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Representative:

, International Maize and Wheat Improvement Center (CIMMYT)

Background of the problem:

If we want to provide the growing global population with healthy, affordable and socially acceptable diets while staying within planetary boundaries (Raworth 2017) under climate change, we are facing a huge challenge. Especially in low and lower middle-income countries, affordability is a major issue. Suggestions made by the EAT-Lancet commission (Willett et al. 2019) are not affordable (Hirvonen et al. 2020). Discussions on this topic are filled with emotion while there is a clear need for scientific evidence.                         

Challenges:

There is ample data available from many different sources on separate components of dynamic complex agri-food systems. Bringing them together to provide insight about food system transformation pathways is challenging. The data is often not yet interoperable, partly because the data is from many different sources with different classifications                      

Main questions to be addressed:  

      1. What are the global patterns of consumption?

Changing consumption patterns have been analyzed in isolation at national level in low and middle income countries. Examples include but are not limited to major food staples (Khondoker A Mottaleb et al. 2017; Mottaleb et al. 2018; Khondoker A. Mottaleb, Rahut, and Mishra 2017)

      2. How are these patterns changing over time and space in relation to major drivers of change?

Major primary drivers of change include population growth, income growth, urbanization, climate change. Is it possible to identify tipping points in the past that can provide insight into what happens when there are tipping points in future.

      3. How are dietary transition pathways linked to affordability, health and sustainability?

Complex Systems Science aspects:

Identification of specific emerging patterns and providing forecasts on the possible pathways. The way to do this is open for discussion, this can involve traditional statistical methods (multi stage regression analysis), machine learning (neural networks, random forest) in combination with simulation techniques.   

Possible societal importance/impact:  

The outcomes of this project will feed directly into the current priority setting of international agricultural research of CGIAR, affecting the focus of research for the next decade, and hence have a major impact on food and nutrition security of the resource poor in low and middle-income countries.

Initial literature: 

• Hirvonen, Kalle, Yan Bai, Derek Headey, and William A. Masters. 2020. “Affordability of the EAT–Lancet Reference Diet: A Global Analysis.” The Lancet Global Health.
• Mottaleb, K. A., D. B. Rahut, G. Kruseman, and O. Erenstein. 2018. “Changing Food Consumption of Households in Developing Countries: A Bangladesh Case.” Journal of International Food and Agribusiness Marketing 30(2):156–74.
• Mottaleb, Khondoker A, Dil Bahadur Rahut, Gideon Kruseman, and Olaf Erenstein. 2017. “Changing Food Consumption of Households in Developing Countries: A Bangladesh Case.” Journal of International Food & Agribusiness Marketing 0(0):1–19.
• Mottaleb, Khondoker A., Dil Bahadur Rahut, and Ashok K. Mishra. 2017. “Consumption of Food Away from Home in Bangladesh: Do Rich Households Spend More?” Appetite.
• Raworth, Kate. 2017. “A Doughnut for the Anthropocene: Humanity’s Compass in the 21st Century.” The Lancet Planetary Health.
• Willett, Walter, Johan Rockström, Brent Loken, Marco Springmann, Tim Lang, Sonja Vermeulen, Tara Garnett, David Tilman, Fabrice DeClerck, Amanda Wood, Malin Jonell, Michael Clark, Line J. Gordon, Jessica Fanzo, Corinna Hawkes, Rami Zurayk, Juan A. Rivera, Wim De Vries, Lindiwe Majele Sibanda, Ashkan Afshin, Abhishek Chaudhary, Mario Herrero, Rina Agustina, Francesco Branca, Anna Lartey, Shenggen Fan, Beatrice Crona, Elizabeth Fox, Victoria Bignet, Max Troell, Therese Lindahl, Sudhvir Singh, Sarah E. Cornell, K. Srinath Reddy, Sunita Narain, Sania Nishtar, and Christopher J. L. Murray. 2019. “Food in the Anthropocene: The EAT–Lancet Commission on Healthy Diets from Sustainable Food Systems.” The Lancet.

Representative: Dr. Carla Do Rosario Costa, School of Economics (UU)

Background of the problem:

Knowledge flows within regions are expected to favor the appearance of related industries more than unrelated industries (Neffke et al., 2011). For regions where an industry clusters significantly, most of the knowledge flows are directed into that industry, and it is common to observe high indices of spinoffs (Klepper, 2009; 2010) and entrepreneurship (Feldman, 2001) into that same industry. However, there are cases of regions where more than one cluster succeeds to develop and thrive. We aim to understand such cases and analyze knowledge flows between co-located clusters, unveil their relatedness level, and reveal the types of interchanged skills.

Challenges:

Employees moving across firms have skill profiles. We aim to identify them through machine learning classification. Furthermore, worker mobility allows to determine the network of knowledge flows among firms in the cluster and unsupervised learning algorithms could be used to identify cohesive groups of individuals or firms.

Main questions to be addressed:  

1. What are the skill patterns of workers moving into and out of the cluster industry?
2. Which types of skill profiles move among cohesive groups in the network?

Complex Systems Science aspects:

Embodied knowledge flows in cluster regions are complex to define and categorize, due to the multitude of job classifications, as well as factors such as age, gender, education, experience, tenure, and job function. Identifying patterns is the first step to understanding their impact on firm performance and cluster performance.

Possible societal importance/impact: 

This project aims to deepen our understanding of knowledge flows inside industrial clusters, which contribute to explain firm and cluster performance. Our findings will allow to improve policy designed to foster the creation or development of industrial clusters in economically deprived regions, thus avoiding common wasteful development policy schemes.

Initial literature: 

• Feldman M. The Entrepreneurial Event Revisited: Firm Formation in a Regional Context. Industrial and Corporate Change 2001;10:1-31.
• Hidalgo C., Balland P.-A., Boschma R., Delgado M., Feldman M., Frenken K. et al. The Principle of Relatedness. Papers in Evolutionary Economic Geography (PEEG) 2018;1830.
• Hidalgo C.A., Klinger B., Barabási A.-L., Hausmann R. The Product Space Conditions the Development of Nations. Science 2007;317:482-487.
• Klepper S. Spinoffs: A review and synthesis. European Management Review 2009;6:159-171.
• Klepper S. The origin and growth of industry clusters: The making of Silicon Valley and Detroit. Journal of Urban Economics 2010;67:15-32.
• Neffke F., Henning M., Boschma R. How Do Regions Diversify over Time? Industry Relatedness and the Development of New Growth Paths in Regions. Economic Geography 2011;87:237-265.
• Zhu S., He C., Zhou Y. How to jump further and catch up? Path-breaking in an uneven industry space. Journal of Economic Geography 2017;17:521-545.

Representative: Dr. Erik van Sebille,  Institute for Marine and Atmospheric research Utrecht (UU)

Co-supervisor: Ir. Mikael Kaandorp, Institute for Marine and Atmospheric research Utrecht (UU)

Background of the problem                            

The plastic pollution littering our oceans has been one of the most visible signs of our society’s destructive footprint on natural systems. In the ‘Tracking Of Plastics In Our Seas’ () project, we aim to map the whereabouts of this plastic in our ocean. We do this by creating computer simulations of virtual plastics. These simulations, however, need to be trained with data of observed plastic abundances in the real ocean. But these observations are buried in the scientific literature, and no complete and up-to-date databases exist.

Challenges:

The challenge is to build a data mining workflow that trawls through the peer-reviewed scientific literature and returns data of plastic observations in the ocean and on beaches, geo-tagged to their location and time of sampling.

Main questions to be addressed:  

1. How to automatically identify peer-reviewed papers that contain data on observations of plastic in the ocean and on beaches?
2. How to automatically parse that data into a database?
3. How to geo-tag the plastic observations to location and time of sampling?

Complex Systems Science aspects:

To answer the three questions above requires a wide range of skills in computer science and natural language processing, but also in geoscience and data interpretation.

Possible societal importance/impact:  

A publicly available workflow and database of plastic observations can form the basis of a map of the plastics polluting our oceans. Such a map would help with assessing the efficacy of policy/technological solutions to the plastic problem.

Initial literature:  

• Kaandorp et al (2020) on data assimilation of plastic observations into model for the Mediterranean Sea: and associated article at
• Litterbase portal of curated plastic observations:
• Law (2017) review paper about plastics in the marine environment:
• Nasar et al (2018) on Information extraction from scientific articles: a survey
• Tkaczyk et al (2015) on automatic extraction of structured metadata from scientific literature

Representative: , Van 't Hoff Institute for Molecular Sciences (UvA)

Background of the problem:

Systems chemistry studies transformations with large number of intermediate steps, as in the origin of life problem, for example. Aged oil paintings (think Rembrandt) is a natural laboratory for systems chemistry. They began with several distinct molecules and complexify to include several thousand of chemical species. Our graph automata can automatically ‘discover’ these species and compose a system of non-linear ODEs for reaction kinetics. This reaction network is similar to food web describing how prey and predators coexist in ecosystems: reactant molecules contribute their concentration to the product molecules, similarly to prey contributing its biomass to the biomass of the predators.

Challenges:

Challenges with the reaction network arise from its size. If a reaction network consists of n molecules, then the system of ODEs consists of n equations. The sizes of real world chemical systems grow to several thousands of molecules and few millions of reactions. Moreover, the parameters that describe the speed of reactions (rate parameters) have uncertainty.

Main questions to be addressed:  

1. Can the model be reduced based on the underlying structure of a reaction network?
2. How the uncertainty of rate parameters propagates through the solution of the model and influences final result?

Complex Systems Science aspects:

In this problem we view chemical processes as a complex system represented as a graph and a dynamical system. We provide the reaction network and a dynamical system for further analysis.

Possible societal importance/impact:  

The modeling approach is applicable to other complex chemical and biological processes, such as metabolic processes, protein interactions and prebiotic scenarios. Such systems are big and troublesome when it comes to optimization of system parameters against experimental data. Being able to perform model reduction and uncertainty quantification on such systems may help to improve the match to the experimental data.

Initial literature:  

• Y. Orlova, A.A. Gambardella, R.E. Harmon, I. Kryven & Iedema P.D. (2020). Finite representation of reaction kinetics in unbounded biopolymer structures. Chemical Engineering Journal, doi: 10.1016/j.cej.2020.126485.
• Dmitry Yu Zubarev, Dmitrij Rappoport & Ala ́n Aspuru-Guzik (2015) Uncertainty of Prebiotic Scenarios: The Case of the Non-Enzymatic Reverse Tricarboxylic Acid Cycle. Scientific Reports, 5: 8009, doi: 10.1038/srep08009
• Ralph C. Smith. Uncertainty Quantification: Theory, Implementation, and Applications. Volume 12 of Computational Science and Engineering. SIAM, 2013, ISBN: 1611973228, 9781611973228
• Keith Edwards, T.F.Edgar, V.I.Manousiouthakis (1998)  Kinetic model reduction using genetic algorithms. Computers & Chemical Engineering. Volume 22, Issues 1–2, 1998, Pages 239-246, doi: 10.1016/S0098-1354(96)00362-6
• PengZhao, Samuel M.Nackman, Chung K.Law. On the application of betweenness centrality in chemical network analysis: Computational diagnostics and model reduction. Combustion and Flame, Volume 162, Issue 8, August 2015, Pages 2991-2998. Doi: 10.1016/j.combustflame.2015.05.011