Model Thinking and Formal Modeling to Improve Our Mental Models in Population Health Research

2020 ◽  
pp. 107-118
Author(s):  
Michael K. Lemke
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Mental Models ◽  
Population Health ◽  
Real World ◽  
Cognitive Abilities ◽  
Health Research ◽  
Computational Simulation ◽  
Human Mind ◽  
Formal Modeling

When encountering complex systems, the human mind applies various heuristics, and from these heuristics, mental models—how people understand phenomena in the real world—emerge, which shape their decisions. Unfortunately, the same limitations that confound heuristics similarly cloud people’s mental models and result in fundamental misunderstandings and lead to flawed decisions. The development of models, through the act of modeling, provide means to mitigate inherent shortcomings in people’s cognitive abilities and mental models and can stimulate new ways of understanding and acting in population health, grounded in model thinking. For the study of complex systems in particular, computational simulation modeling approaches enable novel scientific inquiry and facilitate decision-making. Engagement in modeling can also overcome the difficulties in learning imposed by complex systems, leading to transformed mental models and the proliferation of model thinking in population health research and action.

Ethics for Complex Systems

2021 ◽  
pp. 62-82
Author(s):  
James Wilson
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Social Norms ◽  
Real World ◽  
External Validity ◽  
Thought Experiments ◽  
Social Processes ◽  
Causal Models ◽  
Government Action ◽  
Evidence Collection

This chapter argues that the scale of the challenge posed by external validity requires a similarly sizeable response. Not only should practitioners approach evidence collection and interventions in policy differently, but philosophers should also change the way they conceive of ethics. The default should no longer be to start from simplistic causal models or thought experiments, while being dimly aware that these approaches will exclude some features that would be relevant for real-world decision-making. Rather, both practitioners and philosophers should start from the premise that social processes are complex systems. Moreover, complex systems are in important aspects performative: for example, what counts as a breach of trust, or a violation of privacy, is not something that can be discovered once and for all, but is partly constituted by social norms and individual expectations, which will themselves change in response to government action.

E-Government Dynamics

2010 ◽  
pp. 261-276
Author(s):  
Petter Gottschalk ◽  
Hans Solli-Saether
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Organizational Performance ◽  
Mental Models ◽  
Complex World ◽  
Dynamic Complexity ◽  
Causal Loop ◽  
Business Dynamics ◽  
Effective Decision ◽  
Effective Decision Making

It was Sterman’s (2000) book entitled “Business Dynamics: Systems Thinking and Modeling for a Complex World” that introduced the term business dynamics. Business dynamics is concerned with learning in and about complex systems. Effective decision-making by growing dynamic complexity requires executives to become systems thinkers – to expand the boundaries of their mental models and develop ways to understand how the structure of complex systems creates behavior. We start this chapter discussing how to overcome barriers to E-Government. Then we present a framework for assessment of E-Government projects. In the context of system dynamics, we discuss causal loop diagramming, modeling and organizational performance.

scholarly journals Situating agent-based modelling in population health research

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Eric Silverman ◽  
Umberto Gostoli ◽  
Stefano Picascia ◽  
Jonatan Almagor ◽  
Mark McCann ◽  
...  
Keyword(s):  
Complex Systems ◽  
Population Health ◽  
Health Research ◽  
Intervention Development ◽  
Epidemiological Methods ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Complex Systems Theory ◽  
Current State ◽  
Core Concepts

AbstractToday’s most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method’s conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the ‘wicked’ problems in population health, and could make significant contributions to theory and intervention development in these areas.

Complex Systems and Population Health

2020 ◽  
Keyword(s):  
Complex Systems ◽  
Knowledge Base ◽  
Population Health ◽  
Computational Simulation ◽  
Health Sciences ◽  
Health Science ◽  
Systems Science ◽  
University Professors ◽  
Complex Population ◽  
Analytical Approaches

Currently, population health science is an integral part of academic curricula around the world. For over a century, the principles of the reductionist paradigm have guided population health curricula, training, research, and action. Researchers continue to draw upon these principles when theorizing, conceptualizing, designing studies, analyzing, and devising interventions to tackle complex population health problems. However, unresolved impasses in delineating and managing pressing population health challenges have catalyzed calls for the integration of complex systems science–grounded theoretical, methodological, and analytical approaches into population health science. Mounting evidence denotes that a complex systems paradigm can bring about dramatic, multipronged changes for education and training and lead to innovative research, interventions, and policies. Despite the large and untapped promise of complex systems, the haphazard knowledge base from which academics, researchers, students, policymakers, and practitioners can draw has slowed their integration into the population health sciences. This volume fulfills this growing need by providing the knowledge base necessary to introduce a holistic complex systems paradigm in population health science. As such, it is the first comprehensive book in population health science that meaningfully integrates complex systems theory, methodology, modeling, computational simulation, and real-world applications, while incorporating current population health theoretical, methodological, and analytical perspectives. It is intended as a programmatic primer across a broad spectrum of population health stakeholders—from university professors and graduate students to researchers, policymakers, and practitioners. This book also aims to provoke long-overdue discourse on the need for updated new curricula in the population health sciences.

Designing Population Health Research Grounded in Complex Systems Science

2020 ◽  
pp. 93-106
Author(s):  
Leah Frerichs ◽  
Natalie R. Smith
Keyword(s):  
Complex Systems ◽  
Population Health ◽  
Research Design ◽  
Health Research ◽  
Systems Approach ◽  
Systems Science ◽  
Dynamic Complexity ◽  
Traditional Research ◽  
Computational Modeling And Simulation ◽  
Systems Mapping

There have been increasing calls to use complex systems research approaches to address pressing population health problems. To respond to this call, the authors argue that researchers must shift their thinking in how they design population health research studies. Although designing research from a complex systems approach follows the same basic steps as traditional studies that are anchored in statistical, deductive research, new elements of dynamic complexity must be taken into account. This chapter provides an overview of designing research grounded in complex systems. It details the research design steps following a complex systems approach, emphasizing the initial stages of defining and narrowing the research focus. Complex systems issues compel researchers to define objectives and ask questions about how factors such as interconnections, delays between cause and effect, and nonlinear relationships influence outcomes of interest. The initial questions subsequently shape the research design and approach, where systems mapping and computational modeling and simulation methods are often highly relevant. The chapter concludes with a discussion about the complementary and synergistic nature of complex systems and traditional research approaches.

Beérkezett könyvek

2008 ◽  
Vol 63 (3) ◽  
pp. 607-608
Author(s):  
Csaba Pléh
Keyword(s):  
Decision Making ◽  
Human Mind ◽  
Frankfurt Am Main ◽  
Conscious Decision ◽  
Better Than

ErősFerenc, LénárdKataés BókayAntal(szerk.) Typus Budapestiensis. Tanulmányok a pszichoanalízis budapesti iskolájának történetéről éshatásáról. Thalassa, Budapest, 2008, 447 oldalHargittaiIstván: Doktor DNS. Őszinte beszélgetések James D. Watsonnal. Vince Kiadó, Budapest, 2008, 223 oldalKutrovátzGábor,LángBenedekésZemplénGábor: A tudomány határa. Typotex,Budapest, 2008, 376 oldalEngerl, C. andSinger, W. (eds) Better than conscious? Decision making, the human mind, and implications for institutions . MIT Press, Cambridge, 2008, xiv + 449 oldalKondor, Zsuzsanna: Embedded thinking. Multimedia and the new rationality. Peter Lang, Frankfurt am Main, 2008, xi + 169 oldalSíklakiIstván(szerk.): Szóbeli befolyásolás. I–II. Typotex, Budapest,_n

THE INTEGRAL INDEX OF PROBLEMS IN THE EVALUATION OF THE HEALTH STATE

2018 ◽  
pp. 12-14
Author(s):  
A.E. Vlasenko ◽  
N.M. Zhilina ◽  
A.A. Kozhevnikov ◽  
G.I. Chechenin
Keyword(s):  
Decision Making ◽  
Population Health ◽  
Economic Indicators ◽  
Health State ◽  
Integral Index ◽  
Municipal District ◽  
Risk Areas

The article presents the algorithm for calculating the integral index of problems in the evaluation of indicators of population health and identifying risk areas. The integral indices for Novokuznetsk municipal district were calculated. The index can be used by specialists of various levels and regions in assessing the level of health, environmental and socio-economic indicators for appropriate decision-making.

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