The Resilience Equation and the Missing Variable: Adaptive Capacity

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Resilience has been a hot topic for the last decade as the public and private sectors have worked to incorporate it within their organizations and communities. In addition, there has been much research conducted, best practices and lessons learned developed, and initiatives launched to support these efforts. For example, in 2013, the Rockefeller Foundation launched the 100 Resilient Cities initiative that helped place Chief Resilience Officers in 100 of the largest cities worldwide and subsequently develop a supporting program to help sustain their efforts. However, I continue to question the ultimate outcomes achieved when implementing resilience in practice and evaluating its success.

One of the primary concerns is the trend toward over-codifying and -simplifying resilience concepts to the point that they look more like the processes and capabilities from the last few decades. The trend is understandably driven by the prior success of these processes and capabilities in some form or fashion. However, this regressive trend binds us to principles that hinder meaningful progress for improving the resilience of complex socio-technical systems-of-systems in which interdependence, emergence, and dynamism are core characteristics. COVID-19 and many other disasters in the past five years continue to demonstrate the inadequacies of approaches primarily grounded in the a priori reduction of uncertainty or risk.

The Resilience Equation

Instead, resilience work today needs to also build adaptive capacity within a system’s steady-state operating paradigm by embracing uncertainty and risk. This is a significant shift in thinking from working to protect a steady-state system toward enabling the real-time evolution of a steady-state system as it faces externalities that challenge the status quo. However, embracing adaptive capacity above all else is a short-sided endeavor. The desire to protect steady-state conditions that allow our societies and systems to thrive must be balanced with the ability to gracefully adapt and evolve when needed. I capture this balance in the following formula:

Adaptive Capacity x (Risk Management + Standby Capabilities) = Resilience

These three variables complete the resilience “pie,” but their relative contributions or dominance is driven by the context, which then fluctuates as the system and operating environment changes over time.

Without adaptive capacity in the equation, we will continue chasing the impossible dream of resilience that we appear to achieve…until the next disruption. With adaptive capacity, the steady-state system’s resilience baseline is natively improved as impacts naturally become less disruptive because the system can better adapt, risks become easier to manage, and idle crisis capabilities are needed less.

Here is how I define the variables:

  1. Adaptive Capacity - The ability of a steady-state system to successfully adapt to externalities. The system, as it presently operates, contains features (designed or natural) that enable adaptability through latent capability and capacity. In addition, the features are primarily considered in relation to the system’s intended goals/outcomes and the desired degree of future modification to enable. Features are not linked to specific threats, hazards, vulnerabilities, or impacts, but may have some weak linkages to broad risk archetypes exist (e.g., public health emergency).

  2. Risk Management - The sustained capability to identify threats, hazards, vulnerabilities, and impacts to a steady-state system, and then develop targeted strategies to mitigate, transfer, accept, or avoid them. Risks are investigated to the fullest extent possible with the goal to reduce uncertainty about the risks as much as possible a priori.

  3. Crisis Capabilities - Standby capabilities ready to support materialized risks, whether the risks are specific (e.g., hurricane knocking out power to primary distribution facility) or a risk archetype (e.g., public health emergency). Risk archetypes can range in their level of specificity, but are identifiable a priori.

Building Adaptive Capacity

The approach to building adaptive capacity looks very different from risk management and crisis capabilities in that it becomes an embedded part of steady-state systems and is not linked to specific risks. The goal is to build a steady-state system with properties that demonstrate adequate adaptability to emergent externalities. Building adaptive capacity is not an easy task, though. It is highly contextual and needs to balance systems thinking concepts with optimization to make targeted changes that yield the largest impacts to adaptability. The challenge is to find the right balance between adaptive capacity and optimization as the relationship between the concepts is highly interdependent (i.e., yin and yang).

The questions now become how to invest in adaptive capacity and to what extent? Let me know your thoughts in the comments below.