Global Dynamics 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
A world model: economies, trade, migration, security and development aid.
This bookprovides the analytical capability to understand and explore the dynamics of globalisation. It is anchored in economic input-output models of over 200 countries and their relationships through trade, migration, security and development aid. The tools of complexity science are brought to bear and mathematical and computer models are developed both for the elements and for an integrated whole. Models are developed at a variety of scales ranging from the global and international trade through a European model of inter-sub-regional migration to piracy in the Gulf and the London riots of 2011. The models embrace the changing technology of international shipping, the impacts of migration on economic development along with changing patterns of military expenditure and development aid. A unique contribution is the level of spatial disaggregation which presents each of 200+ countries and their mutual interdependencies – along with some finer scale analyses of cities and regions. This is the first global model which offers this depth of detail with fully work-out models, these provide tools for policy making at national, European and global scales.
Global dynamics:
- Presents in depth models of global dynamics.
- Provides a world economic model of 200+ countries and their interactions through trade, migration, security and development aid.
- Provides pointers to the deployment of analytical capability through modelling in policy development.
- Features a variety of models that constitute a formidable toolkit for analysis and policy development.
- Offers a demonstration of the practicalities of complexity science concepts.
This book is for practitioners and policy analysts as well as those interested in mathematical model building and complexity science as well as advanced undergraduate and postgraduate level students.
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Veröffentlichungsjahr: 2016
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Table of Contents
Cover
Title Page
Copyright
Notes on Contributors
Acknowledgements
Part One: Global Dynamics and the Tools of Complexity Science
Chapter 1: Global Dynamics and the Tools of Complexity Science
Reference
Part Two: Trade and Economic Development
Chapter 2: The Global Trade System and Its Evolution
2.1 The Evolution of the Shipping and Ports' System
2.2 Analyses of the Cargo Ship Network
2.3 A Complex Adaptive Systems (CASs) Perspective
2.4 Conclusions: The Benefits of a Systems Perspective
References
Appendix
A.1 Complexity Science and Complex Adaptive Systems: Key Characteristics
Chapter 3: An Interdependent Multi-layer Model for Trade
3.1 Introduction
3.2 The Interdependent Multi-layer Model: Vertical Integration
3.3 Model Layers
3.4 The Workings of the Model
3.5 Model Calibration
3.6 Result 1: Steady State
3.7 Result 2: Estimation and Propagation of Shocks in the IMM
3.8 Discussion and Conclusions
References
Chapter 4: A Global Inter-country Economic Model Based on Linked Input–Output Models
4.1 Introduction
4.2 Existing Global Economic Models
4.3 Description of the Model
4.4 Solving the Model
4.5 Analysis
4.6 Conclusions
Acknowledgements
References
Appendix
A.1 Modelling the ‘Rest of the World’
A.2 Services Trade Data
Part Three: Migration
Chapter 5: Global Migration Modelling: A Review of Key Policy Needs and Research Centres
5.1 Introduction
5.2 Policy and Migration Research
5.3 Conclusion
References
Appendix
A.1 United Kingdom
A.2 Rest of Europe
A.3 Rest of the World
Chapter 6: Estimating Inter-regional Migration in Europe
6.1 Introduction
6.2 The Spatial System and the Modelling Challenge
6.3 Biproportional Fitting Modelling Methodology
6.4 Model Parameter Calibration
6.5 Model Experiments
6.6 Results
6.7 Conclusions and Comments on the New Framework for Estimating Inter-regional, Inter-country Migration Flows in Europe
References
Chapter 7: Estimating an Annual Time Series of Global Migration Flows – An Alternative Methodology for Using Migrant Stock Data
7.1 Introduction
7.2 Methodology
7.3 Results and Validation
7.4 Discussion
7.5 Conclusions
References
Part Four: Security
Chapter 8: Conflict Modelling: Spatial Interaction as Threat
8.1 Introduction
8.2 Conflict Intensity: Space–Time Patterning of Events
8.3 Understanding Conflict Onset: Simulation-based Models
8.4 Forecasting Global Conflict Hotspots
8.5 A Spatial Model of Threat
8.6 Discussion: The Use of a Spatial Threat Measure in Models of Conflict
References
Chapter 9: Riots
9.1 Introduction
9.2 The 2011 Riots in London
9.3 Data-Driven Modelling of Riot Diffusion
9.4 Statistical Modelling of Target Choice
9.5 A Generative Model of the Riots
9.6 Discussion
References
Chapter 10: Rebellions
10.1 Introduction
10.2 Data
10.3 Hawkes model
10.4 Results
10.5 Discussion
References
Chapter 11: Spatial Interaction as Threat: Modelling Maritime Piracy
11.1 The Model
11.2 The Test Case
11.3 Uses of the Model
Reference
Appendix
A.1 Volume Field of Type Ship
A.2 Volume Field of Naval Units
A.3 Pirates Ports and Mother Ships
Chapter 12: Space–Time Modelling of Insurgency and Counterinsurgency in Iraq
12.1 Introduction
12.2 Counterinsurgency in Iraq
12.3 Counterinsurgency Data
12.4 Diagnoses of Space, Time and Space–Time Distributions
12.5 Concluding Comments
References
Chapter 13: International Information Flows, Government Response and the Contagion of Ethnic Conflict
13.1 Introduction
13.2 Global Information Flows
13.3 The Effect of Information Flows on Armed Civil Conflict
13.4 The Effect of Information Flows on Government Repression
13.5 Conclusion
References
Appendix
Part Five: Aid and Development
Chapter 14: International Development Aid: A Complex System
14.1 Introduction: A Complex Systems' Perspective
14.2 The International Development Aid System: Definitions
14.3 Features of International Development Aid as a Complex System
14.4 Complexity and Approaches to Research
14.5 The Assessment of the Effectiveness of International Development Aid
14.6 Relationships and Interactions
14.7 Conclusions
References
Chapter 15: Model Building for the Complex System of International Development Aid
15.1 Introduction
15.2 Data Collection
15.3 Model Building
15.4 Discussion and Future Work
References
Chapter 16: Aid Allocation: A Complex Perspective
16.1 Aid Allocation Networks
16.2 Quantifying Aid via a Mathematical Model
16.3 Application of the Model
16.4 Remarks
Acknowledgements
References
Appendix
A.1 Common Functional Definitions
Part Six: Global Dynamics: An Integrated Model and Policy Challenges
Chapter 17: An Integrated Model
17.1 Introduction
17.2 Adding Migration
17.3 Adding Aid
17.4 Adding Security
17.5 Concluding Comments
References
Index
End User License Agreement
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Guide
Cover
Table of Contents
Part One: Global Dynamics and the Tools of Complexity Science
Begin Reading
List of Illustrations
Chapter 2: The Global Trade System and Its Evolution
Figure A.1 Graph of systems showing their ability to evolve and adapt.
Chapter 3: An Interdependent Multi-layer Model for Trade
Figure 3.1 A multi-layer network's functional dependencies (a) and ‘events’ (b).
Figure 3.2 Functional relationship between networks.
Figure 3.3 A nested hierarchical multi-layer network.
Figure 3.4 Multi-layer interactive process.
Figure 3.5 Trade network (a), migration network (b), cultural network (c) and network of common borders (d). Link width is proportional to trade and migration flows, while node size and colour are proportional to national GDP in the trade network and to national population in the migration network. Node size and colour for colonial and common border networks are proportional to the number of connections.
Figure 3.6 Weighted in-degree
s
+ and out-degree
s
−
distributions for trade (a) and migration (b).
Figure 3.7 IMM results for the steady state. GDP forecast versus observed data for European countries (a and b), Baltic states (c and d) and six major world economies (e and f).
Figure 3.8 Effect of 2% decrease of US GDP over four time steps (6–9) for major world economies (a), European countries (b) and Baltic states (c).
Figure 3.9 Effect of 2% decrease of German GDP over four time steps (6–9) for European countries (a), major world economies (b) and Baltic states (c).
Chapter 4: A Global Inter-country Economic Model Based on Linked Input–Output Models
Figure 4.1 The model algorithm: total production, imports and exports are calculated for a given set of fixed coefficients. Note that square brackets have been introduced to designate the iteration variable , which tracks the quantities that are iteratively recalculated as the algorithm runs.
Source
: Reproduced with permission from Levy et al. (2014)
Figure 4.2 The total response of the global economy to a reduction in final demand, averaged across every sector in a country. ‘Response’ is defined as the total production lost across all sectors. Here, response has been divided into domestic, where the response is measured only in the country whose final demand has been reduced, and foreign, where the response is measured in all other countries only.
Source
: Reproduced with permission from Levy et al. (2014)
Figure 4.3 Figure showing the positive relationship between economic self-reliance as defined by Equation (4.27) and population. More populous countries tend to be more self-reliant.
Source
: Reproduced with permission from Levy et al. (2014)
Figure 4.4 A network representation of the seven most-affected countries following a reduction in final demand for the Chinese vehicles sector. Node size is proportional to eigenvector centrality and edge width is proportional to the change in flow.
Source
: Reproduced with permission from Levy et al. (2014)
Figure 4.5 A comparison of the results of the Chinese vehicle experiment performed on an MRIO (on the left) and on the present model (on the right). Vertical position represents the rank of the country in terms of how affected it is by a reduction in demand for Chinese vehicles.
*
Taiwan is ranked last in the present model. This is because the United Nations provides no trade data for the territory. All Taiwan's import ratios are therefore zero (i.e. Taiwan is modelled as being entirely self-sufficient).
Source
: Reproduced with permission from Levy et al. (2014)
Chapter 6: Estimating Inter-regional Migration in Europe
Figure 6.1 The 287 NUTS2 regions of EU 27 + 3 counties.
Figure 6.2 Example migration data availability within Europe
Figure 6.3 Sample system in Figure 6.2 using defined notation
Figure 6.4 Expanded sample system with margins and sub-margins
Figure 6.5 Sample system including all sub-margin and margin elements
Figure 6.6 Collapsed matrix showing only region-to-country sub-margins depicted in Figure 6.5
Figure 6.7 Collapsed matrix showing only country-to-region sub-margins depicted in Figure 6.5
Figure 6.8 values modelled using the entropy-maximising model in (6.21)
Figure 6.9 values modelled using the entropy-maximising model in (6.22)
Figure 6.10 values calibrated on inter-regional, intra-country migration data, 2006
Figure 6.11 values calibrated on inter-regional, intra-country migration data, 2006
Figure 6.12 Correlation between internal (‘Place of residence changed outside the NUTS3 area’) and international (‘Place of residence changed from outside the declaring country’) migrant distributions for NUTS2 regions, selected EU countries, 2001.
Figure 6.13 Distribution of NUTS 2 regions where shares of internal and international in-migrants differ, selected EU countries, 2001
Figure 6.14 Correlation between the NUTS2 regional share of internal in- and out-migration flows across EU countries, 2006
Figure 6.15 Flows greater than 200 migrants entering UK regions from other EU system regions, 2006
Figure 6.16 Flows greater than 200 migrants leaving UK regions for other EU system regions, 2006
Chapter 7: Estimating an Annual Time Series of Global Migration Flows – An Alternative Methodology for Using Migrant Stock Data
Figure 7.1 Comparison between bilateral stock and flow proportions for European countries
R
2
= 0.794
Figure 7.2 Comparison between total global migration flows (5-year periods) as estimated by Abel (2013b) and UN net migration positive balance (5-year periods), 1965–2010
Figure 7.3 Smoothing options for total migrant estimation
Figure 7.4 Flow data estimate comparison with UN flow data, 1970–2005
Chapter 9: Riots
Figure 9.1 The geography of the 2011 riots in Greater London. The shading of each point indicates the number of offences that occurred at each location.
Figure 9.2 The timing of the 2011 riots in Greater London with reported police numbers on London streets for each day of unrest
Figure 9.3 Results of the Knox test for space-time interaction. Six different spatial grid sizes are used and all demonstrate significant levels of space-time clustering.
Figure 9.4 The distribution of distance travelled between rioter residence and offence location
Figure 9.5 The distribution of rioter age
Figure 9.6 Four patterns of geographic diffusion
Figure 9.7 Example of null model output compared against the empirical data (dashed line) for the four diffusion patterns in Figure 9.6
Chapter 10: Rebellions
Figure 10.1 Temporal distribution of the event data
Figure 10.2 The distribution of each parameter, as obtained from the bootstrap procedure, for the period 2000–2003 (in blue) and 2005–2008 (in red). The shaded distributions are density plots obtained from 100 re-estimations of the parameters from simulations of the process in Equations (10.5) and (10.6). The dashed lines represent the point estimates in Table 10.1 and correspond to the parameters with which these simulations are specified
Figure 10.3 QQ-plots comparing the residual process to a Poisson process for the two time periods. The black lines represent the 95% confidence interval of the quantiles of the residual process in comparison to a Poisson process. These are obtained from 1,000 samples of the residual process. The grey shaded region represents an equivalent Poisson process
Chapter 11: Spatial Interaction as Threat: Modelling Maritime Piracy
Figure 11.1 Area under analysis
Figure 11.2 Vessels volume field
Figure 11.3 Navy field
Figure 11.4 Threat field
Figure 11.5 Observed attacks in 2010
Figure A.6 Vessels ideal route
Figure A.7 Pirates ports and mother ships
Figure A.8 Distances with (values in metres/1,000,000)
Chapter 12: Space–Time Modelling of Insurgency and Counterinsurgency in Iraq
Figure 12.1 Spatial distribution of IED and COIN events (projected relative to an arbitrary origin point). *The ‘All COIN’ set excludes the IED found/cleared events (see text for details)
Figure 12.2 Temporal distribution of IED and COIN events (auto-correlation function (ACF) values for lag 1 indicate the strength of serial correlation in the series). *The ‘All COIN’ set excludes the IED found events (see text for details)
Figure 12.3 Univariate Knox analyses of six event types. *The ‘All COIN’ set excludes the IED found events. **For caches found, the Knox ratios for event pairs that occurred up to 1500 m and 1 week of each other exceed 2.5 (the actual values are 4.49, 2.99 and 2.73 – in that order), but we truncate the scale bar at 2.5 to make observed patterns clearer
Figure 12.4 Bivariate Knox analyses of six event types. *The ‘All COIN’ set excludes the IED found events
Chapter 13: International Information Flows, Government Response and the Contagion of Ethnic Conflict
Figure 13.1 The yearly number of ongoing inter- and intrastate conflicts in the world
Figure 13.2 Government repression in the world
Figure 13.3 The per capita average of radio and TV in the world
Figure 13.4 The rate of media freedom in the world
Figure 13.5 The measure on information flows in the world
Figure 13.6 The effect of information about foreign civil wars
Figure 13.7 The effect of information about foreign civil wars moderated by mutual discrimination
Figure 13.8 The effect of the availability of radio and TV on conflict contagion between discriminated groups
Chapter 14: International Development Aid: A Complex System
Figure 14.1 Total net disbursements by DAC countries (USD million).
Figure 14.2 Total net disbursements by World Bank (USD million).
Figure 14.3 Total net disbursements by UN agencies (USD million).
Figure 14.4 Total net disbursements by regional development banks (USD million).
Figure 14.5 OECD's multi-dimensional country review framework for aid effectiveness
Chapter 16: Aid Allocation: A Complex Perspective
Figure 16.1 Complete bipartite graph (a) with vector weights and node-specific information detail (b)
Figure 16.2 A schematic model representation with recipients and donor . Arrows show information flows; dashed lines, feedback; hyphens, inter-country relationships
Figure 16.3 Scenario 1 set-up emphasising the role of node-specific information and inter-country relationships. The table notes the data included in this scenario, while each network diagram encodes relevant allocation data
Figure 16.4 Model allocation for parameter choices 1, 2 and 3
Figure 16.5 Model allocation for parameter choices 4, 5 and 6
Figure 16.6 Model allocation for parameter choices 7, 8 and 9
Figure 16.7 Model allocation for parameter choice 10
Figure 16.8 Model allocation for parameter choice 11
Figure 16.9 Aid allocation incorporating aid usage
Figure 16.10 Poverty and trade levels of recipients following aid investment
Chapter 17: An Integrated Model
Figure 17.1 Changes in GDP and balance of trade for the three largest recipient of WIOD-country migrants in 2010 for various levels of the linear combination parameter in Equations (17.1), (17.2), (17.3)
Figure 17.2 Changes in GDP for the five most positively affected and five most negatively affected countries due to a change in exportness of Ethiopia
Figure 17.3 Scatter plot showing the relationship between the minimum distance of a recipient country to a regional power (the United States, China, Germany or Australia) and the results of the exportness experiment outlined in Section 17.3.3. Vietnam with an exportness difference of 5.6 and minimum distance of 0 is not shown on the graph, but does contribute to the regression line
Figure 17.4 Scatter plot showing the relationship between the normalised Herfindahl index of export partner concentration and the results of the exportness experiment outlined in Section 17.3.3. Vietnam with an exportness difference of 5.6 and an of 0.07 is not shown on the graph, but does contribute to the regression line
Figure 17.5 A part of a network of changes resulting from an increase in exportness in Vietnam. Blue arrows show increases and red arrows show decreases. Nodes are either countries (labelled in light blue) or country/sectors (CSs, labelled in black). Edges between CSs represent changes in trade flows. Those between countries and CSs represent changes in total production. All changes are logarithmic
Figure 17.6 The 399 largest dyadic threat relationships (those with ). Node size is proportional to the weighted out-degree, meaning that larger nodes create more threat. The thickness of the edges is proportional to the logarithm of . Nodes are grouped into three clusters using a community detection algorithm
Figure 17.7 A network diagram of the 50 dyadic threat relationships to which the GDP of Guinea is most sensitive. Node size (and colour) is proportional to node degree. The weight of the edges is proportional to the increase in Guinea's GDP following a fixed-percentage increase in , the dyadic threat measure
List of Tables
Chapter 2: The Global Trade System and Its Evolution
Table 2.1 Overview of the main features of the GCSN as proposed in the studies of Kaluza et al. 2010 and Ducruet and Notteboom 2012
Table 2.2 Comparison of Complex Adaptive Systems (CAS)s features with shipping
Chapter 3: An Interdependent Multi-layer Model for Trade
Table 3.1 IMM variables and sources
Table 3.2 List of countries
Table 3.3 Parameter estimates (
β
coefficient) and Pearson coefficient of correlation (in parenthesis)
Table 3.4 Parameter estimates for linear regression on bilateral migration
Table 3.5 Main statistics of trade and migration networks: average link weight <
w
>, weighted and topological clustering <
c
w
> and
C
, and average traffic intensity per node <
s
>
Table 3.6 Country ranking of eigenvector centrality index for the bilateral trade network: the highest is the eigenvector centrality index and the most important is a node
Chapter 4: A Global Inter-country Economic Model Based on Linked Input–Output Models
Table 4.1 Coefficients which define the economy of country and their source in WIOD national input-output tables
Table 4.2 The four groups of fixed coefficients in the model and their construction from data. All other flows, including imports and exports, are derived from these coefficients mathematically
Table 4.3 Response to a $1M reduction in final demand in terms of the difference induced in the total output, , of other sectors
Chapter 6: Estimating Inter-regional Migration in Europe
Table 6.1 Goodness-of-fit statistics for model experiments
Table 6.2 Goodness-of-fit statistics for inter-regional migration data modelled with a doubly constrained model with a power distance decay
β
parameter
Table 6.3 Goodness-of-fit statistics for Model (i) with and parameters
Chapter 7: Estimating an Annual Time Series of Global Migration Flows – An Alternative Methodology for Using Migrant Stock Data
Table 7.1 Comparison of Abel's 5-year migration flow estimates with IMEM data
Table 7.2 Sample migration flows between countries in a three-country system
Table 7.3 Goodness-of-fit statistics for new estimates and Abel's estimates when compared with flows in Europe in 2002 and 2008 from the IMEM project
Table 7.4 Descriptive statistics for flow data estimate comparison with UN flow data, 1970–2005
Table 7.5 Estimates versus recorded flows, Italy to Australia, 1960–2006
Chapter 9: Riots
Table 9.1 Odds ratios of selected parameter values
Chapter 10: Rebellions
Table 10.1 Point estimates for parameters in Equations (10.5) and (10.6). The calibration is performed separately for two different time periods
Chapter 12: Space–Time Modelling of Insurgency and Counterinsurgency in Iraq
Table 12.1 Counts and characteristics and summary hypotheses regarding insurgency and counterinsurgency events, January–June 2005
Chapter 14: International Development Aid: A Complex System
Table 14.1 Comparison between science of simplicity and complexity science approaches to development studies
Chapter 15: Model Building for the Complex System of International Development Aid
Table 15.1 Comparison of the results of AD model and our three models
Chapter 16: Aid Allocation: A Complex Perspective
Table 16.1 Parameters determining recipient investment
Chapter 17: An Integrated Model
Table 17.1 A list of the independent variables used by a selection of recent papers explaining migration flows. Each column represents one of the works cited in Section 17.2 with an ‘x’ showing that this variable is used by the author(s)
Table 17.2 The 10 biggest winners and 10 biggest losers from the final demand effect of global migration in GDP terms, measured in millions of $US. Also shown is the change in each country's balance of trade (BoT). Both model and migration flows are using 2010 data
Table 17.3 The 10 biggest increases and reductions in emigration between 2009 and 2010 across all destinations. Changes are shown as a percentage of the country's 2010 population. Results are only shown for the 40 countries of WIOD, but emigrations were calculated across all countries in the data set
Table 17.4 The biggest gains and losses from applying the trade channel of the familiarity effect, governed by Equation (17.2). Only results for the 40 countries of WIOD are shown, and the trade channel was only calculated across these countries. GDP changes are shown in $US millions
Table 17.5 The biggest gains and losses from applying the both channels (demand and trade) of the familiarity effect. Only results for the 40 countries of WIOD are shown. GDP changes are shown in $US millions
Table 17.6 A summary of the ‘exportness’ measure describing how attractive a country is an export partner beyond the effects included in the regression specified in Section 17.3.3. The measure is unitless and runs from 6.5 for the United States to 0.6 for Bhutan
Table 17.7 The effect of increasing exportness by 0.1 on the affected country itself. Effect is measured in terms of change in GDP, both absolute and percentage. The top 10 by percentage are shown
Table 17.8 Increases in trade flows from Togo, due to the exportness experiment, in which Togo's exportness was increased by 0.1
Table 17.9 Other-country positive effects from the exportness experiment. The country in the ‘Recipient’ column had its exportness increased by 0.1. The ‘Total effect’ is the sum of all the positive changes in GDP from countries other than the recipient itself. The 10 largest by this measure are shown. The top three other-country effects are shown per recipient country
Table 17.10 Total effects from the exportness experiment. The country in the ‘Recipient’ column had its exportness increased by 0.1. The ‘Total effect’ is the sum of all the changes in GDP from all countries in the model. The 10 largest by this measure are shown. The top three other-country effects are shown per recipient country
Table 17.11 The 10 most commonly occurring sources and 10 largest targets of threat among the subset of
Table 17.12 The largest total beneficiaries of increased threat across the 399 dyadic threat relationships under experiment. Only third-party benefits are considered. Benefits are summed across all threat increases
Table 17.13 The threat dyads causing the biggest increase in the GDP of Guinea, following a fixed percentage increase in the threat level. The 10 largest effects are show
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.
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Behavioral Computational Social Science
Riccardo Boero
Tipping Points: Modelling Social Problems and Health
John 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 Evolution
Vladimir Batagelj, Patrick Doreian, Anuska Ferligoj, Natasa Kejzar
Analytical Sociology: Actions and Networks
Gianluca Manzo (Editor)
Computational Approaches to Studying the Co-evolution of Networks and Behavior in Social Dilemmas
Rense Corten
The Visualisation of Spatial Social Structure
Danny Dorling
Global Dynamics
Approaches from Complexity Science
Edited by
Alan Wilson
This edition first published 2016
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A catalogue record for this book is available from the British Library.
Notes on Contributors
Peter Baudains is a Research Associate at the UCL Department of Security and Crime Science. He obtained his PhD in Mathematics from UCL in 2015 and worked for five years on the EPSRC-funded ENFOLDing project, contributing to a wide range of research projects. His research interests are in the development and application of novel analytical techniques for studying complex social systems, with a particular attention on crime, rioting and terrorism. He has authored research articles appearing in journals such as Criminology, Applied Geography, Policing and the European Journal of Applied Mathematics.
Janina Beiser obtained her PhD in the department of Political Science at University College London. During her PhD, she was part of the security workstream of the ENFOLDing project at the UCL's Centre for Advanced Spatial Analysis for three years. Her research is concerned with the contagion of armed civil conflict as well as with government repression. She is now a Research Fellow in the Department of Government at the University of Essex.
Jyoti Belur is a Senior Research Associate and Senior Teaching Fellow. She served as a senior officer of the Indian Police Service for several years. Her experience and understanding of policing has contributed to her research interests in various aspects of policing, counter-terrorism, crime prevention in the United Kingdom and overseas. She has conducted research on a wide variety of topics including police use of deadly force, police investigations, police misconduct, policing left-wing extremism and crimes against women and has published a book titled Permission to Shoot? Police Use of Deadly Force in Democracies, as well as a number of journal articles and book chapters.
Steven Bishop is a Professor of Mathematics at UCL where he has been since arriving in 1984 as a post-doctoral researcher. He published over 150 academic papers, edited books and has had appearances on television and radio. Historically, his research investigated topics such as chaos theory, reducing vibrations of engineering structures and how sand dunes are formed, but has more recently worked on ‘big data’ and the modelling of social systems. Steven held a prestigious, ‘Dream’ Fellowship funded by the UK Research Council (EPSRC) until December 2013 allowing him to consider creative ways to arrive at scientific narratives. He was influential in the formation of a European network of physical and social scientists in order to investigate how decision-support systems can be developed to assist policy-makers and, to drive this, has organised conferences in the United Kingdom and European Parliaments. He has been involved in several European Commission funded projects and has helped to forge a research agenda which looks at behaviour of systems that cross policy domains and country borders.
Alex Braithwaite is an Associate Professor in the School of Government and Public Policy at the University of Arizona, as well as a Senior Research Associate in the School of Public Policy at University College London. He obtained a PhD in Political Science from the Pennsylvania State University in 2006 and has since held academic positions at Colorado State University, UCL, and the University of Arizona. He was a Co-Investigator on the EPSRC-funded ENFOLDing project between 2010 and 2013, contributing to a wide range of projects under the ‘security’ umbrella. His research interests lie in the causes and geography of violent and nonviolent forms of political conflict and have been published in journals such as Journal of Politics, International Studies Quarterly, British Journal of Political Science, Journal of Peace Research, Criminology and Journal of Quantitative Criminology.
Simone Caschili has a PhD in Land Engineering and Urban Planning, and after being a Research Associate at Centre for Advanced Spatial Analysis (UCL) and Senior Fellow of the UCL QASER Lab, he is currently an Associate at LaSalle Investment Management, London. His research interest covers the modelling of urban and regional systems, property market, spatio-temporal and economic networks and policy evaluation for planning in both transport and environmental governance.
Adam Dennett is a Lecturer in Urban Analytics in the Centre for Advanced Spatial Analysis at University College London. He is a geographer and fellow of the Royal Geographical Society and has worked for a number of years in the broad area of population geography, applying quantitative techniques to the understanding of human populations; much of this involving the use of spatial interaction models to understand the migration flows of people around the United Kingdom, Europe and the world. A former secondary school teacher, Adam arrived at UCL in 2010 after completing a PhD at the University of Leeds.
Robert J. Downes is a MacArthur Fellow in Nuclear Security working at the Centre for Science and Security Studies at the Department of War Studies, King's College London. Trained as a mathematician, Rob received his PhD in mathematics from UCL in 2014; he studied the interplay between geometry and spectral theory with applications to physical systems and gravitation. He also holds an MSc in Mathematics with Theoretical Physics awarded by UCL. As a Postdoctoral Research Associate on the ENFOLDing project at The Bartlett Centre for Advanced Spatial Analysis, Rob studied the structure and dynamics of global socio-economic systems using ideas from complexity science, with particular emphasis on national economic structure and development aid.
Shane Johnson is a Professor in the Department of Security and Crime Science at University College London. He has worked within the fields of criminology and forensic psychology for over 15 years and has particular interests in complex systems, patterns of crime and insurgent activity, event forecasting and design against crime. He has published over 100 articles and book chapters.
Rob Levy is a researcher at the Centre for Advanced Spatial Analysis at University College London. He has a background in quantitative economics, database administration, coding and visualisation. His first love was Visual Basic but now writes Python and Javascript, with some R when there is no way to avoid it.
Elio Marchione is a Consultant for Ab Initio Software Corporation. Elio was Research Associate at the Centre for Advanced Spatial Analysis at University College London (UK). He obtained his PhD at the University of Surrey (UK) at the Centre for Research in Social Simulation; MSc in Applied Mathematics at the University of Essex (UK); MEng at the University of Naples (ITA). His current role consists, among others, in designing and building scalable architectures addressing parallelism, data integration, data repositories and analytics while developing heavily parallel CPU-bound applications in a dynamic, high-volume environment. Elio's academic interests are in designing and/or modelling artificial societies or distributed intelligent systems enabled to produce novelty or emergent behaviour.
Francesca Romana Medda is a Professor in Applied Economics and Finance at University College London (UCL). She is the Director of the UCL QASER (Quantitative and Applied Spatial Economics Research) Laboratory. Her research focuses on project finance, financial engineering and risk evaluation in different infrastructure sectors such as the maritime industry, energy innovation and new technologies, urban investments (smart cities), supply chain provision and optimisation and airport efficiency.
Pablo Mateos is Associate Professor at the Centre for Research and Advanced Studies in Social Anthropology (CIESAS) in Guadalajara, México. He is honorary lecturer in the Department of Geography, University College London (UCL), in the United Kingdom where he was Lecturer in Human Geography from 2008 to 2012. At UCL, he was a member of the Migration Research Unit (MRU) and Research Fellow of the Centre for Research and Analysis of Migration (CReAM). His research focuses on ethnicity, migration and citizenship in the United Kingdom, Spain, the United States and Mexico. He has published over 40 articles and book chapters, and a book monograph titled Names, Ethnicity and Populations: Tracing Identity in Space published by Springer in 2014.
Thomas Oléron Evans is a Research Associate in the Centre for Advanced Spatial Analysis at University College London, where he has been working on the ENFOLDing project since 2011. In 2015, he completed a PhD in Mathematics, on the subject of individual-based modelling and game theory. He attained a Masters degree in Mathematics from Imperial College London in 2007, including one year studying at the École Normale Supérieure in Lyon, France. He is also an ambassador for the educational charity, Teach First, having spent two years teaching mathematics at Bow School in East London, gaining a Postgraduate Certificate in Education from Canterbury Christ Church University in 2010.
Alan Wilson FBA, FAcSS, FRS is Professor of Urban and Regional Systems in the Centre for Advanced Spatial Analysis at University College London. He is Chair of the Home Office Science Advisory Council and of the Lead Expert Group for the GO-Science Foresight Project on the Future of Cities. He was responsible for the introduction of a number of model building techniques which are now in common use – including ‘entropy’ in building spatial interaction models. His current research, supported by ESRC and EPSRC grants, is on the evolution of cities and global dynamics. He was one of two founding directors of GMAP Ltd. in the 1990s – a successful university spin-out company. He was Vice Chancellor of the University of Leeds from 1991 to 2004 when he became Director-General for Higher Education in the then DfES. From 2007 to 2013, he was Chair of the Arts and Humanities Research Council. He is a Fellow of the British Academy, the Academy of Social Sciences and the Royal Society. He was knighted in 2001 for services to higher education. His recent books include Knowledge Power (2010), The Science of Cities and Regions and his five volume (edited) Urban Modelling (both 2013) and (with Joel Dearden) Explorations in Urban and Regional Dynamics (2015).
Belinda Wu holds a PhD in Geography and has a broad interest in modelling and simulating complex systems in socio-spatial dimensions, with a focus on quantitative strategic decision-support systems. Currently, she works as a Research Associate on the development aid workstream of the ENFOLDing project in CASA, UCL. She was appointed as the main researcher on a series of transport planning and policy research projects in Northern Ireland, before working as a main researcher at two nodes of UK National e-Social Science Centre: Genesis (Generative Simulation for the Social and Spatial Sciences) and MoSeS (Modelling and Simulation of e-Social Science). In 2012, she became the named researcher of the ESRC project SYLLS (Synthetic Data Estimation for the UK Longitudinal Studies) to produce the synthetic microdata to broaden the usage of the valuable UK Longitudinal Studies data for ONS (Office of National Statistics). She is also a Fellow of Royal Geographical Society (FRGS) and a Member of Institute of Logistics and Transportation (MILT).
Acknowledgements
I am grateful to the following publishers for permission to use material.
INDECS, Interdisciplinary Description of Complex Systems, Scientific Journal: A Review of the Maritime Container Shipping Industry as a Complex Adaptive System, INDECS, 10(1), 1–15, used in Chapter 2.
CASA, UCL: Shipping as a complex adaptive system: A new approach in understanding international trade, CASA Working Paper 172, used in Chapter 2; Global migration modelling: A review of key policy needs and research centres, CASA Working Paper 184, used in Chapter 5.
Pion Ltd: A multi-level spatial interaction modelling framework for estimating inter-regional migration in Europe, Environment and Planning A 45: 1491–1507, used in Chapter 6.
Springer: Space-time modelling of insurgency and counterinsurgency in Iraq, Journal of Quantitative Criminology, 28(1), 31–48, used in Chapter 12.
I am very grateful to Helen Griffiths and Clare Latham for the enormous amount of work they have put into this project. Helen began the process of assembling material which Clare took over. She has been not only an effective administrator but an excellent proof reader and sub-editor!
I also acknowledge funding from the EPSRC grant: EP/H02185X/1.
Part OneGlobal Dynamics and the Tools of Complexity Science
Chapter 1Global Dynamics and the Tools of Complexity Science
Alan Wilson
The populations and economies of the 220 countries of the world make up a complex global system. The elements of this system are continually interacting through, for example, trade, migration, the deployment of military forces (mostly in the name of defence and security) and development aid. It is a major challenge of social science to seek to understand this global system and to show how this understanding can be used in policy development. In this book, we deploy the tools of complexity science – and in particular, mathematical and computer modelling – to explore various aspects of change and the associated policy and planning uses: in short, global dynamics.
What is needed and what is the available toolkit? Population and economic models are usually based on accounts. Methods of demographic modelling are relatively well known and can be assumed to exist for most countries. In this case, we will largely take existing figures and record them in an information system. An exception is the task of migration modelling. National economic models are, or should be, input–output based. We face a challenge here, in part, to ensure full international coverage and also to link import and export flows with trade flows. In the case of security, there are some rich sources of data to report; in the case for development aid, the data are less good. In each case, we require models of the flows – technically, models of spatial interaction.
Flow models represent equilibria or steady states. Our ultimate focus is dynamics. There will be imbalances in the demographic and economic accounts, and these become the drivers of change in dynamic models. Typical combinations of systems and models that we explore are as follows:
multi-layered spatial interaction models of trade flows – in the context of rapidly changing ship, port and route ‘technologies’;
dynamic models of trade and economic impact using a variant of spatial Lotka–Volterra;
input–output models linked by spatial interaction models of imports and exports;
spatial interaction models of migration combined with biproportional fitting;
models of riots (i) using epidemiological and spatial interaction modelling, (ii) using discrete choice models, (iii) using spatial statistics and (iv) using diffusion models;
models of piracy (i) using agent-based models and (ii) using spatial interaction models with ‘threat’ as the interaction;
models of ethnic contagion using spatial statistics;
modelling the impact of development aid through input–output models;
spatial Richardson (arms race) models;
Colonel Blotto game-theoretic security models.
We introduce each of these in a little more detail, noting the actual or potential planning and policy applications of each.
In the case of shipping (Chapters 2 and 3), we can use the models we develop to explore the consequences of changing patterns of trade and changing transport technologies. There are rich, albeit disparate, sources of data. The global trade system is complex – through the variety of goods, commodities and services that are carried and through the set of transport modes deployed – sea, air, rail, road and telecommunications. This means that we have to choose levels of resolution at which to work and particular systems of interest on which to focus. In making these decisions, we are, to an obvious extent, constrained by the availability of data. We also wish to connect – and make consistent – any predictions from a model of trade with the import and export data which form part of the input–output tables to be outlined in the next chapter. We focus on a coarse level of aggregation based on seven economic sectors, and we present these sectors and volumes of trade in money terms. We focus mainly on ‘container shipping’, though container routes usually include road and/or rail elements as well as sea. This covers 80% or more by volume of trade flows. We proceed in two stages beginning with a review of the evolution of the container shipping system (Chapter 2) and then by building a multi-layered model of international trade (Chapter 3).
A key component of an integrated global model will be a submodel that gives us the state of economic development of each country. The ideal model for each country is an input–output model and these, of course, would be linked through trade flows. It seems appropriate, therefore, to report our response to this challenge in this section along with trade (Chapter 4). The basis of this development has to be the existence of national input–output tables. WIOD 2012 provides an excellent source for 40 countries. However, there are enormous gaps of course – 40 out of 220 – and these gaps embrace the whole of Africa. We have sought to handle this situation by developing new tools, based on high-dimensional principal components' analysis, which enable us to estimate the missing data. The detail of this method is presented in Chapter 5 of our companion book Geo-Mathematical Modelling (Wilson, ed., 2016).
The policy challenges facing governments associated with migration are essentially of three kinds: the effective integration of in-migrants; limiting the inflows of some types of migrant; encouraging inflows of others. There are forces driving migration which, from governmental perspectives, are more or less controllable in different circumstances. It is important, as ever, to seek to provide a good analytical base to underpin the development of policy. There has been extensive research on migration, and we first provide a background to our own work by surveying this research in the context of the policy questions that arise (Chapter 5).
A typical problem facing the global systems' modeller is the situation in which the data available are not sufficiently detailed. In this case, bearing in mind the nature of the policy challenges, in Chapter 6 we take on the task of estimating flows at a regional (sub-national) level. We do not have the data to achieve this on a global scale, but we have good European data and so we develop the methodology on this basis. This is a classical biproportional fitting problem. Migration data have to be assembled from a variety of sources and different ones are more or less reliable. In order to build as complete a picture as possible of global migration, we explore a variety of sources and seek to integrate them (Chapter 7).
Security challenges vary in scale from the urban – even street level – to the international, for example, through the global deployment of a country's military forces. These different scales, in general, demand different modelling methods and we seek to illustrate a range of these. Security has rich but disparate data. We have developed a two-pronged approach: first to develop some new theoretical models by taking some traditional ones and adding spatial structures, and second, we have assembled a wide range of data that has allowed us to carry out some preliminary tests. We recognise that in this case, there will be government agencies around the world who are modelling these systems with far richer resources than we can bring to bear. What we hope to have achieved is to demonstrate some new approaches to security modelling that may be taken up by these agencies. In this case as well, we have been able to develop models at finer scales in relation to riots, rebellions and piracy. A key concept in this work is the representation of ‘threat’ and in particular, threat across space. We introduce this in broad terms in Chapter 8.
We then present five distinct applications which between them offer a wide range of methods. In some cases, we can apply different methods to the same problem and so discover the strengths and weaknesses in a comparative framework. Chapter 9 offers a variety of approaches to the London riots of August 2011. We built a three-stage model – propensity to riot (from epidemiology), where to riot (a version of the retail model) and the probability of arrest. We use Monte Carlo simulations to determine whether the counts of observed patterns are more or less frequent than might be expected under conditions in which the extent of spatio-temporal dependency of offences is varied.
In Chapter 10, we shift scale and location again and examine the Naxalite rebellions in India. The data on Naxalite terrorism include the date on which events took place and the district (of which there are 25) within which they occurred. Events include Naxalite attacks and police responses. A key idea in the insurgency literature concerns the contagion of events. This can occur for a number of reasons. For example, conflict may literally spillover from one locality to a nearby other, leading to an increase in the area over which the conflict occurs or moving from one location to those adjacent. In this case, we explore a number of hypotheses. We can test whether there are non-spatial effects of police action on insurgent activity. Moreover, we may test the hypothesis that police action is triggered by insurgent activity. If only the latter is observed, this would suggest that police action is reactive but has little effect on insurgent actions (at least on a short-time scale). For such models, the count of attacks per unit time is described by two components: (i) the first is a baseline risk – which may be time invariant or not, but where it changes it will tend to do so over a relatively long-time scale; and (ii) a self-excitation process, whereby recent events have the potential to increase the likelihood of attacks today considerably.
In Chapter 11, we explore a very different system of interest: piracy in the Gulf. An important question is the security of shipping in relation to pirate attacks. There are two possible approaches to this problem: first, to develop an agent-based model with a given (and realistic) pattern of shipping, and pirates as agents; and secondly, as adopted in this Chapter, to develop a spatial interaction model of ‘threat’ and to use this to explore naval strategies.
In Chapter 12, we explore a different kind of security issue with a different method: the impact of IEDs (improvised explosive devices) in Iraq. The null hypothesis is that they are independent in time and space. We use Knox's method of contagion analysis to seek evidence of clustering – an important issue in the assessment of response to this kind of threat – and find that there is evidence for clustering in space, time and space-time.
Another kind of security issue is posed in nearby countries where there is a threat of cross-border contagion fuelled, for example, by social networks and this is the subject of Chapter 13. We consider whether ethnic conflict is contagious between groups in different countries and if so, how? And then, whether governments react pre-emptively to potential conflict contagion by increasing repression of specific groups? The argument to be tested is that ethnic groups that are discriminated against in a society identify with groups fighting against the same grievance in other countries and become inspired by their struggle to take up arms against their own domestic government as well. For this process, information about foreign struggles is important, not geographic proximity as such. The empirical test involves using a statistical model on country-years from 1951/1981 to 2004 and this gives some support for the argument. The test will be repeated using data on the analytic level of ethnic groups in different years and improving on the measures of information flows. In the case of government reaction, the argument to be tested is whether governments pre-emptively increase repression against ethnic groups they expect to become inspired by foreign conflicts in order to deter them from mobilising. The empirical test in this case is through a strategic model using data on the behaviour of governments towards domestic ethnic groups.
Development aid (Chapters 14 and 15) offers different challenges: first, defining categories; and then assembling data from very diverse sources. In this case, the ultimate challenge is to seek to measure the effectiveness of aid, and a starting point is to connect aid to economic development. This creates a demand to ‘measure’ development, and we have done this by constructing input–output models for each country which can then be integrated with our trade model. It then becomes possible to compare the magnitudes of different kinds of aid flows with other trade flows and with flows within national economies. Not surprisingly, aid is much more significant in developing countries than in those with advanced economies. The value of the global input–output model now becomes apparent: in a selected country, we can compute the multiplier effects of increased demand or investment in particular sectors and then begin to address the question of whether investment aid is most effectively targeted. We model aid allocation in Chapter 16.
We finally seek to move beyond our investigation of the impact of aid on development in particular countries and explore the extent to which it has any impact on trade, on migration flows or on helping to maintain security. It has been necessary to drive our work in developing particular submodels by assembling relevant data in each case. Our global input–output and trade system then provides the basis for integrating the main submodels so that we explore the interdependencies which make the global system so complex. It is foolish to think that we (or anyone else) can offer a detailed and convincing ‘model of the world’ in all its aspects. But what we can do is to offer a demonstration model that reveals some of the complex system consequences of interdependence and points the way to further research, possibly to be carried out by government and inter-governmental agencies that can bring far greater resources to bear. In Chapter 17, therefore, we draw together the different submodels into a comprehensive model which enables us to incorporate the key interactions. Some of the most obvious interdependencies to be picked up are as follows:
the impacts of net migration on economic development through the labour elements of the national input–output models;
security-led pushes in outward migration;
security-led changes in economic development – whether from damage from attack or because of more intensive development of the arms industry;
many aspects of changing trade patterns – for example, from investment in new ports as well as changes in economic development levels;
the re-targeting of development aid.
We proceed by establishing a base model and year – taking 2009 as the latest year for which input–output data are available at the time of writing. As noted in Chapter 4, the model is rooted in WIOD 2012 data but then enhanced through a principal components' technique to cover all countries. The import and export flows are integrated with those from a trade model by a biproportional fitting procedure. We assemble base year data and models (as appropriate) for the flows of migrants, military dispositions and development aid. These become drivers of change for subsequent time periods (which we take to be years). At each year end, a number of indicators are calculated and particular attention is paid to imbalance as these will provide the basis for driving the system dynamics. Each year end ‘model run’, for this reason, is likely to involve iterations driving the system to a new equilibrium.
Reference
WIOD (2012) The World Input–Output Data Base: Content, Sources and Methods, Technical report Number 10.
Wilson, A. (ed) (2016)
Geo-Mathematical Modelling
, Wiley, Chichester.
Part TwoTrade and Economic Development
Chapter 2The Global Trade System and Its Evolution
Simone Caschili and Francesca Medda
2.1 The Evolution of the Shipping and Ports' System
Shipping volumes have grown
