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Formalized Scenario Building Apadpation for Conflict Prevention

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Scenario-building methods are broadly employed to assist prediction and planning across a broad field of applications. Security environment analysis and conflict prevention planning has predominantly relied on long-term trend assessments by experts and infrequently on basic scenario building. The mode of scenario building was characterized by high-volume or extreme case methodology. The high number of possible scenarios and assignment of probabilities present key disadvantages. The paper proposes an adaptation of Trend Impact Analysis (TIA) methodology to security environment analysis and conflict prevention by illustrating this application on a dataset of 12 monitored trend factors specifically tested on a set of 316 cases. The application shows that TIA combines the advantages of quantitative and scenario-building methods to systematically reduce the number of probable scenarios and increase the precision of predictions necessary for effective analysis and conflict prevention. This application is highly relevant to both state and international medium and long-term conflict prevention and threat mitigation strategies.
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VOLUMUL
I
“CAROL I” NATIONAL DEFENCE UNIVERSITY
SECURITY AND DEFENCE FACULTY
PROCEEDINGS
THE 16TH INTERNATIONAL SCIENTIFIC CONFERENCE
“STRATEGIES XXI”
STTEGIC CHANGES IN SECURITY
AND INTERNATIONAL RELATIONS
April 09-10, 2020
Volume XVI,
Part 2
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Colonel Professor Ion PURICEL, PhD
Colonel Professor Daniel GHIBA, PhD
Colonel Professor Lucian Dragoş POPESCU, PhD
Colonel Professor Ioana ENACHE, PhD
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Volume XVI, Part 2
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Colonel Professor Daniel GHIBA, PhD
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9
FORMALIZED SCENARIO BUILDING ADAPTATION
FOR CONFLICT PREVENTION
Adriana ILAVSKA
Researcher, Masaryk University, Czech Republic
424083@muni.cz
Martin CHOVANCIK, Ph.D.
Assistant Professor, Masaryk University, Czech Republic
chovancik@fss.muni.cz
Abstract: Scenario-building methods are broadly employed to assist prediction and planning across a broad
field of applications. Security environment analysis and conflict prevention planning has predominantly relied
on long-term trend assessments by experts and infrequently on basic scenario building. The mode of scenario
building was characterized by high-volume or extreme case methodology. The high number of possible scenarios
and assignment of probabilities present key disadvantages. The paper proposes an adaptation of Trend Impact
Analysis (TIA) methodology to security environment analysis and conflict prevention by illustrating this
application on a dataset of 12 monitored trend factors specifically tested on a set of 316 cases. The application
shows that TIA combines the advantages of quantitative and scenario-building methods to systematically reduce
the number of probable scenarios and increase the precision of predictions necessary for effective analysis and
conflict prevention. This application is highly relevant to both state and international medium and long-term
conflict prevention and threat mitigation strategies.
Keywords: conflict prevention, scenario-building, trend impact analysis.
Introduction
Prediction in social sciences and namely impactful security issues has always been a
core challenge of security analysts, military planners, and political scientists. While a plethora
of approaches have been applied, refined, adapted, discarded, and reinvented - the post-Cold
War transformation of addressing conflict has established several dominant patterns. These
are characterized by a rapid and profound transformation of peacekeeping and peace
enforcement, but also quite significantly, a re-focus on conflict early warning, prediction, and
prevention
.
While conflict resolution approaches have been tested by fire throughout the 1990s
and daily ever since, conflict prevention remains much more elusive and overlooked - but
may also be quite effective and cheap. Its methods span from structural prevention programs
such as UN Good Offices or the European neighborhood Policy, to sophisticated quantitative
methods predicting hotspots with machine-learning
, and stand-by mediation teams ready for
deployment.
Bredel, Ralf, Long-term conflict prevention and industrial development: the United Nations and its specialized
agency, UNIDO (Leiden: Brill, c2003); Babbitt, Eileen F. “The Evolution of International Conflict Resolution:
From Cold War to Peacebuilding,” Negotiation Journal 25, no. 4 (October 1, 2009): 53949,
https://doi.org/10.1111/j.1571-9979.2009.00244.x; Gross, Eva.EU conflict prevention and crisis management:
roles, institutions, and policies ( London: Routledge, 2014); Zartman, I. William. Preventing deadly conflict
(Malden, MA: Polity Press, 2015).
Basuchoudhary, Atin, James T. Bang, Tinni Sen, and John David. Predicting hotspots: using machine learning
to understand civil conflict (Lanham, Maryland: Lexington Books, 2018).
10
Whether considered an element of preventive diplomacy or pre-emptive diplomacy
,
most prolifically written about by Michael S. Lund in his many works, tools of conflict
prevention are rapidly developing around the same core concepts - long-term peacebuilding
efforts coupled with predictive methods to concentrate and intensify these efforts in specific
times and locations. These methods are often less understood and even more frequently
laborious and distrusted. If prediction and early warning serve as necessary identifiers of risk
areas where violence is likely; scenario building offers the production of actionable
probability assessments to address emanating threats.
Proactive engagement in early stages of conflict is necessary for both operational and
structural prevention
. In an emerging crisis, fundamentals for early warning are provided by
an analysis of structural causes and triggers. Identification of structural causes supports long-
term structural prevention. On the other hand, operational prevention is based mostly on
detecting proximate causes and triggers. Monitoring them enables faster reaction, crucial for
containing escalation
. However, early warning is in itself inefficient in averting, containing,
or mitigation and almost irrelevant to long-term planning of capacities within the observing
country - despite significant progress in its methodology
. It is a base for creating targeted
responses and guiding decision makers to take the best and most effective action under the
time constraint
. Even in this regard Early warning systems are plagued by the curse of the
“response gap”: the actual follow-up of warnings by action
.
In spite of the considerably long tradition of early warning and conflict predictions,
both topics are still controversial in the field of conflict research
. N.N. Taleb identified
several issues of human predictions and predictions in “soft” sciences. Besides the individual
bias, one of the main problems of qualitative approach is overestimating predictions. Humans
tend to exaggeratedly rely on their own estimations if they are based on a large amount of
information
. To address this issue, the prediction process became more formalized and
engaged quantitative methods. Firstly, traditional extrapolation techniques were introduced
into predictions. Forecasting techniques became more sophisticated since the 1960s
.
However the central issue of quantitative techniques could not be eliminated neither by
improving computational power nor by enlarging used data sets. The problem lies in the main
assumption of extrapolation, that the future will be similar to the past
. Therefore,
Steven A. Zyck and Robert Muggah, “Preventive Diplomacy and Conflict Prevention: Obstacles and
Opportunities,” Stability 1, no. 1 (September 25, 2012): 6875, https://doi.org/10.5334/sta.ac.
Susanna Campbell and Patrick Meier, “Deciding to Prevent Violent Conflict: Early Warning and Decision-
Making within the United Nations,” 2007, 32, https://irevolution.files.wordpress.com/2011/07/campbell-meier-
isa-2007.pdf.
Herbert Wulf and Tobias Debiel. 2009. Conflict Early Warning and Response Mechanisms. A Comparative
Study of the AU, ECOWAS, IGAD, ASEAN/ARF and PIF. no. Crisis States Working Papers Series No.2.
Hegre, H., Karlsen, J., Nygård, H. M., Strand, H., & Urdal, H. 2013. Predicting Armed Conflict, 201020501.
International Studies Quarterly, 57(2), 250270. https://doi.org/10.1111/isqu.12007
Claus Neukirch, “Early Warning and Early Action – Current Developments in OSCE Conflict Prevention
Activities,” 2013.
Wulf, H., & Debiel, T. 2010. Systemic disconnects: Why regional organizations fail to use early warning and
response mechanisms. Global Governance, 16(4), 525547; Bock, J. G. 2014. Firmer Footing for a Policy of
Early Intervention: Conflict Early Warning and Early Response Comes of Age. Journal of Information
Technology & Politics, 12(1), 10311; Rohwerder, B. 2015. Conflict Early Warning and Early Response.
Governance Social Development Humanitarian Conflict Helpdesk Research Report, 13.
Lars-Erik Cederman and Nils B. Weidmann, “Predicting Armed Conflict: Time to Adjust Our Expectations?,”
Science, no. 355 (2017): 47476, https://doi.org/10.1126/science.aal4483.
TALEB, Nassim Nicholas. The black swan: the impact of the highly improbable (London: Penguin, 2008)
William R. Huss, “A Move toward Scenario Analysis,” International Journal of Forecasting, 1988,
https://doi.org/10.1016/0169-2070(88)90105-7.
William R. Huss and Edward J. Honton, “Scenario Planning-What Style Should You Use?,” Long Range
Planning, 1987, https://doi.org/10.1016/0024-6301(87)90152-X.
11
extrapolation techniques can produce only surprise-free predictions. But our world, especially
the world of security issues and conflicts, is definitely not surprise-free.
Jointly, the deficiencies of - early warning, the response gap, and surprise-free
extrapolation techniques in forecasting - create the need for a methodology ascertaining the
risk of said surprise and predicting the possible fallout of that surprise. In the field of security
analysis and conflict prevention, the preferred method is scenario building.
Scenario building allows for planning out possible surprises in the trends as well as the
necessary responses ahead of time. Compared to prediction and early warning, scenario
building offers both - the alternative outcomes of a situation which we might then be alerted
of by an early warning system, and the range of actions to follow these alternatives.
However, much like with forecasting - the determination of probabilities is fraught
with deficiencies. What is more, to adequately cover a security issue, threat, or prediction of
conflict impacts - dozens of scenarios have to be produced. The current method of
minimization rests with expert consultations, worst case scenario building only, or other
eleminitation methods to reduce the number of scenarios
.
The proposed text offers an example of employing a tool not used in conflict
prevention and threat mitigation scenario building - Trend Impact Analysis (TIA) in
conjunction with Qualitative Comparative Analysis (QCA) - as a method of increased
prediction preciseness and a method automatically assigning probabilities to scenarios. The
obvious benefit to security analysis and conflict prevention being the reduced number of
scenarios with already designated probabilities - focusing resources and conflict prevention
capacities to the scenarios deemed most impactful in respective sectors. We introduce a real-
world analysis of a small state’s optimization of scenario building to rationalize resource
dedication to highest impact scenarios in individual spheres - migration is used as an example,
due to the ease of quantification, but the process is applicable to any defined threat possibly
emanating from a conflict scenario.
Scenario-building approach: Trend Impact Analysis
Predictive analyses in the field of security or politics are still rare. It seems scepticism
still prevails originating in the supposed inability of predicting social reality because of its
overwhelming complexity
. A scenario-based approach to the future might be more
acceptable even for sceptics because a scenario is not “a future reality but rather a means to
represent it with the aim of clarifying present action in light of possible [...] futures.”
It
makes scenarios suitable for long-term evaluation of the future in uncertain environments
characterized by lack of data and a considerable number of variables that are extremely
difficult or impossible to quantify. Scenarios were introduced in the 1950s by Herman Kahn
and even though their application is mainly in business, the initial application was related to
military and strategic studies
. The first comprehensive model for scenario-building was
Schwenker, Burkhard, and Torsten Wulf. Scenario-Based Strategic Planning : Developing Strategies in an
Uncertain World (Munich: Springer Gabler, 2013) Martelli, Antonio. Models of Scenario Building and
Planning: Facing Uncertainty and Complexity (New York: Palgrave, 2014).
Kalous Miroslav, “Analysis of several pioneering studies in the field of Czech political and security scenario-
building.” Obrana a Strategie. 18(1):131 - 146. doi:10.3849/1802-7199.18.2018.01.131-146.
Philippe Durance and Michel Godet, “Scenario Building: Uses and Abuses,” Technological Forecasting and
Social Change, 2010, p.1488 https://doi.org/10.1016/j.techfore.2010.06.007.
William R. Huss, “A Move toward Scenario Analysis,” International Journal of Forecasting, 1988,
https://doi.org/10.1016/0169-2070(88)90105-7.
12
published in 1975
but it took almost another 3 decades for the use of the method to spread.
Only in the last 15-20 years scenarios are on the upswing
.
In the context of a gradual evolution of scenario-building, three main alternative
approaches can be identified. The first category of techniques is based on intuitive logic, the
second category is more formalized, and engages cross-impact analysis. The third category,
trend impact analysis based scenarios, combines more traditional forecasting techniques with
qualitative factors
). This is a desirable combination for predictions in security studies.
However, it is important to emphasize once again, scenarios are not forecasts. Their
primary task is not to anticipate the future but they do promote thinking of the environment as
a network of independent relationships rather than a cluster of variables. A scenario exercise
is more a simulation than a forecast, it is a model which duplicates structure and actions of the
environment
. It can be a huge advantage in combination with their strong narrativity
because through scenarios using trend impact analysis, issues identified by formalized and
precise methods can be translated into terms of the real world and become actionable.
Intelligibility of results of scenario exercise in conflict prevention for all relevant actors in the
process is the crucial factor for interconnecting long-term planning, early warning, and early
and appropriate action.
Trend impact analysis seems to be the best candidate for meeting the goal of
integrating forecasting into planning. The common practice of TIA application follows rules
of traditional surprise-free extrapolation, combines them with inputs from qualitative methods
and ties both together by narrative targeted to future actions. The very first step to final
scenarios is identifying key scenario drivers for the chosen problem. By doing that, the
researcher demarcates scenario space and can work with time series and trends in the defined
space. So-called naive extrapolation follows. Variables and their trend is analyzed by
traditional quantitative methods. After surprise-free extrapolation, the innovation of TIA
transpires. The next step is introducing impacting events, there are a few ways to identify
these events, i.e. literature review, experts’ opinions, results of the Delphi method. When the
set of events is assembled, every event must be specified in more detail. Namely, when will
the event’s impact on trend occur, how long will it take till the event causes the most
significant shift in the trend, what is the highest possible impact and how long will it take
until the shifted trend becomes a new standard. Equally important is to define the probability
of every event and also probabilities of details on the event’s development. With the specified
scale of possible impact and probabilities, it is possible to revisit the original extrapolation
and adjust it to different events. At this point, a single extrapolation breaks up into dozens of
scenarios.
Unlike the other approaches to scenario-building, trend impact analysis assigns
probability and impact to every scenario and significantly facilitates the process of choosing
relevant options and developing narratives in particular scenarios
.
From the method description, it is clear TIA offers a solution for many issues that
need to be addressed to achieve the integration of forecasting into planning. Unquantifiable
variables cannot be neglected and tools for the prediction cannot be indifferent to the
unexpected turning points. Both points are addressed by integrating experts' opinions,
George Wright, Ron Bradfield, and George Cairns, “Does the Intuitive Logics Method - and Its Recent
Enhancements - Produce ‘Effective’ Scenarios?,” Technological Forecasting and Social Change, 2013,
https://doi.org/10.1016/j.techfore.2012.09.003.
Martelli, Models of Scenario Building and Planning: Facing Uncertainty and Complexity.
William R. Huss and Edward J. Honton, “Scenario Planning-What Style Should You Use?,” Long Range
Planning, 1987, https://doi.org/10.1016/0024-6301(87)90152-X.
Martelli, Models of Scenario Building and Planning: Facing Uncertainty and Complexity.
William R. Huss and Edward J. Honton, “Scenario Planning-What Style Should You Use?”; Martelli, Models
of Scenario Building and Planning: Facing Uncertainty and Complexity.
13
literature reviews comprising mostly case studies, and taking into account possible twists in
trends caused by unexpected events. Another important point for linking both aspects of
successful conflict prevention and management is macro environment analysis and
interconnection of long-term analysis and consideration of short-term changes
. TIA, which
successfully covered previous issues, is not so strong in addressing the latter, however, in the
conflict prevention macro environment it becomes crucial. Therefore we decided to modify
TIA in a few steps to increase the chance it will comprehensively cover a broader
environment and combine long-term and short-term factors.
Trend Impact Analysis adaptation: small state action in conflict prevention and threat
mitigation - example of migration to Czechia
Small states are the ideal users of TIA in conflict prevention. With limited resources to
conduct extensive assessments of threats through scenario building and equally limited
resources to engage in threat impact mitigation and prioritization - TIA offers a rationalization
of both types of resources. Exemplified by a threat of irregular migration, Czechia is utilized
as a small state with limited capacities pro-actively seeking to improve its security in guarding
against negative impacts of possible armed conflict in the proximate neighborhood. The
impacting “surprise” therefore is established to be an armed conflict. Conflicts and especially
civil wars are well-recognised drivers of forced migration
and for preventing forced
migration it is important to focus on conflict prevention
. Irregular migration is identified by
Czechia as a security risk it wishes to mitigate. A lot of attention is paid to migration as a
factor increasing crime and causing the growth of the labor black market, but there is also an
issue of cultural consequences. If it is combined with the rise of xenophobia and lack of
integration, social cohesion can be endangered
and also stability of state institutions if the
migration is massive and institutions are failing to manage it. Therefore it is necessary to
monitor how migration evolves in time
.
Regarding monitoring migration, countries usually have to focus primarily on
geographically close regions. For European countries the potential threat can come from
Europe itself, Middle East, North Africa, Central Asia, Caucasus, and Russia. Those countries
should be regularly inspected and if an internal conflict, as a source of migration, may occur -
preventive action to manage the conflict and attenuate possible consequences should be
launched. For the case of the Czech Republic regions of interest remains the same, in three
main groups - Western Europe, Post-Communist space, Middle East and North Africa - 86
individual countries will be included into analysis and inspected on possibility of conflict
William R. Huss, “A Move toward Scenario Analysis.”
Christina Davenport, Will Moore, and Steven Poe, “Domestic Threats and Forced Migration, 1964-1989,”
International Interactions 29, no. 1 (2003): 2755, https://doi.org/10.1080/03050620304597; Will H Moore and
Stephen M Shellman, “Whither Will They Go? A Global Study of Refugees’ Destinations, 1965 - 1995,” vol. 51,
2007; Timothy J Hatton, “The Rise and Fall of Asylum: What Happened and Why?,” Source: The Economic
Journal, vol. 119, 2009; Mathias Czaika and Mogens Hobolth, “Do Restrictive Asylum and Visa Policies
Increase Irregular Migration into Europe?,” European Union Politics 17, no. 3 (2016): 34565,
https://doi.org/10.1177/1465116516633299; Tilman Brück et al., “Determinants and Dynamics of Forced
Migration to Europe: Evidence from a 3-D Model of Flows and Stocks,” 2018, www.iza.org.
Tilman Brück et al., “Determinants and Dynamics of Forced Migration to Europe: Evidence from a 3-D Model
of Flows and Stocks”.
Khalid Koser, “Irregular Migration, State Security and Human Security A Paper Prepared for the Policy
Analysis and Research Programme of the Global Commission on International Migration and Does Not
Represent the Views of the Global Commission on International Migration,” 2005.
Khalid Koser, “When Is Migration a Security Issue?,” Brookings, 2011,
https://www.brookings.edu/opinions/when-is-migration-a-security-issue/ Sergei Metelev, “Migration as a Threat
to National Security,” Indian Journal of Science and Technology 9, no. 14 (2016),
https://doi.org/10.17485/ijst/2016/v9i14/91086.
14
escalation. Analysis is performed within the time frame 1989 - 2017. Area specification
emerged directly from the problem and also helped to define scenario space. These definitions
themselves determined general conceptualization of a variable for analysis - migration to the
Czech Republic per year.
Data on migration are available on website of The United Nations’ Refugee Agency
.
Fig.1 presents basic extrapolation of the trend in the horizont of 5 years. This concerns the
first step of TIA - establishing a trend for a particular threat identified by the country in
question (Czechia):
Fig. 1: Migration to the Czech Republic: 1989 - 2023 extrapolation
In classical scenario-building, at this point it is usual to engage a group of experts or
confront literature in order to list a set of events which in case of occurrence would have an
impact on the migration to the CR. However, keeping in mind not only general critique of
possible researchers' biases and issues of validity and reliability but also one of requirements
to achieve effective conflict prevention - necessity to incorporate macro environment analysis,
we decided to choose a different approach in this step.
Credible capturing of the environment is challenging, it is a very tangled task which
cannot be completed by neither qualitative nor quantitative methods exclusively. However if
both approaches are combined, it can minimize pitfalls of an attempt to cover as much of
environment complexity as possible. One of a few effective and transparent methods
combining qualitative and quantitative approach is Qualitative comparative analysis. QCA is
by definition qualitative a comparative methods approach. The main focus is on the
systemizing of the process of comparison in order to increase the number of cases that can be
actually compared. The method is still case oriented
but thanks to the formalized analysis by
mathematical apparatus of set theory, a large number of cases can be analyzed. Therefore
QCA also resembles a quantitative approach and combines the advantages of both. The main
Data available on website: http://popstats.unhcr.org/en/overview.
De Meur, Giséle, Benoît Rihoux, and Charles C. Ragin. “Qualitative Comparative Analysis (QCA) as an
Approach,” In Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related
Techniques, ed. by Benoît Rihoux and Charles C. Ragin (Thousand Oaks: Sage, 2009), 1-18.
15
principle lies in treating the case as a combination of factors producing a specific outcome.
Examined factors are the same for all cases, but factors and also outcomes may acquire
different values. As every case is represented by combinations of factors, it is possible to
systematically compare many complex situations. QCA comprises more techniques, often
used is csQCA - QCA on crisp sets where values of factors and outcome are dichotomous
.
The aim of the analysis is to structurally look for patterns in empirical data
.
The method was chosen because it allows us to describe the environment of every case
in detail but at the same time, the description is structured and formalized. If only experts’
opinions are considered there is a change of neglecting some variables or overlooking
complex relations among them. Analysis with set theory apparatus also enables testing of
different combinations and evaluating their relevance.
In the case of the Czech Republic, 316 cases of escalation opportunities between 1989
- 2017 in countries of Western Europe, Postcommunist space, Middle East and North Africa
were a basis for creating the QCA model. The result of the analysis is a set of causal paths
leading to escalation into an armed conflict. They are combinations of economic (youth
unemployment; GDP at purchasing parity power; income inequality), social and demographic
(population growth; ethnic power relations), political (conflict in last 50 years in the country;
conflict in neighbourhood; irredentist or secession claims; political violence and terror;
repressiveness of regime; institutionalized democracy), environmental (conflict because of
basic sources) and military (global militarization index) conditions. These conditions had
been chosen from the larger set of possible relevant factors, the original set was composed in
order to cover as broad a range of environment characteristics as possible in correspondence
with literature on sources of conflict. 12 aforementioned conditions were chosen out of the set
based on the results of the testing of their combinations by Boolean algebra apparatus.
Tab. 1: Conflict causal paths
No.
Conflict causal path
path 1
GDP_PPP*IRED_CLAIM*~TER_CLAIM*NEIGH_CONF
path 2
GDP_PPP*CONF_50*DEM_POLITY*ZAKL_ZDROJE
path 3
~GINI_DISP*EPR_ED*~NEIGH_CONF*~GMI_BICC
path 4
~GINI_DISP*CONF_50*~DEM_POLITY*ZAKL_ZDROJE
path 5
~POP_GROWTH*~EPR_ED*TER_CLAIM*PTS_S
path 6
~YUEMP*~GINI_DISP*IRED_CLAIM*~TER_CLAIM*NEIGH_CONF
path 7
~YUEMP*GINI_DISP*IRED_CLAIM*TER_CLAIM*~NEIGH_CONF
path 8
YUEMP*POP_GROWTH*~NEIGH_CONF*DEM_POLITY*~ZAKL_ZDROJE
path 9
YUEMP*IRED_CLAIM*CONF_50*NEIGH_CONF*DEM_POLITY
path 10
~GDP_PPP*EPR_ED*~TER_CLAIM*~NEIGH_CONF*~GMI_BICC
path 11
GDP_PPP*CONF_50*NEIGH_CONF*PTS_S*~GMI_BICC
path 12
GINI_DISP*IRED_CLAIM*~NEIGH_CONF*ZAKL_ZDROJE*GMI_BICC
path 13
~POP_GROWTH*IRED_CLAIM*~NEIGH_CONF*PTS_S*ZAKL_ZDROJE
path 14
~IRED_CLAIM*TER_CLAIM*NEIGH_CONF*~DEM_POLITY*ZAKL_ZDROJE
path 15
YUEMP*~GDP_PPP*GINI_DISP*POP_GROWTH*~NEIGH_CONF*GMI_BICC
Berg-Schlosser, Dirk, and Giséle De Meur. “Comparative Research Design: Case and Variable Selection”. In
Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques, ed.
by Benoît Rihoux and Charles C. Ragin (Thousand Oaks: Sage, 2009), 19-33.
Schneider, Carsten Q, and Claudius Wagemann. Set-Theoretic Methods For The Social Sciences:
A Guide To Qualitative Comparative Analysis. (Cambridge:Cambridge University Press, 2012)
16
No.
Conflict causal path
path 16
YUEMP*GDP_PPP*POP_GROWTH*CONF_50*NEIGH_CONF*PTS_S
path 17
YUEMP*GDP_PPP*~TER_CLAIM*NEIGH_CONF*PTS_S*~DEM_POLITY
By discovering causal paths leading to conflict in different parts of the world (Tab. 2)
many factors describing macro environment and its changes were combined into 2 types of
impacting events - low intensity conflict escalation and high intensity conflict escalation. All
conditions are structural, therefore their changes are not as dynamic and allow long-term
analysis. On the other hand, some structural factors can be significantly changed by external
impact or internal disruption. The advantage of QCA model is that such changes can be
immediately incorporated and reflected in results. In this respect, the short-term aspect is also
taken into consideration.
Knowing causal paths leading to the conflict escalation enables collection of more up
to date data for countries of interest and checks whether any of the countries’ combinations
correspond with conflict causal paths. If so, it is an important early warning element because
it is possible to identify potential threats in pursuance of evolution of conflict sources. It is a
way to identify a potential escalation opportunity even before existing early warning systems
start to detect early stages of conflict. The result of this step in analysis is not only the list of
countries which may be at risk of conflict escalation but also the number of conflict causal
paths which complies with the current situation in the country. The probability of escalation
can be partially estimated based on simple logic, the more corresponding causal paths, the
higher probability of escalation. Another part of probability estimation is focused on cases
from the original set of 316 cases in model. All causal paths are empirically anchored and can
be matched with particular cases in the original set and then it is possible to count the cases
which correspond with the conflict causal path in the past.
“The strength” of the causal path
can be defined by this number (Tab. 2) and the second component of probability is thus
estimated. Tab. 2: The strength of causal paths
No.
Matched cases32
Strength of the
path
path 1
MDA2016,MDA2017
1
path 2
MLI2017
3
path 3
ISL2016,IRL2016,ISL2017,IRL2017,LUX2017
1
path 4
AFG2017,DZA2017,UKR2017
9
path 5
IRN2016,AZE2016,RUS2016,TKM2016,UZB2016,
IRN2017,ARM2017,AZE2017,RUS2017,TKM2017,UZB2017
4
path 6
NOT FOUND
1
path 7
GBR2016
1
path 8
NOT FOUND
2
path 9
IRQ2016,GEO2016,IRQ2017,GEO2017
4
path 10
ISL2016,IRL2016,ISL2017,IRL2017,LUX2017
1
path 11
MLI2016,TJK2016,UZB2016,MLI2017,TJK2017,
UZB2017
5
The results of QCA includes for every path also calculation of the frequency of cases when conflict causal
path occurred but did not lead to the escalation. If the rate exceeded 80% the causal path was not evaluated as
conflict causal path.
Cases codes are composed of ISO Alpha 3 countries’ codes and the examined year.
17
No.
Matched cases32
Strength of the
path
path 12
YEM2017
3
path 13
NOT FOUND
3
path 14
SYR2016,AFG2017,DZA2017,EGY2017,MAR2017,SSD2017,SYR2
017
8
path 15
NOT FOUND
2
path 16
MLI2016,SDN2016,SSD2016,TJK2016,MLI2017,
SDN2017,SSD2017,TJK2017
7
path 17
NOT FOUND
1
This approach, assessing the potential risk of conflict escalation in every country,
leads to the first reduction of relevant cases which need to be reflected. It significantly
decreases the number of cases which need attention and in the next step suffices to evaluate
impact only for countries which were determined by previous analysis. From all countries
entering the analysis, only 24 face an increased risk of conflict escalation in the examined
time period (in this case 2 years). Instead of 86 cases to further evaluation, only 24 will be
subjected to further analysis.
After the probability assessment, countries are divided into 3
groups: high probability, medium probability and low probability. The last group will be
taken into consideration only if these cases have potentially significant impact on migration.
The major reduction of countries staying in the “perimeter” of analysis reduces the number of
scenarios needed in the final phase. It also lower expenses of early warning and conflict
prevention and focus can be shifted to the operational planning and early action.
Every causal path can be matched with cases in the original set. Thanks to that, it is
possible to retrospectively ascertain intensity (expressed by battle deaths) of every particular
escalation and with this in mind is possible to assess potential impact of other cases of
escalation via the same causal path. Many authors examined the relation between conflict
intensity and the volume of migration flows and found positive correlation
. Conte and
Migali
analyzed, along with many different factors, the role of the medium-level (25-1000
battle deaths) and high-level (1000+ battle deaths) conflict intensity in international
migration. According to their results, high-level intensity conflicts increase the migration flow
significantly more than medium-level intensity conflict. Abel et al.
chose a different
approach, they utilized different variables for different conflict intensity but worked with only
variable “Battle Deaths” and examined how relation of all independent variables to the
dependent variable evolves in 2 years subperiods. Numbers differed slightly for subperiods,
but in each of them the variable “Battle Deaths” had a positive influence on migration flows.
The impact of the conflict on migration will be calculated for each case as the combination of
the mean of coefficients presented by Abel et al.
and intensity of conflicts in causal path
which correspond with combination of conditions in particular case. Estimated value of the
MDA, MLI, ISL, AFG, IRN, GBR, TJK, IRQ, YEM, SYR, SSD, AZE, RUS, TKM, UZB, ARM, IRL, LUX,
DZA, UKR, GEO, SDN, EGY, MAR.
Timothy J Hatton, “The Rise and Fall of Asylum: What Happened and Why?,” Source: The Economic
Journal, vol. 119, 2009; Guy J. Abel et al., “Climate, Conflict and Forced Migration,” Global Environmental
Change, 2019, https://doi.org/10.1016/j.gloenvcha.2018.12.003; Alessandra Conte and Silvia Migali, “The Role
of Conflict and Organized Violence in International Forced Migration,” Source: Demographic Research 41:
393-424, accessed February 27, 2020, https://doi.org/10.4054/DemRes.2019.41.14.
Alessandra Conte and Migali S, “The Role of Conflict and Organized Violence in International Forced
Migration”.
Guy J. Abel et al., “Climate, Conflict and Forced Migration.
Ibidem.
18
impact (the presented example uses a 3% increase) will be used to calculate the overall
increase in international migration and the respective increase of migration to the Czech
republic will define trend modification.
After the impact estimation for every case, particular situations for scenario-building
can be chosen based on impact. Situations can be combinations of more cases, taking into
account cases with high probability (even if they have low impact) but also for the cases with
high impact (even if they have low probability). Estimating the other details of the impact
follows - time frame of when the impact on migration becomes evident is according to
aforementioned studies 1-2 years and the same is true for the highest impact - the basic
surprise-free trend extrapolation can be modified. Fig. 2 presents an example of a case of
South Sudan which has been attributed with a high possibility of conflict escalation and at the
same time, the escalation would have considerable impact on migration flows. Modifying a
trend based on the results of one country is not a complex situation for scenario-building, it
merely demonstrates an increase with a single country source. The course of the trend did not
change, because Fig. 2 presents a situation when South Sudan is the only country
experiencing conflict escalation. If a complex situation is described (e.g. if all countries with
higher probability than South Sudan or higher impact than South Sudan are included) the
displayed trend would change more significantly and likely even reverse.
Fig. 2: Comparison of surprise-free extrapolation and Adapted-TIA trend modification
Modified trend extrapolation is the base for building a scenario. It exposes possible
future dangers and leads the narrative in scenario. Using the QCA brings another advantage
which is revealed in the last step. Thanks to the method’s affiliation to the qualitative
methods, it is case oriented and the practice of the QCA requires knowledge of every case.
Familiarity with cases and their context is a very good starting position for scenario-building,
it enables incorporating operational planning and setting the main course of preventive actions
which are more relevant for the particular situation. Looking at the bigger picture in situations
improves reactivity of the scenario and it contributes to achieving another goal of successful
conflict prevention better integration of planning and forecasting.
19
Conclusion
Conflict prevention needs to incorporate early warning and prediction with measures
and actions addressing the identified threat to be effective. Achieving successful
interconnection of both aspects is one of main goals of successful and effective conflict
prevention. For smaller countries, taking into account their limited resources and the need of
prioritization, scenario-building with application of Trend Impact Analysis offers a superior
method. To demonstrate the relevance of the method for small states’ conflict prevention and
threat mitigation, an example of migration to Czechia was chosen.
Trend impact analysis in scenario-building reacts to criticism of prediction and
forecasting by a systematized methodology. In order to avoid surprise-free predictions and
neglecting unquantifiable variables, TIA combines quantitative and qualitative methods.
However, there are still pitfalls the original TIA method does not address. It still produces a
considerable amount of scenarios and by engaging experts’ opinions brings back human
imperfections excluded before by relying on quantitative approach in the earlier phase. To
prevent these problems from decreasing effectiveness and success of conflict prevention, we
decided to modify the Trend Impact Analysis technique by engaging qualitative comparative
analysis into the process.
QCA has proven to be a powerful tool in decreasing the number of scenarios. The
method’s formalized procedure and structured results enable systematic minimization of
scenarios. Unlike the current methods of minimizing the number of scenarios in TIA, which
are dependent on experts’ assessments, QCA-led reduction is no less based on expert
knowledge than the aforementioned one but it also incorporates classical probability
calculation. It has been also demonstrated how this systematic minimization reveals patterns
which could have been unnoticed. The example of the Czech Republic shows that traditional
focus on major conflict-prone countries like Egypt, Libya or Sudan is insufficient, and other
sources of migration should be considered with assessed probabilities. Mali, Algeria or Sudan
which are not primary interests of Czechia have a high probability of conflict escalation in
spite of their medium impact potential. On the other hand, attention should be also paid to
countries with a lower probability of conflict escalation but high possible impact such as
Azerbaijan. By systematizing the procedure, we thus arrive at an impact-driven (the impact
being the likelihood of armed conflict escalation) assessment surpassing the weakness of
classical extrapolation techniques - the assumption that the future will be similar to the past -
and better defining the source of perceived threats - in this case irregular migration.
Another articulated advantage of QCA directly addresses the main prerequisite for
conflict prevention. The interoperability of predictions and planning and actions is more
coherent because of QCA practice. Because of its qualitative aspect, the deep knowledge of
cases is required which indirectly adds value to planning. To summarize, there are fewer
scenarios which are more relevant and their impact on planning is better targeted, therefore
conflict prevention has more potential to be successful.
Adapted-TIA makes the method more flexible regarding sensitivity. In every step of the
analysis the researcher or examining body may control to what extent the number of scenarios will
be reduced - meaning sensitivity may be adjusted by controlling thresholds of probability and
thresholds of impact and setting them as low or as high as preferred. This is a highly relevant result
for smaller countries which may opt for higher sensitivity in one threat area and lower sensitivity in
another - yet still retaining the same methodology and procedure. On the input side, in this
particular case, also the intensity of the armed conflict can be set to low, medium or high or even
the combination of these intensities thus producing the desired level of sensitivity to migration..
These parameters are defined while defining the QCA model.
Incorporating Qualitative Comparative Analysis into Trend Impact Analysis based
scenario-building brought significant progress in addressing issues central to conflict
20
prevention in small countries with limited sources. The illustrated case of migration to
Czechia above serves as demonstration of the transparency and systemic nature of steps in
Adapted-TIA application. Engagement of QCA does not disrupt the structure of TIA
technique or scenario-building, therefore suggested adaptation can be easily applied to the
broad spectrum of threats. The main advantage is that the Adapted-TIA model can be further
developed and trained not only to achieve better results but also to cover more topics central
to conflict prevention.
BIBLIOGRAPHY
1. Abel, Guy J., Michael Brottrager, Jesus Crespo Cuaresma, and Raya Muttarak.
“Climate, Conflict and Forced Migration.” Global Environmental Change, 2019.
https://doi.org/10.1016/j.gloenvcha.2018.12.003.
2. Babbitt, Eileen F. “The Evolution of International Conflict Resolution: From Cold
War to Peacebuilding.” Negotiation Journal 25, no. 4 (October 1, 2009): 53949.
https://doi.org/10.1111/j.1571-9979.2009.00244.x.
3. Basuchoudhary, Atin, James T. Bang, Tinni Sen, and John David. [2018]. Predicting
Hotspots: Using Machine Learning To Understand Civil Conflict. Lanham, Maryland:
Lexington Books.
4. Berg-Schlosser, Dirk, and Giséle De Meur. “Comparative Research Design: Case and
Variable Selection”. In Configurational Comparative Methods: Qualitative
Comparative Analysis (QCA) and Related Techniques, edited by Benoît Rihoux and
Charles C. Ragin, 19-33. Thousand Oaks: Sage, 2009. ISBN 9781412942355
5. Bock, J. G. 2014. Firmer Footing for a Policy of Early Intervention: Conflict Early
Warning and Early Response Comes of Age. Journal of Information Technology &
Politics, 12(1), 103111.
6. Bredel, Ralf. c2003. Long-term conflict prevention and industrial development: the
United Nations and its specialized agency, UNIDO. Nijhoff law specials, 57. Leiden:
Brill. ISBN 978-90-04-13619-9.
7. Brück, Tilman, Kai M Dunker, Neil T N Ferguson, Aline Meysonnat, and Eleonora
Nillesen. “Determinants and Dynamics of Forced Migration to Europe: Evidence from
a 3-D Model of Flows and Stocks,” 2018. www.iza.org.
8. Campbell, Susanna, and Patrick Meier. “Deciding to Prevent Violent Conflict: Early
Warning and Decision-Making within the United Nations,” 32, 2007.
https://irevolution.files.wordpress.com/2011/07/campbell-meier-isa-2007.pdf.
9. Cederman, Lars-Erik, and Nils B. Weidmann. “Predicting Armed Conflict: Time to
Adjust Our Expectations?” Science, no. 355 (2017): 47476.
https://doi.org/10.1126/science.aal4483.
10. Conte, Alessandra, and Silvia Migali. “The Role of Conflict and Organized Violence
in International Forced Migration.” Source: Demographic Research 41: 393424.
Accessed February 27, 2020. https://doi.org/10.4054/DemRes.2019.41.14.
11. Czaika, Mathias, and Mogens Hobolth. “Do Restrictive Asylum and Visa Policies
Increase Irregular Migration into Europe?” European Union Politics 17, no. 3 (2016):
34565. https://doi.org/10.1177/1465116516633299.
12. Davenport, Christina, Will Moore, and Steven Poe. “Domestic Threats and Forced
Migration, 1964-1989.” International Interactions 29, no. 1 (2003): 2755.
https://doi.org/10.1080/03050620304597.
21
13. De Meur, Giséle, Benoît Rihoux, and Charles C. Ragin. “Qualitative Comparative
Analysis (QCA) as an Approach”. In Configurational Comparative Methods:
Qualitative Comparative Analysis (QCA) and Related Techniques, edited by Benoît
Rihoux and Charles C. Ragin, 1-18. Thousand Oaks: Sage, 2009. ISBN
9781412942355
14. Durance, Philippe, and Michel Godet. “Scenario Building: Uses and Abuses.”
Technological Forecasting and Social Change, 2010.
https://doi.org/10.1016/j.techfore.2010.06.007.
15. Gross, Eva, and Ana E. Juncos. 2014. Eu Conflict Prevention And Crisis
Management: Roles, Institutions, And Policies. London: Routledge. ISBN
9781138829893.
16. Hatton, Timothy J. “The Rise and Fall of Asylum: What Happened and Why?”
Source: The Economic Journal. Vol. 119, 2009.
17. Hegre, H., Karlsen, J., Nygård, H. M., Strand, H., & Urdal, H. 2013. Predicting Armed
Conflict, 201020501. International Studies Quarterly, 57(2), 250270.
https://doi.org/10.1111/isqu.12007
18. Herbert Wulf and Tobias Debiel. 2009. Conflict Early Warning and Response
Mechanisms. A Comparative Study of the AU, ECOWAS, IGAD, ASEAN/ARF and
PIF. no. Crisis States Working Papers Series No.2.
19. Huss, William R. “A Move toward Scenario Analysis.” International Journal of
Forecasting, 1988. https://doi.org/10.1016/0169-2070(88)90105-7.
20. Huss, William R., and Edward J. Honton. “Scenario Planning-What Style Should You
Use?” Long Range Planning, 1987. https://doi.org/10.1016/0024-6301(87)90152-X.
21. Kalous, M. How (Not) to Predict the Future? Analysis of several pioneering studies in
the field of Czech political and security scenario-building. Obrana a Strategie.
18(1):131 - 146. doi:10.3849/1802-7199.18.2018.01.131-146.
22. Koser, Khalid. “Irregular Migration, State Security and Human Security A Paper
Prepared for the Policy Analysis and Research Programme of the Global Commission
on International Migration and Does Not Represent the Views of the Global
Commission on International Migration,” 2005.
23. ———. “When Is Migration a Security Issue?” Brookings, 2011.
https://www.brookings.edu/opinions/when-is-migration-a-security-issue/.
24. Metelev, Sergei. “Migration as a Threat to National Security.” Indian Journal of
Science and Technology 9, no. 14 (2016).
https://doi.org/10.17485/ijst/2016/v9i14/91086.
25. Martelli, Antonio. 2014. Models Of Scenario Building And Planning: Facing
Uncertainty And Complexity. New York: Palgrave.ISBN 978-1-137-29349-7.
26. Moore, Will H, and Stephen M Shellman. “Whither Will They Go? A Global Study of
Refugees’ Destinations, 1965 - 1995.” Vol. 51, 2007.
27. Neukirch, Claus. “Early Warning and Early Action Current Developments in OSCE
Conflict Prevention Activities,” 2013.
28. Rohwerder, B. 2015. Conflict Early Warning and Early Response. Governance Social
Development Humanitarian Conflict Helpdesk Research Report, 13.
29. Schneider, Carsten Q, and Claudius Wagemann. 2012. Set-Theoretic Methods For The
Social Sciences: A Guide To Qualitative Comparative Analysis. [1st ed.]. Strategies
For Social Inquiry. Cambridge: Cambridge University Press.ISBN 9781139004244.
22
30. Schwenker, Burkhard, and Torsten Wulf. Scenario-Based Strategic Planning :
Developing Strategies in an Uncertain World. Munich: Springer Gabler, 2013. ISBN
978-3-658-02874-9
31. Taleb, Nassim Nicholas. 2008. The black swan: the impact of the highly improbable.
2nd edition. London: Penguin. ISBN 9780141034591.
32. Wright, George, Ron Bradfield, and George Cairns. “Does the Intuitive Logics
Method and Its Recent Enhancements Produce ‘Effective’ Scenarios?
Technological Forecasting and Social Change, 2013.
https://doi.org/10.1016/j.techfore.2012.09.003.
33. Wulf, H., & Debiel, T. 2010. Systemic disconnects: Why regional organizations fail to
use early warning and response mechanisms. Global Governance, 16(4), 525547.
34. Zyck, Steven A., and Robert Muggah. “Preventive Diplomacy and Conflict
Prevention: Obstacles and Opportunities.” Stability 1, no. 1 (September 25, 2012): 68
75. https://doi.org/10.5334/sta.ac.
35. Zartman, I. William. 2015. Preventing deadly conflict. Malden, MA: Polity Press.
ISBN 978-0745686929.
ResearchGate has not been able to resolve any citations for this publication.
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