a system might possess high inherent resilience, the actual response to a specific event depends on well-defined plans, trained personnel, and efficient coordination. A resilient system without effective response mechanisms might still experience prolonged downtime and cascading failures.
Every Event is Unique: Even with robust inherent resilience, each disruptive event presents unique challenges. Pre-defined response plans need to be adaptable, and recovery processes must be tailored to address the specific consequences of the event.
Recovery is Crucial for Long-Term Resilience: The post-disruption recovery phase is not just about restoring functionality but also about learning from the event, adapting systems, and enhancing resilience for future disruptions. Neglecting this phase could lead to repeated vulnerabilities.
Balancing Capacity and Response:
To avoid this pitfall, a balanced approach is essential:

Integrate Response and Recovery in Resilience Planning: Event-specific response plans and recovery procedures should be developed in conjunction with efforts to enhance inherent resilience.
Regular Training and Drills: Conduct regular training exercises and drills to test and refine response mechanisms, ensuring that personnel are well-prepared to handle various disruption scenarios.
Post-Event Analysis and Improvement: After each disruptive event, conduct thorough post-mortem analyses to identify lessons learned, adjust response plans, and improve recovery processes.
In essence, resilience is not just about withstanding a disruption; it's also about bouncing back effectively and emerging stronger. A holistic approach that considers both inherent capacity and dynamic response mechanisms is crucial for building truly resilient critical infrastructures.

If resilience is a dynamic and evolving capacity, how can we measure its rate of change and identify factors that contribute to its growth or decline over time?

Measuring the rate of change in resilience and identifying the factors influencing its dynamics is crucial for effective resilience management. Here's a breakdown of how to approach this:
1. Establishing a Baseline:

Initial Resilience Assessment: Conduct a comprehensive initial assessment of the system's resilience using the tools and metrics discussed earlier (ensemble modeling, property-based assessments, etc.). This establishes a baseline for comparison.
Define Key Resilience Indicators: Select a set of quantifiable indicators that reflect the most critical aspects of resilience for the specific system. These indicators should be sensitive to changes in the system's capacity over time.
2. Continuous Monitoring and Data Analysis:

Track Indicator Values Over Time: Continuously monitor the selected resilience indicators, collecting data at regular intervals. This could involve real-time data feeds, periodic assessments, or a combination of both.
Time-Series Analysis: Apply time-series analysis techniques to the collected data to identify trends, patterns, and significant changes in resilience indicator values over time.
Statistical Process Control: Utilize statistical process control methods to establish control limits for resilience indicators. Deviations beyond these limits can signal significant changes in resilience, prompting further investigation.
3. Identifying Influencing Factors:

Correlational Analysis: Investigate potential correlations between changes in resilience indicators and other system variables or external factors. This could involve analyzing historical data, conducting sensitivity analyses, or using machine learning techniques.
Causal Loop Diagrams: Develop causal loop diagrams to visualize the complex interrelationships between system components, external factors, and their influence on resilience. This can help identify feedback loops that contribute to resilience growth or decline.
Expert Knowledge and Stakeholder Input: Engage experts familiar with the system and stakeholders who rely on its services to gain insights into potential drivers of resilience change.
4. Example:
Consider a supply chain. To measure its resilience dynamics:

Establish baseline resilience by assessing its ability to handle disruptions like supplier failures, transportation disruptions, or demand fluctuations.
Select indicators like inventory levels, supplier diversity, production flexibility, and delivery time reliability.
Continuously monitor these indicators, analyzing data for trends and deviations.
Investigate correlations between resilience changes and factors like geopolitical events, economic conditions, supplier relationships, or internal process improvements.
By combining continuous monitoring, data analysis, and expert knowledge, we can gain valuable insights into the dynamic nature of resilience, identify factors driving its change, and make informed decisions to enhance the long-term resilience of critical infrastructures.