COMPLEX-IT: A software package for case-based temporal analysis, including the usage of QCA

Much thanks to Sofia Pagliarin and Lasse Gerrits for the chance to present at the Time-in-QCA workshop at Erasmus University.




The Time-in-QCA (TiQ) international workshop is an opportunity for scholars to discuss the different ways in which time and process can be integrated into the logic and workings of Qualitative Comparative Analysis (QCA) as a research method and approach. The TiQ workshop is organised as a series of roundtables to foster constructive exchanges and discussions. Because of the specialised, and at the same time informal format of the workshop, we welcome work-in-progress ideas and ongoing empirical research integrating the time dimension into QCA both theoretically and methodologically. 



For my talk, I presented on COMPLEX-IT: A software package for case-based temporal analysis, including the usage of QCA.







Health CASCADE Workshop: Co-producing complex systems interventions for public health

On 28 and 29 Sept 2022, I had the opportunity to present on Co-producing complex systems interventions for public health, as part of the Health CASCADE three-day workshop in Amsterdam on co-creation.


It was a lot of fun and a really brilliant group of faculty and students. Thanks again to Mai Chin A Paw, Kunshan Goh and the rest of the team for organising the event, and to Sebastien Chastin for the invite, and to everyone that attended the event. I really enjoyed the discussion and I hope the ideas we discussed prove somewhat useful.




Before turning to my talk, here is a bit more about Health CASCADE. It is a brilliant project and something others should explore and promote! It is very much at the leading edge of co-creation for health.


Health CASCADE is a Marie Skłodowska Curie Innovative Training Networks project funded by the European Union (H2020 MSCA ITN) (Project number 956501).


The aim of Health CASCADE is to foster the next generation of highly trained research leaders to develop evidence based guiding principles, novel tools, and new technologies to make co-creation an effective tool to fight complex public health problems through a European Joint Doctoral Programme.


Global health challenges confront us all as individuals and communities – from obesity to pandemics, cancer to dementia – magnified by climate change and increasing inequality. These challenges are complex problems arising from multiple interconnected factors and feedback loops, resistant to existing public health programmes. We need new ideas and new approaches such as co-creation. By bringing together citizens, academics, businesses, and civil organisations, the project aims to co-create effective solutions to these complex problems.



This two-day workshop explored the value of integrating complexity science and co-production for developing effective, evidence-based tools for addressing complex public health problems. (HERE IS THE LINK to the PDF of my Presentation)




A complex systems approach has been proposed as a powerful toolkit for addressing complex public health problems, including the important role of place. In turn, co-creation has gained traction for addressing the complexities of public health policy, practice, and promotion, particularly around issues of inequality and inequity. While both approaches offer vital strategies for addressing complexity in public health, researchers are only beginning to explore their integration. Hence the purpose of this workshop.



Day 1 provided a framework for thinking about complexity in public health. To develop this framework, we began with an introduction to the complexity sciences, including a map of its present-day trajectories. From there we examined the current challenges the field faces. Particular focus was given to the failure of most complexity science approaches – particularly in terms of computational modelling – to effectively engage stakeholders in the model building process, as well as the development or evaluation of public health policies and practices. Given our public health focus, the COVID-19 pandemic was used as our case study. We ended the day highlighting some examples where progress has been made in integrating complexity science and co-production, particularly participatory systems mapping and case-based complexity – which attendees got a chance to explore.


Day 2 involved a series of break-out, small-group discussions. The first explored, from both an epistemological and practical level, which approaches to co-creation and complexity science might work best together (or not), or critically inform or challenge one the other, including different methods and tactics. The second session explored what sorts of methods or research projects, or case studies participants could develop to advance the integration of these two approaches to address complex public health problems.


HERE IS A KEY POINT – WHICH WE SOUGHT TO ARRIVE AT, BUT STILL HAVE LOTS TO DO TO GET THERE. Co-creation emerged of late in response to the limitations of science and policy and practice. Those same limitations are often found in the complexity sciences. How can co-creation address those similar limitations in the complexity sciences? In turn, given its focus on collective decision making, co-creation struggles with complexity and systems thinking. How can the tools of complexity science help? Be it systems mapping, computational modelling, or network analysis?





·      Here is an open-access book by Barbrook-Johnson and Penn that is the gold standard on practical guidelines for doing systems mapping, including participatory systems mapping.


·      Here is an article by two CASCADE members (Lead author, Niamh Smith and co-author, Sebastien Chastin), using systems mapping and complex network analysis.


·    Here is the link to CECAN and its resources (complexity evaluation toolkit, tools for choosing appropriate evaluation methods) for engaging stakeholders and doing policy evaluation from a complex systems perspective. CECAN stands for the Centre for the Evaluation of Complexity Across the Nexus.


·      Here is an open-access article by me and my colleagues that used participatory systems mapping to develop a co-created policy agenda for air quality and brain health and dementia.


·     See the community engagement work around co-creation and systems thinking being done by Sharon Zivkovic and colleagues.

  • Here is the link to COMPLEX-IT, the software package my colleagues and I developed for helping evaluators, civil servants, healthcare experts and public and third-sector


Mapping Complexity’s Adjacent Possible: Where Are We (Not) Headed?

On 27 Sept 2022, Lasse Gerrits and I had the opportunity to present on our Atlas of Social Complexity project for the RICH, the Radboud Interfaculty Complexity Hub. The focus of our presentation was, Mapping Complexity’s Adjacent Possible: Where Are We (Not) Headed?

It was a lot of fun and a really brilliant group of faculty and students. Thanks again to Marcos Ross, Hitomi Shibata, and Jerrald Rector for organising the event, and to everyone for the excellent discussion, which helped us to develop our ideas further. In particular, we enjoyed the discussion on the need for vertical (multi-level) systems thinking (Jerrald Rector) and the importance of integrating complexity thinking into primary, secondary and university education (Hubert Korzilius), as well as the possibility of developing a series of formalisms for systems thinking (Fred Hasselman).


The origin and development of the complexity sciences is well-documented. Once deemed a minority interest, the complexity sciences have been taken up in many fields such that some of its aspects have become adopted generally. The complexity map, which we have developed (2021), demonstrates this widespread popularity.A major challenge remains: the complexity sciences have run into a series of intellectual traps – e.g., quants over qual, poor knowledge of social science – which presently have the study of complexity on a bit of a problematic course. Looking to the future, can these present challenges be addressed? If so, to what extent or in what ways? What do such near and distant future adjacent possibilities look like? Can we map them? Where is the frontier of the complexity sciences headed, or not headed? The purpose of this highly interactive workshop is to explore the future (the adjacent possible) of the complexity sciences, and for participants to have an active hand in shaping what that future map looks like.


CLICK HERE for the PDF of the presentation



The psychology of complexity: a few notes from forthcoming Atlas of Social Complexity

My colleague, Lasse Gerrits and I are working on the forthcoming Atlas of Social Complexity, to be published in Autumn 2023 with Edward Elgar Publishing.


The focus of the book is how a global network of researchers, scholars, artists, social activists, policy makers, and civil servants have been working variously over the last decade to overcome to venture into new territories in the study of social complexity, creating entirely new fields of social science synthesis and advance, as well as inspiring the complexity imagination we presently need to address the significant global social problems we currently face.


Psychology of Complexity

In the process, one of the major sections of the book is on the progress being made in the study of key topics in psychology and the cognitive sciences, from cellular cognition and network immunology to the gut-brain axis and the emotional self to our embodied minds and the dynamical systems of human psychology.


ISCIA Seminar Series: Grappling with Complexity, 2021

I was kindly invited by Andrea Hurst and colleagues to present my initial ideas on the topic for the ISCIA Seminar Series: Grappling withComplexity, 2021, hosted by Nelson Mandela University, South Africa.

While my ideas have certainly progressed since the presentation, most of the main points remain core to my argument.


Here is a copy of the PDF of the presentation

Here is a link to a video of my lecture and the conversation that followed




The emergence of SMART methods -- non-expert platforms for social science and health research

I would like to thank Mark Elliot, Claire Spencer and the MethodsCon team and the National Centre for Research Methods for the opportunity to run my session on  The emergence of SMART methods -- non-expert platforms for social science and health research.

I presented on a new avenue of methods development that my colleague Corey Schimpf (Department of Engineering Education, University at Buffalo) first identified, which he and I are calling smart and approachable methods or AM-Smart for short.



Advances in the integration of smart technology with interdisciplinary methods has created a new genre, approachable modeling and smart methods – AM-Smart for short. AM-Smart platforms address a major challenge for applied and public sector analysts, educators and those trained in traditional methods: accessing the latest advances in interdisciplinary (particularly computational) methods. AM-Smart platforms do so through nine design features. They are (1) bespoke tools that (2) involve a single or small network of interrelated (mostly computational) methods. They also (3) embed distributed expertise, (4) scaffold methods use, (5) provide rapid and formative feedback, (6) leverage visual reasoning, (7) enable productive failure, and (8) promote user-driven inquiry; all while (9) counting as rigorous and reliable tools. Examples include R-shiny programmes, computational modeling and statistical apps, public-sector data management platforms, data visualisation tools, and smart phone apps. Critical reflection on AM-Smart platforms, however, reveals considerable unevenness in these design features, which hamper their effectiveness. A rigorous research agenda is vital. After situating the AM-Smart genre in its historical context and introducing a short list of platforms, we review the above nine features, including a use-case on how AM-Smart platforms ideally work. We end with a research agenda for advancing the AM-Smart genre.

This session will introduce this newly emerging field, provide some examples, and then explore with attendees how to critically engage and develop new smart methods for social science and health research.
The goal is to
Examine the utility of this field
Identify key concerns
Sketch out ideas for possible AM-Smart methods
Explore possible collaborations or venues for future research


CLICK HERE for the PDF of the Power Point

CLICK HERE for the paper ON AM-Smart Methods (Open Access)

CLICK HERE to explore COMPLEX-IT and its software, tutorials, etc.

CLICK HERE for a published article on COMPLEX-IT

CLICK HERE for Big Data Mining and Complexity


Much thanks to those who participated in the event. 

Here are the questions we came up with as a function of the workshop discussions:

How do AM-Smart methods impact learning due to the speed at which we they work?
The value or ramifications of datasets that have not been understood?
The value of pausing and slow science.
When is it good to have slow versus fast science?
In terms of scaffolding how do we make sure of not cutting corners.
How do we decide what to use based on different context and users and different levels of expertise.
The importance of co-production.
Throwing the baby out with the bathwater by critiquing conventional methods without being as critical of AM-Smart method. Are they actually learning what we want them to learn?
Where is the learning taking place or not taking place?
Are we smart enough for AM-Smart methods?
The value of gaming environments for AM-Smart environments?
This tends to favour fast processing.

One of the outcomes of the workshop was the value of figuring out how to add qualitative information to the clustering or classification methods regularly used in many AM-Smart methods.

Another was how to integrate, via smart design, qualitative and quantitative information to evidence both aspects of corroboration of insights as well as gaps in understanding.


COMPLEX-IT for rethinking the boundaries of methods in health and social science research (MethodsCon University of Manchester)


I would like to thank Mark Elliot, Claire Spencer and the MethodsCon team and the National Centre for Research Methods for the opportunity to run my session on COMPLEX-IT: A Case-Based Modelling and Scenario Simulation Platform for Health Research.



Attendees will learn how to use COMPLEX-IT, which is a free online R-Studio suite of computational social science techniques for classification, scenario simulation, and prediction. COMPLEX-IT was created for users who are non-experts in these techniques, including cluster analysis, topographical neural nets, agent-based modelling, micro-simulation, data visualisation, and prediction/forecasting methods. Using a small public health dataset, this session will introduce attendees to COMPLEX-IT (and its online resources and tutorials) sufficient for them to leave the session able to use this method on their own.


CLICK HERE for the PowerPoint of the presentation.


CLICK HERE to explore COMPLEX-IT and its software, tutorials, etc.


CLICK HERE for a published article on COMPLEX-IT 


CLICK HERE for Big Data Mining and Complexity


CLICK HERE for the Sage Handbook of Case-Based Methods


Smart Methods for Complex Policy Evaluation

I would like to thank Wayne Wakeland and his colleagues in the systems science program at Portland State University for the chance to present at their noon systems science and complexity seminar.

I presented on a new avenue of methods development that my colleague Corey Schimpf (Department of Engineering Education, University at Buffalo) first identified, which he and I are calling smart and approachable methods or AM-Smart for short.

My presentation was a quick summary of a paper on the topic we have in review and its application to policy evaluation. In particular, I focused on our computational modelling software, COMPLEX-IT.


TITLE: Smart Methods for Complex Policy Evaluation
ABSTRACT:  Advances in the integration of smart technology, computational modelling and statistical platforms has created a new methods environment, approachable modelling and smart methods – AM-Smart for short. The AM-Smart environment consists of bespoke tools that facilitate user-driven learning of a topic, creating an intuitive, supported but open-ended environment designed to solve specific tasks. Unlike most statistical platforms, AM-Smart methods focus on a single technique or small network of closely interrelated methods (mostly computational in focus), which help users to simultaneously use and learn new methods. They do so by providing scaffolding while allowing for user-exploration, rapid and formative feedback and by requiring modest technical skill, while still being rigorous, authentic, and reliable. A major focus of AM-Smart methods is policy evaluation, as demonstrated in participatory systems mapping tools, complex evaluation toolkits, and R shiny programmes and fast ABM modelling. For this talk, I will introduce the world of AM-Smart methods and their value for policy evaluation. I will specifically introduce a package we have developed called COMPLEX-IT.


Examples of AM-Smart methods are as follows:

COMPLEX-IT, a computational modelling environment for case-based modelling and policy evaluation for non-experts.


SOMbrero for artificial intelligence


PRSM for participatory systems mapping


SAGEMODELER for learning systems dynamics through designing models


FactoShiny for statistical analysis and visualization


Occam, a Discrete Multivariate Modeling (DMM) tool based on the methodology of Reconstructability Analysis (RA).


COMPASS for comparative methods software


NetLogo Web for agent-based models


MAIA for designing multiagent models for institutional analysis


Cytoscape for modeling complex networks


Several platforms that meet many of the AM-smart attributes but may be less representative on a few attributes including


IQAir, the world's largest real-time, air-quality information platform, including a suite of tools – IQAir Earth, Map, App and AirVisual


UK CDRC Mapmaker, including indices of multiple deprivation


Gapminder for exploring global trends statistically and visually




Health Policy Evaluation: A complex systems perspective



David Byrne and I would like to thank James Noble and Alexandra Potts for the opportunity to present at the third Systems Evaluation Network meeting for the 28th of January 1-3pm. The focus of this session was “the process for planning an evaluation of a complex systems approach”.

The overview of our presentation included the following – which you can click on to get further information. CLICK HERE for a PDF of our presentation.


       What is CECAN (The Centre for the Evaluation of Complexity Across the Nexus)

       What is a complex systems approach to evaluation?

       What is the role of methods?

       Three examples:

       PRSM participatorysystems mapper


       ComplexEvaluation Toolkit

*    Place and Health as Complex Systems

*    Map of the complexity sciences 



Q & A for the 2021 version of the map of the complexity sciences

As each new version of the map of the complexity sciences is released, there are questions regularly asked of us. Social media is not the best place for having such discussions -- particularly when people get mean or aggressive or do not actually take the time to read the Map Legend or explore things before commenting. Also, even when there are great questions asked, others may miss our response. We therefore thought it usefult to try and answer the questions typically posed to us on social media or email.

Brian Castellani and Lasse Gerrits




What is this map? 


This map is an introduction to the complexity sciences – from physics and biology to sociology and psychology to computational modelling and policy evaluation. We purposely use the term ‘sciences’ in the plural because there is no one complexity science and no one boundary around it. The map was created as an educational tool. It is to be treated as an introduction, not an in-depth investigation into the field. Experts in the field will also find the map useful for exploring new areas and for teaching.


What about the Arts and Humanities?


Unfortunately, we cannot address the Humanities or Arts as the map would become unwieldy. Complex systems thinking, fractals, chaos theory and other areas of investigation have been used in the arts; and the Humanities have added key insights, for example, Buddhist meditation, deep ecology, fractal architecture, urban design, and assemblage art.


Is the map historical? 


It is roughly historical. The five lineages, running from left to right, are based on Fritjof Capra’s  The web of life: A new synthesis of mind and matter (1996), which organises the field into: (1) dynamical systems theory and complexity in mathematics (purple), (2) systems thinking/systems science (blue), (3) the core concepts of complexity (yellow), (4) cybernetics (grey), and (5) artificial intelligence/methods (orange). While not perfect, we’ve kept this basic framework as it provides a nice skeleton on which to assemble the map.


How should the map be read? 


It should be read left to right, moving from the early 1900s to the present. Topics are placed approximately at the point when they became a major area of study. For each topic, we have provided a handful of top scholars, including, when possible, the individual or team that was instrumental in advancing the topic.


How was the map compiled? 


Between the two of us, the map represents over forty years of combined research and reading, as well as in-depth discussions with colleagues across the various fields and around the world. Castellani launched the first version of the map in 2009. Since then, it has been revised every several years, as the field has massively expanded over the last decade. The current version, which is an update on the 2018 map, is rooted in our fellowship at the Institute for Advanced Study of the University ofAmsterdam.


Why didn’t you use a bibliometric analysis to make the map? 


Bibliometric analyses are all the rage, and they can be powerful tools. There are a couple of reasons why we don’t use those tools. First, bibliographic analyses struggle to construct a history of a topic. They are better at providing cross-sectional snapshots. For a good overview, see Thomas, J., & Zaytseva, A. (2016). Mapping complexity/Human knowledge as a complex adaptive system. Complexity, 21(S2), 207-234. Second, those tools depend heavily on articles (as opposed to books), recognised journals (which is an issue, as many complexity works are often in obscure journals or blogs, etc) and sources that are online as opposed to libraries, archives, conference letters, and so forth. They also do not capture historical impact beyond citations. Third, there is range of more minor technical problems that make us unsure if those tools can do a better job than we did manually.


Is this map complete? 


The map is not complete, and was not designed to be – in fact, we are not sure what would even entail. The complexity sciences represent a loose (but connected) and quickly evolving body of knowledge that intersects with almost every field in the sciences and the social sciences. A map created in 2019 will not look like a map in 2020. We focus on providing a reasonably comprehensive introduction to the field. The nice thing about the online version is that, by clicking on the links, users are taken into even more in-depth reviews that link to an even wider range of information.


How about diversity in terms of age, gender, ethnicity, and nationality?


We made a concerted effort to make sure the map highlighted the work of a wide variety of scholars around the world and up-and-coming researchers. We also sought gender and stage of career balance as well as ethnicity and nationality. We will continue to advance the work of everyone we can.


Why is author x not on the map? 


We often receive questions about why a certain author is not on the map. Sometimes the scholar is missing because of the limitations in our knowledge. Most of the time it is because the map is an educational tool, and of limited size and space, and can only include so many people.


Could you please include author x on the map? 


Unfortunately, we are unable to fulfil such requests.


Could you please include me on the map? 


Unfortunately, we are unable to fulfil such requests.


I know how to improve the map.


We invite everyone who believes that our map needs to be improved to make an alternative one themselves. We are looking forward to such initiatives.


This is not a good map / I don’t like your map / Your understanding of the field is incorrect.


As stated above, we are looking forward to alternatives. In the meantime, in the brave new world of social media, it is easy to be cruel. Please do not be mean.


Can I use your map? 


You can always use the map if you attribute correctly with this reference:

Castellani, B and Gerrits, L (2021). Map of the complexity sciences. Art and Science Factory, LLC.

While we encourage sharing of the map, we want to point out that the map may not be used for commercial purposes and / or without proper attribution.


CLICK HERE for downloads of the map as a jpg and pdf, which are located on the Map Legend page.



Under what license is the map released? 


The map is licensed on Wikipedia under the Creative Commons Attribution-Share Alike 4.0 International license.


Will you continue to update the map?


Yes, we plan to update the map regularly, as was done since the original version from 2009.


How do you know what is complexity and what is not? 


As others have said before us, and we agree with, the complexity sciences are not defined by clear boundaries. It is a sprawling and growing group of theories, methods, findings, and big and small ideas, that permeates in almost every field imaginable. The boundaries between what is about complexity and what is not are amorphous.


The map is skewered / biased towards…


Bias is real. For starters, we can only cover English / German / Dutch / French between us, as such we may overlook work published in other languages. There is also the fact that map-making is somewhat path-dependent, where those who are said to have made an impact may remain to be designated as such. Above all, it can be hard to trace the origin of ideas, especially going further back in time. It may very well be that someone took an idea from someone else without proper attribution. Sadly, some labs or research groups tended to build on the work of PhD’s and postdocs without giving much credit to their work. This continues to be an issue until this very day. We’ve tried our best to present a balanced overview of the people who have driven the study of complexity, and those who continue to do so.


I don’t think that all the names on the map are the big names…


This is correct. We purposely include names of upcoming scholars that we find worth following as they push the field into interesting directions. Map-making is not only about charting the terrain just crossed but also an attempt to charter the unknown terrains. That is why we include scholars that we believe have something novel to say.