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Policymaking for the Long-term Future:
Improving Institutional Fit
Maxime Stauffer1,*, Konrad Seifert1, Nora Ammann1, and Jan Pieter Snoeij1
1Simon Institute for Longterm Governance
*max@simoninstitute.ch, Rue des Photographes 2, 1207 Geneva, Switzerland
Abstract
Is policymaking sufficiently concerned with the well-being of future generations? This
paper adapts the concept of institutional fit to identify areas of improvement in policy-
making to foster resilience and progress over the long term. Our assessment suggests that
the impact of current policymaking is limited. Global coordination is fragile and underde-
veloped. Humanity is unprepared to anticipate, prevent or recover from potential global
catastrophes. Siloed structures neglect the governance of cross-cutting challenges. Pervasive
short-termism neglects future generations. And policy learning capacities can be improved.
Building on these observations, we suggest three avenues for improving long-term institu-
tional fit. First, future generations require representation. Second, the prevention and mit-
igation of global catastrophic risks, as well as recovery and learning from inevitable shocks,
need to be embedded in policy agendas. And third, popular narratives could emphasize the
value of fostering transgenerational global public goods and adaptive capabilities. To initi-
ate institutional changes, we highlight key starting points. One can foster moral reflection;
train systems thinking; improve the science-policy interface; train decision-making under
uncertainty; and facilitate group deliberation. We aim to inspire further research and equip
policy practitioners with a sense for the interface between longtermist philosophy, global
catastrophic risk and policy process studies.
Keywords: governance, policymaking, future generations, institutional fit, longter-
mism, global catastrophic risks
1
Introduction
Relative to the potential size and goodness of the future (Beckstead, 2013), humanity is currently
paying insufficient attention to the well-being of future generations. The future could be vast
– millions or billions of years long – and the median quality of life extremely high as a result
of continued intellectual and technological progress. Based on the assumption that the welfare
of individuals matters irrespective of where or when they live, the philosophy of Longtermism
elucidates the moral significance of civilization’s survival and flourishing (Greaves & MacAskill,
2019). Taking this view seriously opens up a myriad of questions about how best to contribute
to long-term flourishing.
Most avenues to long-term flourishing, such as the prevention of collapse or lock-in scenarios,
require some form of coordinated action. This paper is about such coordination; aligning dif-
ferent actors’ goals and actions for collective benefit. More specifically, this paper is about the
most advanced form of deliberate large-scale coordination humans have achieved to date: public
policymaking through governments and international organizations (Gintis, Doebeli, & Flack,
2012).
This paper contributes to building an interface between researchers and policymakers. On one
side, burgeoning scholarship is refining the philosophy of longtermism and investigating global
catastrophic risks. On the other side, policymaking networks have developed to govern societal
progress and cope with shocks but currently fail to address the needs of future generations.
Both, longtermist researchers and policymakers, would benefit from a stronger interface to make
research and policy more impactful.
Building this interface is important because the complexity of policymaking and uncertainty
around long-term trajectories have led many to simplistic, nihilistic or relativist ways of thinking
about policymaking. To foster a constructive debate anchored in science, we link ethical consid-
erations for the long-term future with the literature on policymaking processes. In this paper,
we contribute to the foundations of future research on long-term policymaking by discussing
three guiding questions:
1. Are current policymaking networks fit to ensure resilience and progress?
2. What are the most important avenues for improvement?
3. What are promising ways to implement these improvements?
We tackle these questions in three parts based on our adaptation of existing theoretical
frameworks and evidence synthesis. First, we propose a definition of long-term institutional
fit as a framework to assess the extent to which public policymaking is able to contribute to
resilience and progress. Second, based on our preliminary assessment, we delineate three central
avenues for improving the long-term fit of policymaking. Third, we tackle the question of how
best to bring about these improvements by addressing the common thread that runs through all
policymaking networks and stages: decision-making processes. We articulate five improvements
which, if pursued, could strengthen policymaking and, by extension, lead to a better future.
We aim to pave the way for further research at this science-policy interface and provide
support to policy actors who aim to place the long-term future at the core of policymaking.
As our assessment of long-term institutional fit points out, current policymaking is the most
advanced form of large-scale coordination to date. Yet, its beneficence is constrained by, among
others, structural short-termism and a lack of preventive capacity. This juxtaposition of the
power and limits of current policymaking mechanisms outlines the puzzle which, if solved, would
become a major contributing force to humanity’s progress. Box 1. summarizes the core concepts
used in this paper.
2
Box 1. Core definitions
•Longtermism: the philosophy that elucidates the moral importance of civilization’s
survival and flourishing based on the assumption that the welfare of individuals matters
irrespective of where and when they live.
•Long-term: as far as thousands to billions of years into the future.
•Short-term: five to ten years into the future.
•Policymaking: the process that takes place in local (e.g. city) and national govern-
ments, as well as in international organizations, to craft collective action.
•Institutions: sets of rights, rules and decision-making procedures.
•Global catastrophic risk: risks that might have the potential to inflict serious damage
to human well-being on a global scale.
•Complex system: systems characterized by the interactions and adaptation of many
parts that lead to nonlinear systemic events.
•Institutional fit: defines the capacity of policymaking networks to impact the systems
they aim to govern.
•Long-term institutional fit: defines the capacity of policymaking networks to safeguard
and improve the long-term potential of humanity.
1 Is policymaking fit to shape the long-term future bene-
ficially?
Public policymaking is the process that takes place in local (e.g. city) and national governments,
as well as in international organizations, to craft collective action (Weible & Sabatier, 2018).
It is both a technical and social process that consists of making sense of the world, agreeing
on a set of problems, designing, implementing and evaluating solutions to them, and producing
collective narratives, values and visions (Geyer & Rihani, 2010).
Policymaking is closely related to the notion of institutions which are sets of rights, rules
and decision-making procedures (Young, 2010). This paper solely refers to policymaking within
governments and international organizations, such as the United Nations or the European Union,
and not to private or religious institutions. When we refer to “current” policymaking, we restrict
ourselves to the twenty-first century only. When we use “policymaking”, we refer to the abstract
concept independent of time to treat the subject matter as a general phenomenon. Our use of
“short-term” refers to five to ten years ahead while our use of “long-term” refers to as far as
millions to trillions of years into the future.
Policymaking can be understood as a crowd event (Van Waarden, 1992), i.e. a network
of heterogenous, purpose-driven individuals and organizations whose interactions lead to the
emergence of policies (Weible & Sabatier, 2018). This process does not happen in a vacuum but
within institutional determinants such as national constitutions, international treaties and more
(Moyson, Scholten, & Weible, 2017). Policymaking includes party politics and technical policy
design, as well as the inter-play of governments’ executive, legislative and judiciary branches
(Howlett, 2020). In this paper, we take stock of the aggregate of policymaking processes, from the
3
local to the international level (Simon, 1991), including all types of outputs, from legislation to
customs (Lowi, 1964; Smith, 2002). Our lens, however, is very Western and, at times, European.
Is current policymaking able to prevent extinction or stagnation, and foster resilience and
progress? We propose to approach this question through the notion of institutional fit (Ekstrom
& Young, 2009). Applied to policymaking, institutional fit defines whether policymaking can
adequately impact the systems it is to govern (Galaz, Olsson, Hahn, Folke, & Svedin, 2008). A
lack of fit would mean that policymaking fails to attain the necessary power to resolve a given
issue (Folke, Pritchard, Berkes, Colding, & Svedin, 2007).
Section 1.1 provides a short discussion of the nature of the systems policymaking aims to
govern. In section 1.2, we operationalize the notion of institutional fit by building on existing
definitions (Epstein et al., 2015; Folke et al., 2007) and adapting the concept to our specific
use: long-term institutional fit. We do so by integrating literature on policymaking processes,
institutional design (e.g. Bednar and Page (2018)), global catastrophic risks (e.g. Avin et al.
(2018)) and longtermism (e.g. Greaves and MacAskill (2019)). In section 1.3, we conduct a high-
level assessment of the current strengths and weaknesses of policymaking in view of long-term
institutional fit.
Due to the scope of this paper, our assessment of current long-term institutional fit is ten-
tative. However, it provides a preliminary basis for reflection on the long-term fit of current
policymaking. By illustrating the usefulness of the framework in investigating public policymak-
ing networks, we hope to motivate and inform future work by researchers and practitioners. Our
analysis suggests that current policymaking can be improved to better benefit the long-term
future (summarized in 1.4). This analysis serves as the problem statement and motivation for
sections 2 and 3, delineating a path forward.
1.1 A systems view of policymaking
Talking about systems in the abstract can be confusing. In the context of this paper, it is
helpful to differentiate between “the governing system” and “the system(s) being governed”.
The former – public policymaking – is the primary object of this paper. The latter – the set of
interconnected systems that make up the world at the physical scales relevant to human activity
– is what policymaking is defined in relationship to. Thus, to understand the requirements for
policymaking, we need to understand the structural nature of what it is to govern.
In this paper, we take a systems view on what policymaking is meant to govern (Bertalanffy,
1969; Leveson, 2016). A lot of the most important issues for policymaking – such as climate
change, economic growth, equality, public health or emerging technology – concern systems of
organized complexity. What characterises these systems is that they cannot be decomposed
into independent subsystems due to their complexity (as opposed to simple systems) yet ex-
hibit structure instead of total randomness (as opposed to systems of unorganized complexity)
(Weaver, 1991).
Systems of organized complexity are dynamic and evolving. Their interconnections can lead
to feedback loops (e.g. causing cascading or dampening effects) or common mode failures (when a
single fault causes several systems to fail) (Pines, 2018). For instance, the severity of a pandemic
(e.g. deaths, economic harm) is mediated by the condition of the related systems: the strength
of the health care system; the level of economic inequality; the level of trust in government;
or the individual health of every citizen – all can amplify or dampen the effect of a pandemic
outbreak (Wernli et al., 2021).
As a consequence of feedback loops, systems of organized complexity tend to depict heavy-
tailed distributions, meaning that few events account for most impact (Sornette, 2009). A
reliable system (one that suffers few failures) is not necessarily a safe system, because a single
4
failure might cause enormous harm (Leveson, 2016). Figure 1 summarises the above concepts;
organized complexity, heavy-tailed distribution and system dynamics.
Risk count
Risk severity
+
-
- + Time
System functionality
+
-
Shock
(A)
Organized complexity
& feedback loops
(B)
Heavy-tailed risk
distribution
(C)
System functionality
as a function of shock
Global catastrophe
Resilient recovery
Shock absorbtion
System transformation
Figure 1: Organized complexity, risk distribution and system dynamics
(A) depicts complex networks with nonlinear properties, such as positive and negative feedback loops reinforcing or
absorbing the adverse consequences of a shock. (B) shows a heavy-tailed distribution of risks, where a large proportion
of risks cause shocks of low (green) to medium (orange) severity and a minority of risks extreme shocks (red). (C) depicts
system functionality trajectories when shocks occur (adapted from Grafton et al. (2019)). The green curve shows that
the system absorbs the shock and continues with same levels of functionality. The orange curve shows that the system
suffers from but manages to mitigate damages, recover and adapt. The red curve shows that the system collapses and
cannot recover from damages. The black dashed curve shows that a system can adapt and transform in response to a
shock and can reach higher levels of functionality.
Policymaking must take into account the structural properties of the systems it aims to
govern. The interconnectedness and nonlinearity of systems of organized complexity makes them
particularly vulnerable to cascades of failure and explains why our assessment pays particular
attention to global catastrophic risk cascades (Buldyrev, Parshani, Paul, Stanley, & Havlin, 2010;
Vespignani, 2010). Global catstrophic risks (GCRs) are loosely defined as “risks that might have
the potential to inflict serious damage to human well-being on a global scale” (Bostrom &
Cirkovic, 2011, p. 1). Examples of natural GCRs include solar storms, asteroid impacts and
volcano eruptions that can destroy critical infrastructure, cause violent tsunamis or prolonged
winters (Avin et al., 2018).
Examples of anthropogenic GCRs include adverse effects from climate change, such as ecosys-
tem collapses that could lead to food crises which could, in turn, lead to widespread conflicts
(Farquhar et al., 2017); nuclear wars with the potential for unprecedented destruction of habitat
and nuclear winters (Bostrom, 2013); bioengineered pathogens that can deliberately or acci-
dentally lead to much deadlier pandemics than natural ones (Schoch-Spana et al., 2017); or
mismanagement of other emerging technology, such as transformative advances in artificial in-
telligence which could, for example, lead to the rapid destabilization of the labour market (e.g.
through extensive automation (Frey, 2019)), to an uprooting of political systems (e.g. through
deep fakes accelerating misinformation spread (Howard, 2020)) or to international arms races
(e.g. through heightened uncertainty about state’s technological capacities (Kania, 2019)).
While civilization has grown ever more powerful through scientific and technological progress,
humanity’s collective wisdom – the capacity to use this power wisely – is not necessarily keeping
up. Most ways of preventing or mitigating GCRs require large-scale coordinated action, which
is why it is essential to improve policymaking.
Viewing systems as nonlinearly evolving structures elucidates why safety is a system property,
not a property of one of its components. Consequently, risks ought to be understood as the result
of the interplay of external threats and systemic vulnerabilities. It is this threat-system complex
5
that ultimately is responsible for the effects of a shock (Avin et al., 2018; Linkov & Trump,
2019). If the threat is large and the systemic vulnerable, the shock will be severe. However, the
more resilient the system, the better it will be able to mitigate the severity of the shock, recover
from it and adapt to a changing reality (Cotton-Barratt, Daniel, & Sandberg, 2020; Gao, Barzel,
& Barab´asi, 2016; Grafton et al., 2019).
In sum, understanding the world as a set of systems characterized by organized complexity
informs what is demanded of policymaking. Based on this systems view, we will next define the
notion of long-term institutional fit, to then assess the fit of current policymaking and identify
avenues for improving it.
1.2 Defining long-term institutional fit
We propose the following definition of institutional fit from a longtermist perspective:
Long-term institutional fit defines the capacity of policymaking networks to safeguard and
improve the long-term potential of humanity.
This definition relies on three core tenets. First, today’s policies shape the future (Cotton-
Barratt et al., 2020). Under this view, policymaking becomes an important vehicle for benefiting
the future by reducing the negative and amplifying the positive effects of human activity. For
instance, governments can enforce the use of renewable energy, thereby reducing the likelihood of
adverse climate events. Or, national constitutions can encode moral progress by granting equal
rights to different genders and races. Such legal code preserves moral progress which is likely
to benefit future generations. A way to achieve more good through policymaking is to formally
account for future generations as stakeholders of the policy process. The fate of the long-term
future is thus, to a considerable degree, the responsibility of current institutions.
The second tenet is that one can unpack the long-term benefits of actions into: (1) safe-
guarding and (2) improving the future. To do good, institutions need to prevent existential
catastrophes and improve the quality of life. This paper pays particular attention to the miti-
gation of global catastrophes as a necessary condition for life to flourish. If global catastrophes
are prevented or mitigated, civilization can allocate more resources to fostering progress (Ord,
2020).
The first two tenets imply a third: long-term institutional fit requires some degree of short-
term fit. Why is this so? Policymaking sets precedents and requires political legitimacy. Policies
with future benefit are implemented via current policymaking processes. Thus, they must be
conceived by and implemented by current populations. Discounting the needs and demands of
current populations would not just hinder implementation but is inherently morally problematic
(Hetherington, 2005; Knack & Zak, 2003). To ensure the creation of robustly beneficial path-
dependencies, concern for both, current and future generations, must be brought into agreement.
This is a major puzzle to solve for policy actors aiming to shape the long-term future.
In the next section, we unpack our definition of long-term institutional fit into four dimensions
(summarized in Table 1): spatial, temporal, functional and representational fit (Epstein et al.,
2015).
1.2.1 Spatial fit
Spatial fit defines whether an institution has the geographical scope to solve problems effectively.
6
Dimensions of fit General definition Requirements for long-term fit
Spatial fit Institutions have the geographical scope to
solve problems that require regulation
Institutions achieve global coordination through centric and
polycentric processes
Temporal fit Institutions can address problems at an
adequate speed
Institutions prevent (heavy tail) risks and react quickly
if they manifest
Functional fit Institutions can address the systemic effects
involved in the resolution of a problem
Institutions (1) address interconnectedness; (2) keep up
with technological change; (3) account for the social context;
and (4) handle uncertainty
Representational fit Institutions respond to the needs and values
of their target populations
Institutions account for both current and future generations in
legislative and executive processes
Table 1: Dimensions of institutional fit and their adaptation to long-term fit
As avoiding global catastrophes is key to safeguarding the future (Bostrom, 2013), institutions
need, at the very least, not impede and, ideally, foster global coordination. Which spatial scales
policymaking needs to be able to affect depends on the risk and systems in question. Avin
et al. (2018) suggest a way of categorizing global catastrophic risks according to the critical
systems they affect and the global spread mechanisms involved. As the severity of shocks also
depends on how different populations react to them, there is a need to coordinate responses.
Such coordination can take the form of transnational bodies and local authorities cooperating,
the creation of bilateral treaties or multilateral alliances (Lavelle, 2020).
1.2.2 Temporal Fit
Temporal fit defines whether institutions can address problems at an adequate speed.
Given that the occurrence of global catastrophic risks can lead to collapse scenarios, notably
through cascade effects (Avin et al., 2018), policymaking has to prevent shocks and, if they do
occur, act quickly enough to avoid cascades. This requires early-warning systems and prepared-
ness for a range of possible shocks, even if they are of low probability. As such, it requires to
invest into policies whose value is difficult to assess and in fact often invisible, as their success
would result in the contrast-free continuation of current trend towards ever higher well-being.
1.2.3 Functional fit
Functional fit defines the ability to affect and handle all factors relevant to the resolution of the
problem.
To effect change, policymaking requires the ability to tackle at least four challenges. First,
it is crucial to address the interconnectedness of different risks and systems (Avin et al. (2018),
see section 1.1) and avoid “siloed policies” to manage feedback loops between sectors. Second,
policymaking should encourage technological progress while governing emerging threats from
these technologies (e.g. from advanced artificial intelligence, biotechnology or nanotechnology).
Third, policymaking must fit the current social context, while anticipating future attitudes
(Epstein et al., 2015). Fourth, policymaking must deal productively with high uncertainty
regarding the exact nature of threats and long-term trajectories (Evans et al., 2020).
1.2.4 Representational fit
Representational fit is “concerned with congruence between [policymaking] and the preferences,
values, and needs of human actors” (Epstein et al., 2015, p. 34).
The key question here is whether all beneficiaries are duly accounted for. To reconcile the
7
needs of current and future generations, policymaking requires to foster current well-being and
stability, while ensuring survival and progress. Given uncertainty about what the future will look
like and which policy changes would be best, current policymaking can focus on preserving and
increasing option-value for future generations. In particular, institutions should avoid actions
that could prevent them from adapting to needs of future generations.
1.3 Assessing the long-term institutional fit of current policymaking
In what follows, we conduct a high-level analysis by examining current policymaking through
the lens of long-term institutional fit to motivate further research. We take policymaking mech-
anisms in the twenty-first century as subject of study. We do not expand this timeframe by
considering more historical perspectives because going further back in history brings us closer
to a very different world order (the cold war) and its aftermath. We do not restrict this time-
frame to a shorter period because we want to understand the impact of policymaking in the
short-term, which often requires at least five to twenty years of observation. This qualitative,
high-level assessment serves to identify key questions informing further work on improving long-
term institutional fit.
1.3.1 Assessing spatial fit
We unpack the ability of current institutions to solve global issues into three capacities that can
improve coordination at various scales: codifying rules, pooling resources and building multilat-
eralism.
First, a significant portion of the power of policymaking lies in its ability to codify rules. The
preambles of all national constitutions, the United Nations Charter and the Treaty of Lisbon
articulate grand visions of peace, mutual support and prosperity. The codified rules are meant
to help their constituents achieve those visions (Bednar, 2012; Gintis et al., 2012). Rules include
rights, rewards and punishments, preference aggregation mechanisms (e.g. voting methods), and
powers given to institutional structures such as parliaments or assemblies, executive branches,
and judicial bodies. As such, policymaking produces explicit texts that serve as the aspirational
operating system of society (Nindler, 2019).
Codification and subsequent regulation are powerful instruments to foster coordination across
populations. Their effects are only observable across time and often unnoticeable to individuals
operating within these systems. Since every citizen is subject to the law with equal rights,
changes to legislation affect everybody and mechanisms for reward and punishment ensure their
respect. This reference system for what is good or bad increases trust through reliability and sets
expectations of the behaviour of others, and vice-versa (Bednar, 2012). As such, the codification
of collective norms and welfare enables coordination (North, Wallis, & Weingast, 2009).
Second, governments, and, to some extent, international organizations, have extensive finan-
cial power. Each year, national governments allocate 20 trillion US dollars (IMF, 2018) on top
of which institutions like the European Union and the United Nations respectively spend around
160 and 50 billion dollars 1 2. Altogether, governments allocate around 130 times more money
annually than all foundations combined (P. Johnson, 2018). The sheer size of public budgets
enables coordination by financing administrations that design and implement policies, shape
incentives for the economy, deploy subsidies and safety nets, as well as counter market inefficien-
cies (Howlett, 2020). Public funding enables large research projects on societal challenges. The
1https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/
2https://www.cfr.org/article/funding-united-nations-what-impact-do-us-contributions-have-un-agencies-and
-programs
8
European Union’s budget for innovation and research, for instance, is approximately 96 billion
Euros for six years 3.
In contexts of crisis, governments can launch extensive recovery and stimulus plans. For
instance, after the 2008 economic crisis, the European Union and China respectively implemented
recovery and stimulus plans of 200 billion dollar4and 580 billion dollar5. In the face of COVID-
19, the European Union developed a 750 billion euro recovery plan6, and many countries allocated
billions of dollars to support their economy and deploy emergency measures (Brodeur, Gray,
Islam, & Bhuiyan, 2020). As such, the power of policymaking is unique because it combines
extensive resources with the legitimacy to allocate them in ways that affect future generations.
Examples of recent feats include COVID-19 vaccines, CERN’s Large Hadron Collider and the
International Space Station.
Third, institutions such as the European Union and the United Nations foster multilateralism,
that is, the coordination of national governments, the private sector and civil society to work
towards shared goals (Lavelle, 2020). Beyond laws and budgets, those institutions offer important
spaces for information exchange, negotiation and consensus-building (Kamau et al., 2018). Given
that the vision of those institutions extends beyond national borders, they contribute to the
resolution of problems that bypass artificial frontiers (Ruggie, 1992). A pandemic is a typical
example of such a challenge that is independent of national borders, thus requiring international
coordination.
International institutions foster coordination by setting standards, regulation and treaties
that specify common goals, procedures and expectations (Stone & Moloney, 2019), elaborated
by a wide variety of stakeholders, well beyond national governments. While bottom-up and
polycentric policymaking can achieve global coordination, international and transnational bod-
ies significantly reinforce it by fostering information exchange, building trust, organizing ne-
gotiations and providing transnational reference frames (Lavelle, 2020; Vasconcelos, Santos, &
Pacheco, 2013). Global coordination also has the potential of overcoming zero-sum games and
other inadequate equilibria (Lavelle, 2020; Milner & Tingley, 2013). This can, for example,
take the shape of commitment mechanisms to ensure simultaneous implementation of a policy
once a sufficient number of actors have signed the agreement (Bunzl & Duffell, 2017). Such a
mechanism eliminates worries about free-riding or related losses in competitiveness that usually
deter actors from agreeing to a commitment in the first place. The recent entering into effect
of the Treaty on the Prohibition of Nuclear Weapons is an example of a weak version of such
mechanisms (Sauer & Reveraert, 2018). However, at large, the potential of effective mechanisms
to foster collective action appears largely under-explored and under-used at this point in time.
While international treaties and regulations often lack an enforcement body, they currently
are the most advanced form of value-driven large-scale coordination. The creation of the United
Nations’ Millennium Development Goals (2000 - 2015) and Sustainable Development Goals (2015
- 2030) provide global agendas for action that many countries have integrated into their national
policymaking (Messerli et al., 2019).
However, while current international organizations serve the vision of multilateralism, not
all countries consistently subscribe to it (Lavelle, 2020). International organizations can only
rarely enforce their measures and mostly just encourage coordination. In the face of crises,
national governments tend to resort to nationalistic responses. However, national governments
have limited fit for the governance of cross-border issues. During the COVID-19 pandemic, for
instance, countries did not respect the legally-binding International Health Regulations, which
foster coordination across borders, uniform responses and standards (von Tigerstrom & Wilson,
2020; Wilson, Halabi, & Gostin, 2020).
3https://ec.europa.eu/commission/presscorner/detail/en/IP 20 2345
4https://ec.europa.eu/economy finance/publications/pages/publication13504 en.pdf
5https://www.latimes.com/archives/la-xpm-2008-nov-10-fi-china10-story.html
6https://ec.europa.eu/info/strategy/recovery-plan-europe en
9
The multilateral system currently in place is stretched thinly between preserving its own le-
gitimacy and avoiding its collapse. Its existence is fully dependent on the willingness of member-
states to sustain their support. For example, multilateral norms did not prevent the USA from
cutting its financial support to the World Health Organization in midst of the COVID-19 pan-
demic. Another example of a weak multilateralism is the United Nations Security Council, which
was put in place at the end of World War II to ensure peace and security. It has since lost sig-
nificant parts of its functionality, while also becoming largely unable to reform itself, thereby
failing to adapt to present-day circumstances (Stephen, 2018).
1.3.2 Assessing temporal fit
Through the lens of temporal fit, current institutions show mixed results. Their capacity to
anticipate and respond to crises is difficult to analyse. On the one hand, there likely are many
shocks that have not materialized or did not amplify precisely because of policymaking. Shocks
like the 2008 crisis have not led to global catastrophes presumably because institutions acted
swiftly by, for example, boosting economies through stimulus plans. On the other hand, there are
clear examples of known threats that institutions could have been better prepared for. Examples
include the 2008 financial crisis or the COVID-19 pandemic.
A clear example of extensive preventive policymaking are the programs to manage rising
sea levels in Venice and the Netherlands. After extensive floods in 1966 and 1953 respectively,
which functioned as “wake up calls” (Ferraro, 2012; Pilkey, Pilkey-Jarvis, & Pilkey, 2016), both
places adopted extensive construction plans: the Dutch Delta Works started in 1954 (Pilkey et
al., 2016) and the Italian MOSE started in 1987 (Rinaldo et al., 2008).
While institutions have proven at least some reactive capacity (e.g. through adaptive
decision-making by concentrating power in the hands of the executive for limited time periods
(not without risks of power abuse) (Thomson, 2020), two cases demonstrate that anticipatory
and preventive capacities could clearly be improved.
First, in light of COVID-19, it was evaluated that approximately 30 billion of dollars would
have been sufficient to prevent the global pandemic (Dobson et al., 2020), whereas the pandemic
is expected to cost trillions of dollars to the economy and governments (Cutler & Summers,
2020; Schwab, 2020). Therefore, reacting to crises can be effective and lead to longer term
improvements (Linkov, Fox-Lent, Keisler, Sala, & Sieweke, 2014) but is extremely costly in
terms of resources.
Second, some shocks are impossible to recover from. A hypothetical pathogen ten times
deadlier than COVID-19, transformative artificial intelligence or a nuclear war could undermine
any reactive policymaking capacity (Cotton-Barratt et al., 2020; Schoch-Spana et al., 2017).
Even if these risks have a low probability, their event would be so devastating that they require
preparedness and prevention.
Considering human psychology, the reactive nature of current policymaking processes is un-
surprising (B. D. Jones & Baumgartner, 2005). It is easier to focus social networks and legitimize
action when shocks are observable and harm present populations. However, the prevention of
global catastrophic risks could well be justified as a less costly and more ethical alternative for
current and future generations alike. This is particularly important for such extreme threats.
While reactive capacity to ordinary risks may be sufficient to mitigate them, it is primordial to
avoid much deadlier risks because of their massive costs in case societies are not prepared. Such
cost, however, can only be adequately calculated if policymaking accounts for future generations
as well. The extent to which institutions serve the needs of all their stakeholders is assessed
under representational fit.
10
1.3.3 Assessing functional fit
Our assessment of functional fit suggests that current institutions can improve the way they deal
with systemic crises. Despite efforts in policy coherence and the design of cross-sectoral public
bodies (Labont´e, 2014), they often produced siloed policies. Silos originate, at least in part,
from the way institutions are structured. For instance, national governments have departments
for health, the environment, the economy, etc.
While siloed institutional structures facilitate a clear delineation of mandates and reflect the
disciplinary expertise found in academia, global catastrophic risks are cross-sectoral in nature
(Avin et al., 2018; Messerli et al., 2019; Wernli et al., 2021). It is precisely their interconnect-
edness that leads to amplifying feedback loops and extreme shocks. By reducing the ability to
govern systemic threats and account for uncertainties, siloed policymaking increases the likeli-
hood and severity of risks. For instance, economic austerity policies following the 2008 crisis
led to under-investment in health systems in many countries of Europe. Twelve years later, the
COVID-19 pandemic was likely more severe because of weaknesses in health systems (Wernli et
al., 2021).
One important challenge for institutions is to ensure technological progress benefits society
at large. The development of the European General Data Protection Regulation is an example of
how institutions mostly react to, instead of proactively shaping, technological progress (Moses,
2011). On the one hand, reactiveness makes it likelier that laws tackle real problems and pre-
vents them from becoming more severe (Simon, 1969). On the other hand, institutions risk the
development of technologies that are misaligned with societal values (Carp, 2018; Fenwick, Kaal,
& Vermeulen, 2017; Moses, 2011).
Functional fit also requires policymaking to account for the heterogeneity of society, today
and in the future. Local conditions, such as the structure of communities, define the best ways to
design and implement policy solutions. Polycentric processes are a promising means to achieve
fit with the societal context because they address both cross-contextual problems as well as the
specificity of particular contexts (Vasconcelos et al., 2013). Institutions also have to adapt to the
evolution of cultures over time and avoid deploying overly sticky policies that could be positive
in the present but limiting the future. This calls for adaptive policymaking and mechanisms to
revise policies over time, take into account future generations and anticipate what they might
need. In response, horizon scanning (e.g. Horizon Scanning Programme, UK government 7) and
forecasting (e.g. Good Judgement Project (Tetlock & Gardner, 2016)) are gaining popularity.
Policy theory suggests that policymaking improves over time at individual, team, department
and institutional levels (Dunlop, 2018) - a phenomenon that is called policy learning. This
institutional adaptation manifests via different strategies (Dunlop, Radaelli, & Trein, 2018). For
example, learning occurs through imitation of best practices, a process called policy diffusion
(Marsh and Sharman (2009); e.g. anti-smoking policies or children’s health insurance policies
Volden, Ting, and Carpenter (2008) diffusing among US cities and states respectively; downsizing
of the public-sector diffusing among OECD member nations Lee and Strang (2006)), or through
extensive monitoring and evaluation mechanisms (Sanderson, 2002) (e.g. the Best value regime
adopted by English local governments in 2000 Martin et al. (2006)). Some forms of policy
learning match the notion of learning by seeking to understand the nature of reality (so called
epistemic learning (Dunlop & Radaelli, 2020); e.g. the European Commissions’ Joint Research
Center (JRC) (Topp, Mair, Smillie, & Cairney, 2018)). Other forms of policy learning are the
product of bargaining dynamics, where actors adapt their strategies in reaction to learning about
the strategies, intentions, volition and preferences of other actors (Dunlop and Radaelli (2013);
e.g. negotiations.
However, policy learning can also be biased and focus on the wrong things (Moyson et al.,
7https://www.gov.uk/government/groups/futures-and-foresight
11
2017). Epistemic learning is hindered by groupthink, and the value of learning based on reflective
dialogue heavily depends on identifying and convening the relevant stakeholders (Greenbaum,
2015; Hart, Stern, & Sundelius, 1997; Tetlock, Peterson, McGuire, Chang, & Feld, 1992; Walker
& Watson, 1989). Especially dialogue-based learning often faces difficulties when scaled up (e.g.
from the local to national level). Learning through bargaining processes gets stuck in zero-sum
dynamics and biased by power asymmetries where rigid hierarchical structures stifle innovation
and render it less likely that insights will be adapted to new environments (Dunlop & Radaelli,
2020).
While there are advances in policy learning (e.g. through the advent of evidence-informed
policymaking), there is a lot of room for improvement. Policy learning is a critical ingredient for
long-term functional fit, by contributing to institutional adaptiveness in the face of uncertainty.
The world is changing ever faster, which means that boosting our ability to learn has become
even more important.
1.3.4 Assessing representational fit
Current institutions systematically discount future generations for numerous reasons. As a result,
problems most relevant to future generations, such as global catastrophic risks, are neglected.
Discounting future generations means that policymakers care less about their impact, the
further we look into the future (Arrow et al., 2013). If one considers that future generations
have equal moral value, then discounting them is a form of discrimination (Greaves & MacAskill,
2019). Under-prioritizing global catastrophic risks implies that policy agendas are not designed
proportionately to the severity of risks. Instead, other factors, such as emotional saliency, per-
ceived urgency or political acceptability determine policy agendas (B. D. Jones & Baumgartner,
2005), and lead to the neglect of priorities for present and future generations.
Building on previous scholarship (Caney, 2016; Jacobs, 2016; MacKenzie, 2016), John and
MacAskill (2020) review and propose a typology of determinants that lead to the discounting
of future generations, and subsequent under-prioritization of global catastrophic risks. They
identify three determinants: epistemic, motivational and institutional.
Epistemic determinants suggest that policy actors lack knowledge about the future. On the
one hand, discounting may be a rational choice because the scarce information about future
scenarios reduces the expected value of policies’ long-term effects (Halevy, 2008; Irving, 2009;
Jacobs & Matthews, 2012). On the other hand, policy actors are biased to focus on short-term
effects, neglecting future generations not out of conviction but because they are removed from
their attention (Jacobs, 2016; D. Johnson & Levin, 2009).
Motivational determinants refer to policy actors’ goals and motivations that cause temporal
discounting. Time preference (i.e. impatience), for instance, leads to a focus on short-term
issues and policies (Bidadanure, 2016; John & MacAskill, 2020; MacKenzie, 2016). Other, self-
reinforcing factors are self-interest and the favouring of contemporary stakeholders who also have
short-term preferences (Caney, 2016).
Institutional determinants highlight the effects of political structures that incentivize actors
to discount future generations. For example, the motivation to be reelected is rhythmed by short
election cycles and chances dependent on support from firms that have short-term preferences
(Binder, 2006). Other examples include short media cycles and political polarization that distract
actors from sustained reflection on long-term policy trajectories (John & MacAskill, 2020) or the
use of short-term performance indicators and budget windows that do not support the mitigation
of low-probability, high-impact risks.
In addition to these determinants of short-termism, other challenges reduce representational
12
fit with respect to both, present and future generations. Institutions may be dysfunctional or
instrumentalized for interests that are not prosocial. Corruption, nepotism or unclear separation
of powers increase friction, decrease trust and incentivize policy actors to resort to conservatism
and to optimize for their individual personal career progression instead of collective benefit
(Eberle, 2016; Ghaniy & Hastiadi, 2017; Reilly, 2017).
1.4 Summary: long-term institutional fit
In the previous section, we assessed the long-term institutional fit of current institutions, which
we summarize in Table 2. Using four dimensions of long-term institutional fit – spatial, temporal,
functional and representational – we identify key areas of improvement.
Current institutions are the most influential form of explicit, value-driven coordination mech-
anisms humanity has produced. However, they have significant room for improvement.
We discussed three aspects of spatial fit: codification of rules, pooling of resources and
multilateralism. Each of these are being leveraged to some degree, yet a lot of their potential
remains to be unlocked. Particularly concerning might be the fact that multilateralism remains
a relatively fragile system dependent on the cooperativeness of a few big players, while the
predominant drivers of policymaking remain more nation-centric than multilateralist, despite
salient global challenges. The means to forge new dimensions of alignment and positive-sum
games among actors remains limited. We see an urgent need to strengthen and expand global
coordination to unprecedented levels for global and long-term benefit.
As seen in the assessment of temporal fit, policymaking is largely reactive rather than pre-
ventive. Reactivity is still important: once a shock occurs, its effects can often still be mitigated,
suffering alleviated more quickly and recovery kick-started sooner. However, too few resources
are going into prevention and preparedness. Often, these measures are not only highly tractable
but also significantly cheaper. Prevention becomes increasingly important because of emerging
threats with the potential for immediate global impact that could be too large to recover from
at all. To have more foresight, scientific and policy institutions need to foster the understand-
ing of critical systems and develop mechanisms informed by a systems perspective to ensure
preparedness.
Assessing functional fit, current policymaking is characterized by siloed structures and poli-
cies which reflect an insufficient understanding and appreciation of the complex nature of the
world it aims to govern. Functionally fit policymaking would be able to manage feedback loops
in interconnected systems, effectively identify critical nodes and cope with uncertainty. It is
worth noting that these system properties do not only represent heightened risk (e.g. due to
common mode failures) but can also be leveraged, if properly understood, to increase resilience
(e.g. by fostering redundancies in the right places). An important avenue for strengthening
functional fit is to foster advances in policy learning.
Last but not least, representational fit can clearly be increased by appropriately weighing the
moral value of future generations. Short-termism is driven by a variety of epistemic, motivational
and institutional determinants. By providing decision-makers with a better understanding of the
possible value of the long-term future, temporal discounting could become less defensible despite
remaining epistemic uncertainty about the path to long-term impact. The motivation of policy-
makers to concern themselves with future generations can be increased by discussing the moral
value of future generations despite the fact that they are “voiceless” in the present, expanding
society’s moral circle. Additionally, institutional and procedural reforms can improve the in-
centives under which policymakers act, which can lead to an improvement in representational
fit.
13
Dimensions Criteria Assessment
Spatial fit Foster global coordination
(+) codification
(+) large pooling of resources
(+) multilateralism
(-) fragility of multilateralism
Prevent risks (-) lack anticipatory and preventive capacity
Temporal fit React to shocks (+/-) quick but costly reactive capacity
Address interconnectedness (-) siloed structures and lack of understanding of
interconnected systems
Keep up with (technological) change (-) lack of adequate solutions to anticipate and regulate
technological progress
Account for social context (+) institutional adaptation
(-) neglect of future contexts
Functional fit
Deal with uncertainty (+/-) systematic and biased policy learning
Account for current generations
(+) granted fundamental and voting rights in constitutions
(-) instrumentalization of institutions by actors who are not
prosocial; corruption, nepotism
Representational fit Account for future generations
(-) epistemic determinants of short-termism
(-) motivational determinants of short-termism
(-) institutional determinants of short-termism
Table 2: The long-term institutional fit of current institutions
Zooming out, current institutions enable unprecedented degrees of global coordination but
insufficient to be confident in humanity’s capacity to avoid global catastrophes. While some
may advocate for revolutions or attempt to reinvent the wheel, current institutions do learn and
their development can likely be improved. Further, some of the most promising, multilateral
policymaking institutions are recent developments in the history of human civilization and have
yet to stand the test of time. Last but not least, many short-term improvements that current
policymaking mechanisms can implement appear to contribute to improve the long-term future.
An example of how acting on short-term incentives can be beneficial to the long term is
that, in the past, governments and international organizations have contributed to reducing
poverty, increasing vaccination, avoiding the use of nuclear weapons and more (Boston, Bagnall,
& Barry, 2020; Howlett, 2020) despite the absence of formal systems to take future generations
into account. We do not mean to say that these improvements were sufficient to bring about a
good future – in fact, we believe the opposite to be true – nevertheless, they likely benefit the
long-term future. Thus, one should not throw the baby out with the bath-water. We suggest to
improve on what is already in place.
As our assessment is preliminary, we want to draw attention to its limitations and motivate
further research. First, we considered an abstraction of Western institutions at large which
prevents us from being more precise and account for idiosyncrasies of specific institutional con-
texts. Second, we did not rely on metrics, but on literature reviews, expert interviews and
common sense. Further work should quantify the assessment to establish a benchmark. Third,
our assessment is constrained by the uncertainty around the long-term effects of public policies.
Nonetheless, our assessment seems sufficient to delineate areas for improvement, which will
themselves benefit from further research. How can we deal with global catastrophes in the
short-term while their impact would mostly affect future generations? How can we improve the
representation of future generations and lessen the drivers of short-termism? What characterizes
longtermist policy agendas? How can longtermist thinking be brought into decision-making
processes? The rest of this paper provides a scaffold for further construction of answers to these
questions.
14
2 Avenues for improving long-term institutional fit
Having identified various deficiencies of policymaking with respect to the long-term future, we
ask: what are the most important avenues for improving long-term institutional fit? Potential
improvements are numerous and the details of their implementation context-dependent. To
remain globally relevant, we outline three avenues for improvement before discussing concrete
next steps.
The three avenues for improvement we highlight are: (1) reforming institutional structures
to account for future generations; (2) integrating global catastrophic risks into policy agendas;
and (3) adapting dominant societal narratives to view civilization from a long-term perspective
and nurture existential hope. We describe and justify each avenue in detail.
We suggest these three avenues for improvement not in an attempt to be comprehensive,
but because they tackle what we consider the most important bottlenecks to fostering long-
term policymaking at this point in time. In other words, unlocking any one of them would add
significant amounts of resources and opportunities towards building a thriving civilization by
preparing institutions and their ecosystems to integrate concrete policy recommendations that
aim to shape the long-term future. These avenues for improvement are tractable to varying
degrees, but progress appears feasible on each of them. Scholars or policy practitioners aspiring
to improve the long-term future might want to focus their attention on interventions within one
or several of these avenues. More work is needed to conceive of, test and implement concrete
and context-sensitive interventions within these categories.
2.1 Reforming institutional structures
As seen in our assessment of representational fit, policymaking systematically neglects future
generations. Institutional structures shape individual and collective behaviour and decision-
making processes. Reforming old structures and adding new ones could build beneficial path-
dependencies, as institutions provide incentives, accountability mechanisms, procedures and mo-
tivations to guide policymaking, lasting well beyond average human lifespans. For example, a
way of balancing preemptive and reactive policymaking can be found in adaptive regulation,
which requires continuous experimentation and incremental adjustments to maintain legislation
(Carp, 2018). Regulatory markets are another example of possible ways to govern emerging
technologies (Clark and Hadfield, 2019).
Political philosophers are showing a growing interest in potential institutional changes to
remedy sources of short-termism. Institutions for Future Generations by Gosseries and Gonzalez-
Ricoy (2016) is the most comprehensive overview of these potential institutional changes. In
this section, we briefly illustrate existing and potential mechanisms that could contribute to the
representation of future generations in current institutions.
Several countries have implemented measures to represent future generations. A prominent
example is Wales and its Well-being for Future Generations Act8. The Act specifies goals such as
sustainable development and well-being, as well as the creation of rights and duties for the future
generations commissioner (Davidson, 2020). Among others, the commissioner must produce a
future generations report9which consists of a vision, observations about current policymaking,
monitoring of progress on goals, and recommendations. This mechanism likely contributes to
representing future generations in policymaking. It promotes the value of the future in current
political discourse and offers a soft accountability mechanism towards future generations.
In Singapore, the government created a Centre for Strategic Futures in 2009. The goal of the
8https://www.futuregenerations.wales/about-us/future-generations-act/
9https://www.futuregenerations.wales/wp-content/uploads/2020/05/FGC-Report-English.pdf
15
centre consists of improving policymaking on risks and future scenarios, notably by developing
guidelines and frameworks to make decisions about risk prevention and the future10. Another
example is Finland’s Committee for the Future which was established in 2003. The Committee
is part of the parliament and focuses on research and policy responses related to the future.
The Committee is formed by parliamentarians, which allows it to gain power within parliament
instead of being an external actor11.
There are other promising mechanisms to represent future generations that have not yet
been tested. Examples include sub-majority rules to allow a third of parliamentarians to ask for
policy delays or referendums (Ekeli, 2015); longer election cycles to reduce short-term incentives
of the legislative and executive branches (Gonz´alez-Ricoy & Gosseries, 2016); youth quotas in
parliaments as a proxy to represent future generations (Bidadanure, 2016); Demeny voting to
give parliamentarians an additional vote to use in favour of future generations (Gonz´alez-Ricoy
& Gosseries, 2016); the creation of Ombudspersons for future generations12; or the creation of a
United Nations office for existential risk reduction (Ord, 2020).
It is important to note that these suggestions are up for debate and have not been tested
systematically. They illustrate what institutional reforms may look like and require refinement
before implementation at scale. For example, the creation of a United Nations office for ex-
istential risk reduction would require an analysis of how it fits with the current UN system,
which treaties would be most adequate to put it in place and which type of commitment is both
sufficient and acceptable for member-states for such an office to be born. It is essential to un-
derscore that making institutions more longtermist will require profound changes in their design
and in the popular conception of public policymaking. Overlooking such difficulties may lead
to hasty proposals and changes that could erode the core component ensuring policy legitimacy
and effectiveness: trust (Hetherington, 2005; Knack & Zak, 2003).
Reforms might seem difficult to implement but are needed to improve long-term institutional
fit. Given the immaturity of the field, further exploration of institutional reforms appearto be
of high value.
2.2 Building policy agendas for the long-term future
To promote the safety and well-being of future generations, it is important that policy actors
pay attention to the most relevant problems. While attention often shifts from one problem to
another as a function of exogenous cues, such as extensive media coverage of an event (Cairney,
2019), there are general patterns to the development of policy agendas.
Agendas are a tool for policy actors to signal commitment to specific topics and to each
other, create common knowledge and a common vocabulary, and concentrate the attention of
other actors (B. D. Jones & Baumgartner, 2005). For instance, the attention of many policy
actors around and inside the United Nations is bound by the UN 2030 Agenda. As a foundation
for coordination, the UN 2030 Agenda provides clear directions. Agenda-setting influences much
of the rest of policymaking such as policy design, adoption and implementation (Howlett, 2020).
Based on the systems view and our assessment of long-term institutional fit from section 1,
we suggest two elements to integrate into policy agendas: the importance to (1) anticipate and
prevent global catastrophic risks; and (2) to mitigate, recover and learn from shocks.
First, all policy agendas should assess global catastrophic risk – the likelihood of shocks with
the potential to lead to civilizational collapse – as even local shocks may have the potential
10https://www.csf .gov.sg/our-work/our-approach/
11https://www.eduskunta.fi/EN/valiokunnat/tulevaisuusvaliokunta/Pages/default.aspx
12https://www.un.org/en/chronicle/article/ombudspersons-future-generations-bringing-intergenerational
-justice-heart-policymaking
16
to trigger unforeseen cascades (Avin et al., 2018). Scenarios of total collapse or lock-in are
unprecedented (Bostrom, 2013), hard to grasp – especially emotionally – and therefore neglected
(Salama & Aboukoura, 2018). The inclusion of global catastrophic risk assessments across policy
agendas seems vital to avoid silos that are unable to govern interconnected systems.
Second, all policy agendas should consider how they feed into collective resilience and learning
through prevention, mitigation, recovery and adaptation (Wernli et al., 2021). Concrete problems
and solutions can be identified under each of those properties. Current policymaking tends to be
more reactive than preventive, which can be very costly (Dobson et al., 2020). However, investing
all resources in prevention would be misled, as it would neglect the strengthening of anticipation,
response and adaptation capacities, and thus lead to lower resilience, as not all shocks are
avoidable (Linkov & Trump, 2019). The best division of resources towards strengthening these
capacities is context-dependent but it seems safe to say that both require more investment at
this point.
We suggest that identifying, understanding and targeting systemic vulnerabilities (Ord,
2020), as done by e.g. the UN 2030 Agenda, represents an effective approach to improving
resilience for long-term flourishing. As elaborated in 1.1., shocks are the result of a threat
and vulnerability. Vulnerabilities mediate the severity of a shock when a threat materializes.
Examples of important vulnerabilities include poverty, inequality, conflict, trust in institutions
and social cohesion. Longtermist policy agendas must therefore link identified threats to sys-
temic vulnerabilities that could amplify or reduce risks. This opportunity to align long-term
objectives with short-term agendas requires more research. Some early investigations of the
interaction between shocks, critical systems and diffusion mechanisms contribute to identifying
linkages between global catastrophic risks and system features (Avin et al., 2018).
How would agendas based on such risk and resilience management compare to existing in-
ternational agendas, for example? Given that extreme risks transcend national borders (e.g. a
pandemic), countries must cooperate through the financing of organizations that can foster in-
formation exchange, coordinate policy, support poorer countries, and set international standards
(Farquhar et al., 2017; Ord, 2020). However, it is challenging to make rivaling countries agree
on common policy agendas. The UN 2030 Agenda on Sustainable Development is an attempt to
establish a global agenda and incentivize countries to pursue shared goals. In this context, the
worldwide adoption of the Sustainable Development Goals (SDGs) across sectors and industries
is a noteworthy success (Fehling, Nelson, & Venkatapuram, 2013; Messerli et al., 2019).
How much do the SDGs contribute to the long-term future? With its seventeen goals, the UN
2030 Agenda covers most systemic vulnerabilities that can reduce or amplify risks. If countries
succeed at achieving the set goals, one would expect higher resilience to risk cascades. However,
with the exception of severe consequences from climate change, the 2030 agenda neglects the most
extreme threats, such as engineered pandemics or transformative artificial intelligence (Messerli
et al., 2019). Disarmament and security issues, which are of high relevance to anthropogenic
risks, are only indirectly addressed in the agenda (Mekonnen, 2020) and require continuous effort
to situate within the SDGs in order to receive the attention they should, given their importance
(Nakamitsu, 2018; UNODA, 2018). In short, while the UN 2030 Agenda is heavily focused
on climate change-related risks, it nonetheless neglects other risks and thus is insufficiently
longtermist.
Pursuing and building on the UN 2030 Agenda is a concrete path forward to place long-
term considerations at the heart of policymaking. The SDGs have been picked up by all sectors
as guiding directives for responsible action. Even though they can be instrumentalized for
reputational boosts, their popularity is likely to have contributed to a shift in the societal
narrative around sustainability (Pedersen, 2018; Scheyvens, Banks, & Hughes, 2016). Yet, many
more resources must be allocated to some of the most pressing issues of our time, such as
the governance of emerging technologies. Developing a post-2030 agenda that incorporates our
suggestions would therefore be a priority for policymakers in the 2020s.
17
2.3 Changing societal narratives
Institutions shape public discourse by creating narratives (M. D. Jones & McBeth, 2010; McBeth
& Lybecker, 2018). Narratives are both embedded in historical context and forward-looking
(Peterson & Jones, 2016). They shape human interaction and sense-making, and get shaped
in return. Narratives provide a lens through which the world is interpreted and understood.
They affect anyone who is part of the relevant society, as private citizens or in public functions.
Narratives have important effects on what forms of coordination are feasible. In the language
of game theory, narratives can shape the expectations of how other actors are likely to behave,
perceived options and the payoff matrix (Gr¨une-Yanoff & Schweinzer, 2008). As such, the
creation of narratives illustrates how institutions affect coordination by reifying a set of shared
values, ideals and models of the world that function as Schelling points, improve communication
and facilitate collective action (Potts, 2008).
In the context of policymaking, societal narratives are relevant because they constrain the
range of problems and solutions that receive consideration (Mair et al., 2019; McBeth & Lybecker,
2018). For example, a policy proposal might be scientifically realistic and technically feasible
but not socially credible if it does not fit the current societal narrative. Changing dominant
narratives represents a leverage point for improving long-term institutional fit by shaping which
problems and solutions receive consideration.
To illustrate this idea, existing dominant narratives include, for instance, neoliberalism, sus-
tainable development and welfarism. The neoliberal narrative advances the notions of the free
market, freedom, individualism and limited government (Chandler, 2014; Hall & Lamont, 2013).
The sustainable development narrative emphasises the interactions between social, economic and
environmental systems, finite resources, equality and justice (Messerli et al., 2019). The welfarist
narrative builds on the notion of welfare and actions that aim to maximise it (Mwabu, 2007).
Each narrative presents a worldview that defines the role of policymaking in shaping society;
whether its primary purpose lies in ensuring economic and individual freedom, redistributing
income, guiding an ecological transition, or lifting humans above the poverty line or maximizing
economic growth. Such narratives can be in direct competition with each other, but are likely
to serve more as complementary perspectives, due to their individual incompleteness.
In what follows, we suggest how the current mix of dominant societal narratives can be
expanded on in order to increase the long-term benefits from policymaking.
Longtermism, as introduced at the beginning of this paper, has the potential to function
as a narrative that facilitates collective action. Different societies throughout history have had
different perceptions and visions of the future, which in turn had manifest effects on their actions
in the present. When designing institutions – say, social security, education or knowledge – the
timescales across which such institutions are meant to operate represent an important consider-
ation. Investing in building institutions that last for a long time only makes sense if it is salient
to the people designing them that the future could, in fact, be vast.
Public goods are commonly understood as worth protecting from, for example, misaligned
market incentives. Transgenerational global public goods merit similar prominence and appre-
ciation in public discourse Bostrom (2013). If appropriately valued, their creation also does
not have to come at the cost of present generations. Unfortunately, issues like climate change
are often framed as intergenerational social dilemmas (Wade-Benzoni, Hernandez, Medvec, &
Messick, 2008): present generations incur the costs generated by past generations to safeguard
future generations. But increasing concern for future generations does not imply discarding the
value of present generations. Modifying the common narrative could lead to more adequate
valuations of contributions to the future, allowing to move away from such zero-sum thinking.
Further, a systems view of the world emphasizes the role of risk and resilience management
which in turn direct attention to prevention, mitigation, recovery and adaptation, while leaving
18
room for value maximisation (Linkov & Trump, 2019; Trump & Linkov, 2020). This under-
standing could make it easier to implement cost-effective preparedness measures whose value
only makes sense through the lens of expected value. Understanding the world as shaped by
complexity can also help humans to move away from unrealistic requirements of certainty and
control. Instead, humanity needs to acknowledge and learn to deal productively with uncertainty.
This, for example, implies a shift to more adaptive planning and focus on learning mechanisms
(Folke, Hahn, Olsson, & Norberg, 2005; Janssen & van der Voort, 2020; Renn & Klinke, 2015).
In order to develop and foster such narratives, we highlight the diverse roles of various
aspects of society, ranging from continuously expanding humanity’s understanding of relevant
issues through scientific research, to the political, civic and cultural spheres nurturing societal
discourse about what humanity collectively considers valuable, virtuous and worth protecting.
***
Summarizing section 2, we have outlined three avenues for improvement – institutional re-
forms, policy agendas and societal narratives – which could increase the long-term institutional
fit of policymaking. More specific improvements could be listed for each dimension of long-term
institutional fit and our proposals are unlikely to be exhaustive. Yet, with this section, we
provide directions to improve policymaking and a basis for scholars to build upon.
While institutional structures, agendas and societal narratives shape and are produced by
policymaking processes, we have so far taken such processes as a black-box. Yet, the realization
of improvements is subject to the constituent parts of policymaking: individual actors whose
interactions and decisions lead to policy outputs. The next section delves into improvements of
decision-making processes with the aim to boost long-term institutional fit.
3 Boosting long-term institutional fit by improving
decision-making processes
Much of the literature on policymaking for future generations takes a high-level view. It examines
the nature of democracy (Boston et al., 2020), the current state and potential reforms of insti-
tutional structures (Gonz´alez-Ricoy & Gosseries, 2016), or intertemporal public choice (Jacobs,
2016). These publications clear up important philosophical issues concerning the promotion of
future generations but, with a few exceptions (John & MacAskill, 2020), do not propose con-
crete policymaking improvements or insights as to how to pursue them. With our assessment
of long-term institutional fit in section 1 and the avenues for improvement outlined in section 2,
we help to bridge abstract, theoretical considerations and wicked policy problems.
To translate general insights into practice, they need to be embedded within the very sub-
strate of policymaking: decision-making processes. Such processes happen within policy net-
works which consist of elected politicians, civil servants, academics, funders, beneficiaries, inter-
est groups and more (Geyer & Rihani, 2010). The coalescence of the perspectives and actions
of these actors shape policymaking. The ensemble of cognitive, social and political forces that
shape policy networks (B. D. Jones & Baumgartner, 2005) also shape the way improvements can
be implemented. Therefore, starting with decision-making processes is a concrete way to boost
the implementation of larger-scale changes.
In this section, we focus on decision-making processes in policy networks as a key vector
for the implementation of improvements. We first outline a simple model of decision-making
processes and then suggest improvements to them.
19
3.1 A simple model of decision-making processes
Decision-making processes define how policy actors navigate uncertainty and weigh competing
preferences to arrive at individual and collective decisions. In their most disaggregated form,
decision-making processes refer to bounded-rational actors processing information and reaching
decisions (B. D. Jones, 2003; Oppenheimer & Kelso, 2015). Bounded rationality means that
actors have limited processing capacity; meaning that anyone, even groups, can only try to
make sense of a fraction of all available information. The selection of relevant information and
the processing thereof are mediated by social cues and the environment (Hertwig & Pedersen,
2016; Simon & March, 1976). These processes of information selection and processing occur at
all levels of policymaking. They concern elected and non-elected actors, and affect day-to-day
as well as pivotal decisions. Whether conscious or subconscious, individual or collective, small
or large; policy is the result of uncountable decisions.
Decision-making processes are shaped by at least five drivers, some endogenous and others
exogenous to policy actors.
First, individual motivation defines the interests of policy actors that drive them to make
decision A instead of decision B (e.g. values or career goals) (Moyson et al., 2017). Second, cogni-
tion and behavioural profiles describe how actors make sense of the world, their underlying beliefs
and their expectations of how systems function. People’s past experiences, their education and
their professional background, among others, influence how they think and behave (Dente, 2014).
Third, the quality of information available imposes limits on the accuracy of results. Consuming
misinformation will lead to a different understanding of the world than reading peer-reviewed
publications and result in different decisions (Ansell & Geyer, 2017; Sanderson, 2002). Fourth,
how actors cope with uncertainty defines the quality of decisions. Common reactions to uncer-
tainty are action aversion or overconfidence (Hafner-Burton, Hughes, & Victor, 2013), which can
be mediated via robust decision-making processes (Kwakkel, Walker, & Haasnoot, 2016). Fifth,
policy emerges from collective dynamics as the aggregation of many micro-decisions and their
interaction (Kerr & Tindale, 2004; Stasser & Abele, 2020). Some properties of collective decision-
making, such as competing preferences, information asymmetries or groupthink, may constrain
decision-making while diversity of perspectives and effective communication, for example, may
improve it.
3.2 Transforming decision-making processes
In this section, we delineate improvements for the five drivers of decision making processes
outlined. These improvements are (in no particular hierarchy): (1) fostering moral reflection;
(2) training systems thinking; (3) improving the science-policy interface; (4) training decision-
making under uncertainty; and (5) facilitating group deliberation. We discuss how we selected
these dimensions and how they can boost long-term institutional fit. We provide general frame-
works to approach these challenges, as well as pointers to the relevant literature. It is, however,
outside of the scope of this paper to treat each of these improvements in detail. Further, the
suggested improvements are, by the nature of the systems in question, inevitably intertwined.
3.2.1 Fostering moral reflection
First, as individual motivation is a core driver of individual decision-making, a key improvement
lies in motivating actors to reflect on the future and expand their circle of compassion to future
lives. Research on intergenerational behavioural ethics has shown that the so called legacy
motive motivates people to act more prosocially towards future generations (Fox, Tost, & Wade-
Benzoni, 2010; Wade-Benzoni, 2006). Independently of how people define the value of the future,
20
it seems that cooperation could be increased if future generations become a more common factor
in decision-making.
Motivating actors to care about future generations can appear like a tedious task. Policy en-
vironments tend to be politicized and policy actors do not always focus on the same core values.
Furthermore, a lot of the features of long-term issues – such as their intertemporality, inter-
personality and high epistemic uncertainty – do not mesh well with human cognitive structures
(Wade-Benzoni, 2019). Biases, such as scope insensitivity, availability bias, egocentricity bias,
optimism bias, or salience bias cause humans to systematically neglect core features of long-term
issues (Aspinwall, 2005; Wade-Benzoni, 2019; Wade-Benzoni et al., 2008; Wiener, 2016).
Given cognitive biases, how can the circle of concern in policymaking better include future
generations? The model of the four psychological determinants of moral action (Baker, 2020;
Rest & Narvez, 1994) paints a first step forward. To take good actions, Rest (1994) proposes a set
of necessary conditions: (1) moral sensitivity (an awareness of the possible moral implications
of an action); (2) moral motivation (the desire to prioritize moral values over self-interest);
(3) moral judgement (the capacity to reason explicitly about moral dilemmas); and (4) moral
character (the courage and willingness to figure out the best plausible option and enact it). The
absence of any one of them can explain a failure to act in line with one’s value system. This
framework thus constitutes a useful starting point for identifying why moral action might be
failing, and how it could be mediated.
Moral philosophy is unlikely to become the primary driver of policymaking, nor does it
have to in order to contribute to progress. Despite constraints and competing motivations,
strengthening moral reflection can increase good faith among policy networks, and incrementally
reinforce standards of moral integrity. A potential benefit is that increasing attention will be
paid to the uncertain nature and scale of problems, which would ease trade-offs in agenda-setting
by, for instance, prioritizing global catastrophic risk mitigation.
3.2.2 Training systems thinking
As a second improvement, actors can improve their systems thinking. Fostering risk and re-
silience management (Linkov & Trump, 2019) requires an understanding of how various parts
of a system relate to one another and how intervening would affect the system (Freeman &
Yearworth, 2014; Monat & Gannon, 2015). For example, if the global temperature increased
by five degrees Celsius, what repercussions would that have on public infrastructure, and how
would that affect the economy, public budgets and social security?
While systems thinking already is in use by many policy institutions including the Food and
Agriculture Organization, the World Bank, the Sendai Framework for international disaster risk
reduction policy and the Paris Agreement from the United Nations Framework Convention on
Climate Change (Grafton et al., 2019), it is far from dominant. As a result, it is not a common
element in policy education or customs.
To correctly interpret and improve systems, policy actors do not need to study systems
science. The core requirement rather consists of building appropriate mental models of systems.
That way, actors can develop realistic expectations of the system at play, calibrate information
search, and effectively identify system properties (D¨orner & Funke, 2017; Doyle, Radzicki, &
Trees, 2008). Examples of system properties include the Pareto principle (Mandelbrot, 1995),
the influence of leverage points (Meadows, 1997), nonlinear dynamics (Helbing et al., 2015),
redundancy (Cook, 1998), path dependency (Bednar, Jones-Rooy, & Page, 2015) and cascading
effects (Turalska, Burghardt, Rohden, Swami, & D’Souza, 2019). Mental models can efficiently
bridge the discrepancy between systemic complexity and the cognitive limitations of actors within
these systems. A goal for governments and organizations would be to provide training to their
teams, allowing them to develop an arsenal of mental models and know when to use which one(s).
21
3.2.3 Improving the science-policy interface
Third, actors should privilege scientific evidence whenever possible, and be able to identify the
appropriate type of evidence given their particular situation. When forming an understanding of
a given issue, actors are dealing with vast amounts of information ranging from meta-analyses,
to think tank reports, case studies, speeches, media articles and more. Good evidence results
from a systematic approach to making sense of reality, which is why the scientific method offers
the most reliable insights on how the world works (Sanderson, 2002).
However, the scientific goal of pushing the knowledge frontier is often difficult to reconcile
with more practical policy goals (Cairney, 2016). Widely reported barriers that prevent effective
research uptake include the lack of access to and relevance of research, the mismatch between
research timelines and policy deadlines, and the lack of funding schemes to support policy-
relevant research projects (Oliver, Innvar, Lorenc, Woodman, & Thomas, 2014).
To facilitate research uptake in policymaking, science-policy ecosystems can be enhanced
especially through the work of interface actors that facilitate lasting collaborations between
scientists and policy actors (Bednarek et al., 2018; Oliver & Cairney, 2019). Collaborations
between policymakers and researchers of complex systems seem of particular relevance. Such
work could yield empirically validated computational simulations to explore nonlinearities and
identify leverage points (Helbing et al., 2015) to improve our understanding of resilience and
cascade dynamics (Egli, Weise, Radchuk, Seppelt, & Grimm, 2019; Fraccascia, Giannoccaro,
& Albino, 2018; Naghshbandi et al., 2020; Nasrazadani & Mahsuli, 2020). Other examples
include synthesis methods that provide state-of-the-art evidence combining multiple disciplines
in a timely manner (Nicolescu, 2014). Building such collaborations should be a joint effort of
policymakers and scientists.
Other practices provide faster access to scientific evidence and thus fit the timing of policy-
making better. Expert opinion aggregation methods, such as the Delphi method for example,
can generate robust decisions based on the current body of knowledge (Adler & Ziglio, 1996).
Or, living systematic reviews, which are semi-automatically updated over time, can provide
real-time state-of-the-art knowledge on policy matters (Elliott et al., 2017). Lastly, just like any
citizen, individual policymakers have to be aware of the challenges posed by the abundance of
information in the digital age and remember their civic responsibility to vet information sources
and avoid misinformation 13).
3.2.4 Training decision-making under uncertainty
Fourth, actors must be equipped to deal with uncertainty and time constraints when prioritizing
information and actions.
Here, the ultimate question is how policy actors make decisions with limited information.
Uncertainty may lead actors to be overly risk-averse or overconfident which, in turn, hinders
decision-making. Time constraints can increase stress, cloud judgement and lead established
decision procedures to break down. Therefore, there is a need for adaptive tools and training of
intuitions to make decisions in uncertain and unstable environments (Khatri & Ng, 2000).
The field of decision-making under deep uncertainty has developed cognitive and digital
tools with empirical backing, for example: multi-criteria decision analyses, dynamic adaptive
planning, or engineering options analysis (Marchau, Walker, Bloemen, & Popper, 2019). While
better forecasts themselves do not seem to be the most significant bottleneck in decision-making
(Dessai, Hulme, Lempert, & Pielke, 2009), the literature on superforecasting provides a treasure
13e.g.see The World Health Organization’s advice on minimising infodemics https://www.who.int/news-
room/spotlight/let-s-flatten-the-infodemic-curve
22
trove of information on robust, adaptive decision-making under uncertainty (Tetlock & Gardner,
2016). People who tend to perform extraordinarily well at this type of judgement are humble,
self-aware, pragmatic analysts with a probabilistic worldview who continuously strive to improve
(Mellers, Tetlock, Baker, Friedman, & Zeckhauser, 2019).
Decision-makers often have to rely on cognitive heuristics (Hertwig, Pleskac, & Pachur, 2019).
This is unavoidable when decisions have to be made under real-world constraints, such as limited
time or processing power. By systematically assessing the performance of these heuristics, we
can identify and retain successful ones and discard those that prove maladaptive. We can
strengthen the use of performant heuristics by providing training to decision-makers. This should
be regarded as a dynamic and iterative learning process which emphasizes the importance of
each of its stages, from data collection and evaluation, to updating and refinement, and training
and deployment.
3.2.5 Facilitating group deliberation
Fifth, decisions are usually made collectively. While the first four improvements mostly pertain
to individual motivations and behaviour, it is important to understand political decision-making
as a collective phenomenon and thus improve how consensus is reached by alleviating information
asymmetries and supporting deliberation processes. Improving decision-making processes is thus
a function of individual motivation, information, individual judgement and collective factors.
Groups can produce better decisions as they bring together different sources of information
(Bang & Frith, 2017) but they also suffer from yet different biases and fallacies (Kerr & Tindale,
2004). Therefore, decision-making support must account for group-specific particularities. The
field of social psychology has aggregated a number of promising leverage points for improving
collective decision-making.
To counteract the tendency to seek consensus without critical reflection (Janis, 1972), per-
spective sharing and constructive criticism have to be fostered in group settings (Kerr & Tindale,
2004). Explicit approaches to facilitating conversations and information exchange can create
common knowledge (Liang, Moreland, & Argote, 1995) and participative processes can boost
performance by facilitating communication and fostering trust (Durham, Knight, & Locke, 1997;
Ludwig & Geller, 1997; Yammarino & Naughton, 1992).
***
All in all, these improvements constitute the building blocks of a decision-making culture that
is more likely to accelerate long-term institutional fit. Many of those improvements are already
under way and find numerous proponents outside and inside policymaking institutions. Our
proposed approach is based on empowerment: equipping policy actors to deal with complexity
and uncertainty more easily, and anchored in recent advances in the behavioural sciences and
decision-making support. Overall, the aim is to boost decision-making processes in policymaking
based on explicit values and tested tools for dealing productively with uncertainty and group
dynamics. The implementation of the five improvements to decision-making processes requires
to be cognizant of the reality of policymaking processes: it must be informed by what we know
about cognition and political realities. Only then will one be able to reliably and effectively
improve policymaking.
23
Conclusion
The philosophy of Longtermism emphasises the importance of coordinating societies across space
and time. Policymaking processes in local and national governments and international organi-
zations are the current most advanced form of explicit value-based coordination at scale. They
foster coordination and affect citizens anywhere in the world. The same processes, however,
suffer from important constraints and shortcomings that prevent them from benefiting future
generations; what is sometimes referred to as political short-termism. In this paper, we artic-
ulated improvements to policymaking that would reduce current constraints and contribute to
unlocking the potential of policymaking for safeguarding and building an exciting future.
In section 1, we introduced the concept of long-term institutional fit as a framework for as-
sessing the capability of policymaking to effectively promote the well-being and safety of future
generations. Long-term institutional fit is unpacked into four dimensions – spatial, temporal,
functional and representational. We conduct a high-level assessment of the current strengths
and weaknesses of current policymaking. In conclusion, we identified some central shortcom-
ings: the relative fragility of multilaterialism (reduced spatial fit), the lack of anticipatory and
preventive capacities (reduced temporal fit), siloed structures inapt for tackling challenges that
emerge from organized complexity (reduced functional fit), room for improvement in policy learn-
ing capacity (reduced functional fit) and lack of formal representation of future generations in
decision-making processes (reduced representational fit). While our assessment is not meant to
be comprehensive or conclusive, it constitutes the foundation upon which we formulate sugges-
tions for improvement. Further, we hope that this framework will motivate and support future
research.
In section 2, based on our preliminary assessment of long-term institutional fit, we formu-
late three avenues for improvement which we consider key bottlenecks to fostering long-term
policymaking. The reform of institutional structures is required to consider future generations
in formal decision-making processes. While such reforms are ambitious and hard to achieve
quickly, it is indispensable to start making progress towards them, by developing and testing
reform proposals and normalizing the consideration of future generations in policymaking.
Policy agendas are the crystallization of attention patterns of policy networks and the re-
sult of a complex and disputed process. Once crystallized, agendas shape which topics policy
actors pay attention to and thus constitute an important leverage point for influencing policy
outcomes. We propose to improve current policy agendas by accounting for the importance of
global catastrophic risks, integrating the core tenets of risk and resilience management (preven-
tion, mitigation, recovery and adaptation) and identifying and targeting systemic vulnerabilities
which play a critical role in mediating shocks.
The third and final avenue of improvement we highlight are societal narratives. Societal
narratives are the substrate based on which democracies decide which societal problems are
pressing and which solutions to consider. We advocate for the extension of current societal
narratives to integrate a due appreciation for the long-term future (longtermism), the creation
of transgenerational global public goods, a systems view of the world and a focus on risk and
resilience management.
Having spelled out avenues for improvement for long-term policymaking, section 3 discusses
how to bring about improvements. We posit that decision-making processes are a common de-
nominator of all policymaking networks and can therefore hinder or accelerate the adoption of
improvements. We outline five dimensions along which decision-making processes can be en-
hanced: fostering moral reflection among individual policy actors, training systems thinking,
improving the science-policy interface, training decision-making under uncertainty and facilitat-
ing group deliberation. Based on a series of semi-systematic literature reviews, we provide an
overview of the relevant literature and pointers for further research.
24
Our work draws on literature reviews, expert interviews and the adaptation of existing theo-
retical frameworks from political science and beyond. In doing so, we aim to inspire research on
long-term policymaking and to equip policy practitioners with an overview of how to transform
policymaking for the long term, thereby improving the interface between policy and science.
Acknowledgements
This research did not receive any specific grant from funding agencies in the public, commercial,
or not-for-profit sectors.
25
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