Uncertainty Analysis in Population-Based Dynamic
Microsimulation Models: A Review of Literature
Miia Rissanen
1,2
and Jyrki Savolainen
1,3
1
Lappeenranta-Lahti University of Technology, Yliopistonkatu 34, Lappeenranta, Finland
2
Keva, Unioninkatu 43, Helsinki, Finland
3
CSC – IT Center for Science, Tehdaskatu 15, Kajaani, Finland
Keywords: Dynamic Microsimulation, Population-Based Modeling, Uncertainty Analysis, Monte Carlo Simulation.
Abstract: This paper reviews population-based dynamic microsimulation (DMs) models used in policy analysis and
decision support of social systems and demographics. The application of uncertainty analysis (UA) methods
is examined focusing on how probabilistic Monte Carlo (MC) simulation technique is being used and reported.
Secondly, inspired by the expanding possibilities of data, this analysis examines the models' capability to
uncover finer temporal variations beyond traditional yearly intervals and the use of near real-time data in the
reported studies. The analysis of the 44 studies included in this preliminary literature review reveals a lack in
the rigorous application of UA and transparent communication of results, particularly in the social sciences.
Despite the advances of data availability and modeling, no research attempts were found that would indicate
a shift of paradigm from historical data-driven models to real-time data. It is suggested that DM studies in
this context could benefit from some mutually agreed standardized reporting guidelines for UA. This literature
review serves as a preliminary exploration of the topic, highlighting the need for a more comprehensive and
systematic survey to thoroughly assess the current state of research.
1 INTRODUCTION
Dynamic microsimulation (DM) models are
analytical tools to simulate the behavior of individual
units over time and predict recurring events based on
historical data. These models integrate data analysis,
computational methods, and computer experiments to
support ex-ante policy analysis, government planning
and decision making. (Brown & Harding, 2002;
Harding, 2007; O’Donoghue, 2014; O’Donoghue &
Sologon, 2023; Sauerbier, 2002; Spielauer and
Duplirez, 2019). Throughout the simulation, each
micro-unit, representing diverse population
characteristics (e.g., age, employment, health status),
evolves independently through stochastic processes,
with their states updated over time according to
current conditions and attributes—a phenomenon
referred to as "dynamic aging" (see e.g., Burgard et
al., 2020; Dekkers, 2015).
Many popular DMs (see in detail e.g., Harding,
2007; O’Donoghue, 2001) were initially developed to
address concerns about population aging and to assess
affordability of the future social protection system.
Over the last decade, their applications in health and
labour market studies have been growing
(O’Donoghue & Dekkers, 2018). Unlike population-
aggregating macroscopic approaches, DMs consider
individuals separately, which is crucial for
understanding the complex interconnections between
factors such as demographics, education,
employment, and health that influence future
economic and health outcomes. For a general
introduction to DMs and their applications, the reader
is advised to refer to, e.g., O’Donoghue (2001, 2014),
O’Donoghue and Dekkers (2018), Klevmarken
(2008), and Zaidi and Rake (2001).
Times of uncertainty, such as the Ukraine war,
COVID-19 and past financial crises, have created
new demands for real-time simulation and
"nowcasting" (O'Donoghue & Sologon, 2023; see
also Navicke et al., 2014) to facilitate timely decision-
making in rapidly evolving economic landscape.
Digital trace data from web browsing and mobile
applications provide unprecedented regional and
temporal data granularity, enabling close-to-real time
modeling of social phenomena, such as predicting
disease spread (Burgard et al., 2021; Kashyap &
Zagheni, 2023; Li et al., 2024; O'Donoghue &
74
Rissanen, M. and Savolainen, J.
Uncertainty Analysis in Population-Based Dynamic Microsimulation Models: A Review of Literature.
DOI: 10.5220/0012995200003838
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024) - Volume 3: KMIS, pages 74-84
ISBN: 978-989-758-716-0; ISSN: 2184-3228
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

Dekkers, 2018). With more real-time data, simulation
models could better capture short-term fluctuations
instead of producing predictions only on an annual
level, thus hiding seasonal variations and timely
insights, e.g., related to healthcare demands or labour
force participation. However, it seems common that
administrative data used in many popular DMs
targeted to public policy analysis (see again e.g.,
Harding, 2007) typically has a time lag (O'Donoghue
& Sologon, 2023), even if such data is generated
constantly as by-products of administrative
transactions.
Despite the data revolution enhancing simulation
capabilities (Crato, 2023; Margetts & Dorobatu,
2023; O’Donoghue & Sologon, 2023), the proper
accounting of modeling uncertainty in DMs remains
challenging. To address the inherent stochasticity
when simulating individual behaviour and
demographic and economic changes is complex,
particularly given the (too) high expectations for
perfect modeling accuracy (Burgard and Schmaus,
2019; Gilbert et al., 2018; O’Donoghue, 2014;
O'Donoghue & Dekkers, 2018; Sharif et al., 2012,
2017). In modeling studies, this often shifts the focus
from probabilistic thinking back to traditional,
deterministic scenario analysis with single-point
estimates, although it is well-known (see e.g.,
Burgard and Schmaus, 2019; Sharif et al., 2012) that
for DMs to be useful, they must thoroughly analyze
potential impacts on populations under various
scenarios. It's crucial to examine not just the
outcomes but also the processes leading to them,
incorporating comprehensive uncertainty analysis
(UA) of various sources of variation. The authors
discussing uncertainty and stochasticity in
demographic modeling include Alho and Lassila
(2023), Xue et al. (2021), Sabelhaus and Topoleski
(2007), and Lee and Tuljapurkar (1994).
Monte Carlo (MC) simulation is a key method for
handling uncertainty in DMs, offering a robust
approach to systematically explore how variations in
inputs affect model outputs. This numerical method
involves random sampling from distributions and
repeated simulations using the sampled values. The
Markov Chain Monte Carlo (MCMC) method, in
turn, draws mutually dependent samples to generate
random sequences of state transitions based on
probabilistic rules (e.g., from logit models build on
historical data). This process is repeated hundreds or
thousands of times to simulate the expected behavior
of the object of interest over time, with calibration
performed at each step using newly generated
parameters. As such, the MC simulation mitigates
misinterpretations from single simulations by
examining a broad spectrum of possible outcomes,
thereby capturing the inherent variability in simulated
population dynamics (Burgard and Schmaus, 2019;
Marois & Aktas, 2021; Rutter et al., 2011).
Confidence intervals (CIs) communicate variability
in outcomes, with larger sample sizes and higher
number of simulation iterations leading to narrower
CIs and more precise estimates (Burgard et al., 2020;
Smithson, 2003; Spielauer & Dupriez, 2019).
Previous literature reviews and surveys on DMs,
such as O’Donoghue (2014), Li and O’Donoghue
(2013), and O’Donoghue and Dekkers (2018),
provide a comprehensive overview of DMs
developed over decades (see also Spielauer, 2007;
Zaidi & Rake, 2001). In past reviews, the lack of
standardization in reporting practices and incomplete
validation of models stay as ongoing topic (see also
Burgard & Schmaus, 2019; Lee et al., 2024).
However, past studies have not delved deeper into the
use of probabilistic methods, specifically MC
approach and related reporting in demography DM
studies, although best practices of UA have been
proposed by e.g., Burgard and Schmaus (2019), Lee
et al. (2024) and Caro (2012). Another gap pertains to
the scarcity of literature examining whether enhanced
data accessibility in terms of granularity and
timeliness have spurred advancements in models
capable of delivering more accurate and timely
forecasts, compared to “traditional” DMs those run
simulations in yearly intervals and are initialized
using historical data with a time lag of several years
(see O'Donoghue & Sologon 2023).
This paper addresses these mentioned gaps by
conducting a preliminary literature review using the
Scopus Database, targeting publications from 2000
onwards with “Dynamic Microsimulation” and
“Population” or “Demography” in the title, abstract,
or keywords. The search was limited to peer-
reviewed journals, conference proceedings, books,
and reviews in English, yielding 158 results. After
content analysis, based on the title and abstract, 44
documents focused on dynamic microsimulation
modeling works targeted mainly to model
demographic dynamics were selected. In this initial
review, the focus is on addressing aspects of
uncertainty analysis rather than technical details.
Thus, technical/model introduction reports about the
model construction (e.g., Andreassen et al., 2020;
Münnich et al., 2021.) were not reviewed since these
do not focus primarily on conducting simulations, but
rather introduce e.g., the modules and data
requirements. Also, as the focus is primarily on DMs,
some publications utilizing combined micro-macro
Uncertainty Analysis in Population-Based Dynamic Microsimulation Models: A Review of Literature
75

simulations are not included and, duplicates were also
excluded.
In the following section, preliminary findings of
the review and discussion with the objective to
inspect the scale and scope of the MC applications are
provided with aggregated knowledge on the
conventions such as number of simulations run and
the use of CIs. Additionally, the modelers' decisions
regarding the number of simulations in MC, and other
possible discussion of uncertainty aspects together its
mitigation methods are emphasized. Secondly, the
review reveals the time span of the forecasts (e.g.,
annual) and the possible specification of being spatial
or agent-based model (ABM). These reflects (from
one perspective) to the data aspects in terms of
timeliness and granularity. The paper also identifies
studies that aim to utilize near real-time information
or continuously updating models. Considering future
research, other findings related to emerging
technologies, mainly ML-oriented works, are
acknowledged although this research mainly omits
the technical details about the models.
The paper concludes with suggestions for future
research. Conclusions are drawn from available
publication details, and while the literature review is
not comprehensive, it lays the groundwork for a more
in-depth study on these schemes.
2 RESULTS AND DISCUSSION
In the following results section and related
discussion, the reader may find it helpful to refer to
Table 1, which presents basic information of the
modeling works (author, year), the brief summary of
main modeling purpose and the findings related to the
MC simulation and data aspects, as detailed in the
previous section. We do not specify whether the MC
is used only in some model parts. Also, if the use of
MC method is not reported, but repeated simulations
are applied, it is categorized under the MC. If other
methods are clearly reported, such as bootstrapping,
they are marked.
2.1 Results
In most of the reviewed studies (30 out of 44)
MC/repeated simulations is applied (see, Table 1). In
the set of these 30 studies reporting practices vary:
seven works did not directly report on using the MC
method, but it was shown that the simulation had been
indeed run repeatedly. In 13 entries the number of
simulations run was not reported and notably, 16
studies (out of 30) did not report CIs. Yet only two
Table 1: Reviewed studies of DMs. Legend: [MC]=Monte
Carlo method used (Yes/No or “-” if unclear and additional
NR=not reported, if repeated simulation applied without
reporting the method or “B” if bootstrapping is applied
instead of MC), [Simrun]=number of simulations run
(NR=not reported and “-” if MC not applied),
[CI]=confidence intervals used (if MC used, otherwise “-”)
[Simstep]=Forecast period (A=annual, M=monthly,
D=daily) + Detail (spatial (S)/ agent-based (AB). *In
progress = not yet available since study ongoing but
reported to be applied.
Auth. &
Year
Study purpose
MC/Simrun/
CI/Simstep +
Detail
Aransiola
et al. 2024
To assess if expanding Social
Assistance could reduce infant and
child mortality in Brazil.
Yes/10000/Y
es/A
Archer et
al. 2021
To project the prevalence of chronic
diseases and their economic impacts
using the Future Elderly Model (FEM)
in the UK.
Yes/100/Yes/
A
Atella et
al. 2021
To project future individual health
status across OECD countries by
applying several FEM models.
Yes/NR/Yes/
A
Baldini et
al. 2008
To assess the characteristics of the
long-term disabled in Italy and the
evolution of public expenditure for
long-term care.
Yes/NR/No/
A
Ballas et
al. 2005
To simulate the basic components of
population change in Ireland using
spatial SMILE model.
Yes/NR/No/
A + S
Ballas et
al. 2005
To simulate urban and regional
populations in UK.
No/-/-/A + S
Becker et
al. 2024
To assess the efficiency of COVID-19
mitigation strategies with the
CEACOV model in U.S.
-/-/-/D
Bonin et
al. 2015
To model monetary value of family
policy measures with ZEW model in
Germany.
No/-/-/A
Böheim et
al. 2023
To model the impact of health and
education on labor force participation
in US and Germany.
Yes/12/No/A
Brouwers
et al. 2016
To study the effects of an ageing
population on inpatient and elderly
care with SESIM-LEV model in
Sweden.
No/-/-/A
Chen et al.
2019
To model fiscal sustainability of
healthcare by projecting the health of
future elders using FEM model for
Singapore.
No/-/-/A
Craig et al.
2022
To simulate the long-term health
impacts in UK.
Yes/10000/Y
es, In
progress*/A
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Table 1: Reviewed studies of DMs. Legend: [MC]=Monte
Carlo method used (Yes/No or “-” if unclear and additional
NR=not reported, if repeated simulation applied without
reporting the method or “B” if bootstrapping is applied
instead of MC), [Simrun]=number of simulations run
(NR=not reported and “-” if MC not applied),
[CI]=confidence intervals used (if MC used, otherwise “-”)
[Simstep]=Forecast period (A=annual, M=monthly,
D=daily) + Detail (spatial (S)/ agent-
b
ased (AB). *In
p
rogress = not yet available since study ongoing but
reported to be applied. (cont.).
Ernst et al.
2023
To analyse migration impacts on
demographics in Germany.
Yes/NR/No/
A + S
Flannery
&
O'Donogh
ue 2011
To study the fiscal and redistributive
impacts of different higher education
finance structures using the LIAM
model in Ireland.
No/-/-/A
Fukawa
2011
To project health/long-term care
expenditures with the INAHSIM-II
model in Japan.
Yes/NR/No/
A
Head et al.
2024
To model time individuals spent in
different health states in UK.
Yes/100/Yes/
A
Horvath et
al. 2023
To project healthcare costs over the
lifecycle using microWELT model in
Austria.
No/-/-/A
Ben
Jelloul et
al. 2023
To forecast morbidity of population
aged +60 and identify causing factors
in France.
No/-/-/A
Jiang & Li
2024
To project the population size and
share of late middle-aged/older people
with difficulties/dependence on
activities of daily living (DL) and
instrumental activities of DL with the
CHARISMA model in China.
Yes,
NR/1000/No/
A
Keegan
2021
To simulate the distributional impact o
f
pension policy scenarios on
superannuation savings using the
APPSIM model in Australia.
No/-/-/A
Khalil et
al. 2024
To predict demographic dynamics in
Canada with STELARS model.
No/-/-/A +
AB
Kingston
et al. 2018
To predict the survival and
(risk/disease) characteristics and
related health expectancies in UK
using PACSim model.
Yes, NR/10/-
/A
Kirn
&Dekkers
2023
To simulate with MIDAS_CH model
the distribution of pension income and
its underlying processes in
Switzerland.
No/-/-/A
Knoef et
al. 2013
To analyse the income distribution of
the Dutch elderly.
Yes/NR/No/
A
Kopasker
et al. 2024
To project changes in psychological
distress given predicted economic
outcomes. from a tax-benefit UKMOD
model with SimPaths model in UK.
Yes,
NR/1000/Yes
/A
Lawson
2016
To model how demographic change is
likely to affect household spending
patterns in the UK.
Yes/5/Yes/A
Li et al.
2024
To model the spread of COVID-1 in
China.
Yes,
NR/10/Yes/D
+ S, AB
Maitino et
al. 2020
To study the future socio-demographic
structure and the effects of social
security programmes in Italy.
Yes/NR/No/
A + S
Marois &
Aktas
2021
To project the health of cohorts for
selected EU countries to study the
effects of risk factors and education on
future health trajectories using
ATHLOS-Mic model.
Yes/NR/No/
A
May et al.
2022
To project the health and service use
among elderly in Ireland using TILDA
model.
Yes/25/No/A
Milne et
al. 2016
To model child development from birth
to age 13 with MELC model and
studying e.g., changes in family
circumstances and early education in
New Zealand.
Yes,
NR/10/Yes/A
Nadeau et
al. 2013
To model physical activity to inform
population health policies using
POHEM-PA model in Canada.
Yes,
B/40/Yes/An
nual
Patxot et
al. 2018
To model the impact of retirement
decision and demographics on pension
sustainability in Spain.
-/-/-/A
Rasella et
al. 2021
To analyse the prospective effects of
fiscal policies on childhood health in
the EU countries and in Italy.
Yes/1000/Ye
s/A
Rephann
& Holm
2004
To model economic-demographic
effects of immigration in Sweden using
the SVERIGE model.
Yes/NR/No/
A + S
Spielauer
& Dupriez
2019
To model a variety of demographic and
health characteristics with DYNAMIS-
POP model in Canada.
Yes/NR/No/
A
Spooner et
al. 2021
To model epidemics with spatial
SPENSER model in UK.
Yes,
NR/1000/Yes
/D + S
Tamborini
et al. 2022
To analyze socioeconomic gaps in
retirement benefits using the MINT
model in U.S.
No/-/-/M, A
Tikanmäki
et al. 2015
To analyse impacts of the pension
reform on working lives using ELSI
model in Finland.
Yes/NR/No/
A
van
Sonsbeek
& Gradus
2005
To simulate the budgetary impact of
the 2006 regime change in the Dutch
disability scheme.
Yes/NR/No/
A
Walker
2004
To model the likelihood that more
Australians aged 65-70 will work +15
hours per week in a changing
employment environment.
Yes/NR/No/
A
Wu et al.
2011
To model several demographic
processes under various scenarios with
a Moses in UK.
Yes/NR/No/
A + S, AB
Zhang &
Miller
2024
To predict the location of new housing
supply in U.S.
No/-/-/- + S,
AB
Zhang et
al. 2023
To model the processes of developing
depression and care-seeking behaviors
among U.S children and adolescents.
Yes,
NR/20/Yes/
M
Uncertainty Analysis in Population-Based Dynamic Microsimulation Models: A Review of Literature
77

studies reported using 10 000 and four studies 1000
simulation runs. In the remainder, the number of
simulations vary from five to forty.
Additionally, there is a notable variability in depth
across publications about the discussion of the
sources and mitigation of uncertainty. Many studies
indirectly or directly, but briefly, address uncertainty
when discussing issues like data availability and
sample size (see e.g., Becker et al., 2024; Kirn and
Dekkers, 2023; Rephann & Holm, 2004) or mention
it broadly as parameter/statistical/MC uncertainty.
Also, authors commonly discuss of "model error" or
“model-based bias”, which intersects with the
uncertainty concept (see e.g., Atella et al., 2021; Jiang
et al., 2021; Knoef et al., 2013; Kopasker et al., 2024;
Lawson, 2016; Marois & Aktas, 2021; Spielauer &
Dupriez, 2019).
Focusing on data aspects in terms of data
timeliness, only three pandemic-related models and
two exceptions from other disciplines seems to offer
sub-annual observation periods in models, reaching
daily or monthly level accuracy in simulation results.
To mention, in their multi-morbidity modeling study,
although simulation results are presented in yearly
interval, Kingston et al. (2018) updated individual’s
characteristics monthly over the simulation time
period “to achieve a more realistic evolution for
characteristics which jointly influence each other”,
similarly than Böheim et al. (2023) regarding labour
force status.
Also, to the best of our understanding, only
epidemiology models by Becker et al. (2024) and
Spooner et al. (2024) target to produce forecasts using
near-real-time data with updates. In rest of the
models, it seems common to use administrative
statistics with a time lag of at least 2-3 years in model
initialization.
Lastly, nine studies reviewed are by their nature
spatial, including epidemiology studies. Four studies
combined the ABM method with DMs, three of them
being also spatial (see again Table 1).
2.2 Results Analysis
2.2.1 Uncertainty Analysis
The literature covered indicates varying practices in
the application of the MC method and related
reporting, highlighting a need for common standards
and/or strategies to improve the transparency and
comparability of demographic models in the research
field. This will not only improve the accuracy of
individual studies but also facilitate more robust
analyses and comparisons across different research
efforts in demographic modeling. We justify this
claim by the often inadequate depth of the discussion
(and missing information) of MC related details, such
as the number of simulation rounds (and reasons that
led to the number) and lack of CIs. The lack in
reporting CIs aligns also with Smithson (2003), who
noticed that different disciplines vary considerably
how frequently they report CIs in published research
(see also Lappo, 2015; O’Donoghue 2014, 332;
O’Donoghue & Dekkers, 2018). Kingston et al.
(2018) notes the lack of CIs as one of their study
limitations, although the authors also highlight that
running the simulation iteratively reveals a small
range of prevalence for multi-morbidity (less than 1
%), even when the error in transition rates is
disregarded. Knoef et al. (2013) reported not using
CIs due the “computational reasons”. Lappo (2015),
however, states that the omission of reporting CIs
may be since many microsimulation users are not
statisticians, perhaps so be in the case of social
sciences. However, this indicates a prevalent lack of
established practices in employing methods to convey
information on result variability across study
disciplines (see e.g., Li & O’Donoghue, 2013).
To further explore practices related to the MC
method, some authors provide the basis (or tests
made) for selecting the number of simulations such as
Rasella et al. (2021), who state that a thousand
simulation runs was chosen after ensuring that the
estimates were stable and additional runs did not alter
the point estimates (see also Van Sonsbeek & Gradus,
2005). Spielauer and Dupriez (2019) claimed that 24
iterations make MC variation neglectable, whereas
Aransiola et al. (2024) performed 10k rounds to
ensure the variation of the parameter values. Overall,
the selection of the number of MC simulation runs has
received only limited attention even though it is a
crucial factor for generating meaningful predictions
(see e.g., Byrne 2013; Kennedy 2019, Kennedy et al.
2000). There is a position to analyze more
comprehensively the specific factors contributing to
the large variation in the number of simulations,
especially within studies investigating the same
phenomena and “sharing” the same uncertainty
elements. Overall, we can concur with O’Donoghue
and Dekkers (2018) who noted that alignment
techniques (not a focus of this study) are so common
in DMs that most reports do not even mention them,
despite their significant impact on simulation results.
This oversight is similar to the treatment of the MC
method (see also Byrne, 2013; Lorscheid et al., 2012;
Kennedy, 2019).
When analyzing the overall use of the MC
approach, studies reveal differing perspectives on the
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objectives of modeling: some prioritize analyzing
current systems without accounting for variations or
forecasting goals, thus considering repeated
simulations unnecessary (see e.g., Ben Jelloul et al.,
2023; Flannery & O'Donoghue, 2011). In contrast,
the majority (30 out of 44) employ the probabilistic
method to understand system functionality under
uncertainty. Studies focusing on individual behavior
and future trends through predefined scenarios and
single-point estimates may fail to capture the full
spectrum of potential outcomes or convey the
inherent uncertainty of modeled phenomena. Such
approaches might overlook rare yet impactful events,
whereas the MC accounts for these events and their
potential consequences (see Fuchs et al., 2018;
Gilbert et al., 2018; Marois & Aktas, 2021;
O’Donoghue, 2014; Rutter et al., 2011).
2.2.2 Data Granularity and Timeliness
Considering data aspects, the shortcomings of the
models running yearly intervals have been
recognized.
Salonen et al. (2021) highlight challenges
in capturing gradual changes such as increase in
pension age or short social security spells with a
model allowing transitions in one year time intervals.
For instance, based on data, the average duration of
sickness and unemployment spells is one week,
although these periods accumulate over an
individual's life course (see also Perhoniemi et al.,
2023; Zaidi and Rake, 2001). Although Kingston et
al. (2018) provides forecasts in yearly intervals, they
enhanced the accuracy of their simulation results by
updating health behaviors and disease conditions on
a monthly basis. Chen et al. (2019) acknowledges the
limitation of not modeling shorter disease dynamics
similarly than Andreassen et al. (2020), who suggest
that with improved data access and today’s
computing power, monthly time units could be
preferable in the MOSART model (renowned for
evaluating the Norwegian pension system) to avoid
aggregating data annually and potentially
overlooking nuances.
To continue, in an ideal world, employing close-
to-real time data for model calibration would reduce
the risk of obsolete information affecting transition
probabilities – an issue that is especially important
when addressing rapidly evolving matters, such as
changes in labour market status during economic
crisis (see e.g., O'Donoghue & Sologon, 2023). To
the best of understanding, no research efforts in this
direction were found in this review except
epidemiology models. The findings align also with
O'Donoghue and Loughrey (2014), who observed
that microsimulation models tend to be built on
historical data (see also Klevmarken, 2008), limiting
researchers' ability to analyze and monitor recent
changes and developments.
However, it's important to note that not many
phenomena require the daily/monthly forecast
accuracy and frequent data calibration typical in
pandemic research. Instead, “traditional” social
policy models could aim to reduce the delay between
data collection and utilization, moving from a lag of
several years to using more recent statistics. This shift
would better reflect contemporary issues, such as the
interconnections between labour force participation
and health status (see O’Donoghue & Sologon, 2023).
Admittedly, increased granularity and the use of
more timely data to update transition probabilities
(together with MC method) add to model complexity
in regards of model calibration and computational
demands. Nevertheless, many renowned models in
the field already require substantial computing power
and resources for maintenance due to their high
modularity. Today’s technological capabilities, such
as cloud computing and big data analytics can help
overcoming this issue (see Andreassen et al., 2020;
O'Donoghue & Dekkers, 2018; Richiardi et al.,
2023).
Looking forward, there may be a trend towards
simpler models that allow for agile calibration with
detailed, current data, albeit sacrificing some
modularity (Harding, 2007; Li & O’Donoghue, 2013;
Zaidi & Rake, 2001). For instance, localized
projections (with ABM approach) are vital for
addressing regional disparities and tailoring policies
to specific areas. They enhance the relevance of
simulations and allow for more detailed evaluations
of policy impacts (see Ballas et al., 2005; Birkin et al.,
2017; Ernst et al., 2023; Wu & Birkin, 2011). Agile
calibrated models providing timely forecasts could
potentially be recognized also at the tactical decision-
making level.
2.2.3 Other Findings
Machine learning (ML) techniques, in addition to
being utilized in model calibration tasks, can aid in
addressing complexity arising from models’ non-
linearities, a topic of ongoing discussion (see e.g.,
Jiang & Li, 2024; Klevmarken, 2008; Kopasker et
al., 2024; O’Donoghue & Dekkers, 2018; Wolfson &
Rowe, 2014). The integration of ML could enable the
development of more dynamic and predictive models,
which could better address complex societal
challenges and facilitate faster decision-making.
These methods could uncover unobserved, detailed
Uncertainty Analysis in Population-Based Dynamic Microsimulation Models: A Review of Literature
79

behavioural patterns among individuals thus
improving simulation granularity and supporting e.g.,
ABM constructions (see discussion of Margetts &
Dorobatu, 2023). That is, model structures where
individual model components interact with each other
instead of being passive and detached (see e.g., Axtell
2000). Although there are few applications within the
reviewed works, Khalil et al. (2024) provide an
innovative application of explainable artificial
intelligence (xAI) with the aim to interpret ML
models, elucidating input-output relationships in
complex settings. This study can be regarded as a
pioneering effort in integrating ML within DM
schemes in the research domain. Other studies like
Rodriguez et al. (2022) in healthcare and other ML-
assisted models (see e.g., Shi et al., 2015) offer also
insights into applying advanced methods, potentially
inspiring social science research.
3 CONCLUSION
This paper presented a literature analysis on the use
of probabilistic methods such as Monte Carlo
simulation in dynamic microsimulation models and
related reporting practices of probabilistic outcomes.
This study, to the best of our knowledge, is the first
review that addresses the use of such methods and
related challenges in reporting the analysis findings in
the given context.
It was shown that the current literature often lacks
a statistical treatment of the model and if given, there
are no standard practices on how a (MC) simulation
is conducted and presented. As another important
finding, we did not find evidence that attempts were
made to develop DMs towards nowcasting with the
help of extensive real-time datasets in other study
contexts than epidemiology.
The results imply that population-based modeling
studies, a predominant focus of the review conducted,
could adopt probabilistic thinking to address the
inherent uncertainty associated with complex socio-
economic processes to make the modeling results
more robust and reliable. Common guidelines for UA
application and related communication/reporting
practices could enhance the transparency of modeling
insights as the vulnerability of results would become
better communicated to policymakers and less weight
could be put on single-point estimates. Also,
transition probabilities calculated sub-annual periods
can lead to more accurate simulations by
incorporating finer temporal variations, e.g., monthly
updates can capture short-term trends or immediate
impacts of policy changes that yearly intervals might
miss. In this regard, it was concluded that the research
field could benefit from the development and
application of smaller, more targeted models that
could offer greater agility in terms of maintenance,
particularly in incorporating updated data.
This paper has limitations, notably not being a
fully comprehensive systematic review. Nonetheless,
it provides some preliminary directions for future
research efforts to improve probabilistic treatment of
DMs in the context of demographic models.
Additionally, future research could assess the role of
emerging technologies, such as cloud computing,
machine learning techniques, and big data analytics.
The final limitation of this research to be pointed
out is the method used to assess data granularity,
categorizing models as agent-based or spatial. Future
evaluations could provide an extended analysis of
variables like demographic precision. A more
comprehensive review could delve deeper into
whether the nature of the phenomena being modeled
warrants more frequent updates to transition
probabilities. The review focused only on the MC
method with frequentist viewpoint, omitting
Bayesian methods or distinguishing bootstrapping
from MC approach. It also did not cover the
alignment techniques used together with the MC, or
other reporting practices such as goodness-of-fit or
standard error statistics.
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