Complex Systems Science
Comment
Stakeholder Type
GESDA
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Topic

Complex Systems Science

Anticipation Committee Chair:

Dirk Helbing

Professor of Computational Social Science

ETHZ

Anticipation Committee:

Carlos Gershenson, Chang-Won Ahn, Francois Grey, Hans J. Herrmann, Indra Spiecker, Isamu Okada, Jeroen van den Hoven, Jose Jacob Kalayil, Lorenzo Fioramonti, Maria Rosa Mondardini, Mark Klein, Ranga Yogeshwar, Sanjana Hattotuwa, Sarah Spiekermann, Yvonne Hofstetter
Topic Brief

Complex Systems Science

Society consists of a wide variety of densely connected, interdependent systems. These networks of networks enable the flow of information, ideas, goods, services and money. In turn, this leads to huge benefits in the form of free media, open democracy, global trade and international finance.
Society consists of a wide variety of densely connected, interdependent systems. These networks of networks enable the flow of information, ideas, goods, services and money. In turn, this leads to huge benefits in the form of free media, open democracy, global trade and international finance.

However, this connectedness also makes our world vulnerable to extreme events in ways that are hard to imagine and even more difficult to avoid. Examples of the negative consequences of networked society include the 2008 global financial crisis, the ongoing climate crisis and the COVID crisis. In each case, the disaster unfolded over a range of interconnected networks with powerful but difficult-to-predict feedback patterns.

The science of complex systems can help here. This discipline seeks to characterise, understand and ultimately manage systems with emergent, self-organised behaviour that cannot be characterised as the sum of their parts. Human society falls squarely into this category, giving this science the potential to help understand and improve it. In particular, the science of complex systems can help us build our future by alternative scenarios and opportunities, while putting humans, their values and a democratic, participatory-governance approach in the centre. It should also allow us to embrace desirable emergent behaviour such as coordination, cooperation, co-evolution, collective intelligence and truth.

KEY TAKEAWAYS

The complexity of human society in the 21st century poses a challenge for those trying to plan and direct its evolution to ensure positive individual and societal experiences. They do, however, have a number of tools at their disposal, one of which is the discipline of Computational social science. This examines and models the multifold relationships at work within society, enabling research to develop better theories and understanding. The complexities of democratic societies are deepened with moves toward Digital democracy, where organisations, individuals and governments make use of digital tools. This requires new insights into issues such as how digital participation can be maximised, and how security of democratic processes can be established and maintained. Our deepening understanding of the roots of Collaborative behaviour can help here, providing insights from computer models and real-world experiments that will encourage healthy collective behaviour. Another key movement is Design for values, which finds ways to create desirable individual and social experiences, and sustainable, affirming social environments.

Topic:

Anticipation Potential

Complex Systems Science

Sub-Fields:

Computational social science
Digital democracy
Collective intelligence
Design for Values
The increasing digitisation of all aspects of life is opening up new opportunities to re-engineer our societies. These efforts are not expected to reach maturity for over a decade, with timelines ranging from between 10--14 years. Getting there will be more about social innovation and building up infrastructure than scientific breakthroughs. Most of the required technical capabilities already exist and the challenge will be more to increase the scale of existing activities. Smart cities were judged to have particularly high disruptive potential, but the anticipatory need was tempered by the fact that this is a field that has already received considerable attention from policymakers.

Anticipatory Impact:

Three fundamental questions guide GESDA’s mission and drive its work: Who are we, as humans? How can we all live together? How can we ensure the well-being of humankind and the sustainable future of our planet? We asked researchers from the field to anticipate what impact future breakthroughs could have on each of these dimensions. This wheel summarises their opinions when considering each of these questions, with a higher score indicating high anticipated impact, and vice versa.

  • Anticipated impact on who we are as humans
  • Anticipated impact on how we will all live together
  • Anticipated impact on the well-being of humankind and sustainable future of our planet
SubField

Computational social science

Social science explores the relationships among individuals within societies and the forces that influence them. For this reason, it has close relationships with network science. However, the networks at play are multifold and complex. They include social, cultural and institutional networks that encompass activities playing out not only between individuals, but also at local, regional and global scales.

Future Horizons:

×××

5-yearhorizon

Data-collection protocols are agreed

The creation of an international forum for computational social science leads to broad agreement between academia, industry and government on the ethics of data collection and data use. This leads to greater collaboration. Grass-roots data-privacy organisations play a key role in these discussions.

10-yearhorizon

Modelling finds increasing success

Models of certain classes of techno-socio-economic-environmental phenomena become increasingly used by diverse stakeholders and civil-society initiatives to explore potential outcomes of a large variety of applications.

25-yearhorizon

Outcome-testing guides social interventions

Computational models of complex techno-socio-economic-environmental systems that simulate networks and interactions become progressively more capable. These models lead to a number of innovative approaches to manage complex dynamical systems that prove the power of the suggested approach: for example to improve sustainability and resilience, or to prevent or mitigate the spread and impact of diseases.
In recent years, increasingly powerful computational models have allowed researchers to capture many properties of these networks and to study the transitions from one type of collective behaviour to another. This has led to the emergence of the new discipline of computational social science, which aims to develop better social theories, gather more meaningful datasets in an ever-growing range of experiments and to create increasingly useful models. These models have already given us a better understanding of a wide range of phenomena, such as pedestrian and traffic flows, social inequality and the spread of diseases.1 The hope is that this approach will help predict the feed-forward effects too, allowing stakeholders such as researchers, commercial entities and governments to collaborate on modelling the potential outcomes of alternative decisions and putting solutions into practice more successfully.

Computational social science - Anticipation Scores

The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:

  1. The uncertainty related to future science breakthroughs in the field
  2. The transformative effect anticipated breakthroughs may have on research and society
  3. The scope for action in the present in relation to anticipated breakthroughs.

This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.

SubField

Digital democracy

One of the challenges for democracy is to engage the widest range of people in its practice and activity. Digital tools offer powerful new ways to do this by offering alternative means for citizens to debate and discuss, to communicate, to find solutions, to allocate resources and, ultimately, to govern.

Future Horizons:

×××

5-yearhorizon

Digital tools become commonplace in local community projects

Small-scale institutions such as town councils and community associations increasingly rely on digital tools that gather data from and about communities to decide how to allocate resources, such as for maintaining roads, funding schools and reducing crime. Concerns about late adopters of digital technologies are given proper consideration.

10-yearhorizon

Digital-aware politicians gain an advantage

Machine-learning algorithms trained on the output of digitally-gathered data provide new insights into community priorities. Politicians engaging with these priorities grow in popularity, thereby reinforcing the importance of digital inputs and participatory frameworks.

25-yearhorizon

Algorithms become vital tools in the democratic process

Advances in the science of complex systems combine with digitally-gathered data and increased access to machine learning algorithms. The result is a mechanism that prompts politicians and policy-makers towards solving real-world problems collaboratively and to measure the success of measures taken.
This creates the potential for dramatic changes in democracy, making it more representative, more efficient and more capable. That said, challenges will remain. Much effort will be needed to engage the broadest range of citizenry so that no groups are disenfranchised, particularly the elderly and technologically disadvantaged. 2 Furthermore, digital tools also open the way for malicious actors to subvert democracy and to undermine society: securing public confidence will require a transparent design and operation of a robust, reliable and trustable, sufficiently participatory framework.

Digital democracy - Anticipation Scores

The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:

  1. The uncertainty related to future science breakthroughs in the field
  2. The transformative effect anticipated breakthroughs may have on research and society
  3. The scope for action in the present in relation to anticipated breakthroughs.

This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.

SubField

Collective intelligence

Technology that enhances collective behaviour clearly has an important role to play in bringing people together, in supporting their collective action and in bringing it to fruition, which is why so much work is being done on collaborative tools.

Future Horizons:

×××

5-yearhorizon

Modelling of complex systems seeds responsive urban infrastructure

Certain areas in global cities become “smart”: they monitor citizen behaviour in a privacy-respecting way and adapt accordingly, such as increasing phone and data capacity for large gatherings, adapting transport timetables and redeploying resources for street cleaning.

10-yearhorizon

Frameworks for ethical research into collective intelligence are agreed

An international forum allows researchers to reach an agreement on a comprehensive set of ethical rules that will govern future large-scale social and collective-intelligence experiments.

25-yearhorizon

Computer models assist transnational collaboration

Online collaborative tools build trust in a way that allows small businesses to span the globe, with individuals working towards common goals with others they have not met.
However, collective behaviour does not always produce the intended or best results. Groupthink and herding behaviour can push groups towards dangerously wrong-headed actions and amplify negative trends, such as racism, unhealthy behaviours and online hate.3 Computer modelling provides a way to study how collective intelligence emerges (and why it sometimes doesn’t).4 Large-scale real-world experiments can help to calibrate these models, provided they can be carried out within a suitable ethical framework. The same models can be used to explore negative outcomes, making it possible, in principle, to find ways to avoid problematic scenarios.

Collective intelligence - Anticipation Scores

The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:

  1. The uncertainty related to future science breakthroughs in the field
  2. The transformative effect anticipated breakthroughs may have on research and society
  3. The scope for action in the present in relation to anticipated breakthroughs.

This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.

SubField

Design for Values

The design-for-values movement is based on the idea that technology can promote certain values and discourage others.5 6 Desirable values include, for example, equality between men and women, healthy living, personal safety, sustainable living, environmental responsibility and valuing democracy. The hope is that, with this approach, positive conversations about such values would be amplified on a suitably designed social media platform, for example, while fake news and and cyber-bullying would be diminished.

Future Horizons:

×××

5-yearhorizon

International design-for-values efforts demonstrate first successes

International forums such as the IEEE see their agreed design-for-values standards increasingly adopted by developers of products and services.7 Discussions on the future of artificial intelligence begin to see progress towards designing for values in AI systems.

10-yearhorizon

Awareness campaigns amplify the interest in design for values

Grass-roots organisations highlight negative issues associated with poorly designed intelligent machines, such as the development of inappropriate relationships with nature and humans, and between them, including poor quality of information sharing. This drives greater interest in the design-for-values approach. Major institutions of higher education provide courses on design for values, complex dynamical systems and global systems.

25-yearhorizon

Policy-makers require design for values as a mandatory part of technology development

Positive results from various high-profile demonstrations of successful technological design-for-values solutions lead to the formation of a global forum aiming to extend the approach to all intelligent machinery.

The technologies of intelligent cities that monitor their citizens in a privacy-respecting way and adapt to their behaviour have the scope to embody values of one kind or another. Such cities are already evolving, and it is important for us to consider — and influence — the values they will promote, in accordance with national constitutions and human rights, as well as the UN Sustainable Development Goals.

Design for values is a complex undertaking, however, and (due to feedback and side effects) such interventions are not always guaranteed to achieve their intended purpose from the beginning. As researchers in the field of machine learning have pointed out, without careful, deeply considered design, technologies can create unanticipated and perhaps unwanted consequences. In any cases, design for values has become an urgent approach to master the challenges in our increasingly technological age more successfully.

Design for Values - Anticipation Scores

The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:

  1. The uncertainty related to future science breakthroughs in the field
  2. The transformative effect anticipated breakthroughs may have on research and society
  3. The scope for action in the present in relation to anticipated breakthroughs.

This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.

References

Citations

Computational social science

  1. D. Lazer. Computational Social Science: Obstacles and Opportunities https://doi.org/10.1126/science.aaz8170

Digital democracy

  1. B. S. Noveck. Five Hacks for Digital Democracy https://doi.org/10.1038/544287a

Collective intelligence

  1. R. P. Mann, D. Helbing. Optimal Incentives for Collective Intelligence https://doi.org/10.1073/pnas.1618722114
  2. A. Williams. Collective Intelligence and Group Performance https://doi.org/10.1177/0963721415599543

Design for Values

  1. N. Tromp, P. Hekkert. Designing for Society: Products and Services for a Better World
  2. European Commission Directorate General for Research and Innovation. Values for the Future: The Role of Ethics in European and Global Governance https://data.europa.eu/doi/10.2777/595827
  3. IEEE. IEEE 7000-2021 - IEEE Approved Draft Model Process for Addressing Ethical Concerns During System Design’ https://standards.ieee.org/standard/7000-2021.html