Cover
Title Page
List of Contributors
Preface
Introduction: The Why, What and How of Social Systems Engineering
1. The Very Idea
2. Epistemic Notions on the Engineering of Social Systems
3. Using Engineering Methods
4. Into Real‐World Applications
5. References
Part I: Social Systems Engineering: The Very Idea
1. 1 Compromised Exactness and the Rationality of Engineering
  1. 1.1 Introduction
  2. 1.2 The Historical Context
  3. 1.3 Science and Engineering: Distinctive Rationalities
  4. 1.4 ‘Compromised Exactness’: Design in Engineering
  5. 1.5 Engineering Social Systems?
  6. References
2. 2 Uncertainty in the Design and Maintenance of Social Systems
  1. 2.1 Introduction
  2. 2.2 Uncertainties in Simple and Complicated Engineered Systems
  3. 2.3 Control Volume and Uncertainty
  4. 2.4 Engineering Analysis and Uncertainty in Complex Systems
  5. 2.5 Uncertainty in Social Systems Engineering
  6. 2.6 Conclusions
  7. References
3. 3 System Farming
  1. 3.1 Introduction
  2. 3.2 Uncertainty, Complexity and Emergence
  3. 3.3 Science and Engineering Approaches
  4. 3.4 Responses to CSS Complexity
  5. 3.5 Towards Farming Systems
  6. 3.6 Conclusion
  7. References
4. 4 Policy between Evolution and Engineering
  1. 4.1 Introduction: Individual and Social System
  2. 4.2 Policy – Concept and Process
  3. 4.3 Human Actors: Perception, Policy and Action
  4. 4.4 Artefacts
  5. 4.5 Engineering and Evolution: From External to Internal Selection
  6. 4.6 Policy between Cultural Evolution and Engineering
  7. 4.7 Conclusions and Outlook
  8. Appendix: Brief Overview of the Policy Literature
  9. References
5. 5 ‘Friend’ versus ‘Electronic Friend’
  1. References
Part II: Methodologies and Tools
1. 6 Interactive Visualizations for Supporting Decision‐Making in Complex Socio‐technical Systems
  1. 6.1 Introduction
  2. 6.2 Policy Flight Simulators
  3. 6.3 Application 1 – Hospital Consolidation
  4. 6.4 Application 2 – Enterprise Diagnostics
  5. 6.5 Conclusions
  6. References
2. 7 Developing Agent‐Based Simulation Models for Social Systems Engineering Studies: A Novel Framework and its Application to Modelling Peacebuilding Activities
  1. 7.1 Introduction
  2. 7.2 Background
  3. 7.3 Framework
  4. 7.4 Illustrative Example of Applying the Framework
  5. 7.5 Conclusions
  6. References
3. 8 Using Actor‐Network Theory in Agent‐Based Modelling
  1. 8.1 Introduction
  2. 8.2 Agent‐Based Modelling
  3. 8.3 Actor‐Network Theory
  4. 8.4 Towards an ANT‐Based Approach to ABM
  5. 8.5 Design Guidelines
  6. 8.6 The Case of WEEE Management
  7. 8.7 Conclusions
  8. References
4. 9 Engineering the Process of Institutional Innovation in Contested Territory
  1. 9.1 Introduction
  2. 9.2 Can Cyber Security and Risk be Quantified?
  3. 9.3 Social Processes of Innovation in Pre‐paradigmatic Fields
  4. 9.4 A Computational Model of Innovation
  5. 9.5 Discussion
  6. Acknowledgements
  7. References
Part III: Cases and Applications
1. 10 Agent‐Based Explorations of Environmental Consumption in Segregated Networks
  1. 10.1 Introduction
  2. 10.2 Model Overview
  3. 10.3 Results
  4. 10.4 Conclusion
  5. Acknowledgements
  6. References
2. 11 Modelling in the ‘Muddled Middle’: A Case Study of Water Service Delivery in Post‐Apartheid South Africa
  1. 11.1 Introduction
  2. 11.2 The Case Study
  3. 11.3 Contextualizing Modelling in the ‘Muddled Middle’ in the Water Sector
  4. 11.4 Methods
  5. 11.5 Results
  6. 11.6 Discussion
  7. Acknowledgements
  8. References
3. 12 Holistic System Design: The Oncology Carinthia Study
  1. 12.1 The Challenge: Holistic System Design
  2. 12.2 Methodology
  3. 12.3 Introduction to the Case Study: Oncology Carinthia
  4. 12.4 Insights, Teachings and Implications
  5. Acknowledgements
  6. Appendix: Mathematical Representations for Figures 12.5, 12.6 and 12.7
  7. References
4. 13 Reinforcing the Social in Social Systems Engineering – Lessons Learnt from Smart City Projects in the United Kingdom
  1. 13.1 Introduction
  2. 13.2 Methodology
  3. 13.3 Case Studies
  4. 13.4 Discussion
  5. 13.5 Conclusion
  6. References
Index
End User License Agreement

List of Tables

Chapter 04
1. Table 4A.1 Threads of theoretical approaches to policy
Chapter 06
1. Table 6.1 Abstraction–aggregation hierarchy
2. Table 6.2 Mnemonics for abstraction–aggregation hierarchy
Chapter 07
1. Table 7.1 The main elements that constitute a state chart
2. Table 7.2 Violence factors affecting South Sudanese people (derived from CNRCD, 2011)
3. Table 7.3 Scope to be considered for the toolkit design
4. Table 7.4 Citizen stereotypes
Chapter 08
1. Table 8.1 Actor‐networks in Colombia’s WEEE management system
Chapter 09
1. Table 9.1 List of example knowledge artefacts and their position in I‐space
Chapter 10
1. Table 10.1 Effect of model parameters on saturation times, by firm strategy (DV = average wait time)
2. Table 10.2 Effect of model parameters on differences in wait times between high‐ and low‐propensity groups (DV = difference in average wait time)
3. Table 10.3 Effect of switching from FTA to random strategy at different points during the process on overall saturation times and equity between high‐ and low‐propensity groups
Chapter 11
1. Table 11.1 Summary of the four models, displayed in terms of the number (no.) of stock variables (with the relative percentage of the stock variables in relation to total variables in brackets); the number of feedback loops; and references for model documentation
Chapter 12
1. Table 12.1 Stakeholders, goals and critical factors
2. Table 12.2 Recursive distribution of tasks in the oncological care system

List of Illustrations

Chapter 03
1. Figure 3.1 Emergence resulting from syntactic complexity plus abstraction.
2. Figure 3.2 CA rule 30 (Wolfram, 1986). This is iteratively applied to an all‐dark line with a single white cell (time is downwards, so the development is clear). What state the original cell (shown with a blue line) is in after a number of iterations seems to be only retrievable by calculating the whole.
3. Figure 3.3 The engineering and maintenance phases before and after system creation.
4. Figure 3.4 A plot of scaled standard deviation against different population sizes averaged over 24 runs over 500 cycles for each point in the El Farol Bar model.
Chapter 04
1. Figure 4.1 The policy process as control loop.
2. Figure 4.2 Different threads of policy research.
3. Figure 4.3 Human actors form a minimal social system.
4. Figure 4.4 Artefacts.
Chapter 06
1. Figure 6.1 Architecture of public–private enterprises.
2. Figure 6.2 Healthcare systemigram.
3. Figure 6.3 Two components in the agent‐based decision support model for hospital consolidation: simulation model and user interactive system.
4. Figure 6.4 Mergers and acquisitions process.
5. Figure 6.5 CCSE Immersion Lab at Stevens Institute of Technology.
6. Figure 6.6 Interactive visualization using d3.js: left figure shows payment, charges and market share information for each hospital (rows) and diagnostic group (columns), with sorting enabled for both rows and columns; right figure explores M&A transactions database, from M&A activities, to top acquirers’ financials, to news reports.
7. Figure 6.7 Hospitals’ frequency as market leaders based on net patient revenue. Each dot represents one instance of a particular hospital entering the top tier.
8. Figure 6.8 Introduction screen.
9. Figure 6.9 Analysis interface with available data window, evidence windows and abstraction filter.
10. Figure 6.10 Accuracy results for each era.
11. Figure 6.11 Accuracy results for each car.
12. Figure 6.12 Accuracy results for each era for each level of expertise.
13. Figure 6.13 Accuracy results for each car for each level of expertise.
Chapter 07
1. Figure 7.1 Overview of the framework.
2. Figure 7.2 Toolkit design part of the framework.
3. Figure 7.3 Application design part of the framework.
4. Figure 7.4 Citizen in conflict areas (early prototype).
5. Figure 7.5 Citizen in conflict areas (final template).
6. Figure 7.6 Variables included in the decision to study the dynamics of South Sudanese citizens.
7. Figure 7.7 Screenshot of the decision tool main screen.
8. Figure 7.8 Sample output.
9. Figure 7.9 Example of the use of the tool to test actions towards creating jobs and increasing safety and representativeness.
Chapter 08
1. Figure 8.1 Agent‐based model representation.
2. Figure 8.2 Abstraction of steps for applying ANT to the design of an ABM.
3. Figure 8.3 Graph of the mobilization of A‐Ns in local and global networks.
4. Figure 8.4 WEEE management processes in Colombia.
5. Figure 8.5 The general timeline of WEEE management in Colombia.
6. Figure 8.6 Mobilization of actors in local and global networks.
7. Figure 8.7 ABM elements based on the application of ANT for the case study.
Chapter 09
1. Figure 9.1 Generic agent algorithm for a single time step.
2. Figure 9.2 The 2D lattice of ‘technologies’ in our percolation model of innovation after 1200 simulation steps. One region in the lattice around the best‐practice frontier (BPF) is magnified. See Figure 9.3 for details. Greyscale code: black = impossible, white = possible but not yet discovered; very dark grey = possible but not reachable; lightest grey = discovered but not yet viable; medium‐light grey = discovered and viable; medium‐dark grey = discovered, viable and on best‐practice frontier (BPF). Medium‐dark grey cells are the loci of R&D. The two horizontal lines show the ‘average’ level of innovation (i.e., BPF) across all ‘technologies’ (columns). The dark line is the average (i.e., mean) BPF, while the dark‐grey line is the mean + standard deviation of the BPF.
3. Figure 9.3 Detailed view of the magnified region in Figure 9.2.
4. Figure 9.4 Algorithm for R&D activity in a single time step.
5. Figure 9.5 A single run at different time steps, given an initial state (a), with a time‐series chart (d) showing innovation rate for mean BPF (black horizontal line in b and c) and upper BPF (mean+standard deviation, dark‐grey horizontal line in b and c).
6. Figure 9.6 Screenshots of two treatments tested on the same lattice configuration: (a) quant–slow learning vs. (b) quant–fast learning. In (b), the innovation rate increases as fidelity increases due to learning. Therefore, even though it started out slower, the quant– fast learning treatment wins this innovation race. (The black horizontal lines are mean BPF. The dark‐grey horizontal lines are upper BPF = mean + standard deviation.)
7. Figure 9.7 Violin plot of experimental results for four treatments, each tested with ten random lattice initial conditions and twenty runs per lattice condition: (a) innovation rate at the end of the simulation run; (b) time to complete a simulation run.
Chapter 10
1. Figure 10.1 A sample network connecting agents in a segregated system.
2. Figure 10.2 Distribution of average wait times by firm strategy.
3. Figure 10.3 Adoption dynamics by firm strategy: (a) FTA strategy; (b) random strategy.
Chapter 11
1. Figure 11.1 Overview causal loop diagram, with three feedback loops identified.
2. Figure 11.2 Simulation output from the GKWS model illustrating the time without potable water, differentiated across supply zones 1, 2 and 3, 4 and 5 [see D’Hont (2013) and Clifford‐Holmes (2015) for details].
3. Figure 11.3 The left‐hand graph (a) displays the simulated population dynamics in the Greater Kirkwood region. The right‐hand graph (b) displays the simulated water supply and the quantity of ‘billable water’ in relation to the gap between demand and supply [see Clifford‐Holmes et al. (2015) for full model].
4. Figure 11.4 Expanded CLD showing an additional three feedback loops (B2, R3 and R4) that build on Figure 11.1. The emboldened arrows show the causal relations between the additional variables, with the two arrows dashed for the sake of clarity where the emboldened arrows overlap with other arrows.
Chapter 12
1. Figure 12.1 Integrative Systems Methodology for dealing with complex issues – a process diagram.
2. Figure 12.2 Map of Carinthia with hospitals.
3. Figure 12.3 Causal loop diagram showing the dynamics of the system‐in‐focus.
4. Figure 12.4 Three main levers for the development of the oncological care system.
5. Figure 12.5 The Viable System Model.
6. Figure 12.6 Oncology Carinthia as a recursively structured system.
7. Figure 12.7 The constitution of tumour boards.
8. Figure 12.8 Survival rates for main types of cancer in Carinthia 1995–2013.

This edition first published 2018
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List of Contributors

Ricardo A. Barros‐Castro
(Faculty of Engineering, Pontificia Universidad Javeriana). Email: ricardo‐barros@javeriana.edu.co

Heike I. Brugger
(Department of Politics and Public Administration, University of Konstanz). Email: heike.brugger@uni‐konstanz.de

William M. Bulleit
(Department of Civil and Environmental Engineering, Michigan Technological University). Email: wmbullei@mtu.edu

Jai K. Clifford‐Holmes
(Institute for Water Research, Rhodes University). Email: jai.clifford.holmes@gmail.com

Bruce Edmonds
(Centre for Policy Modelling, Manchester Metropolitan University). Email: bruce@edmonds.name

Grazziela P. Figueredo
(School of Computer Science, University of Nottingham). Email: Grazziela.Figueredo@nottingham.ac.uk

César García‐Díaz
(Department of Industrial Engineering, Universidad de los Andes). Email: ce.garcia392@uniandes.edu.co

John S. Gero
(Krasnow Institute of Advanced Study, George Mason University and University of North Carolina, Charlotte). Email: john@johngero.com

Steven L. Goldman
(Departments of Philosophy and History, Lehigh University). Email: slg2@Lehigh.edu

Rafael A. Gonzalez
(Faculty of Engineering, Pontificia Universidad Javeriana). Email: ragonzalez@javeriana.edu.co

Zeynep Gurguc
(Digital City Exchange, Imperial College London). Email: z.gurguc@imperial.ac.uk

Adam Douglas Henry
(School of Government and Public Policy, University of Arizona). Email: adhenry@email.arizona.edu

Miwa Hirono
(Department of International Relations, Ritsumeikan University). Email: hirono‐1@fc.ritsumei.ac.jp

Johann Klocker
(Landeskrankenhaus Klagenfurt). Email: j.klocker@ipso.at

Chen Liu
(Center for Complex Systems and Enterprises, Stevens Institute of Technology). Email: cliu16@stevens.edu

Sandra Méndez‐Fajardo
(Faculty of Engineering, Pontificia Universidad Javeriana). Email: sandra.mendez@javeriana.edu.co

Jenny O’Connor
(Digital City Exchange, Imperial College London). Email: j.oconnor@imperial.ac.uk

Mehrnoosh Oghbaie
(Center for Complex Systems and Enterprises, Stevens Institute of Technology). Email: moghbaie@stevens.edu

Peer‐Olaf Siebers
(School of Computer Science, University of Nottingham). Email: Peer‐Olaf.Siebers@nottingham.ac.uk

Camilo Olaya
(Department of Industrial Engineering, Universidad de los Andes). Email: colaya@uniandes.edu.co

Carolyn G. Palmer
(Institute for Water Research, Rhodes University). Email: tally.palmer@ru.ac.za

Michael J. Pennock
(Center for Complex Systems and Enterprises, Stevens Institute of Technology). Email: mpennock@stevens.edu

Joseph C. Pitt
(Department of Philosophy, Virginia Polytechnic Institute and State University). Email: jcpitt@vt.edu

William B. Rouse
(Center for Complex Systems and Enterprises, Stevens Institute of Technology). Email: wrouse@stevens.edu

Martin F.G. Schaffernicht
(Faculty of Economics and Business, Universidad de Talca). Email: martin@utalca.cl

Markus Schwaninger
(Institute of Management, University of St. Gallen). Email: markus.schwaninger@unisg.ch

Anya Skatova
(Warwick Business School, University of Warwick). Email: anya.skatova@wbs.ac.uk

Jill H. Slinger
(Faculty of Technology, Policy and Management and the Faculty of Civil Engineering and Technical Geosciences, Delft University of Technology and the Institute for Water Research, Rhodes University). Email: j.h.slinger@tudelft.nl

Russell C. Thomas
(Department of Computational and Data Sciences, George Mason University and University of North Carolina, Charlotte). Email: russell.thomas@meritology.com

Koen H. van Dam
(Digital City Exchange, Imperial College London). Email: k.van‐dam@imperial.ac.uk

Chris de Wet
(Institute for Water Research and the Department of Anthropology, Rhodes University). Email: c.dewet@ru.ac.za

Zhongyuan Yu
(Center for Complex Systems and Enterprises, Stevens Institute of Technology). Email: zyu7@stevens.edu

Preface

We, the editors of this volume, are trained as both engineers and social scientists, and have contrasted the two different mindsets during our careers. Since the time when we were PhD students in the social sciences, we have felt that ‘praxis’ does not have the same status as theorizing in generating knowledge, as opposed to what happens in engineering. Even the recognition of engineering knowledge as a distinctive kind of knowledge, different from scientific knowledge, seems to remain elusive for both academics and practitioners. In fact, ‘praxis’ embodies a set of differential elements from pure science. This volume is an effort to bring together elements of engineering thinking and social science into the study of social systems, and more importantly, aims to be a vehicle that emphasizes the necessity of developing practical knowledge – through its proper ways and under its own ‘validation’ criteria – to provide feasible, yet informed, paths for intervening and improving social systems; that is, systems created and driven by human beings.

Also, through this volume we would like to make explicit the inherent link between systemic thinking and engineering knowledge through the consideration of multiple perspectives and methods. We believe that the merger of both the qualitative and quantitative worlds is essential in order to cope with the complexity of contemporary social systems. Moreover, we believe that engineering thinking, along with its tools and methods, is one of the best chances that we have for designing, redesigning and transforming the complex world of social systems.

This project would not have been possible without the initial motivation provided by Debbie Jupe (commissioning editor at John Wiley & Sons), who was the first editor we met at the Social Simulation Conference in Warsaw (Poland), back in 2013. Debbie invited us to put our ideas in a written proposal, and encouraged us to come up with a book on engineering perspectives to social systems. Thank you Debbie for your encouragement!

We are grateful to all the authors who contributed to this volume for their patience, commitment and understanding over the course of the many months it took to make this book a reality. We are also indebted to the Department of Industrial Engineering of Universidad de los Andes (Colombia) for their continued support and to Claudia Estévez‐Mujica, who helped us immensely in putting all the chapters together, checking inconsistencies and assembling the whole book.

Wiley Series in Computational and Quantitative Social Science

SOCIAL SYSTEMS ENGINEERING

THE DESIGN OF COMPLEXITY

List of Contributors

Preface