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Science for Society Brief #1, May 2023. This brief presents insights from an event co-organized by the United Nations Development Programme (UNDP), the Austrian Academy of Sciences (ÖAW), and the International Institute for Applied Sciences (IIASA) to mark the Austrian launch of the Human Development Report 2021-2022, Uncertain Times, Unsettled Lives: Shaping our Future in a Transforming World.

Weather extremes and high population growth are challenging the achievement of SDG 2 Zero Hunger in West Africa. It is essential to understand how crop production decisions by farmers affect the reliability of food production and the stability of their livelihoods. • Future food security scenarios are often based on models that ignore annual weather variability and weather extremes. As a result, this approach also disregards the risk of having lower than expected yields, with adverse consequences for food security and farmer livelihoods. • We propose a stochastic modelling framework that allows to study the reliability of food production under crop yield uncertainty, and explore different strategies to increase this reliability at a minimum cost.

The Arctic sea ice is melting with the magnitude and pace of warming being more than twice than the world average. This increases the potential of global trade to cross the Arctic Ocean. Currently, the Arctic does not have the required infrastructure, and investments into the region are limited. It was estimated that US$1 trillion would need to be invested in the Arctic infrastructure over the next 15 years. These developments imply long planning periods, e.g., it may take more than 10 years to build a brand-new icebreaker. Thus, it is of utmost importance to understand what will be shipped, at what risk, at what cost, and by whom and to whom. Whether raw materials and minerals extraction, fisheries, tourism, transport of manufactured goods, or some other type of activity will prevail on the Arctic routes will determine the kind of infrastructure needed. Will it be shipping from Shanghai all the way down to Rotterdam? Or are these supposed to be short voyages calling at different harbors? Working through these issues with the elaboration of potential scenarios is decisive for evaluating infrastructural investment decisions. Is it economics or geopolitics that will mainly drive the Arctic development? Will it be a kind of economic landscape in which countries compete or collaborate? To help decision-makers deal with this high level of uncertainty in the Arctic, we have launched the “Emerging trade routes between Europe and Asia” scenario building project. In this work, we present the resulting alternative narratives describing plausible futures of shipping in the Arctic until 2050: (i) Arctic as a Global Resource Base, (ii) Arctic as a Global Transportation Route, (iii) Arctic as an Abandoned Land, (iv) Arctic as a Sanctuary, and (v) Arctic as a Shortcut. These cross-scale consistent plausible narratives have been co-created together with representatives of policy, business, and academic communities and correspond to different possible volumes of Arctic shipping: high volume of destination and low volume of transit shipping; high volume of destination and transit shipping; low volume of destination and transit shipping; and low volume of destination and high volume of transit shipping. Key factors that determine these scenarios include infrastructure development, navigation conditions, global and regional governance, regulatory and financial barriers, advanced technologies, and decarbonization. The scenarios can inform the development of short-, medium-, and long-term policy measures aimed at the search of common interests and fostering cooperation in the Arctic for strategic planning of infrastructure projects and shipping operations.

Directions of the future development of the Arctic have been heavily debated in research and policy communities. The latest climate projections supported by an already observed significant sea ice retreat are leading various stakeholders to view the Arctic waters as a prospective commercial transportation route. Moreover, the increasing global demand for fossil resources and their abundance in the polar region are driving the development of new extraction projects. On the other hand, there are increasing calls to protect and preserve the delicate environment of the Arctic. The complexity of this environment is further aggravated by often competing interests of various actors: both Arctic and non-Arctic states, businesses, the local population, and indigenous peoples. Therefore, the Arctic is influenced by a plethora of global and regional factors. In the future, each of these factors may develop in several distinctly different directions. The multiplicity of combinations of such plausible developments of factors makes the strategic planning of activities in the Arctic prone to deep uncertainty. It makes it challenging for the governing actors to ensure various forms of security in the Arctic, such as economic security, energy security, environmental security, political security, health security, etc. In this paper, we present five extreme explorative scenarios covering the uncertainty space of the Euro-Asian Arctic development until 2050: (i) “Global Resource Base”, (ii) “Global Route”, (iii) “Abandoned Land”, (iv) “Sanctuary”, and (v) “Transpolar Shortcut”. Each scenario represents a unique combination of global and regional factor developments. The developed scenarios are loosely coupled with well-established global scenarios and are supported by an extensive literature review and expert inputs. In each scenario, the anticipated development of various forms of security in the Arctic is evaluated. The presented scenarios can be used by policymakers to better understand the future opportunities and threats they may face and to develop and evaluate corresponding policy measures. They can serve as an aid to develop a robust policy portfolio aiming at achieving acceptable levels of various forms of security under a range of global and regional developments.

Recently, there has been a growing interest in national well-being (NW) as a comprehensive measure of nations' prosperity. Many countries have incorporated this concept into their policies. However, the links between various NW dimensions, which can be used to maximize synergies and reduce losses from trade-offs, have been less carefully studied. Various non-pharmaceutical interventions (NPIs) introduced by governments to curb the COVID-19 pandemic constitute a prominent example of such trade-offs. While helping to save lives, NPIs have a widespread impact on various NW facets – from social isolation to the increasing onset of depressive symptoms. Although separate effects of NPIs have been extensively studied, there is little evidence on their integral impact on NW overall. Systems thinking and its practical implementation – causal loop diagrams (CLDs) can help address this challenge. CLDs can explain indirect and cumulative effects of policies on NW variables of interest and identify critical feedback loops and leverage points. Using the OECD National Well-being Framework, Complexity Science Hub Vienna COVID-19 Control Strategies List (CCCSL), and 85 literature sources, we developed a CLD visualizing the links between COVID-19 mitigation policies and NW indicators. Its formal analysis identified the prevalence of indirect effects and feedback loops. It suggests that lockdowns have the most detrimental effect on virtually all NW aspects, while life satisfaction, perceived health, and prevalence of depressive symptoms are the most commonly affected NW aspects by all NPIs. The developed framework, especially if supported by empirical data, can be useful to minimize the adverse effects of NPIs on NW and promote its resilient recovery from any similar pandemic.

Global freshwater supplies are increasingly under stress due to the combined effects of population growth, increasing industrialization, and climate change. As a result, water resources must be managed and allocated in ways that are economically efficient and that account for interdependencies between food production, energy generation, and water provision, often referred to as the food-energy-water nexus. Mathematical modeling provides the means to anticipate what might happen in the future based on certain conditions, allowing decision-makers to optimize water use across sectors while taking into account uncertainties over future water availability.

Detailed sectorial and regional models have traditionally been used for planning developments of respective sectors and regions. However, solutions that are optimal for a sub-system may turn out to be infeasible for the entire system. In this talk, we discuss a new modelling approach enabling the linkage of detailed distributed models of subsystems under joint resource constraints, uncertainty, systemic risks, and asymmetric information. The approach is based on a Stochastic Quasigradient (SQG) iterative solution procedure for nonsmooth nondierentiable optimization problems. The models are linked in a decentralized fashion via a central planner (central "hub") without requiring the exact information about models’ structure and data, i.e. in the conditions of asymmetric information and uncertainty. The sequential SQG solution procedure organizes an iterative computerized negotiation between sectorial (food, water, energy, environmental) systems (models) representing Intelligent Agents. The convergence of the procedure to the socially optimal solution is based on the results of nondifferentiable optimization providing a new type of machine learning algorithms. The linkage problem can be viewed as a general endogenous reinforced learning problem of how software agents (models) take decisions in order to maximize the "cumulative reward". The approach is illustrated by linking distributed agricultural, water and energy sector models for food-water-energy nexus security.

Digitalization has brought about a new challenge for the competition authorities around the world. Digital platforms can engage a large number of users and complementors and wield enormous economic and social power through algorithmic collusion, personalized pricing, control of consumer choice, and other features which the ‘automatic pilot’ enables. Antitrust authorities are supposed to regulate digital giants and other actors of the digital economy, but the economics of digital platforms and platform ecosystems is not sufficiently understood and some of its parts are even perceived by regulators as a ‘black box’. Historically, antitrust emerged as a solution to complex economic puzzles. Back in the late 19th century, the Standard Oil trust took over most oil refineries in the U.S. by constantly reorganizing their business and adapting to the new regulatory environment. The Encyclopedia Britannica calls this structure the ‘Mother Trust’ defined as ‘a maze of legal structures, which made its workings virtually impervious to public investigation and understanding’ (Britannica, 2020). The suite of approaches and tools developed at the early age of antitrust was specifically tailored to dealing with the proliferation of such complex trust-based structures. Over time, however, antitrust was growing increasingly detached from the economic reality it is called upon to address and has eventually transformed into a set of rather formalist and unbending practices. In this legal and institutional environment, the new digital ‘trusts’ have been successful in escaping oversight and regulation by exercising new degrees of adaptivity and flexibility enabled by complex webs of locked-in complementors, consumers and even rivals (often turning into what are called ‘frenemies’) which they create and maintain. This challenges the competition authorities to rethink their methods of defining, measuring, and protecting economic competition.

The future of the Arctic region is a subject of heated debates in both scientific and policy circles. The region has an enormous economic potential as a storehouse of mineral resources and as a provider of shorter and more cost-effective transportation between Europe and Asia. The Arctic is therefore an essential strategic element of the domestic and foreign policies of all Arctic states. In addition, there is an increasing economic interest in the region on the part of non-Arctic states. However, at present, the future of the Arctic region development remains highly uncertain. Scenario building is a suitable methodology to imagine alternative plausible futures of such a complex and multi-dimensional process and to elaborate successful and robust development strategies. This paper provides an overview of the scenario frameworks of Arctic futures presented in the literature and analyses key factors that determine these scenarios. Overall, we find a growing interest of the international foresight research community in the Arctic region that is evident from a number of thorough scenario-building exercises published recently. At the same time, we observe two drawbacks. First, the existing studies lack a numerical element, that is, the overwhelming majority of the scenario frameworks that can be found in the literature are fully qualitative. Quantitative estimates would strengthen the scenario narratives and enrich communication, which make them a useful addition to support a qualitative scenario framework. Second, the existing studies use a mixture of both internal and external factors to describe the underlying uncertainty. This limits the number of factors that can be taken into consideration and may be confusing for a potential user of these scenario frameworks due to the lack of a systemic view. Such a confusion can happen, for example, if some of the external factors underpinning a particular scenario suddenly develop in a direction that was not anticipated within the scenario framework. The effect of such a change on the set of scenarios and the validity of the scenarios despite this change will be of interest to the user, and a clear systems perspective would be conducive to address these questions. Separating internal and external factors in a scenario building exercise is particularly useful given that the volatility of the global geopolitical, geoeconomic and environmental dynamics is only increasing. It is our intention to address these two drawbacks in a scenario building exercise within the “Emerging trade routes between Europe and Asia” scenario-building project led by IIASA within the Northern Dimension Institute (NDI) Think Tank Action co-funded by the European Union and coordinated by Aalto University, Finland.

Collaboration between researchers from IIASA, WU, WIFO, and the IHS provides scenarios of the medium-run economic effects of the lockdown in Austria using the IIASA macroeconomic simulation model. The analysis suggests that the return to the business-as-usual trend may take up to three years after a steep initial economic downturn due to the lockdown, and a gradual recovery thereafter.

Policymakers are confronted with hard-to-address questions, such as • What is the ultimate impact of very different policies on the well-being of citizens? • How to anticipate, which policies will promote well-being the most and which ones will lead to tough trade-offs? • How to focus scarce resources and maximize the positive impact on the well-being of citizens? Economic growth is ceasing down, and, moreover, in most of the developed countries additional growth does not promote the well-being of citizens as much as it used to. But what is well-being? According to a dictionary, well-being is a state of feeling happy, healthy or prosperous. In 1980s, a group of sociologists, philosophers and economists led by Amartya Sen and Martha Nussbaum suggested a framework to understand well-being beyond the economic indicators , such as the GDP. In fact, in the modern world, wellbeing itself becomes a prerequisite for economic growth and for social and economic stability. Governments begin to focus their attention directly on the multi-dimensional national well-being including and going beyond economic and material aspects. They look for new under-utilized resources that would raise the national well-being even despite weak economic growth. To discover effective and efficient solutions, one needs to maximize synergies and reduce losses from trade-offs . Systems analysis offers tools to do so. This challenge was presented to the International Institute for Applied Systems Analysis (IIASA) by the National Economic Council of Israel in 2018. In response, IIASA developed a pilot version of a systems description of the national well-being system that covers four major subsystems: economic subsystem, natural subsystem, human capacity subsystem, and social subsystem, each described by a set of indicators. This Working Paper presents the results of this pilot work. We rely on the OECD well-being framework as a basis to measure multi-dimensional well-being and work with 68 factors, of which 39 represent the OECD indicators. Based on evidence we collate from solid scientific literature, we connect these 68 factors by causal relationships and obtain a comprehensive systems map of the National Well-being System (NWS) (a causal loop diagram) comprising 208 directed links between factors. This systems map allows to trace all indirect effects and feedback loops between factors in a systematic fashion thus helping acquire a holistic understanding about the national well-being system. Empirical evidence clearly indicates that systems thinking is difficult to practice when causal interconnections become more complex, especially when it involves indirect effects and feedback loops. As a formal tool from qualitative systems analysis, our NWS map can assist policymakers to reveal trade-offs and synergies, reduce the problem’s “wickedness” and discipline a dialogue. This version 1.0 can and should be developed further. This includes expert validation and fine-tuning, as well as customizing it to particular national and policy contexts. Eventually, our ambition is to develop a policy simulation tool that enables comparison of different policy options and their ultimate impact on well-being. We invite interested parties to join us in this endeavour!

This paper overviews research being done at IIASA with use of machine learning (ML) methods. We elaborate on promising areas of application and advantages and challenges of using ML. These reflections are done as a part of strategic planning process going on at IIASA at the moment, which aims to come up with a new research strategy for 2021-2030, as well as a supporting research plan. It has been recognized that while applications of ML in commercial sector are numerous and become more and more powerful day to day, it is not yet so common to use ML for creating societal impact. To explore the opportunities in this context and to reflect on what IIASA’s role might be, an internal working group was initiated. This paper emerged from the internal workshop held by the working group at IIASA on June 24, 2019; the workshop invited all IIASA scientists to contribute. The workshop program can be found in Appendix A to this paper.

Humans continually face catastrophes involving natural disasters, such as floods, droughts, hurricanes, and large-scale fires. In today’s highly interconnected world, losses from such incidents have increased greatly due to growing population densities, asset concentration in disaster-prone areas, and environmental change from anthropogenic impacts.

Environmental modelling is a commonly used tool to assist the policy dimension of sustainability problems. Model evaluation is an important process in such decision-making contexts to ensure credibility, and it is often considered a purpose-dependent activity. Models are increasingly used for the purpose of scenario generation to deal with non-probabilistic uncertainties. This change in the model purpose implies a change in the evaluation process, and may require different validation techniques. In this paper, we investigate the existing validation viewpoints and approaches, and compare the general modeling context to the scenario generation purpose in particular. We employ three methods for this investigation: (i) A literature review about validation in environmental modeling, (ii) a text-mining analysis on a large dataset of validation-related publications, and (iii) an online survey conducted among model developers and users in academia, governmental and non-governmental policy organizations, and industry. The results indicate a data-orientation for systematic validation, and that conventional validation approaches are adopted even for models used for the purpose of scenario exploration. Future studies can develop a validation framework for models used specifically for generating scenarios and decision-making under non-probabilistic uncertainty.

Perceived intergenerational unfairness is one of the obstacles for a rapid transition to a low carbon economy whereby current generations have to carry the burden of paying for mitigation, while the next generations will enjoy the benefits for free. Green bonds are believed to be able to distribute the burdens over generations more evenly. In this paper, we examine whether green bonds can indeed resolve the intergenerational inequity challenge. To do so, we employ the DICE model and supplement it with bonds and green tax through which future generations repay the debt. We show that bonds can reduce but cannot completely eliminate the intergenerational inequities. Lower interest rates shorten the initial time period when the society is worse off if a mitigation policy is implemented. Additional compensation mechanisms ensuring that the current generation retains the consumption level equal to the one without mitigation are needed to achieve a Pareto improvement of the mitigation scenario for all generations.

From the social-ecological nexus perspective, environmental inequality is embedded in its root of social problem arising from income inequality. “The urgent global challenges of sustainability and equity must be addressed together” (IPCC2011). This paper intends to explore the link between house income inequality and environmental vulnerability in Rural of China. In the process of rural to urban dominated economy transformation, social structures are changing, and ecosystems are facing stress. Given China's dynamic economic and environmental situation, we aim to provide an assessment in the inequality of energy use and environmental effects in two different systems of urban and rural region in China. The paper deals with three questions: 1. how has household expenditure linked with the energy use directly and indirectly; 2. how has China challenged by inequalities between rural and urban household on the residential energy consumption; 3. how high is the emission estimated in the rural residential energy use? The analysis results indicate obviously that almost half rural family still use no-commercial energy and thus coal is the main commercial energy sources; the per capital CO2 emissions of rural region is much higher than urban region, which is driven by low energy efficiency and less advanced public infrastructure. It address the equity issues that policy should focus on energy affordability and promoting a transition away from biomass to other modern energy sources in rural China. In the paper, the input-output table is employed for accounting the indirect residential energy use and emissions, which is associated with the eight sectors of household expenditure. The data sources are from various household serveries and energy statistics in the period of 1990 to 2016.

This paper is a product of the International Dialogue on Science and Technology Advice in Foreign Ministries (Vienna Dialogue) in October 2016, involving more than twenty nations and several international organisations. The event was a key step to further develop the Foreign Minister Science and Technology Advisor Network (FMSTAN), growing from an initial group of five nations. The Vienna Dialogue was convened by the Fletcher School of Law and Diplomacy, Tufts University, and the International Institute for Applied Systems Analysis (IIASA) at the Vienna headquarters of IIASA, bringing together diplomats from foreign ministries to consider the value of evidence for informed decision-making by nations with regard to issues, impacts and resources within, across and beyond national boundaries. The evidence comes from the natural and social sciences with engineering and medicine as well as other areas of technology. By building common interests among nations, science is a tool of diplomacy, promoting cooperation and preventing conflict in our world. Science diplomacy was discussed as an international, interdisciplinary and inclusive process to help balance national interests and common interests in view of urgencies today and across generations in our globally-interconnected civilization.

Dynamic global vegetation models (DGVMs) are now indispensable for understanding the biosphere and for estimating the capacity of ecosystems to provide services. The models are continuously developed to include an increasing number of processes and to utilize the growing amounts of observed data becoming available. However, while the versatility of the models is increasing as new processes and variables are added, their accuracy suffers from the accumulation of uncertainty, especially in the absence of overarching principles controlling their concerted behaviour. We have initiated a collaborative working group to address this problem based on a ‘missing law’ – adaptation and optimization principles rooted in natural selection. Even though this ‘missing law’ constrains relationships between traits, and therefore can vastly reduce the number of uncertain parameters in ecosystem models, it has rarely been applied to DGVMs. Our recent research have shown that optimization- and trait-based models of gross primary production can be both much simpler and more accurate than current models based on fixed functional types, and that observed plant carbon allocations and distributions of plant functional traits are predictable with eco-evolutionary models. While there are also many other examples of the usefulness of these and other theoretical principles, it is not always straight-forward to make them operational in predictive models. In particular on longer time scales, the representation of functional diversity and the dynamical interactions among individuals and species presents a formidable challenge. Here we will present recent ideas on the use of adaptation and optimization principles in vegetation models, including examples of promising developments, but also limitations of the principles and some key challenges.

Recessions are economic downturns that can be recognized from macro-indicators such as the Dow Jones Industrial Average (DJIA) and the Federal Reserve Interest Rate (FRIR). To provide early-warning signals of recessions and similar systemic transitions, here we propose a new approach based on pattern recognition, called inclination analysis [1, 2]. For this purpose, we develop a stochastic model based on time-series analysis to assess the probability of a recession to occur at a given moment in the past, present, or future. Calibrating our model to data proceeds in three steps, involving the coarse-graining of the available input time series, the identification of short series motifs that foreshadow recessions, and the optimization of key model parameters according to the model’s desired forecasting horizon.