This chapter describes ways to enhance the operational potential of Industrial
Ecology. Attention is paid to specific challenges for sustainable development beyond
the broad aims of achieving economic competitiveness, ensuring environmental
stewardship, and promoting both intra- and inter-generational equity. The focus is on
the distributed decision making practices of individual agents within networks of
industry, business and government, which, in various combinations, provide the
underlying structure of any industrial ecology. Here, we consider the need for design
and analysis tools to engage with the dynamics and uncertainty which characterize
complex hierarchical socio-technical systems, including the ability to observe and
interrogate system behaviour over multiple spatial and temporal scales, and to embrace
the vitality which comes from human judgment in decision making within these
systems. This capability should support the transition of such networks to ones which
are both resilient and adaptive whilst pursuing.sustainability goals. We explore the role
of both simulation and optimization toolkits in this regard, and conclude that there is
value in a dual approach. Agent-based models of industrial ecosystems can be coupled
with scenario analysis techniques to engage with future uncertainties. The way in which
individual agents internalize such “world view” scenarios in their own distributed
decision making practices, is highlighted. The effectiveness of agent interventions to
support successful transitions to more sustainable practices can be measured against
goal programming objectives, which in turn are defined by exploring the dynamic
multiple objective optimisation decision space.
Keywords: Industrial Ecology, Sustainable Development, Decision Making,
Simulation, Agent-based modelling, Uncertainty industrial eco-systems, industrial
networks, value chains, resource efficiency, robustness, resilience, adaptiveness,
sustainability assessment, decision support frameworks, objectives hierarchies,
decision trees, network structure, network characteristics, network performance,
embeddedness, norms, routines, scenario analysis, cognition, mental models,
bounded rationality, strategic choice.
