Case study description

AI NAVI includes four case studies or respectively one case study in four countries.

Basic conceptual embedding in the project

At the core of AI NAVI is the examination of behavioural choices in dealing with complex societal challenges. This makes it necessary to examine the empirical situation more closely. To this end, four case studies will take place within AI NAVI, which will serve to better understand the relationship between behavioural decisions, the use of applications that rely on artificial intelligence algorithms, and societal negotiation.

In the four case study countries, empirical data will be generated in parallel and closely integrated with each other, which will inform the research in WP3, and conversely also provide concrete starting points and details for the latter. It is of central importance that the results of the German case study are complemented by the three other case studies. Therefore, the case studies are closely linked conceptually and organisationally in order to generate data that is comparable with each other, but sufficiently culturally different from the situation in Germany. This makes it necessary to select case study countries where the standard of living is comparable to Germany but have a geographical distribution and cultural background sufficiently different from Germany without deviating too much from each other. The choice of the remaining case study countries therefore fell on the USA, Australia and the UK, which fulfil these conditions.

The two thematic focal points, climate change and pandemics, come into play particularly in the case studies and form the foundation on which the examination of the contexts that AI NAVI investigates takes place.



With the increasing clarity of how complex systems express themselves on societal realities, such as climate change or global pandemics, the idea of innovative, AI-based solutions is increasingly coming to the fore. In a review article (Rolnick et al 2019) aimed at wide circles within and outside academia, the authors call for a research effort into more research on how AI systems can be used to combat climate change. From electricity networks increasingly controlled by AI to the machine-assisted management of forestry plants, the review article contains a variety of possible application areas in which AI systems can be profitably used to combat climate change. Another point is the possibilities of individual behavioural change.

However, ideas to influence behaviour with the help of AI suffer from a fundamental research gap: that the cognitive and social foundations of such AI-based behavioural adaptation are not clear. What would be needed, in order to influence behaviour not only in the sense of permanent advertising, would be algorithms that could be called “socially informed neural networks”. Such algorithms would allow behavioural adjustments to be made not only on an individual basis, but also on a social basis, e.g. when which means of transport would be most climate-friendly for a journey, depending on social behavioural patterns that follow from multiple cultural, economic, legislative, and normative underpinnings such as commuting.

The AI NAVI case studies serve as an empirical sandbox for such behavioural adaptations that come about with the help of AI algorithms. They are intended to create the possibilities for developing such “socially informed neural networks” by investigating both the behavioural changes influenced by AI systems and the social and individual repercussions of such use.

 Case study countries









(very high)


(very high)


(very high)


(very high)


56.39 (medium)




(very low)


(very low)

Greenhouse gas emissions per capita[3]

9.72 t

6.80 t

18.44 t

24.63 t

Covid infections per 100,000 inhabitants[4]





The project includes four case studies or respectively one case study in four countries, i.e. Germany, the US, the UK, and Australia. These case studies are intended to provide a background for the study of the research objectives and a specific behavioural domain for the experimentation and empirical research. They were chosen because of their similarity in standard of living, which promises to find common behavioural patterns or lifestyles, and their dissimilarity in terms of their reaction to climate change and the Covid19-pandemic. This may provide the opportunity to detect cultural components of the behavioural domain and the associated decision-making.


Case study phases

The case studies are divided into four phases, the first of which will only take place in Germany, while the following three phases will run in parallel in all four case study countries. Each of these phases is built around a workshop, the content preparation, conception and organisation of which is conditional on the work in the corresponding phase. The workshops in the different case study countries will take place close to each other towards the end of the phase, but not completely at the same time, in order to be able to react flexibly to results from the other case studies. The follow-up to the workshops held in each phase forms the starting point for the following phase – with the exception of the last one, which is primarily for project presentation and evaluation. Each of the phases is projected to last between 7 and 9 months, with a buffer that can be used as needed to respond to individual adjustments. These workshops will follow a “safe space”-concept that was developed by the partners in previous projects and will be adjusted to AI NAVI.

Phase 1:

The first phase initially takes place only in the German case study, serves to lay the foundation for the further empirical work and is divided into two components, with the associated workshop planned as a workshop with two conceptual foci.

The first component consists of processing the research on the connection between lifestyles, consumer behaviour and previous behavioural adaptations on one hand and climate change and pandemics on the other. For this purpose, the research landscape will be examined with the help of desk research, central experts, such as climate researchers or virologists, will be identified, and a first preliminary mapping of the social interaction with complex systems and involved AI use will be carried out. This culminates in an expert workshop.

The second strand mirrors the first with a similar focus but reversed perspective and deals in particular with the psychological dimension of the relationship between lifestyles, consumer behaviour and behavioural adaptations, i.e. in particular the cognitive bases of behavioural influence. This strand will be combined with the first strand in the above-mentioned workshop in order to identify behavioural patterns and behavioural adaptations that are particularly suitable for investigation and on which the further phases can focus.

This is informed and extends on the work of the Corona+ module in the planning grant that already provided first insights into these relationships, but falls short of allowing the more extensive work in the full project.

Phase 2

Based on the more precise identification of appropriate behavioural patterns, Phase 2 will begin the actual research in all case study countries, which will amount to participatory workshops with different, heterogeneous stakeholders. The aim is to add a more general perspective to the expert perspective of the first workshop, to work out possible culture-specific differences and to conduct gamification and psychological experiments as well as include first inputs from the AI research in WP3.

Therefore, phase 2 is divided into three parts: First, the results from phase 1 will be processed, in particular to provide a demonstrator, or a “wizzard of oz” system, with which the connection between behavioural adaptations and AI applications can be investigated. In the second part, the exchange between the work packages takes place and the methodological preparation of the workshop with regard to the results from phase 1 is completed. The third part is the organisational preparation and conduction of the workshop, in which the connection between behaviour and the effects of complex systems with regard to the identified behavioural patterns is worked out by the workshop participants with the help of participatory systems mapping. Crucially, possible gaps are to be identified and the concrete handling of the prepared demonstrator system is to be evaluated in order to develop a more precise understanding of the possibilities for influencing behaviour. Furthermore, the workshop participants will not only be tested as users, but through the specific methodological approach, the participants’ ideas will also serve as an impetus for further AI and experimental research and elements of AI co-design and co-creation will be used.

In the course of the workshop the designed gamification and psychological experiments will be conducted, as well as further investigation into the complexity competences. This is done especially under the guidelines set by the primary research questions of the project, in particular the investigation of the cognitive foundations and the individual and social influence of active or passive AI use, the conditions of a complexity “sweet spot” and behavioural adaptation through the use of applications.

Phase 3

Phase 3 builds on the outcomes of the previous phase and begins to process the workshop, particularly in terms of the findings for the experiments in WP4 and the research in WP3. The possibilities for influencing behaviour from the previous phases are processed in this phase to identify possible intervention points that can be managed with the use of AI systems. This will be presented and adjusted in a further workshop with stakeholders mainly from NGOs, administration and politics.

In turn, specific possibilities for the use of such AI systems will be developed through systems mapping and other methodological approaches, which will further deepen the results from phase 2 and add a macro perspective to them.

Phase 4

Finally, Phase 4, in close cooperation with WP5, prepares the results of the previous workshops so that stakeholders and decision-makers from politics, industry and civil society are presented with concrete options for action to deal with such societal challenges, as well as which possibilities there are for using innovative AI-based solutions for behavioural adaptations in the context of “smart climate change” and “smart pandemics”. In a concluding workshop, the conditions for innovations will also be discussed and how they can be implemented in society – based on the research that has taken place on their cognitive and cultural foundations – or what resistance they may encounter.


Case study responsibilities


Johannes Gutenberg University Mainz

The Johannes Gutenberg University was founded in 1477 and is located in the capital of the federal state of Rhineland-Palatinate, where Johannes Gutenberg invented printing more than 500 years ago. Today, some 32.500 students, 10 percent from abroad, study at JGU (, making it one of Germany’s largest universities. With 75 fields of study and more than 260 degree courses, JGU offers an extraordinarily broad range of courses. JGU enjoys global eminence as a researchdriven university and regularly achieves solid positions in international research rankings. Successes in the Excellence Initiative of the German federal and state governments have confirmed JGU’s academic status. Annually, about 700 PhD students complete their studies at JGU. Another attribute of JGU is its research-oriented teaching – which incorporates research-based topics in the curricula early on. Similar emphasis is placed on promoting and mentoring young research talents. JGU also considers the exchange of knowledge with society as one of its key duties. As an open university, JGU offers the populace a unique portfolio of information dissemination concepts that extend far beyond the scope of standard popular academic formats. Through its system of university governance, JGU makes sure that its members participate in the strategic planning and that outstanding academics get involved.

Research expertise related to AI NAVI

Based in the Institute of Sociology is the Chair of Sociology of Technology and Innovation. With its attached Social Simulation infrastructure (TISSS Lab) it is engaged in the investigation of complex social systems. Analysing social phenomena around the production, the structures and the consequences of social innovations, helps to understand, describe and explain the complex dynamics and long-term effects of innovative change. For research, these complexity aspects require a computer-based lab research infrastructure, which supports a mix of quantitative and qualitative empirical methods combined with innovative methodological approaches from Computational Social Science such as social simulation. Especially, long term impact assessment of changes in interactional behaviour between stakeholders can be valuably addressed and investigated by such methodology.

Publications of institution

Ahrweiler, P., Frank, D., & Gilbert, N. (2019). Co-Designing Social Simulation Models For Policy Advice. In 2019 Spring Simulation Conference (SpringSim) (pp. 1–12). Tucson, AZ, USA, USA: IEEE. (peer-reviewed publication published in July 2019)

Ahrweiler, P., Frank, D., & Gilbert, N. (2019). Co-Designing Social Simulation Models For Policy Advice. 2019 Spring Simulation Conference (SpringSim) Tucson, AZ, USA (peer-reviewed accepted conference paper)

Herget, F., Kleppmann, B., Ahrweiler, P., Gruca, J., & Neumann, M. (2021/22). How perceived complexity impacts on comfort zones in social decision contexts – Combining gamification and simulation for assessment. Journal of Artificial Societies and Social Simulation special SSC2021 issue (peer-reviewed, accepted, to be published in 2021/22)


University of Surrey / CRESS

The University of Surrey has excellent academics whose mission is to lead pioneering research and innovation to create new thinking around, and to provide practical solutions for, some of the world’s main technological challenges. It works in partnership with international academia, industry, policy makers and commerce. Innovative and dynamic, and with around 15,000 students, SURREY is the Times and Sunday Times University of the Year 2016. It also ranks fourth in the Guardian University Guide 2016 and eighth in the Complete University Guide 2016. In the 2015/2016 QS World University Rankings, it is awarded five stars, the highest rating achievable, and is placed within the top one per cent of global higher education institutions. Involved in EC projects for more than 25 years, including around 190 funded from the FP7 and ongoing Horizon2020 programmes, SURREY has extensive experience of acting as both coordinator and beneficiary. It excels at multidisciplinary and cross border research and benefits from excellent professional and administrative support. The Centre for Research in Social Simulation CRESS, headed by Professor Nigel Gilbert, is involved in a number of research projects applying simulation to areas such as environmental management, understanding value chains, the governance of science, web-based social networks, and basic research on modelling the evolution of social structure. It has a strong reputation in the methodology of and application of agent-based modelling. Its work has been supported by the European Commission through sixteen project grants over the past 14 years and also by grants from the UK Research Councils.

Research expertise related to AI NAVI

CRESS provides expertise in social simulation including Agent Based Modelling (ABM), qualitative and quantitative social science methods, complexity science and expertise in participatory modelling, systems thinking and co-production approaches to aid individuals and groups in their understanding of, interaction with and steering of complex adaptive systems. Our innova-tive participatory systems mapping (PSM) and subjective network analysis methods, allow be-spoke design of workshops, complex system representation and analysis for multiple stake-holder and system contexts and are extensively used by the UK government and diverse stakeholder groups. Our freely available PRSM software,, allows collaborative mapping and analysis workshops to be run online.

Publications of institution

Ahrweiler, P., Frank, D., & Gilbert, N. (2019). Co-Designing Social Simulation Models For Policy Advice: Lessons Learned From the INFSO-SKIN Study. In 2019 Spring Simulation Conference (SpringSim) (pp. 1–12). Tucson, AZ, USA, USA: IEEE.

Gilbert, N., Ahrweiler, P., Barbrook-Johnson, P., Narasimhan, K. P., & Wilkinson, H. (2018). Computational Modelling of Public Policy: Reflections on Practice. Journal of Artificial Societies and Social Simulation, 21(1), 14.

Calder, M., Craig, C., Culley, D., de Cani, R., Donnelly, C. A., Douglas, R., Edmonds, B., Gascoigne, J., Gilbert, N., … Wilson, A. (2018). Computational modelling for decision-making: Where, why, what, who and how. Royal Society Open Science, 5(6), 172096.

Barbrook-Johnson, P., Badham, J., & Gilbert, N. (2017). Uses of Agent-Based Modeling for Health Communication: the TELL ME Case Study. Health Communication, 32(8), 939–944.

Kolkman, D. A., Campo, P., Balke-Visser, T., & Gilbert, N. (2016). How to build models for government: criteria driving model acceptance in policymaking. Policy Sciences, 49, 1–16.

Rowden, J., Lloyd, D. J. B., & Gilbert, N. (2014). A model of political voting behaviours across different countries. Physica A: Statistical Mechanics and Its Applications, 413, 609–625. Gilbert, N. (2007). A generic model of collectivities. Cybernetics and Systems: An International Journal, 38(7), 695–706.


Arizona State University

Arizona State University (ASU) is a public metropolitan research university on five campuses across the Phoenix metropolitan area and four regional learning centers throughout Arizona. ASU’s charter is based on the “New American University” model created by ASU President Michael M. Crow upon his appointment as the institution’s 16th president in 2002. It defines ASU as “a comprehensive public research university, measured not by whom it excludes, but rather by whom it includes and how they succeed; advancing research and discovery of public value; and assuming fundamental responsibility for the economic, social, cultural and overall health of the communities it serves.” ASU is one of the largest public universities by enrollment in the United States. As of fall 2019, the university had nearly 90,000 students attending classes across its metro campuses, more than 38,000 students attending online, including 83,000-plus undergraduates and more nearly 20,000 postgraduates. The university is organized into 17 colleges, featuring more than 170 cross-discipline centers and institutes. ASU offers 350 degree options for undergraduate students, as well as more than 400 graduate degree and certificate programs. The 2019 university ratings by U.S. News & World Report rank ASU No. 1 among the Most Innovative Schools in America for the fourth year in a row. Since 2005, ASU has been ranked among the top research universities in the U.S., public and private, based on research output, innovation, development, research expenditures, number of awarded patents and awarded research grant proposals. ASU is currently ranked among the top 10 universities—without a traditional medical school—for research expenditures. It shares this designation with schools such as Caltech, Georgia Tech, MIT, Purdue, Rockefeller, UC Berkeley, and the University of Texas at Austin. ASU is classified as “R1: Doctoral Universities – Highest Research Activity” by the Carnegie Classification of Institutions of Higher Education. The university is one of the fastest growing research enterprises in the United States, receiving $618 million in fiscal year 2018.

Research expertise related to AI NAVI

The Center for Smart Cities and Regions’ (CenSCR) mission is to advance urban and regional innovation to make more inclusive, vibrant, resilient and sustainable communities. CenSCR collaborates with researchers, policy-makers, planners, entrepreneurs, industry and the public to enhance the ability of cities and regions to responsibly use emerging technological infrastructures and improve quality of life. “Smart technologies” and “big data” have rapidly emerged as hoped for solutions to many of the challenges cities and regions face. Yet, there is often a disconnect between the efforts of technology innovators and the local needs and context of policy-makers and communities. Leveraging resources from across ASU, CenSCR bridges this gap between innovations in data, technologies and urban governance to develop anticipatory capacities and responsible innovation processes to create positive futures for cities, regions and their diverse communities. CenSCR generates ideas, methods, scenarios, networks and spaces for collaboration, engagement, educational programs and other research products to enable our partners to leverage technological innovation to create the urban and regional futures they want. The center serves as a living laboratory for ASU’s own efforts in creating a smart campus, with opportunities for undergraduate and graduate students to work with multi-disciplinary teams and cross-sectorial teams on real world problems, as well as providing continuing and professional education to city officials on innovation, entrepreneurship and governance.


SFIA / Dr. Alex Smajgl

Managing Director, Sustainable Futures Institute Australia (SFIA) & Mekong Region Futures Institute (MERFI), North Warrandyte 4810 Victoria, Australia

Dr Alex Smajgl is the Managing Director of SFIA and MERFI. His work is focused on natural resource management in the context of climate change adaptation involving highly participatory policy and planning approaches to effectively bridge research and policy. His project work involves the assessment of sustainable development and climate adaptation strategies, based on advanced integrated assessment modelling. He worked in many parts of Australia and Asia on climate change adaptation, the water-food-energy Nexus and the implementation of Sustainable Development Goals, which was largely funded by DFAT, USAID, CGIAR, ADB, World Bank, and GIZ. Most recently, several of his research projects delivered towards transboundary water management solutions for the GEF and FAO involving improved governance and innovative incentive mechanisms to improve the resilience of communities to climate change.

Prior to 2014, Dr Smajgl worked as a senior research scientist (and intermitted as research director) for the CSIRO in Townsville, Australia. He also established and managed offices in Jakarta, Indonesia, and Bangkok, Thailand. He coordinated large-scale participatory research projects on the water-food-energy Nexus in the context of climate adaptation, sustainability, resilience, poverty and environmental outcomes in Australia and in Southeast Asia (i.e. Mekong region, Indonesia). Scientifically, his work focused on testing participatory process designs, decision making processes, and integrated modelling to effectively link policy and research. His work in Australia focused on climate adaptation, water management, coastal ecosystems and livelihoods in the Great Barrier Reef region.

Prior to 2003, Dr Smajgl worked as a Research Associate/Assistant at the University of Münster where he developed macro-economic models to advise the European Commission and several German Ministries on climate policy outcomes, natural resource dynamics, energy security, and international trade. At the University he lectured Environmental Economics, Natural Resource Management, Microeconomics, and Computational Modelling in the context of Energy and Climate Change Economics. His PhD in climate change economics was funded by the VW Stiftung.

Relevant publications of institution

  • Moallemi A., de Haan F. J., Hadjikakou M., Khatami S., Malekpour S., Smajgl A., Stafford Smith M., Voinov A., Bandari R., Lamichhane P., Miller K. K., Nicholson E., Novalia W., Ritchie E. G., Rojas A. M., Shaikh M. A., Szetey K., and Bryan B. A. 2021. Evaluating Participatory Modeling Methods for Co-creating Pathways to Sustainability. Earth’s Future, 9, e2020EF001843.
  • Voinov, A., et al., 2018. Tools and methods in participatory modeling: selecting the right tool for the job. Environmental Modelling and Software, 109, 232-255.
  • Smajgl A, Barreteau O, 2017. Framing options for characterising and parameterising human agents. Environmental Modelling and Software, DOI:1016/j.envsoft.2017.02.011.
  • Smajgl, A., & Ward, J. (2015). Evaluating participatory research: Framework, methods and implementation results. Journal of Environmental Management, 157, 311-319.
  • Smajgl, A., Foran, T., Dore, J., Ward, J., & Larson, S., 2015. Visions, beliefs and transformation: Exploring cross-sector and trans-boundary dynamics in the wider Mekong region. Ecology and Society, 20(2):15.
  • Smajgl A, 2015. Simulating Sustainability: Guiding Principles to Ensure Policy Impact. Lecture Notes in Artificial Intelligence, 9086, 3-12.
  • Hassenforder, E., Smajgl, A., Ward, J. 2015. Towards understanding participatory processes: Framework, application and results. Journal of Environmental Management, 157, 84-95.
  • Bohensky E, Smajgl A, Brewer T, 2013. Patterns in household engagement with climate change in Indonesia. Nature Climate Change. DOI: 10.1038/nclimate1762.

[1] Human Development Index 2021 (figures for 2020)

[2] Climate Change Performance Index 2021

[3] Figures for 2018 based on (Ritchie & Roser, 2020)

[4] Based on official figures by the Johns Hopkins University September 13th, 2021