Geoscience Reference
In-Depth Information
importance of human decision making to coupled human-environment systems
given the role of refl exivity in decision making. As noted by Nigel Gilbert (1995),
'people are routinely capable of detecting, reasoning about and acting on the
macro-level properties (the emergent features) of the societies of which they form
part' (p. 71).
Complexity and Geographical Divides
The conceptual breadth and multidisciplinarity of complexity research offer the
potential for greater integration and reconciliation among human geography, physi-
cal geography and geographic information science (O'Sullivan, 2004).
Researchers in the humanities and social sciences using interpretivist approaches
have long used complexity concepts such as chaos and catastrophe (Hayles 1991).
This view of complexity often relies on social constructionism, which contends that
our understanding of reality is molded through societal features such as language
and power, focusing on 'understanding the plurality of constructions, how various
assertions are made, how these are related to various interests of stakeholder groups
and how outcomes are affected by power relations' (Jones, 2002, p. 248). Post-
modern, post-structural and other interpretivist perspectives explore systems through
the rubric of knowledge, language and power. Features such as sensitivity and non-
linearity are powerful as metaphors because they capture the importance of nuance,
context and contingency, all bywords of an interpretivist and post-modern under-
standing of the world (Portugali, 2006). The importance of interactions among
entities, particularly to aggregate complexity, also maps well onto various fl avors
of research that examines networks defi ned by relationships among individuals
and communities that form and contest the larger social, cultural and human-
environmental systems of which they are part (Cilliers, 1998; Thrift, 1999; Urry,
2003; Byrne, 2005; Nowotny, 2005; Braun, this volume).
Complexity is also a wellspring for quantitative research. This is especially true
for computer simulations that act as virtual laboratories for exploring 'would-be'
worlds as they unfold (Casti, 1997). Geographers use simulation and modelling for
research, policy and education. Computer simulations allow examination of how
systems appear and of their many possible futures or pasts. They additionally allow
researchers to determine what we do and do not know about the system in question.
Growth in complexity science relies to a great extent on advances in approaches
such as computational intelligence, neural networks, cellular modelling and agent-
based modelling. These methods or their antecedents have been available for decades,
but their use has exploded with complexity research as such and the better avail-
ability of computer processing power and tools (Manson and O'Sullivan, 2006).
Complexity offers a way to bridge divides - including quantitative/qualitative or
among subdisciplines like human and physical geography - because it accommo-
dates a range of ontological perspectives and highlights that understanding complex
systems requires triangulation among approaches and viewpoints. O'Sullivan (2004)
argues that complexity points to the potential for greater engagement between
groups ordinarily having little engagement, such as post-structuralist human geo-
graphers and modelers, because both approaches allow for competing explanations
of many systems. More generally, this work courts the notion of complexity as
seeing the world through a lens of 'imaginable surprise' that treats seemingly unex-
pected system outcomes as explainable when taking into account characteristics of
