Social Systems Engineering E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley Series in Computational and Quantitative Social Science
- Sprache: Englisch
Uniquely reflects an engineering view to social systems in a wide variety of contexts of application
Social Systems Engineering: The Design of Complexity brings together a wide variety of application approaches to social systems from an engineering viewpoint. The book defines a social system as any complex system formed by human beings. Focus is given to the importance of systems intervention design for specific and singular settings, the possibilities of engineering thinking and methods, the use of computational models in particular contexts, and the development of portfolios of solutions. Furthermore, this book considers both technical, human and social perspectives, which are crucial to solving complex problems.
Social Systems Engineering: The Design of Complexity provides modelling examples to explore the design aspect of social systems. Various applications are explored in a variety of areas, such as urban systems, health care systems, socio-economic systems, and environmental systems. It covers important topics such as organizational design, modelling and intervention in socio-economic systems, participatory and/or community-based modelling, application of systems engineering tools to social problems, applications of computational behavioral modeling, computational modelling and management of complexity, and more.
- Highlights an engineering view to social systems (as opposed to a “scientific” view) that stresses the importance of systems intervention design for specific and singular settings
- Divulges works where the design, re-design, and transformation of social systems constitute the main aim, and where joint considerations of both technical and social perspectives are deemed important in solving social problems
- Features an array of applied cases that illustrate the application of social systems engineering in different domains
Social Systems Engineering: The Design of Complexity is an excellent text for academics and graduate students in engineering and social science—specifically, economists, political scientists, anthropologists, and management scientists with an interest in finding systematic ways to intervene and improve social systems.
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Veröffentlichungsjahr: 2017
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Table of Contents
Cover
Title Page
List of Contributors
Preface
Introduction: The Why, What and How of Social Systems Engineering
The Very Idea
Epistemic Notions on the Engineering of Social Systems
Using Engineering Methods
Into Real‐World Applications
References
Part I: Social Systems Engineering: The Very Idea
1 Compromised Exactness and the Rationality of Engineering
1.1 Introduction
1.2 The Historical Context
1.3 Science and Engineering: Distinctive Rationalities
1.4 ‘Compromised Exactness’: Design in Engineering
1.5 Engineering Social Systems?
References
2 Uncertainty in the Design and Maintenance of Social Systems
2.1 Introduction
2.2 Uncertainties in Simple and Complicated Engineered Systems
2.3 Control Volume and Uncertainty
2.4 Engineering Analysis and Uncertainty in Complex Systems
2.5 Uncertainty in Social Systems Engineering
2.6 Conclusions
References
3 System Farming
3.1 Introduction
3.2 Uncertainty, Complexity and Emergence
3.3 Science and Engineering Approaches
3.4 Responses to CSS Complexity
3.5 Towards Farming Systems
3.6 Conclusion
References
4 Policy between Evolution and Engineering
4.1 Introduction: Individual and Social System
4.2 Policy – Concept and Process
4.3 Human Actors: Perception, Policy and Action
4.4 Artefacts
4.5 Engineering and Evolution: From External to Internal Selection
4.6 Policy between Cultural Evolution and Engineering
4.7 Conclusions and Outlook
Appendix: Brief Overview of the Policy Literature
References
5 ‘Friend’ versus ‘Electronic Friend’
References
Part II: Methodologies and Tools
6 Interactive Visualizations for Supporting Decision‐Making in Complex Socio‐technical Systems
6.1 Introduction
6.2 Policy Flight Simulators
6.3 Application 1 – Hospital Consolidation
6.4 Application 2 – Enterprise Diagnostics
6.5 Conclusions
References
7 Developing Agent‐Based Simulation Models for Social Systems Engineering Studies: A Novel Framework and its Application to Modelling Peacebuilding Activities
7.1 Introduction
7.2 Background
7.3 Framework
7.4 Illustrative Example of Applying the Framework
7.5 Conclusions
References
8 Using Actor‐Network Theory in Agent‐Based Modelling
8.1 Introduction
8.2 Agent‐Based Modelling
8.3 Actor‐Network Theory
8.4 Towards an ANT‐Based Approach to ABM
8.5 Design Guidelines
8.6 The Case of WEEE Management
8.7 Conclusions
References
9 Engineering the Process of Institutional Innovation in Contested Territory
9.1 Introduction
9.2 Can Cyber Security and Risk be Quantified?
9.3 Social Processes of Innovation in Pre‐paradigmatic Fields
9.4 A Computational Model of Innovation
9.5 Discussion
Acknowledgements
References
Part III: Cases and Applications
10 Agent‐Based Explorations of Environmental Consumption in Segregated Networks
10.1 Introduction
10.2 Model Overview
10.3 Results
10.4 Conclusion
Acknowledgements
References
11 Modelling in the ‘Muddled Middle’: A Case Study of Water Service Delivery in Post‐Apartheid South Africa
11.1 Introduction
11.2 The Case Study
11.3 Contextualizing Modelling in the ‘Muddled Middle’ in the Water Sector
11.4 Methods
11.5 Results
11.6 Discussion
Acknowledgements
References
12 Holistic System Design: The Oncology Carinthia Study
12.1 The Challenge: Holistic System Design
12.2 Methodology
12.3 Introduction to the Case Study: Oncology Carinthia
12.4 Insights, Teachings and Implications
Acknowledgements
Appendix: Mathematical Representations for Figures 12.5, 12.6 and 12.7
References
13 Reinforcing the Social in Social Systems Engineering – Lessons Learnt from Smart City Projects in the United Kingdom
13.1 Introduction
13.2 Methodology
13.3 Case Studies
13.4 Discussion
13.5 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 04
Table 4A.1 Threads of theoretical approaches to policy
Chapter 06
Table 6.1 Abstraction–aggregation hierarchy
Table 6.2 Mnemonics for abstraction–aggregation hierarchy
Chapter 07
Table 7.1 The main elements that constitute a state chart
Table 7.2 Violence factors affecting South Sudanese people (derived from CNRCD, 2011)
Table 7.3 Scope to be considered for the toolkit design
Table 7.4 Citizen stereotypes
Chapter 08
Table 8.1 Actor‐networks in Colombia’s WEEE management system
Chapter 09
Table 9.1 List of example knowledge artefacts and their position in I‐space
Chapter 10
Table 10.1 Effect of model parameters on saturation times, by firm strategy (DV = average wait time)
Table 10.2 Effect of model parameters on differences in wait times between high‐ and low‐propensity groups (DV = difference in average wait time)
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
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
Table 12.1 Stakeholders, goals and critical factors
Table 12.2 Recursive distribution of tasks in the oncological care system
List of Illustrations
Chapter 03
Figure 3.1 Emergence resulting from syntactic complexity plus abstraction.
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.
Figure 3.3 The engineering and maintenance phases before and after system creation.
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
Figure 4.1 The policy process as control loop.
Figure 4.2 Different threads of policy research.
Figure 4.3 Human actors form a minimal social system.
Figure 4.4 Artefacts.
Chapter 06
Figure 6.1 Architecture of public–private enterprises.
Figure 6.2 Healthcare systemigram.
Figure 6.3 Two components in the agent‐based decision support model for hospital consolidation: simulation model and user interactive system.
Figure 6.4 Mergers and acquisitions process.
Figure 6.5 CCSE
Immersion Lab
at Stevens Institute of Technology.
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.
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.
Figure 6.8 Introduction screen.
Figure 6.9 Analysis interface with available data window, evidence windows and abstraction filter.
Figure 6.10 Accuracy results for each era.
Figure 6.11 Accuracy results for each car.
Figure 6.12 Accuracy results for each era for each level of expertise.
Figure 6.13 Accuracy results for each car for each level of expertise.
Chapter 07
Figure 7.1 Overview of the framework.
Figure 7.2 Toolkit design part of the framework.
Figure 7.3 Application design part of the framework.
Figure 7.4 Citizen in conflict areas (early prototype).
Figure 7.5 Citizen in conflict areas (final template).
Figure 7.6 Variables included in the decision to study the dynamics of South Sudanese citizens.
Figure 7.7 Screenshot of the decision tool main screen.
Figure 7.8 Sample output.
Figure 7.9 Example of the use of the tool to test actions towards creating jobs and increasing safety and representativeness.
Chapter 08
Figure 8.1 Agent‐based model representation.
Figure 8.2 Abstraction of steps for applying ANT to the design of an ABM.
Figure 8.3 Graph of the mobilization of A‐Ns in local and global networks.
Figure 8.4 WEEE management processes in Colombia.
Figure 8.5 The general timeline of WEEE management in Colombia.
Figure 8.6 Mobilization of actors in local and global networks.
Figure 8.7 ABM elements based on the application of ANT for the case study.
Chapter 09
Figure 9.1 Generic agent algorithm for a single time step.
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.
Figure 9.3 Detailed view of the magnified region in Figure 9.2.
Figure 9.4 Algorithm for R&D activity in a single time step.
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).
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.)
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
Figure 10.1 A sample network connecting agents in a segregated system.
Figure 10.2 Distribution of average wait times by firm strategy.
Figure 10.3 Adoption dynamics by firm strategy: (a) FTA strategy; (b) random strategy.
Chapter 11
Figure 11.1 Overview causal loop diagram, with three feedback loops identified.
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].
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].
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
Figure 12.1 Integrative Systems Methodology for dealing with complex issues – a process diagram.
Figure 12.2 Map of Carinthia with hospitals.
Figure 12.3 Causal loop diagram showing the dynamics of the system‐in‐focus.
Figure 12.4 Three main levers for the development of the oncological care system.
Figure 12.5 The Viable System Model.
Figure 12.6 Oncology Carinthia as a recursively structured system.
Figure 12.7 The constitution of tumour boards.
Figure 12.8 Survival rates for main types of cancer in Carinthia 1995–2013.
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Wiley Series in Computational and Quantitative Social Science
Computational Social Science is an interdisciplinary field undergoing rapid growth due to the availability of ever increasing computational power leading to new areas of research.
Embracing a spectrum from theoretical foundations to real world applications, the Wiley Series in Computational and Quantitative Social Science is a series of titles ranging from high level student texts, explanation and dissemination of technology and good practice, through to interesting and important research that is immediately relevant to social / scientific development or practice. Books within the series will be of interest to senior undergraduate and graduate students, researchers and practitioners within statistics and social science.
Behavioral Computational Social ScienceRiccardo Boero
Tipping Points: Modelling Social Problems and HealthJohn Bissell (Editor), Camila Caiado (Editor), Sarah Curtis (Editor), Michael Goldstein (Editor), Brian Straughan (Editor)
Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network EvolutionVladimir Batagelj, Patrick Doreian, Anuska Ferligoj, Natasa Kejzar
Analytical Sociology: Actions and NetworksGianluca Manzo (Editor)
Computational Approaches to Studying the Co‐evolution of Networks and Behavior in Social DilemmasRense Corten
The Visualisation of Spatial Social StructureDanny Dorling
SOCIAL SYSTEMS ENGINEERING
THE DESIGN OF COMPLEXITY
Edited by
César García-Díaz
Department of Industrial Engineering
Universidad de los Andes
Bogotá, Colombia
Camilo Olaya
Department of Industrial Engineering
Universidad de los Andes
Bogotá, Colombia
This edition first published 2018© 2018 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of César García‐Díaz and Camilo Olaya to be identified as the authors of the editorial material in this work has been asserted in accordance with law.
Registered OfficesJohn Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USAJohn Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
Editorial Office9600 Garsington Road, Oxford, OX4 2DQ, UK
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
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Library of Congress Cataloging‐in‐Publication data applied for
Hardback ISBN: 9781118974452
Cover Image: © Henrik Sorensen/Gettyimages
List of Contributors
Ricardo A. Barros‐Castro(Faculty of Engineering, Pontificia Universidad Javeriana). Email: ricardo‐[email protected]
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: [email protected]
Jai K. Clifford‐Holmes(Institute for Water Research, Rhodes University). Email: [email protected]
Bruce Edmonds(Centre for Policy Modelling, Manchester Metropolitan University). Email: [email protected]
Grazziela P. Figueredo(School of Computer Science, University of Nottingham). Email: [email protected]
César García‐Díaz(Department of Industrial Engineering, Universidad de los Andes). Email: [email protected]
John S. Gero(Krasnow Institute of Advanced Study, George Mason University and University of North Carolina, Charlotte). Email: [email protected]
Steven L. Goldman(Departments of Philosophy and History, Lehigh University). Email: [email protected]
Rafael A. Gonzalez(Faculty of Engineering, Pontificia Universidad Javeriana). Email: [email protected]
Zeynep Gurguc(Digital City Exchange, Imperial College London). Email: [email protected]
Adam Douglas Henry(School of Government and Public Policy, University of Arizona). Email: [email protected]
Miwa Hirono(Department of International Relations, Ritsumeikan University). Email: hirono‐[email protected]
Johann Klocker(Landeskrankenhaus Klagenfurt). Email: [email protected]
Chen Liu(Center for Complex Systems and Enterprises, Stevens Institute of Technology). Email: [email protected]
Sandra Méndez‐Fajardo(Faculty of Engineering, Pontificia Universidad Javeriana). Email: [email protected]
Jenny O’Connor(Digital City Exchange, Imperial College London). Email: [email protected]
Mehrnoosh Oghbaie(Center for Complex Systems and Enterprises, Stevens Institute of Technology). Email: [email protected]
Peer‐Olaf Siebers(School of Computer Science, University of Nottingham). Email: Peer‐[email protected]
Camilo Olaya(Department of Industrial Engineering, Universidad de los Andes). Email: [email protected]
Carolyn G. Palmer(Institute for Water Research, Rhodes University). Email: [email protected]
Michael J. Pennock(Center for Complex Systems and Enterprises, Stevens Institute of Technology). Email: [email protected]
Joseph C. Pitt(Department of Philosophy, Virginia Polytechnic Institute and State University). Email: [email protected]
William B. Rouse(Center for Complex Systems and Enterprises, Stevens Institute of Technology). Email: [email protected]
Martin F.G. Schaffernicht(Faculty of Economics and Business, Universidad de Talca). Email: [email protected]
Markus Schwaninger(Institute of Management, University of St. Gallen). Email: [email protected]
Anya Skatova(Warwick Business School, University of Warwick). Email: [email protected]
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: [email protected]
Russell C. Thomas(Department of Computational and Data Sciences, George Mason University and University of North Carolina, Charlotte). Email: [email protected]
Koen H. van Dam(Digital City Exchange, Imperial College London). Email: k.van‐[email protected]
Chris de Wet(Institute for Water Research and the Department of Anthropology, Rhodes University). Email: [email protected]
Zhongyuan Yu(Center for Complex Systems and Enterprises, Stevens Institute of Technology). Email: [email protected]
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.
Introduction: The Why, What and How of Social Systems Engineering
César García‐Díaz and Camilo Olaya
The Very Idea
The expression ‘social systems engineering’ is not new. As far as we know, its first appearance in the literature dates from the mid‐1970s. In 1975, the Proceedings of the IEEE published a special issue on social systems engineering (Chen et al., 1975). Here, Chen and colleagues referred to social systems engineering as the application of systems engineering concepts to social problems. Likewise, the special issue seemed to emphasize that the potential contribution of engineering to social issues was predominantly based on the consideration of quantitative modelling as the workhorse for intervention. Although we concur with some of these points, for us the expression ‘social systems engineering’ has a broader connotation, not meaning that we advocate exclusively for the application of engineering methods to social issues, but rather that we stand up for the consideration of design perspectives as a pivotal way to generate knowledge and transform systems. The intrinsic engineering orientation to action and transformation as its ultimate goals for improving a system, for meeting needs, for addressing successfully a specific problematic situation that someone wants to improve, etc. are emphases that this book highlights. Such goals demand the recognition of specific engineering considerations and their implications for addressing social systems. We want to emphasize the complexity of engineering ‘social’ (human) systems (as opposed to engineering mechanical systems, electrical systems, etc.), since such systems are then in fact ‘social’ (formed by purposeful actors that display agency, with diverse, clashing interests and goals) and therefore their design, redesign and transformation, unlike in other engineering domains, cannot be completely determined or planned beforehand. These designs are formal and informal, emergent, always ‘in progress’, adapting and evolving out of diverse dynamics.
Social systems engineering has a paradoxical status. On the one hand, it is an under‐researched topic whose theoria has rarely been explored. On the other hand, it is perhaps one of the most common endeavours in society since it concerns the praxis that seeks to design, create and transform human organizations. Consequently, we need to understand what engineering thinking means, and how it relates to social systems. Steven Goldman, one of the contributors to this book, stated more than 20 years ago regarding the autonomy of engineering (as distinct from other activities such as science or arts) that ‘while engineering has a theoria, analogous to, but different from, that of the physical sciences, unlike science, engineering is quintessentially a praxis, a knowing inseparable from moral action’ (Goldman, 1991, p. 139). The recognition of engineering as an autonomous activity, independent from science (though related in many ways), seems just a recent explicit realization that can be identified with what can be called a ‘philosophy of engineering’ (Bucciarelli, 2003; Goldman, 2004; Miller, 2009; Sinclair, 1977; Van de Poel and Goldberg, 2010). Perhaps the key word to understand the autonomy of engineering is design (Goldman, 1990; Layton, 1984, 1991; Pitt, 2011b; Schmidt, 2012; Van de Poel, 2010). Engineering, being driven by design, shows a distinct rationality, as Goldman shows in Chapter 1 of this book. He characterizes engineering design as ‘compromised exactness’, since its formal apparatus delivers approximate ‘solutions’ that are subject to their context of application, which means that they are always subjective, wilful and contextual. Social systems, as belonging to the realm of artificial systems, exhibit design, which means that they are, and can be, engineered, but not in the traditional sense (Remington et al., 2012; Simon, 1996). Traditional engineering, design‐based methods, which essentially aim at control and prediction, cannot be applied to social systems due to the very nature of these systems – unlike mechanical systems, social systems do not ‘obey laws’, as Galileo imagined (Galileo Galilei, 1623), but are driven by the agency of human beings. Yet, engineering thinking can be used in several other ways, for instance for steering social systems towards a given direction, for influencing action (Pennock and Rouse, 2016), for opening new possibilities, for driving conversations among its members, for imagining different futures, for learning about the complexity that social systems entail, etc.
Whenever engineering concerns social systems (i.e., firms, public and private organizations, urban systems, etc.) it implies the design of social artefacts and social constructions such as management structures, incentive schemes, routines, procedures, ways of working (formal and informal, planned and spontaneous), agreements, contracts, policies, roles and discourses, among others (Jelinek et al., 2008; March and Vogus, 2010). Therefore, such types of engineering face a special type of complexity, since these artefacts depend on and are constructed through human action, meaning that not only individuals but also their emotions, language and meanings are involved.
This book seeks to offer an overview of what social systems engineering entails. The reader might hasten to think that this is a mechanistic approach to social systems. However, there is no such thing as optimal design in social systems (Devins et al., 2015). In contrast, the very idea of social systems engineering, although it emphasizes action, does not necessarily rely on prediction; it is context‐dependent, iterative, builds upon different modelling perspectives and decisively aims at influencing the path of, rather than deliberatively designing, the evolving character of self‐organization of human societies. This is a starkly different approach from a purely scientific viewpoint. The book encompasses three sections that follow an intuitive inquiry in this matter. The first section deals with the very idea of what social systems engineering might be and the need for addressing the topic in its own terms. The second section samples illustrative methodologies and methods. The final section illustrates examples of the challenge of designing the complexity that results from systems created through human action.
Epistemic Notions on the Engineering of Social Systems
There are diverse beliefs regarding what engineering is about. Perhaps the most popular is to believe that engineering is ‘applied science’. However, this would mean assuming that ‘scientists generate new knowledge which technologists then apply’ (Layton, 1974, p. 31) and therefore would suggest that what makes an engineer an engineer, and what an engineer delivers, is (applied) scientific knowledge, instead of a different type of knowledge (Davis, 2010), which is, at best, misleading (Goldman, 2004; Hansson, 2007; Layton, 1974; McCarthy, 2010; Pitt, 2010; Van de Poel, 2010). The recognition that science and engineering stand on different epistemic grounds (Goldman, 1990; Koen, 2003; Krige, 2006; Layton, 1984, 1987, 1991; Petroski, 2010; Pitt, 2011b; Vincenti, 1990; Wise, 1985) is perhaps the first step in thinking of social systems engineering and requires a brief overview.
If it is not ‘applied science’, what are the defining characteristics of engineering? We can start by realizing that engineering and science usually pursue different goals: scientists, first and foremost, look for systematic explanations of phenomena; engineers, on the other hand, pursue the transformation of a situation through the design of artefacts that serve as vehicles to solve problems. In short, as Petroski (2010) puts it, scientists seek to explain the world while engineers try to change it. The scientist deals primarily with the question ‘what is it?’ The engineer deals with ‘how must this situation be changed?’ and ‘what is the right action to do?’ Engineering is concerned ‘not with the necessary but with the contingent, not with how things are but with how they might be’ (Simon, 1996, p. xii). Such different missions lead to different values, norms, rules, apparatus for reasoning, considerations, type of knowledge, methods, success criteria, standards for evaluating results; in short, different epistemologies.
Engineering knowledge is intrinsic to engineering and different from scientific knowledge. Engineering know‐how is a distinctive type of knowledge, different from the scientific know‐that (Ryle, 1945). For example, ‘engineering knowledge is practice‐generated… it is in the form of “knowledge‐how” to accomplish something, rather than “knowledge‐that” the universe operates in a particular way’ (Schmidt, 2012, p. 1162). Knowledge‐how is not concerned with the truth or falsehood of statements, ‘you cannot affirm or deny Mrs. Beeton’s recipes’ (Ryle, 1945, p. 12). Engineers know how to do things. It is a type of practical knowledge. Therefore, the resources and information to get the job done can be varied and diverse, in principle they are not rejected under any a‐priori principle, ‘resolving engineering problems regularly requires the use of less than scientifically acceptable information’ (Mitcham, 1994). The scientific ‘empirical evidence’ might be useful, but it is not a necessary requirement. Such a practical approach requires also that designs must work in real life; the effects of friction or air resistance cannot be ignored (Hansson, 2007). Since the task of the engineer is to be effective, to accomplish, then mathematical precision and analytical solutions are not required. Unlike the scientist, the engineer does not assume ideal conditions, s/he knows what to do in imperfect situations.
Engineers address practical problems: their know‐how is constructed contingently and for very specific contexts (McCarthy, 2010). Engineering deals with particulars in its particularity, they are not taken as instantiations of a universal (Goldman, 1990). This implies that engineering design faces a variety of constraints related to idiosyncratic values and factors (economic, cultural, political, reliability, viability, ethical) that co‐define and specify the design problem, unlike scientific research in which such constraints are absent in the definition of a scientific question (Kroes, 2012). This singularity of each design problem explains why there is no unique solution for an engineering problem: ‘an engineer who understands engineering will never claim to have found the solution… This is why there are so many different‐looking airplanes and automobiles and why they operate differently… they are simply one engineer’s solution to a problem that has no unique solution’ (Petroski, 2010, p. 54). Moreover, there is usually more than one way to solve an engineering problem. Such diversity of possibilities, methods and solutions contrasts with the goal of scientific communities that typically pursue the one best theory, at any given time, for explaining a phenomenon; when a theory is shown to be erroneous, it can be replaced with a better one.
The activity of engineering does not need epistemic justifications. The intentional creation of artefacts is done by experimental methods that are more fundamental than (and not derived from) any type of theory (Doridot, 2008). The origin of design is irrelevant, it does not necessarily have to be a priori supported by anything, including theories or data. Design can be freely generated with the help of any procedure, sourced from reason, or guided by previous expectations – ‘theoretic’ or not (Stein and Lipton, 1989), guided with the help of a model, or just based on imagination, or instincts. ‘Empirical evidence’, or any other indirect mechanism of representing the world, is just another option, but it is not a requisite. For instance, ‘the inventor or engineer… can proceed to design machines in ignorance of the laws of motion… These machines will either be successful or not’ (Petroski, 2010, p. 54). Engineering handles a pragmatic concept of ‘truth’ (Doridot, 2008). An artefact or an engineering solution is not false or true (or closer to), simply it works or it doesn’t. If it works, engineers succeed. The popular notion of knowledge as ‘justified true belief’ means nothing in a pragmatic approach in which knowledge is unjustified. In the words of Pitt: ‘If it solves our problem, then does it matter if we fail to have a philosophical justification for using it? To adopt this attitude is to reject the primary approach to philosophical analysis of science of the major part of the twentieth century, logical positivism, and to embrace pragmatism’ (2011a, p. 173).
We are interested in particular in the engineering of social systems. What are the implications of the recognition of such philosophy of engineering for the domain of social systems? Let us consider, for instance, that the predictive logic of scientific causal models relates to the idea that prediction is a requirement of control (Sarasvathy, 2003). A fundamental question is how much prediction, derived from causal explanations, is needed to transform a social system. Before the apparent unpredictability of the behaviour of social systems, one idea is to operate under a different logic and to drop the very idea of prediction in design, as Sarasvathy (2003) puts it. Sarasvathy (2003) claims that, in relation to endeavours of enterprise creation, a design logic highlights the fact that ‘to the extent we can control the future, we do not need to predict it’ (Sarasvathy, 2003, p. 208), implying that ‘a large part of the future actually is a product of human decision‐making’ (Sarasvathy, 2003, p. 209). And yet, the future remains uncertain. How to deal with such uncertainty of social systems? William Bulleit offers a possible answer in this book. The unpredictable and complex nature of human action means to face a special type of uncertainty that is, as Bulleit develops in Chapter 2, much larger than that found in other engineered systems. The uncertainty that engineers usually confront resembles an explorer in a jungle with unknown dangers; this explains why engineers consider as part of their design considerations, elements such as ‘safety factors’, ‘safety barriers’, ‘unforeseen factors’, etc. (Doorn and Hansson, 2011; Hansson, 2009a,b). However, unlike probabilistic risk analysis, the design of social systems deals with true uncertainty under unknown probabilities. As Hansson (2009a) pictures it, such uncertainty is unlike that which a gambler faces at the roulette wheel. Social systems represent perhaps the extreme case, whose design and maintenance requires a distinct mindset that brings together bottom‐up and top‐down solutions, along with the recognition of the adaptive nature of social systems, as Bulleit suggests.
How to engineer problem‐solving designs in such unpredictable social systems? The recognition of adaptive and evolutionary dynamics leads us to think of the possibility of producing designs without ‘knowing’ beforehand the way in which the system to be designed or transformed ‘works’. Perhaps the main contribution of Charles Darwin is in the realm of philosophy, indicating a way to produce a design without a ‘designer’ (Ayala, 2007; Dennett, 1995; Mayr, 1995, 2001). Evolution already shows how and why the selection of blind variations explains the success of any system that adapts to changing and unknown environments (Campbell, 1987; Harford, 2011; Popper, 1972). Perhaps we must resist the apparent requisite of having knowledge beforehand for doing something. Bruce Edmonds makes an analogy in Chapter 3 that compares social systems engineering with farming. Since there is no such thing as ‘designing’ a farm, farmers instead know that they must continuously act on their farms to achieve acceptable results. Edmonds underlines that, since we are far from even having a minimal and reliable understanding of social systems, then engineers of social systems must recur to system farming. Edmonds emphasizes that traditional design‐based engineering approaches are simply not possible to be applied to social systems; a systems farming lens should rely more on experience rather than on system control, should operate iteratively rather than as a one‐time effort, and should make use of partial rather than full understanding, among other considerations.
Yet, the notion of evolution challenges the very idea of whether humans can deliberately improve social systems. Is it possible to control, manage or at least direct an evolutionary process? Martin Schaffernicht deals with this question in Chapter 4. Like Edmonds, Schaffernicht questions whether deliberate social system designs can actually be made and if they can really be translated into improvement. Schaffernicht rather suggests that engineering can contribute to influence the pace of the evolutionary nature of social systems through policy engineering. He underlines that collective policies are evolving artefacts that drive behaviours – they are never definitive but in constant revision and adaptation – and become the central elements for developing an interplay between evolution and engineering that ends up shaping open‐ended social systems.
These brief ideas indicate the immense challenge in ‘engineering’ (designing and redesigning, that is) social systems, or as put by Vincent Ostrom, it means a problem of ‘substantial proportions… In Hobbes’s words, human beings are both the “matter” and the “artificers” of organizations. Human beings both design and create organizations as artifacts and themselves form the primary ingredient of organizations. Organizations are, thus, artifacts that contain their own artisans’ (Ostrom, 1980, p. 310). Human beings co‐design the social systems that they form, this is why those designs might be intentional up to some point but they are also emergent, dynamic, incomplete, unpredictable, self‐organizing, evolutionary and always ‘in the making’ (Bauer and Herder, 2009; Garud et al., 2006, 2008; Kroes, 2012; Krohs, 2008; Ostrom, 1980). The ultimate challenge is to address the complexity posed by the relations between human beings. Joseph Pitt illustrates this concern with a concrete example: what does it mean to be a friend of someone? This question will lead us to challenge the very possibility of designing a social system. In Chapter 5, Pitt suggests that we can only design an environment in which a social system emerges and evolves, a suggestion that is in line with the first part of this book that calls for the need to recognize the experimental, evolving and open‐ended nature of social systems. This is the first requisite for anyone aspiring to transform a social system.
Using Engineering Methods
How to engineer social systems? The second part of this book introduces different methods for engineering social systems. Engineers proceed in a distinctive way. Billy Vaughn Koen in his book The Discussion of the Method (2003) defines engineering by its method. For him, the engineering method is any ‘strategy for causing the best change in a poorly understood situation with the available resources’ (p. 7). Engineers call such strategies ‘heuristics’. ‘A heuristic is anything that provides a plausible aid or direction in the solution of a problem but is in the final analysis unjustified, incapable of justification, and potentially fallible’ (Koen, 2010, p. 314). Koen highlights the distinctive nature of heuristics as opposed to other ways of facing the world; in particular, he considers the differences from scientific theories. A heuristic does not guarantee a solution, it may contradict other heuristics (Koen 2009); it does not need justification, its relevance depends on the particular situation that the heuristic deals with and its outcome is a matter of neither ‘truth’ nor generalizability. The engineering method – as opposed to the scientific method – is a heuristic; that is, unjustified, fallible, uncertain, context‐defined and problem‐oriented. Hence, the second part of this book can be seen as a small sample of heuristics that in particular share a common preferred strategy of engineers: modelling.
Engineering design requires the capacity to ‘see’ and imagine possible (both successful and unsuccessful) futures. Zhongyuan Yu and her colleagues show in Chapter 6 how policy flight simulators may help to address ‘what if…’ questions through model‐based interactive visualizations that enable policy‐makers to make decisions and anticipate their consequences. Policy flight simulators drive the exploration of management policies according to possible factors that contribute to an existing or potential state of a system. Through two detailed cases, the chapter shows how such simulators can be developed and how groups of people (rather than individuals) interact with them. These interactions are the central piece of the method, since the involved stakeholders and policy‐makers bring conflicting priorities and diverse preferences for courses of action. The chapter illustrates with practical cases the mentioned idea of Schaffernicht: the centrality of the evolution of ‘collective policies’ for transforming social systems and the way in which such evolution can be enhanced through learning. Yu and her colleagues underline that the key value of their models and visualizations lies in the insights that they provide to those intending to engineer their own social systems.
Models are powerful tools for supporting design activities (Dillon, 2012; Dodgson et al., 2007; Elms and Brown, 2012; Will, 1991). Unlike scientific models that are usually built for analysis of observations and generating ‘true’ explanations (Norström, 2013), engineering models are judged against their usefulness for specific, diverse (Epstein, 2008) purposes. For engineers, they serve as focal points ‘for a story or conversation about how a system behaves and how that behaviour can be changed. It is by mediating in this process – acting to focus language by stressing some features of the real system while ignoring others – that models contribute to new shared understandings in a community of engineering practice’ (Bissell and Dillon, 2012, p. vi). Chapter 7 by Peer‐Olaf Siebers and colleagues introduces a structured framework for guiding such conversation processes through model development, from conceptual design to implementation. In particular, this framework organizes both the process of building and using agent‐based models and the way in which the resulting simulation models can be used as decision‐support tools for exploring the application of policies. Being a heuristic, they adapt what they consider appropriate for developing their framework; in particular, they borrow ideas from software engineering for tackling problem analysis and model design. The chapter uses international peacebuilding activities in South Sudan as an example to illustrate the practical possibilities of their proposal.
There are diverse ways of building models. Sandra Méndez‐Fajardo and colleagues show, in Chapter 8, how social systems engineering can employ (social) science through a methodological framework that uses actor‐network theory as a heuristic for designing and building agent‐based models. They use an applied case in waste of electrical and electronic equipment management as an illustrative example. Their proposal presents a way to overcome the distinction between human and non‐human actors, and underlines the centrality of ‘actor‐networks’ (rather than just actors) in social systems. Although these theoretic contributions stand on their own as valuable results, they unmistakably underline the engineering character of their proposal, which concerns the pragmatic usefulness of modelling rather than its theoretical validity. They frame the application of actor‐network theory as a heuristic for intervening social systems through the use of simulation models to enact policy changes.
Engineering may use scientific theories but may also contribute to science. Computational modelling can complement diverse theoretic approaches, for instance it is useful for supporting theory building in social science (e.g., Schwaninger and Grösser, 2008). To complete the second part of the book, in Chapter 9 Russell Thomas and John Gero use social theory to explore the process of institutional innovation and how to influence innovation trajectories in pre‐paradigmatic settings (which the authors call ‘contested territories’), where there are rival worldviews regarding the nature of problems and innovations. The authors illustrate their methodological approach with the case of cyber security and the problem of quantifying security and risk under two rival worldviews: the ‘quants’ (for whom cyber security and risk can and should be quantified) and the ‘non‐quants’ (who believe that cyber security and risk either cannot be quantified or its quantification does not bring enough benefits). The chapter frames the process of institutional innovation in Boisot’s theory of the social learning cycle and the role of knowledge artefacts during the cycle. A computational model helps to explore how knowledge artefacts of different characteristics affect innovation rate and learning. The chapter makes provocative suggestions regarding not only how social science can contribute to social systems engineering but also the other way around: how this latter approach can contribute to deal with scientific questions, such as the assessment of the scientific merit of each school of thought (in terms of explanatory coherence) and the possibility of addressing further theoretic issues of social dynamics such as legitimization, power struggles and structuration, among others.
Into Real‐World Applications
Since social systems engineering is praxis, then real‐world applications become perhaps the true way to depict this type of engineering. The last part of the book places the emphasis on practical applications that illustrate the richness and possibilities that the first two parts suggest.
Chapter 10 by Adam Douglas Henry and Heike Brugger deals with developing strategic scenarios for the adoption of environmentally friendly technologies. Through agent‐based computational modelling, they inspect non‐trivial policy answers to two simultaneously desirable outcomes regarding sustainable technologies: the speed of their adoption and the guarantee of equal access to them. Chapter 11 by Clifford‐Holmes and colleagues combines ethnographic data collection with participatory system dynamics modelling in the design of potential strategies in water resource management in South Africa. Clifford‐Holmes and colleagues emphasize the ‘muddled middle’ between policy and implementation, and propose new directions in participatory modelling. In Chapter 12 Markus Schwaninger and Johann Klocker provide an account of the 30‐year evolution of the oncological care system in Klagenfurt, Austria, exposing the threat of organizational over‐specialization in patient treatment and highlighting the importance of holistic approaches to healthcare system design by using causal loop diagrams and organizational cybernetic concepts. Last but not least, in Chapter 13 Jenny O’Connor and colleagues explore four case studies of smart city projects in the United Kingdom and highlight the importance of understanding the unpredictability of individual and societal behaviour when confronted with new sustainable‐related policies derived from technical aspects only. O’Connor and colleagues explicitly call for the inclusion of the social dimension in the engineering of social systems.
In summary, social systems engineering goes beyond the application of engineering methods to social problems. In different instances there has been a tendency to equate engineering a social system with a traditional, mechanistic, one‐shot undertaking that attempts to reach optimality according to some well‐pre‐established objective (Devins et al., 2015). That is not what social systems engineering is about. In contrast, we aim to highlight the importance of trial and error, failure, iteration, adaptability and evolution as salient features of any design‐oriented process. Stimulating self‐organization (as opposed to direct intervention) as a way to foster growth of desirable properties (e.g., adaptability and resilience) is also intrinsic to any design‐oriented endeavour. Engineering a social system implies ‘steering’ a system towards a desirable state (Penn et al., 2013), even if such a state is not completely understood and is subject to different interpretations (e.g., a sustainable community), and even if the journey towards it is filled with unexpected occurrences. We hope that this book will provide a broader, multidisciplinary, conceptual approach to social systems design, and stimulate the growth of ideas towards solution‐oriented perspectives (Watts, 2017) in dealing with social systems issues.
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Part ISocial Systems Engineering: The Very Idea
1Compromised Exactness and the Rationality of Engineering
Steven L. Goldman
1.1 Introduction
In the spring of 1929, on the occasion of the Gifford Lectures at Edinburgh University, John Dewey asked: ‘Are there in existence the ideas and the knowledge that permit experimental method to be effectively used in social interests and affairs?’ (Dewey, 1988, p. 218). By ‘experimental method’, Dewey meant systematic reasoning about effective means for achieving a specified end. This was problem‐solving reasoning par excellence for Dewey, because it was reasoning that was reflexively shaped by its consequences in a cognitive positive feedback loop characteristic of applied science and engineering. It was just this ‘experimental method’, Dewey argued, that by uniting the results of experiment‐validated scientific knowledge with the objectives of engineering practice had enabled the society‐ and culture‐transforming accomplishments of nineteenth‐century technological innovations. What Dewey was asking in the Gifford Lectures, then, was: Do we know enough, not in science and engineering, but about
