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SIG Session [clear filter]
Wednesday, August 5

17:00 CEST

The Value of the Frame: Painting Complexity using two Chronic Disease Models

As with all chronic diseases, it is now recognized that Type-II diabetes is a complex health issue, the etiology of which involves numerous risk factors operating at different ecological levels of analysis. However, this ecological complexity of the problem seldom manifests itself in the interventions (leverage points) for preventing the problem, which typically focus on changing behavior through universal health education, which assumes a homogeneous population. This paper examines the limitations of this way of framing the problem of Type-II diabetes, particularly its failure to capture the way in which this problem emerges as a result of dynamic interactions between individuals and their environments and how these interactions vary in fundamental ways depending upon the context within which they occur.  Specifically, the paper examines the ways in which Type-II diabetes in the Lower Rio Grande Valley (LRGV) is framed (understood, interpreted, and applied) and how framing affects which systems modeling method one uses to understand the problem and to help guide policy-makers to ameliorate it.

Each systems model has a paradigm characterizing it by a set of fundamental rules and underlying concepts.  That is, each method bases on assumptions of how the model should be constructed and the knowledge obtainable from such. By assuming the model should be constructed in a certain way, the modeler (whether implicitly or explicitly) frames the problem by making assumptions about the phenomenon of interest. Choosing to develop any model asserts a model proscribes to paradigmatic assumptions for how that model would contribute something useful (of value) in some capacity (for a purpose), which is ultimately affected by understanding, interpretation, and application (framing) of the problem. Selecting a particular modeling paradigm implies part of the conceptualization process of a system modeling study is in selecting a model paradigm based on these assumptions. For example, selecting to create a system dynamics model assumes the system-of-interest is comprised of rates, aggregates or stocks, and feedback loops (at least for the model’s purpose).

The LRGV was selected as the predominantly poor Mexican American population that resides there has the highest diabetes-related death rate in Texas and, in certain areas of this region, 50% of the Hispanic population aged 35 years and older suffer from Type-II diabetes. Addressing the problem of diabetes in this area is especially problematic as it ranks among the most socially and economically disadvantaged areas of the United States. Given this high prevalence and limited economic resources, a model capturing the extent of the health problem and analyzing an array of possible leverage points could be crucial to reducing Type-II diabetes in this population.  The question is: What should such a model capture? More specifically, how does framing affect understanding of systems models of Type-II diabetes in the LRGV and the type of leverage points should it be identifying?

The paper describes how specific types of systems methods, those using agent-based models (ABM) and system dynamics models (SDM), can produce very different ways of understanding the problem of and the leverage points for Type-II diabetes in the LRGV.  Additionally, it moves beyond simply outlining the general differences in the use and applications of ABM and SDM, to presenting models demonstrating how framing of the problem  and model paradigmatic assumptions affect understanding of the problem of Type-II diabetes in the LGRV and its potential leverage points. While the examples are specific to a health problem in a specific community, the significance of such an approach is in its generalizability to how understanding social system behavior depends upon how framing the problem and the paradigmatic assumptions of the modeling method selected for modeling that social system.

Wednesday August 5, 2015 17:00 - 17:15 CEST
Elk Scandic Berlin Potsdamer Platz, Gabriele-Tergit-Promenade 19, 10963 Berlin, Germany

17:30 CEST

Understanding Mechanisms of the Anthropocene – Systems Science Should Meet Social Ecology !

Aiming to govern the anthropocene (Crutzen 2002) implicates to refer to an appropriate model of our global socio-ecological system (briefly: human ecosystem), that might be similar to the famous “world models” (Meadows et al. 2004). Usually, policies assume that the subsystems  nature (soil, water, air; plants animals), economy and society are deterministic machines where any system-specific input (pesticides / taxes/ law etc.) evokes a certain intended operation mode or output (e.g. “sustainability”) without considering side effects and feedbacks. In this context, any steering intervention is guided only by easily available quantitative indicators that are supposed to represent the function state of the respective system validly (comp. “big data” hype). Additionally, it is commonly believed that deterministically operating and (growing) economy is also the determining driver of human ecosystems: “If we ‘kick-start’ the economy everything changes for the better!”  In contrast, self-conditioned dynamics seems to be significant for operations of each of these systems. Therefore, understanding the interconnected but buffered dynamics between the subsystems nature, economy and society needs  the proper identification of major players in the respective and coupled systems dynamics. This knowledge must also encompass boundary conditions of the systems in order to reduce climate change and /or  keep biodiversity effectively (comp. Rockström 2009). Maybe, only with this knowledge it is effective/efficient to design and apply causally oriented intervention strategies.

However, already at the stage of modeling of regional human ecosystems, still epistemic deficiencies de-validate some usual models: theory-free collecting obtainable data across several domains (temperature, % land use, biomass & energy consumption /capita,  population size/density, DGP, Gini-coefficient etc.) and then building formal models for computer simulations without referring to theories of the respective academic disciplines   (geography, sociology, economy   etc.)  seems to be too pragmatic even if modeling methodologies of systems science are used systematically. In this context, it has to be admitted that there are not enough interdisciplinary (or better: “interfacultary”) conceptual frameworks that allow an integrated view on the world and that connect views of ecology, economy and social science. One option is the academic field of social ecology (or human ecology) that studies the  interrelations between population (or: men), society and “environment” (Hawley, Duncan,  Odum; comp. Glaser 1989, Serbser 2004) or the “societal metabolism” (Schaffarzik et al. 2014). Interestingly, the level of development of quantitative theories and models  in social ecology is  rather low (Tretter & Halliday 2012). Additionally, the methodological gap between natural and social sciences  should be minded more (Simon & Tretter 2015).

In the talk, referring to case studies several of such methodological issues are raised that might help to proceed towards a more sophisticated and epistemologically sound theoretical modeling of socio-ecological systems as a basis of ecosystems management.

Presenter / Artist
avatar for Prof. Dr. Dr. Dr. Felix Tretter

Prof. Dr. Dr. Dr. Felix Tretter

Fellow, Bertalanffy Centre for the Studies of Systems Sciences (BCSSS)
SIG Co-Chair: Socio-Ecological Systems (see below for details)This SIG Socio-Ecological Systems intends to help advance a sound epistemology and methodology for socio-ecological systems design in conjunction with socio-technological systems design. At the interface of science... Read More →

Wednesday August 5, 2015 17:30 - 18:00 CEST
Elk Scandic Berlin Potsdamer Platz, Gabriele-Tergit-Promenade 19, 10963 Berlin, Germany