Resilience Science

Resilience in Complex Adaptive Systems

prescribed burn at Barta Brothers Ranch in Nebraska
photo of a figure displaying scale from plant, to field, to farming community, to agricultural landscape

Agricultural systems are biophysical systems, composed of soil, water, nutrients, and light, and social systems, composed of farms, farmers, and the many other economic, policy, and transportation elements of agricultural food systems. Agricultural systems are thus coupled social-ecological systems. They are also complex adaptive systems, and this has direct repercussions on their dynamics and behavior, and how we understand and manage agricultural systems.

Complex adaptive systems (CAS) have specific dynamics and behaviors that distinguish them from simpler, merely complicated systems, such as mechanical vehicles, or a computer. One of the primary challenges of understanding CAS is that there is no centralized system control. Instead, complex behavior emerges from the interaction of the many and varied individual pieces of the system. Despite obvious differences between CAS such as an economy, the human brain, or a forest, there are fundamental principles shaping their structure and dynamics that apply to all CAS, providing valuable opportunities for shared learning across disciplines. Resilience science emerged within ecology in response to the challenge presented by complex adaptive systems—how do we understand these types of systems in a way that adequately encompasses the reality of their behavior?

Complex Adaptive Systems

Resilience science is a suite of interconnected theories, concepts, methods and tools focused on ways to account for how and why systems change over time. It specifically accounts for the following features of complex adaptive systems:

Multi-Scaled and Hierarchical
The key processes that structure complex systems occur at multiple spatial and temporal scales, creating scaled structure and patterns that are nested in a hierarchy.​

Nonlinear
The relationship between cause and effect is not always simple or proportional; a small change in a driver can trigger an abrupt or disproportionate change in the system, resulting in outcomes that are unexpected or a surprise.

Emergent Phenomena
Complex behavior and phenomena can emerge from the simple interactions of individual agents.

Uncertainty
The number of localized interactions amongst system entities means system behavior is never fully knowable or predictable. CAS have inherent, irreducible uncertainty that must always be taken into consideration.

Self-Organized and Adaptive
Order (organization) arises from local interactions between system parts without a central organizing entity; both individual and collective behavior is adaptive in response to a changing environment making self-organization a constant ongoing process.

Feedbacks and Impacts Across System Scales
Processes at one temporal or spatial scale can interact with processes at another scale such that system response is either amplified or dampened.

Non-Stationarity / Nonequilibrium
Complex systems are in a constant state of flux/nonequilibrium because they are open dissipative systems with a source of external energy flowing in (i.e., sunlight); complex systems only achieve equilibrium at death, when the energy flows into the system have ended.