Abstract
The capacity to maintain stability in a system relies on the components which make up the system. This study explores the relationship between component-level resilience and system-level resilience with the aim of identifying policies which foster system-level resilience in situations where existing incentives might undermine it. We use an abstract model of interacting specialized resource users and producers which can be parameterized to represent specific real systems. We want to understand how features, such as stockpiles, influence system versus component resilience. Systems are subject to perturbations of varying intensity and frequency. For our study, we create a simplified economy in which an inventory carrying cost is imposed to incentivize smaller inventories and examine how components with varying inventory levels compete in environments subject to periods of resource scarcity. The results show that policies requiring larger inventories foster higher component-level resilience but do not foster higher system-level resilience. Inventory carrying costs reduce production efficiency as inventory sizes increase. JIT inventory strategies improve production efficiency but do not afford any buffer against future uncertainty of resource availability.
Highlights
Resilience is the ability of a system to recover from shocks
Resilience can be measured using the movement of a system indicator, such as the flow of a key resource through the system [4], the resilience of a system is an emergent property of the resilience of its components: system resilience depends on how the components interact [5, 6], not on their individual resilience metrics
Developed at Sandia National Laboratories to investigate complex adaptive systems (CAS) [15], the exchange model (ExM) provides a framework to abstractly represent systems in which interacting specialists produce and consume resources that flow among entities via continuous markets
Summary
Resilience is the ability of a system to recover from shocks. Economic turmoil, political instability, and natural disasters are examples of shocks which can stress or destabilize a system. Policies which optimize component resilience may not optimize system resilience, and vice versa [7] We explore this tradeoff using a simple model representing two kinds of interdependent entities, each of which consumes the distinct resource produced by the other. Agent-based models (ABMs) are an effective means of modeling, understanding, and measuring resilience of complex systems [9,10,11]. Agents have adaptive processes and interact with other agents in the system resulting in behavior that is complex and difficult to anticipate [13] This modeling methodology lends itself well to understanding how complex adaptive systems work, the interdependencies of system components, and how adaptive entities respond to endogenous and exogenous changes [14]
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