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1. Introduction: Transitions and Transformations in a World of Interconnected Risks
The broader global community is navigating evolving climate risks, rapid energy transitions, and the growing
recognition that sustainable future pathways will require fundamental transformations in our collective manage-
ment of socio-environmental systems (de Vos etal.,2021; Elsawah etal.,2020; Levi etal.,2019; Levin etal.,2021;
Markolf etal.,2018; Mora etal.,2018; Pecl etal.,2017; Trutnevyte etal.,2019). As we navigate the opportunities
and challenges emerging from these issues, there is a need to reflect on our approach to human-Earth systems
science itself. Improving our understanding of how interdependent global-to-local challenges are shaping criti-
cal pathways of societal change is a scientific grand challenge (Aven & Zio,2021; Clarke etal.,2018; Dearing
etal.,2014; Helbing,2013; Moss etal.,2016; Raymond etal.,2020; Scanlon etal.,2017). Keeping pace with the
accelerating complexity of pathways of change requires a deep integration of diverse perspectives and technical
capabilities (Braunreiter etal.,2021; Filatova etal., 2016; Iwanaga etal.,2021; Moallemi & de Haan,2019;
Oikonomou etal.,2021; Trutnevyte etal.,2019). This commentary is an abbreviated vision that draws on a much
longer form report (Reed etal., 2022) that has been developed over the last several years through workshops,
conference sessions, and thematic scientific working groups. Readers interested in additional examples, a more
detailed review of current MultiSector Dynamics (MSD) research, and a longer form summary of our commu-
nity aspirations are encouraged to reference our full report. We put forth a vision for how new modes of inquiry
may yield valuable tools and insights for transforming our understanding of the benefits, risks, and resilience of
complex adaptive human-Earth systems. Given the inherent complexity of human-Earth systems, the plurality
of their candidate pathways of change, and their diverse sources of uncertainty, there is a need to rethink our
traditional disciplinary approaches to human-Earth systems science as well as the ways scientific knowledge is
Abstract The field of MultiSector Dynamics (MSD) explores the dynamics and co-evolutionary pathways
of human and Earth systems with a focus on critical goods, services, and amenities delivered to people through
interdependent sectors. This commentary lays out core definitions and concepts, identifies MSD science
questions in the context of the current state of knowledge, and describes ongoing activities to expand capacities
for open science, leverage revolutions in data and computing, and grow and diversify the MSD workforce.
Central to our vision is the ambition of advancing the next generation of complex adaptive human-Earth
systems science to better address interconnected risks, increase resilience, and improve sustainability. This will
require convergent research and the integration of ideas and methods from multiple disciplines. Understanding
the tradeoffs, synergies, and complexities that exist in coupled human-Earth systems is particularly important in
the context of energy transitions and increased future shocks.
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Multisector Dynamics: Advancing the Science of Complex
Adaptive Human-Earth Systems
Patrick M. Reed1 , Antonia Hadjimichael1,2,3 , Richard H. Moss4, Christa Brelsford5 ,
Casey D. Burleyson4 , Stuart Cohen6 , Ana Dyreson7 , David F. Gold1 ,
Rohini S. Gupta1, Klaus Keller8 , Megan Konar9 , Erwan Monier10,11 , Jennifer Morris12 ,
Vivek Srikrishnan13 , Nathalie Voisin4,14 , and Jim Yoon4
1School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA, 2Earth and Environmental Systems
Institute, The Pennsylvania State University, University Park, PA, USA, 3Department of Geosciences, The Pennsylvania State
University, University Park, PA, USA, 4Pacific Northwest National Laboratory, Richland, WA, USA, 5Oak Ridge National
Laboratory, Oak Ridge, TN, USA, 6National Renewable Energy Laboratory, Golden, CO, USA, 7Michigan Technological
University, Houghton, MI, USA, 8Thayer School of Engineering, Dartmouth College, Hanover, NH, USA, 9Department of
Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA, 10Department of Land,
Air and Water Resources, University of California Davis, Davis, CA, USA, 11Climate Adaptation Research Center, University
of California Davis, Davis, CA, USA, 12Massachusetts Institute of Technology, Cambridge, MA, USA, 13Department of
Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA, 14Department of Civil and Environmental
Engineering, University of Washington, Seattle, WA, USA
Key Points:
• Sustainability, climate change,
and energy transitions are highly
interdependent challenges
• MultiSector Dynamics (MSD) studies
these challenges through the lens
of complex, adaptive human-Earth
systems
• Confronting human-Earth systems
complexity requires a diverse,
transdisciplinary workforce and
community-level open science
Correspondence to:
P. M. Reed,
patrick.reed@cornell.edu
Citation:
Reed, P. M., Hadjimichael, A., Moss,
R. H., Brelsford, C., Burleyson, C. D.,
Cohen, S., etal. (2022). Multisector
dynamics: Advancing the science of
complex adaptive human-Earth systems.
Earth's Future, 10, e2021EF002621.
https://doi.org/10.1029/2021EF002621
Received 17 DEC 2021
Accepted 15 FEB 2022
10.1029/2021EF002621
Special Section:
Modeling MultiSector Dynam-
ics to Inform Adaptive Pathways
COMMENTARY
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produced (Bojórquez-Tapia etal.,2020; Council,2014; Funtowicz & Ravetz,1993; Lubchenco,1998; Nowotny
etal.,2013; Saltelli etal.,2020; Szostak,2017; Wyborn etal.,2019).
Understanding energy transitions and climate challenges requires holistic analyses that account for the complex
mix of human and natural systems they shape and are, in turn, shaped by (Levi etal.,2019). Extreme events,
both naturally occurring and those exacerbated by anthropogenic factors, such as heat waves, droughts, floods,
wildfires or storms, are compounding each other and increasing the potential for long-lived cascading societal
effects (Mora etal.,2018; Raymond etal.,2020). Consequently, we must carefully reconsider our tacit decompo-
sitions and assumptions in the way change itself is studied. Societal change pathways encompass global supply
chains; strained natural resources; infrastructure degradation and investment; growing and migrating population
with evolving vulnerabilities; intensifying natural hazards; technological innovation; changing human values
and preferences and their associated consumption patterns (e.g., dietary preferences). Human decision-making
and actions have important feedback effects that can alter global to local environmental changes and their conse-
quences (e.g., see Dolan etal.,2021; Hallegatte & Engle,2019; Levin etal.,2021; Schweikert & Deinert,2021).
There is a need for science innovations that can aid in exposing, navigating, and prioritizing risk-benefit tradeoffs
across possible multisectoral decisions.
Capturing and navigating the risk-benefit tradeoffs of multisectoral actions warrants a thoughtful reevaluation
of the basic tenets of risk assessment itself (Field etal.,2012; Reisinger etal.,2020; Shukla etal., 2019; Soci-
ety for Risk Analysis,2018). Extreme events and their propagation through complex human-Earth systems can
give rise to systemic failures and “hyper-risks” (Helbing,2013). Hyper-risks refer to threats that emerge across
complex interconnections and dependencies in systems that can give rise to compounding or cascading effects.
The dynamic relationships between agents, systems, and sectors transmit risk from one to another, leading to
new risks or amplifying (or buffering) existing threats (Rinaldi etal., 2001; Vespignani, 2010; Zscheischler
et al., 2018). Figure 1a illustrates a promising framework from Simpson etal. (2021) for the assessment of
complex risks that expands on the traditional definition of risk as emerging from the interaction of hazard, vulner-
ability, and exposure, by explicitly recognizing that human responses to hazards are also a key determinant of
risk. So as illustrated in Figure1a, their framework clarifies that a risk emerges from four primary determinants
(hazard, vulnerability, exposure, and response). Importantly, Simpson etal.(2021) also emphasize how risk can
emerge through interactions across its primary determinants or the determinants' underlying dynamic drivers.
In the MSD context, this framework can enable the qualitative tracing and quantitative assessment of risk as it
emerges from important interactions.
Figures 1b and 1c illustrate the conceptual mapping of risk as proposed by Simpson et al. (2021) using the
specific example of Winter Storm Uri and its risk to electricity supply and to basic electricity dependent services
(heat, food, and water) during the February 2021 Texas power outage. As a hazard, Winter Storm Uri has prece-
dence. The temperature extremes and energy demands during the event were less severe or equivalent to winter
storms in 1951, 1983, and 1989 (Doss-Gollin etal.,2021). But the cold snap in 2021 caused rolling blackouts in
Texas and highlighted systemic vulnerabilities in how the hazard manifested as a risk to utilities and people. In
Figures1b and1c, we distinguish between the risk to the supply of electricity (borne by the electric utilities), and
the risk to having basic energy dependent services for heating and access to water and food (borne by Texans).
This distinction between two kinds of risk resulting from the same events highlights two important considera-
tions. First, depending on the specific measure of risk used and the actors that bear it, the drivers identified as
most critical may differ and, perhaps more crucially, the actions available to respond to the presence of a risk
may be more or less relevant. Second, human responses are not only dominant drivers of potential outcomes, but
also of how risks can interact with each other to buffer or amplify impacts across actors, systems, and sectors. In
this particular example, the actions of electric utilities before and during the storm affected their ability to supply
electricity to people (e.g., poor system weatherization and inadequate resource criteria) and, in turn, the actions of
people (e.g., buying alternative fuels) shaped demand stress on electricity supply. The complex interplay between
diverse objectives and risks are clear (e.g., beyond those illustrated other objectives could include: reducing loss
of life, reliability of services, equity of impacts, minimizing financial volatility, etc.). Figure 1 emphasizes the
need for advances in our ability to model and understand interactions across multiple risks. This requires distin-
guishing if they are linearly aggregated (the accumulation of multiple independent risks), compounding (arising
from the interaction of coincident or sequential hazards), or cascading (causal feedback relationships between
multiple risks).
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Understanding systemic risk is inherently multisectoral and requires consideration of the interactions across
human-Earth systems, as traditional single-sector risk analyses are prone to underestimate both overall risk but
also multisectoral capacities to buffer it (Harrison etal.,2016; Lawrence et al., 2020; Raymond etal., 2020).
Figure2 demonstrates how the risks presented in Figures1b and1c relate to different types of systems (Earth and
environmental, infrastructure, socio-economic, and governance). The risks arising during Winter Storm Uri are
parts of a broader set of complex interactions between sectors and systems. For example, the risk to electricity
supply was driven by processes and actions in Earth systems (climate change affecting local weather and produc-
ing more extreme low temperatures, see Cohen etal.,2021), governance systems (choices around the weatheri-
zation of generation and transmission systems), infrastructure (failures in generation and transmission systems),
and socio-economic systems (people increasing their energy demands as a result of the lower temperatures).
These interactions extend beyond the two risks illustrated and in fact beyond the reduced set of relevant processes
Figure 1. Risk as proposed by Simpson etal.(2021) is a dynamic and emergent outcome of its determinants (hazard,
exposure, vulnerability, response) as well as their underlying drivers. (a) Generic illustration of the different potential
types of interactions across risk determinants and their drivers. (b) Winter Storm Uri illustration of the interactions that
generated risk to the provision of basic energy dependent services (heat, water, and food). (c) Winter Storm Uri illustration
of the interactions that generated risk to electricity supply. “Isolated grid” refers to the power grid managed by the Electric
Reliability Council of Texas, which operates separately from the Western and the Eastern Interconnections. By being isolated
the grid maintains independence from federal regulation.
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shown here, which focus on the state of Texas and a select number of sectors as an illustrative example. Mapping
and quantifying the extent and consequences of these complex interactions is a central challenge to MSD science.
Navigating these complex challenges requires fundamental advances to better understand the risks and the
tradeoffs across multiple sectors. This will enable us to identify pathway opportunities for equitable and sustain-
able futures in the face of changing weather patterns and extremes, major technological advances, f luctuations in
the supply and demand of natural resources and increased interactions between human-Earth systems. We need
diverse perspectives to incorporate the full depth and breadth of multisectoral systems and uncover opportunities
to address clean energy transitions, climate change risks, and sustainability. Embracing this challenge, our ambi-
tion and vision for MSD research are to work broadly and collaboratively across diverse research communities
to make fundamental advances in complex adaptive human-Earth systems modeling, as well as in the analytical
tools needed to accelerate our insights from it. The MSD Community of Practice (CoP) is focused on three scien-
tific strategies for realizing the above research aspirations:
1. Strengthening foundational research capabilities: Through a commitment to and growing capacity for open
science, we seek to accelerate our ability to explore diverse hypotheses by developing interoperable and reus-
able data, models, and analysis methods. Moreover, we want to grow and diversify the MSD workforce to
broaden the backgrounds, technical skills, expertise, and experiences available to advance our understanding
of societal risks
2. Advancing complex adaptive human-Earth systems science: MSD seeks to better understand human-Earth
systems by enhancing our ability to model major dynamic transitions, their dependencies and interactions
across multiple scales, sectors, and systems. The field is focused on exploring a rich array of dynamic and
adaptive behaviors, especially given the potentially compounding or cascading multisectoral effects of
extreme weather and other stressors
3. Providing scientific and decision-relevant insights under deep uncertainty: Through broadening the diver-
sity and availability of human-Earth systems models, MSD seeks to enhance the insights and relevance of
exploratory modeling studies for inferring consequential actions and outcomes for deeply uncertain societal
transitions or transformations. The term deep uncertainty as used here refers to a lack of consensus for MSD
Figure 2. Complex interactions between systems relating to the risk to the provision of basic energy services (heat, water,
and food) and the risk to electricity supply during the 2021 Winter Storm Uri. Process across Earth and environmental, socio-
economic, governance, and infrastructure systems interacted to shape these risks and other outcomes during the 2021 cold
snap.
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problem framings including represented Earth system processes, candidate human actions, as well as the
distributional likelihoods of key input factors (W. Walker etal.,2003)
In this commentary, Section2 provides a brief summary of the origins of the MSD CoP while laying out key defi-
nitions as well as their conceptual connections to the challenges posed in our need to better address interconnected
risks to improve resilience. Section3 initially summarizes recognized research gaps that need to be addressed
by the MSD community and then transitions to an aspirational vision for how to address them. The described
aspirations combine formal mechanisms for growing and diversifying the MSD CoP as well as technical oppor-
tunities for advancing complex adaptive human-Earth systems science. Finally, we conclude in Section4 with a
brief summary of the MSD CoP's planned efforts for formally teaming across US federal agencies and the broader
international research community.
2. MultiSector Dynamics: Origin, Definitions, Questions, and Connections
A key originating event that helped shape the emergence of the MSD CoP is the 2016 US Department of Energy
(DOE) sponsored and US Global Change Research Program (USGCRP) hosted workshop entitled “Understand-
ing Dynamics and Resilience in Complex Interdependent Systems: Prospects for a Multi-Model Framework and
Community of Practice” (USGCRP, Moss etal.,2016). From its origins, the MSD CoP has garnered broad partic-
ipation and interest across many federal agencies as well as leading academic institutions, national laboratories,
and other broader global research groups. The 2016 initiating workshop included representatives of 10 federal
agencies from various USGCRP interagency working groups and 10 universities, labs, and research/consulting
groups. The workshop set a foundation for the MSD CoP's emphasis on open science, advancing our understand-
ing of complex adaptive human-Earth systems, and promoting translational science breakthroughs. The MSD
CoP was formally established in 2019–2020 with DOE support to generate a vision for MSD as a global research
area, clarify key questions, establish and assist scientific working groups, shape a strategy for community devel-
opment, and foster synergies across interested research, government, and user communities.
A key charge for the MSD CoP is to provide a framework for formalizing the field's core terminology and high-
er-order science questions. Formally, we define MSD as:
Complex systems of systems that deliver services, amenities, and products to society. Examples of components of
sectors include infrastructure, governing institutions (public and private), labor force capacity, markets, natural
resources, ecosystem ser vices, supply and distribution networks, finance, and a wide range of actors (e.g., firms,
regulatory agencies, investors, consumers) involved in producing and creating demand for the services and prod-
ucts the sector provides.
Our definition of sectors focuses on the services and products that emerge from the interdependent dynamics of
the underlying systems-of-systems that shape resources, demands, and impacts from global to local scales. Thus,
the term “dynamics” in MSD refers to:
Pathways of change that result from geophysical, biophysical, economic, and socio-technical transitions and
shocks. The emergent complexity of these pathways is shaped by their interdependence-interconnectedness, irre-
versible lock-ins, contested perspectives, cross-scale influences, and effects, as well as the deep uncertainties that
shape their evolution.
Interactions across Earth, environmental, infrastructure, governance, and socio-economic systems shape the
emergent dynamics of change across sectors (Figure3a). Figure3a does not imply that all sectors or systems must
be modeled in every MSD study, it does however emphasize that our decompositions, problem framings, and the
boundaries of our numerical experiments should acknowledge the broader context of the interacting systems-of-
systems and sectors that are not being represented. As illustrated in Figure3a, infrastructure systems are related
to the production and operation of services. They comprise inputs, outputs, technical characteristics of production
systems, including core process operations and management, labor, and capital requirements. Earth and environ-
mental systems capture processes and cycles in the Earth's atmosphere, hydrosphere, cryosphere, lithosphere, and
biosphere. Governance systems include the institutions, national and international agreements, procedures, and
operations through which sectors are managed. Socio-economic systems include demographic processes, such
as population growth and migration, markets, culture, norms, and value systems. Infrastructure, governance, and
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socio-economic systems are central to the behavioral dynamics that emerge from societal action across scales
(from individual to collective). Such dynamics drive changes in consumption preferences, migration, and demo-
graphic patterns, as well as value systems (e.g., growing preference toward decarbonization). These systems-of-
systems and the complex influences they exert across scales are both central to our understanding of pathways of
change and transformation for technologies, infrastructure, and institutions (Andersen etal.,2020; MacKinnon
etal.,2019).
Figure 3. Key concepts for MultiSector Dynamics. (a) MultiSector Dynamics are shaped by co-evolving human and natural
influences that emerge across interactions across sectors and systems. The energy, water, and agricultural sectors are shown as
examples and other non-labeled sectors are shown in gray circles. (b) Adaptive cycles of growth and disruption of a complex
system adapted from (Holling,1985; Holling & Gunderson,2002). (c) Illustration of the relationships between the adaptive
cycle for a system and its key properties (resilience, connectedness, and capital). (d) Conceptual linkage between the risk and
resilience for complex human-Earth systems.
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Connectedness, capital, and resilience are system properties that shape the dynamics of its evolution (Figure3c).
Connectedness reflects the strength and number of interactions between a system's elements, and by extension
the degree of control that can be exerted on the system. As the system grows and accumulates more capital and
resources, connectedness increases and the system becomes more organized and aggregated. In an air transpor-
tation network for instance, connectedness can reflect the degree to which airline flights connect different cities.
The second system property, capital, can be thought of as system potential. It reflects the natural and human
resources, monetary assets, or other capacities that accumulate as the system develops and grows, with the shock
stage triggering a release of this capital. The last property, resilience, is often described as the capacity of a
system to absorb a shock and adapt to maintain essentially the same function, structure, identity, and component
interactions (B. Walker etal.,2004). Most importantly, these three properties of complex adaptive systems are
not static and do not monotonically increase or decrease. As the system evolves and moves through its growth
and disruption phases and through its interactions with other systems, connectedness, capital, and resilience ebb
and flow (Figure3c).
Figure 3d shows how these properties relate to the four determinants of risk and their drivers, presented in
Figure1a. The degree of system interactions (reflected by the connectedness property) can shape resilience to
hazards in both positive and negative ways: increased connectedness between drivers of vulnerability can result
in cascading effects (e.g., critical services all relying on each other for their operation); increased connectedness
in the response space may reflect more available options for flexible adaptation (e.g., readily dispatchable alter-
native sources of electricity or water). Similarly, the capital property may be a measure of more exposed assets
(well looking at determinants of exposure), but it can also mean increased capacity to divert said assets to other
management options. Resilience to hazards and stressors is therefore an emergent property of system interactions
and other properties. It comes about in how hazard drivers are amplified or buffered by drivers of exposure,
vulnerability, and response. Lack of system resilience to a specific hazard or stressor can trigger hazards to other
systems across scales and sectors (see Winter Storm Uri example Figures1b and1c). From a scientific and a
modeling perspective, the implications of acknowledging that human-Earth systems are complex, adaptive, and
have emergent dynamics changing their form and function poses a major challenge. There is a need to advance
how our models “endogenize” the interactive path dependencies of transitions/transformations, shocks, risks, and
differences in resilience (see similar recommendations in Markolf etal.(2018)).
MSD as envisioned here needs to be a diverse transdisciplinary field. However, to ensure that MSD does not
become the science of everything, a broad core set of research questions for the coming decade have emerged
through community interactions over the last several years. Figures4a and4b summarize core MSD research ques-
tions focusing on broader societal and methodological challenges, respectively. As a transdisciplinary endeavor,
the MSD research questions in Figure4 emphasize the need to diversify model-based human-Earth systems
problem framings across a broader array of perspectives, enabling detailed quantitative analyses of a broad suite
of societal objectives (e.g., equity, reliability, resilience, vulnerability, robustness, economic efficiency, financial
risk, stability, etc.). MSD has a distinguished central focus on developing the next generation of open-source
models and analytical tools, and theoretical insights that enhance our ability to trace environmental, technologi-
cal, and societal transitions/transformations. These themes are evident in the diverse published contributions to
the Modeling MultiSector Dynamics to Inform Adaptive Pathways special section of this journal available at the
time of writing this commentary (see Figure5).
Addressing the questions in Figure4 from multiple sectoral perspectives requires care in capturing the dynamic
co-evolutionary pathways of the underlying systems-of-systems governing them. Over the last century many
scientific disciplines have been drawn to the formal framing of their research through the systems-of-systems
perspective (Anderies et al., 2013; Gorod et al., 2008; Haimes,2018; Holling & Gunderson, 2002; Iwanaga
etal.,2021; Pescaroli & Alexander,2018; Simpson etal.,2021), all of which emphasize the importance of captur-
ing the hierarchy of systems' structures and their interdependent state dynamics. These traits are central to the
challenges posed in trying to understand path dependencies, lock-ins, and the potential for emergent behaviors in
natural, engineered, and socio-economic systems.
Figure6 highlights synergies and connections between disciplines that complement and offer important contri-
butions to MSD research. Each discipline represented in the figure explores aspects of complex adaptive human-
Earth systems. Moving outward from the center of the graphic, red text designates analytical challenges that
are common across the disciplines. Orange text emphasizes interactions across human-natural systems, with
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disciplines each giving different weight and attention to individual components. Yellow text describes some of
the research methods and focal points that are explored within sets of individual disciplines. Human systems
contribute to changes in Earth systems that lead to many environmental and human impacts—impacts which
are also shaped by decision feedbacks about how to abate and adapt to detrimental changes. MSD seeks to
apply insights from many different research communities to innovate complex human-Earth system models, for
example, broadening the array of sectors/scales included, diversifying the representation of human systems and
behaviors, and incorporating new ways to evaluate the implications of uncertainty. The integrative modeling
capabilities of the disciplines shown in the left-hand side “feathers” of Figure6 were driven by the need to better
integrate aspects of human-environment systems interactions, in order to inform abatement decisions related
to global environmental issues, such as climate change, acid precipitation, and stratospheric ozone depletion.
Innovations in economics, decision science, and socio-ecological-technical systems analysis are driven by a need
to understand interdependencies between economic sectors, exploring why people make the decisions they do,
and seeking generalizable perspectives on why only some communities succeed in managing complex, coupled
social and ecological systems. Finally, the right-hand side disciplinary “feathers” of Figure6 represent impor-
tant theoretically focused disciplines, exploring the properties and management of systems of systems and the
implications of complex, nonlinear processes for individual and coupled systems. As noted in our definition of
MSD itself above, Figure6 emphasizes the core transdisciplinarity of influences and needs for our research vision
to be realized. It should be noted that our summary of influential disciplines is not meant to be enumerative or
exclusive, but to simply emphasize the breadth of perspectives needed to advance complex human-Earth systems
science. We further elaborate the key research gaps and aspirations in the next section.
3. MSD Research Gaps and Aspirations
Figure7 expands on the core research questions of Figure4 to detail important MSD research gaps that need to
be addressed to enable the field to engage with and better understand the dynamic and adaptive complexity of
human-Earth systems. To address the research gaps summarized in Figure7, the MSD CoP is focused on the
Figure 4. Societal challenges and MSD science questions.
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following strategic investments (see Section1): (a) strengthening foundational research capabilities, (b) advanc-
ing complex adaptive human-Earth systems science, and (c) providing scientific and decision-relevant insights
under deep uncertainty. We provide a more detailed summary for each of these investments and the MSD research
aspirations that underlie them below.
3.1. Strengthening Foundational Research Capabilities
Our foundational capability to model and gain insights for complex co-evolving human-Earth systems is a rate-
and capacity-limited process (Haimes,2018). The necessary lead times for research and development often mean
that modeling and analytic capabilities that adequately capture key dynamics, systems' elements, and their evolv-
ing relationships are often no longer informative for decision making when actually available for use. Intelligently
accelerating our ability to endogenize state-aware changes in the form and function of systems/sectors of focus
represents an outstanding grand challenge for the scientific community. The major societal questions driving
MSD research (Figure4a) present an additional challenge to the rate and capacity limitations. Understanding
transitions and transformations, risk, resilience, and their distributional effects in complex human-Earth systems
requires a significant investment in growing and diversifying the MSD workforce to broaden the backgrounds,
knowledge, and experiences the community can draw on to advance our understanding of societal risks (Batch-
elor etal.,2021; Bernard & Cooperdock,2018; Hofstra etal.,2020; National Academies of Sciences & Medi-
cine,2018, 2020). We must overcome workforce and workflow gaps (Figure7) within the MSD CoP itself as
an enabling mechanism for confronting the complexity of co-evolving human-Earth systems. Fundamentally,
the community needs to exponentially scale inputs to MSD science (workforce, tools, hypotheses, teams, agen-
cies, sectors, and scales) and the resulting outputs (results, papers, insights, and translational science benefits to
society).
Who constitutes the MSD scientific community is integral to the community's capacity to meet its scientific
objectives. Exponentially scaling hypothesis generation and exploration require a broader and deeper work-
force developed using active commitments to diversity, equity, and inclusion (DEI). Figure 8 summarizes the
Figure 5. Contributions to the Modeling MultiSector Dynamics to Inform Adaptive Pathways special section of this journal. As of the time of publication. The
contributions span various geographic and topical focal areas, as well as capture different system and sector processes (Chowdhury etal.,2021; Quinn etal.,2020;
Wessel etal.,2022; Wild etal.,2021).
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properties of community engagement models in a CoP. The continuum from traditional transmissive dissemina-
tion of goals (left-hand side of graphic) to transformative co-creation is fundamentally shaped by a communi-
ty's defined membership, nature of interactions, and the balance of power to make contributions and set goals.
Through community-led co-creation, a CoP can enable the articulation of altogether new modes of framing and
exploring scientific hypotheses that can potentially yield transformative changes. Institutional support of DEI
has been shown to yield direct benefits to scientific outcomes. Nielsen etal.(2017) highlight that increasing the
number of women, especially in team-leadership roles, has been shown to aid collaborative task completion while
improving awareness of social dynamics, membership expertise mapping, and broadening the topics considered
in framing research questions. Adopting DEI goals in MSD will require continuous adaptation to incorporate the
best available information, particularly because most studies to date have focused primarily on the impacts of
greater representation of white women in STEM. More research is needed to identify what practices best support
scientists from other underrepresented groups and the impact of intersectional identities on key outcomes. One
of the initial actions taken by the CoP will be to create a mission statement that addresses DEI and use commu-
nity resources to implement evidence-based practices that support the growth of a diverse body of early career
researchers in this community (Hill etal.,2010; Johnson etal.,2019; National Academies of Sciences & Medi-
cine,2020). DEI work (Tilghman etal., 2021) can support the science mission of MSD and is central to the
aspiration of exponential growth to confront the complexity of human-Earth systems.
Figure 6. Focal and methodological connections of Multisector Dynamics with other disciplines.
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A second element of the MSD CoP's strategic focus on “Going Exponential” is community level support of
training opportunities and improved access to emerging MSD innovations. For example, Murphy etal.(2020)
point out that the collaborative structure and broader social networks of open-science initiatives have led to
more frequent high-status authorship for women, as compared to a narrower focus on reproducibility principles.
To exponentially accelerate collaborative science innovations the MSD CoP needs to undergo a transforma-
tional change in the ways that research is conducted (addressing workflow gaps in Figure7). Elements of this
transformation include: expanding the breadth and scale of explored hypotheses, encouraging researchers from
diverse disciplines and backgrounds to join the MSD community, incorporating new technologies like artificial
intelligence (AI) and emerging computing architectures (e.g., high-performance cloud/edge computing), facil-
itating collaboration across teams and projects, and developing new training and tools to support and sustain
commitments to open science. Open science describes a set of principles around conducting, publishing, and
disseminating science, ranging from open access journals to reproducible research to open science tools like
data repositories and open-source models (National Academies of Sciences & Medicine,2018). Open science
Figure 7. MultiSector Dynamics (MSD) research gaps to be addressed over the next decade to enable the study and improved
understanding of the dynamic and adaptive complexity of human-Earth systems and their implications for a broader array of
societal objectives.
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accelerates progress by reducing barriers to entry, gaining economies of scale, and avoiding duplication of effort
(Allen & Mehler,2019). Two key tenets of open science, reproducibility and extensibility, are central to MSD
CoP's strategic focus on “Going Exponential”. Reproducibility makes it easier to repeat and confirm the findings
of others (McNutt,2014; Pfenninger etal.,2017; Wicherts etal.,2011). Extensibility, the ability to quickly and
easily build from the work of others, aims at reducing the large opportunity costs of adapting models, data, or
analytic tools to a new purpose when they are not publicly available or they are poorly documented. Open science
practices therefore present a major opportunity for innovation scaling in MSD breakthroughs.
3.2. Advancing Complex Adaptive Human-Earth Systems Science
As noted in Section 1, this commentary formalizes a vision for MSD as an emerging transdisciplinary field
advancing our understanding of the local-to-global systems that fundamentally shape the interdependent dynam-
ics, risks, and welfare of our modern world. The aspirations shared here seek to encourage transformative human-
Earth systems research that address the major methodological challenges driving MSD research (see Figures4
and7). There are however methodological, data availability, and computational gaps that are at present limiting the
MSD community's ability to confront the complexity of human-Earth systems and their feedbacks. There is a need
for: (a) better integration with complexity science (Haimes,2018; Meerow & Newell,2015; Montuori,2013), (b)
improved modes of analysis for capturing uncertainties in how human systems shape dynamics (Axelrod,2006;
Filatova etal.,2016; Moallemi & de Haan,2019; Polhill etal., 2016; Trutnevyte etal.,2019; Zellner,2008),
(c) computational advances that enhance representations of highly nonlinear and uncertain “state-action” feed-
backs (Bertsekas,2019; Herman etal.,2020; Oikonomou etal.,2021; Powell,2019), and (d) solutions to over-
come computational scaling and scientific inference barriers to MSD research insights (Bergman etal., 2019;
Hendrickson,2020; McGovern & Allen,2021; National Academies of Sciences & Medicine,2016). Addressing
these gaps will require deeper collaborations with the statistical, mathematical, and computational sciences.
The representation of dynamic and adaptive human actions in human-Earth systems models represents a core
challenge for MSD research (see Figures4 and 7), particularly when considering the uncertainties regarding
human actors and their interaction with the physical environment (Bland & Schaefer, 2012; Osman, 2010).
Human systems uncertainties include: the identification of key individual, collective, and institutional actors; the
representation of diverse objectives and tolerances to risk; and the functional modeling of actors and their actions.
Trutnevyte etal. (2019) note that multisectoral modeling approaches typically represent human development
trajectories in the form of exogenously defined assumptions, such as narrative scenarios of consumption rates
and technology innovations Such approaches may ignore potential human-Earth system feedbacks on the implicit
assumption that human actors do not adapt their land, energy, and water-utilizing activities (and the value-sys-
tems behind them) in the face of changing environmental conditions. In the case of global human-Earth system
Figure 8. Four modes of community member participation based on the community participation model developed by the Center for Scientific Collaboration and
Community Engagement (CSCCE). Center for Scientific Collaboration and Community Engagement(2020) contains the original description and elaborates on the
community participation model. This graphic has been adapted from the original and is used here with permission by its authors.
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models that do attempt to endogenize human action, they typically assume rational actors with complete knowl-
edge, operating within the context of an efficient global commodity market. Recent advances across disciplines
present the MSD community with an opportunity to augment the rational decision maker paradigm and explore
the implications of human actors exhibiting myopia, bounded rationality, incomplete knowledge, and dependence
on past experiences, as well as behavioral heterogeneity across actors (Ajzen, 1991; Barsky etal.,1997; Chan
etal.,2020; de Koning etal.,2019; Kahneman & Tversky,2013; Simon,1972; Weber,2006).
The MSD research community must position itself to take advantage of the explosive growth of emerging data
resources, algorithmic innovations, and analytic advances that facilitate model-based insights. Modeling frame-
works have been rapidly evolving in how they capture dynamic and adaptive representations of human actors,
infrastructures, and natural systems, as well as in how they account for the uncertainties surrounding them (Fila-
tova etal., 2013; Herman et al., 2020; Knox et al., 2018; Morris etal., 2018; Taberna etal.,2020; Trindade
etal.,2020; Turner etal.,2020; Yoon etal.,2021). These advances enable new scientific hypotheses by diver-
sifying theoretical problem framings across a broader array of disciplinary perspectives. Further, they support
quantitative analyses that explore ever-broader suites of societal objectives (e.g., reliability, resilience, robustness,
economic efficiency, financial risk, stability, equity, etc.). The emerging frontier of computational modeling and
analytics has also been embedding AI and agent-based modeling into highly adaptive software development
processes and scientific workflows. Embedded intelligence can facilitate rapid iterative exploration of compet-
ing hypotheses and problem framings, and accelerate scientific insights across the MSD domains (Atkinson
etal.,2017; Brown etal.,2020; Deelman etal.,2019; Yilmaz,2019).
3.3. Providing Scientific and Decision-Relevant Insights Under Deep Uncertainty
The recent advances described above can be applied to carefully assess and trace the effects of our representa-
tions of scales, interactions, and path dependencies (Filatova etal.,2016; Iwanaga etal.,2021; Levi etal.,2019).
Capturing how human systems shape the determinants of risk (hazards, exposure, vulnerability, and response)
even for a single extreme event poses nontrivial scientific challenges (see Figure1). There is to date a dearth of
modeling and analytic tools for better understanding how the co-evolutionary dynamics of multisectoral systems-
of-systems shape risk. More formally, scientific framings of rapidly changing human systems, their multisectoral
demands, as well as their feedbacks within the Earth system are themselves deeply uncertain. As a result, there is
a broad range of plausible futures where there is no clear consensus on their likelihoods and consequences, often
yielding complex tradeoffs across diverse MSD objectives (Dolan etal.,2021; Hallegatte & Engle,2019; Jafino
etal.,2021; Lamontagne etal.,2018; Lempert,2021; Moss etal.,2021).
These challenges question rather common assumptions (either explicit or implicit) about predictability over long-
time scales and for complex human-Earth system dynamics (Hofman etal.,2017; Schneider,2002; Schneider
et al., 1998). For example, recent literature on exploratory modeling under deep uncertainty (Bankes,1993;
Marchau etal.,2019; Moallemi & de Haan,2019) highlights a need for scientific framings and scenario anal-
yses that focus on generating diverse ensembles of plausible futures. These, often large, ensembles are care-
fully designed to capture the compounding and interacting effects of stressors and shocks faced by human-Earth
systems, while encompassing a wide range of possibilities in how they might manifest (e.g., by considering
more extreme conditions than those in the historical record). This shift away from deterministic single-future
predictions moves the focus from predictive questions to questions of discovery, that aim at uncovering what
futures, actions, and outcomes are the most consequential (Lempert,2002). Given the large and long-lived capital
investments associated with energy transitions, managing climate risks, and improving our national infrastruc-
ture systems, exploratory approaches aim at avoiding myopic lock-ins and unintended amplifications of risks by
actions that fail to meet engineered, economic, and social requirements across many plausible futures.
The deep uncertainty around the likelihoods and consequences of pathways of change in human-Earth systems
also implies that there exist irreducible uncertainties around the definition and representation of systems of
focus, their boundaries, and nature of interactions (Kwakkel & Pruyt,2013; Kwakkel etal., 2016; Moallemi
etal.,2020). Consequently, alternative framings of how-if at all-system relationships should be modeled need to
be explored, especially in a multisectoral context, where pathways of change can be differentially relevant to the
range of actors, systems and sectors present, or when modeled at different scales. Exploratory modeling frame-
works, such as robust decision making and its many-objective extension, have iterative analysis of alternative
framings at their core (Kasprzyk etal.,2013; Lempert,2002). As such, exploratory modeling experiments enable
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human-Earth systems' modelers to elucidate the implications of their framing choices through transparent and
traceable comparisons of their differences.
Applying exploratory modeling in MSD research represents a challenge as well as an opportunity to transform
how human-Earth systems modeling is currently done. Innovative approaches to experimental design can (a)
improve the representation of the deep uncertainties affecting a system (for example due to internal variability
as well as uncertainties surrounding model structures and inputs), (b) help to sample potential futures, and (c)
shed light on the impacts of uncertainties on consequential MSD outcomes (e.g., Lehner etal.,2020; Tebaldi
etal.,2021). Applying scenario discovery methods on the generated output space can identify critical combina-
tions of uncertain factors, consequential human actions, or tipping points that drive poor outcomes (e.g., Dolan
etal.,2021; Hadjimichael etal.,2020; Lamontagne etal.,2018,2019). Combined with many-objective optimiza-
tion approaches, these methods create avenues to search through the space of potential actions and uncertainties
to identify adaptive pathways of change across multisectoral objectives (e.g., Herman et al., 2020; Trindade
etal.,2020). This is an area of active research. While previous studies have provided valuable insights, they are
often limited in terms of the considered scales, uncertainties, and multisector interactions. The fast growing body
of research in data analytics and system modeling opens up opportunities to break important new ground.
4. Teaming to Address Complexity
The research challenges identified by MSD CoP include understanding long-term transitions and the effects of
shocks, while capturing a wide range of environmental processes, and integrating knowledge and models of many
systems. These challenges are comparable in complexity to modeling the dynamics of different components of
the Earth system (e.g., oceans, atmosphere, land surface, and subsurface). Successfully addressing the research
vision presented in this commentary will require an open science strategy that encourages collaborations across
diverse fields and research communities. As summarized in Section2, MSD CoP has grown from US DOE spon-
sorship of specific research projects as well as collaborative interactions with other US federal agencies facili-
tated by the USGCRP. Making progress at a rate commensurate with emerging global challenges will require an
even wider set of international collaborations with diverse research communities including systems engineering,
sustainable transitions, socio-environmental systems, socio-ecological systems, urban complexity science, Earth
systems modeling, decision making under deep uncertainty, and others. Over the next decade, our goal is to grow
the MSD CoP to include a broad array of technical working groups, linkages with broader international research
communities, and accelerate innovations in complex, adaptive human-Earth systems science.
Data Availability Statement
Although this commentary does not have data or codes, it is drawn from a longer form vision report where
conceptual graphic figures and related briefing materials are made available in the following Zenodo repository:
https://zenodo.org/record/6144309#.YhjVTZZOlhE.
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Acknowledgments
This research was supported by the U.S.
Department of Energy, Office of Science,
as part of research in MultiSector
Dynamics, Earth and Environmen-
tal System Modeling Program. Any
opinions, findings, and conclusions
or recommendations expressed in this
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