Design for Dynamic Fitness: Archetypes of urban water systems

TL;DR

The paper reframes infrastructure robustness under accelerating global change as dynamic fitness, defined as anticipatory capacity plus responsiveness. It analyzes 35 years of data from 16 U.S. urban water utilities using archetype analysis to link biophysical complexity with institutional design. Findings show that institutional archetypes capable of coping with higher biophysical complexity require concurrent growth in information processing capacity and response diversity, with regional polycentric governance helping offset costs through information sharing. Framing the problem with Ashby's Law of Requisite Variety, the study reveals key design tradeoffs across governance levels and provides a framework for proactive infrastructure governance that can adapt as environmental conditions evolve.

Abstract

In an era of accelerating change, urban water infrastructure systems increasingly operate outside of their design conditions, putting new pressure on systems' institutional designs to weather emerging challenges. Water management institutions must therefore be designed to exhibit dynamic fitness, defined by anticipatory capacity and responsiveness. However, we do not yet understand the specific features of institutional design that enable dynamic fitness, especially in relation to the diverse biophysical characteristics of systems that such fitness is contingent upon. We advance research on dynamic fitness in the context of urban water supply systems by drawing on 35-year data sets of stressors and responses for 16 U.S. urban water utilities using archetype analysis. Here we find that institutional archetypes capable of coping with higher biophysical complexity invest in both information processing capacity and response diversity. While dynamic fitness comes at a cost, balance between information processing capacity and response diversity promotes efficiency, which can be expanded through polycentric regional institutional structures that facilitate information sharing. Lastly, careful consideration should be given to tradeoffs across levels of governance, as institutional structures that facilitate dynamic fitness at the utility level may reduce the control and flexibility of higher levels of governance.

Figures (8)

• Figure 1: Conceptual figure illustrating how urban water supply systems manage variability in both water supply sources and socioeconomic conditions through passive robustness strategies such as drawing on multiple water sources and storage and through active response via feedback control. Adapted from Anderies2015.
• Figure 2: Institutional configurations where each circle is an urban water system and the square is a regional or state level entity: a) centralized, where information flows in one direction and decisions are largely top-down; b) constrained, where information flows in one direction and the decisions on what outcomes to achieve are top-down but lower level make implementation decisions; c) enabled, where authority for decision making is shared across the network, information flows are bidirectional both horizontally and vertically; d) decentralized, where authority for decision making is shared across the network but connectivity and information flows are limited.
• Figure 3: a) An adaptation of the Ashby Space diagram illustrating the requisite response diversity as a function of biophysical complexity where responses are also constrained by available resources (adapted from Boisot2011); b) Three regimes of biophysical complexity (labeled in bold) are projected onto the Ashby space with the types of knowledge mobilization effective in each regime indicated in italics; c) This diagram plots the potential responses of two entities with different levels of response diversity (X) to varying degrees of biophysical complexity (Y). d) This diagram maps institutional configurations to the Ashby Space where each configuration is effective inside of the illustrated boundaries.
• Figure 4: Case study urban water suppliers with the percent surface water in their supply portfolio indicated by color and the utility type indicated by shape.
• Figure 5: Evolution of institutional configurations over time plotted in the Ashby space showing the change in space where it each is effective: a) Charlotte, increased information processing capacity through state level investments in drought information collection and low flow protocols; b) in Harrisburg, investment in backup supply infrastructure shifted the biophysical complexity the institutions must respond to from $Y_1$ to $Y_2$, and over time a decreasing consumer base reduced resources available shrinking the effective space; c) in Indianapolis, investment in additional supply infrastructure shifted the biophysical complexity the institutions must respond to from $Y_1$ to $Y_2$ while institutional capacity increased by working through complex water quality challenges.