Speaker
Dr
Steven Lade
(Stockholm Resilience Center)
Description
‘Resilience’ is emerging as a key concept that researchers
and organisations (including the United Nations and the
World Bank) use to understand and deal with many of the
problems facing contemporary environment and society. In
this talk I provide an overview of research on the
resilience of social-ecological systems and how physicists
could contribute to its future development. I illustrate
this theme with some recent results from my own work.
Local stability concepts of nonlinear dynamics are closely
linked to the original, resistance to shock conception of
resilience. Studies based on nonlinear dynamical approaches
can still provide insights into the dynamics of
social-ecological systems. This is particularly true with
respect to regime shifts, a type of sudden change in a
social-ecological system that is closely related to fold
bifurcations. I summarise one recent work in which I used
the recently developed nonlinear dynamical tool of
generalised modelling to better understand regime shifts of
a social-ecological system. Rather than analyse bifurcations
for a specific model, generic modelling can identify
bifurcations in a model class. The social-ecological system
that we modelled consisted of a community of harvesters that
could each choose whether to harvest a common pool resource
at a community-efficient level or at a higher,
self-interested level that led to overharvesting of the
resource. The co-operators encourage the defectors to
co-operate through a social ostracism mechanism. Among other
results, we show that a nonlinear social-ecological coupling
can lead to a regime shift even if there were none in the
isolated social and ecological subsystems.
Resilience concepts are often used in a qualitative or
heuristic manner to inspire particular research questions or
management approaches. One recent line of research where
resilience has become more quantitative is in the study of
early warning signals for regime shifts. Here, generic early
warning signals for regime shifts are calculated from time
series observations. For example, an increasing variance can
indicate a loss of stability and impending regime shift.
Using the generalised modelling approach introduced above, I
developed a generalised modelling-based early warning signal
that can incorporate system-specific information into a
generic early warning signal approach.
More recently, however, the understanding of resilience has
expanded beyond local stability to include the ability of a
system to adapt and transform in response to threats and
challenges. So far modelling studies have generally not kept
pace with these conceptual developments, but as I will
outline network perspectives show potential to do so.
Brainstorming on other modelling approaches that may meet
modern challenges of resilience research will also be most
welcome.
