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 to 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 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.





The Limits of Evolution and the Modularity of Mind -- Can we please finally move on?

In preparation for writing the 'psychology and complexity' for my forthcoming book with Lasse Gerrits, The Atlas of Social Complexity, I have once again delved into the deep end of the pool on modularity of mind and its links to human evolution.

Modularity Ain't Rev-Evolutionary

Colombo, in an interesting article, Moving forward (and beyond) the modularity debate: A network perspective (2013), nicely sums the purported distinction between modularity and evolutionary psychology:

"At least since Fodor’s 1983 (The Modularity of Mind), the notion of modularity has been one of the most important concepts used to articulate an account of the human cognitive architecture, which provides us with an encompassing theory (a “blueprint”) of the nature, arrangement, and form of the structures and processes that are responsible for cognition and adaptive behavior. This should be distinguished from a theory of the origins of cognitive architectures, which is concerned with the evolutionary and developmental history of the structures and processes that are responsible for cognition and adaptive behavior."

An easy example that makes these differences clear is Chomsky's famous language acquisition device and his agnostic-to-antagonistic views of evolutionary psychology and sociobiology. Massive pages of ink devoted to the complexities of language acquisition aside, Chomsky's basic point is that our brains seem to come ready made, in certain ways, to engage in language -- call it a set of modules if you will. As to how much evolution drives this ability, the answer is probably not as much as we think. We only need so much evolution to survive. While the human brain is a product of evolution, the resulting mind, in all of its massive complexity, is so much more than what we need to evolve and survive. Trying to retrospectively link each little thing we do, from painting a picture to nibbling our nails, to some evolutionary necessity in our past is pointless -- and now it seems, given the current state of the literature, mostly untenable.    

Finding a middle ground

For me, modularity as a strong programme has always been underdetermined by the evidence, with its widespread acceptance appearing to be driven more by academic celebrity and its theoretical simplicity. Modularity as a weak theory has always held my interest, as complex systems are generally comprised of subsystems and lower orders of complexity, working top-down and bottom-up simultaneously, with command centres of limited ability and so forth. The embodied mind being comprised of modules -- that is, some complex network of functional subsystems (from our brains to our gut microbiota), with fuzzy organisational closure, for the purposes of completing a task outside conscious awareness -- just seems to make reasonable sense. Language acquisition, speech, motor function, paleomammalian emotions, microbiota-gut-brain axis, there is an endless list of things needing constant care and attention that our conscious, command-centre selves cannot effectively manage while writing sentences, eating toast and drinking our coffee in the morning.

Evolutionary psychology and sociobiology, however, and their just-so, reverse engineered usge of modularity -- in particular the massive modularity thesis -- have always seemed baffling. That is not to say that they don't have useful things to say, as they do. They just as often say things that are not useful. It is therefore exciting to see, fingers crossed, that the over-usage of modularity to support evolutionary psychology, as well as modularity in its strong form, are finally going the way of the dinosaurs. Or, at least, that is the sense I got from several recent articles. Here is a quick list:

Pietraszewski, D., & Wertz, A. (2021). Why evolutionary psychology should abandon modularity. Perspectives on Psychological Science, 2.

Palecek, M. (2017). Modularity of mind: Is it time to abandon this ship?. Philosophy of the Social Sciences, 47(2), 132-144.

Colombo, M. (2013). Moving forward (and beyond) the modularity debate: A network perspective. Philosophy of Science, 80(3), 356-377.

Bertolero, M. A., & Bassett, D. S. (2020). On the nature of explanations offered by network science: A perspective from and for practicing neuroscientists. Topics in Cognitive Science, 12(4), 1272-1293.

Sosis, R., & Kiper, J. (2014). Why religion is better conceived as a complex system than a norm-enforcing institution. Behavioral and Brain Sciences, 37(3), 275.

The basic story I get from these articles is that, despite the propaganda, modularity as a basis for our evolutionary psychological development has failed to produce the necessary evidence for its assertions and has made little advance in the past decade or so. In short, it is mostly wrong. Colombo sums it up rather nicely:
"Because of mere terminological disputes, because of vagueness surrounding putative central features of modularity such as functional specialization, domain specificity, and informational encapsulation, and especially because of little agreement about the proper empirical methods for discovering and justifying the existence of candidate modules (cf. the controversy around the cheater-detection module: e.g., Fodor 2000, 2008; Sperber and Girotto 2003; Cosmides and Tooby 2008a, 2008b), the modularity debate in the cognitive sciences and philosophy of psychology has often been frustratingly fruitless." (See Moving forward (and beyond) the modularity debate: A network perspective)
In turn, as the theoretical foundation for evolutionary psychology, this has come at a cost. In Why evolutionary psychology should abandon modularity (2021), Pietraszewski and Wertz make this point clear:  
"The upshot of all of this back-and-forth is that both sides in this modularity debate feel as if the other is patently absurd in its convictions. Evolutionary psychologists cannot imagine what else could exist but functional specialization in the mind. Meanwhile, critics on the other side feel as if the bottom has been pulled out from their understanding of evolutionary psychology if it does not intend the attributes of modularity that it now seems to be backing away from. Both sides are left, understandably, exasperated and at a seeming impasse.
The cost of this state of affairs cannot be over-stated. It has misled an entire generation of scientists about how to think about relationship between evolution and the mind, and it actively hinders progress in understanding how the mind works."
An example of this problem is a recent publication in the Proceedings of the National Academy of Science. The article, "The pandemic exposes human nature: 10 evolutionary insights" is of value not only because its authors include some of the key names in the field of evolutionary psychology, but also because it represents the problematic over-valuing of these ideas into the social realm, where simpler and more empirically viable explanations from fields such as public health, social pscyhology, sociology, political science and cultural anthropology already exist. This is not to say that the article does not make some excellent points, such as how quarantine will negatively impact the gut microbiota of kids, which will impact their long-term health. The problem is its reductionist centering of evolutionary theory and its modular understanding of social life as the basis for making sense of human life. 
The first line of the article says it all:
"Nothing in biology makes sense except in the light of evolution, and nothing about the human response to COVID-19 will either (p. 27768). Nothing!

Evolving forward -- Network and more networks!

Image by Bertolero, M. A., & Bassett, D. S. (2020).
I cannot remember where, exactly, Richard Rorty stated it, but his points was this: philosophical arguments generally don't resolve with a winner, instead they get disgarded once they are no longer useful. "Modularity or not" seems to be losing steam as an argument -- and hopefully the antagonistic clash between opposing views will eventually lose sway over evolutionary psychology too. 

What is replacing the "modularity or not" debate? That is not entirely clear, but one productive avenue is complex network modularity and, more generally, a complex systems modularity -- both of which have been around for a while and seem to be gaining ground. I cannot survey this research presently, except to offer a few articles to explore:

Favela, L. H., Amon, M. J., & van Rooij, M. M. (2018). The incommensurability of emergence and modularity in complex systems: A comment on Wastell (2014). Theory & Psychology, 28(4), 559-567.

Wastell, C. A., Purcell, Z., Howarth, S., Paterson, W., & Slocombe, B. (2018). The development of Complex Emergent Modularity: A reply to Favela, Amon, & van Rooij (2018). Theory & Psychology, 28(4), 568-571.
Colombo, M. (2013). Moving forward (and beyond) the modularity debate: A network perspective. Philosophy of Science, 80(3), 356-377.

Zerilli, J. (2019). Neural reuse and the modularity of mind: where to next for modularity?. Biological Theory, 14(1), 1-20. 

Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23), 8577-8582.

The other avenue would be to finally recognise that cognitive science is complexity science and to search for ways to integrate the various ideas and empirical evidence using a complex systems framework. Examples of this view (of which network modality is a part) include embodied cognition, connectionism and ecological psychology, as well as as recasting cognition as complexity and challenging theoretical and methodlogical reductionism. Embodied mind resarch is very well known. The latter is a more general argument. See for example:

Favela, L. H. (2020). Cognitive science as complexity science. Wiley Interdisciplinary Reviews: Cognitive Science, 11(4), e1525. 

Van Orden, G., & Stephen, D. G. (2012). Is cognitive science usefully cast as complexity science?. Topics in cognitive science, 4(1), 3-6.

The utility of these alternative framings come with their own intellectual traps. All scientific ideas do. They are at least more interesting then telling me:

"A key insight of evolutionary thinking is that—in contrast to the metaphor of the invisible hand—the pursuit of lower-level interests, such as short-term individual, corporate, partisan, or nationalistic interests, is far more likely to undermine than contribute to the global common good. . . . [As such,] treat differences in nation/state responses to COVID-19 as natural experiments in evolutionary processes by documenting different phylogenies of responses, measuring the efficacy of each, and then replicating successful approaches in necessary areas and future pandemics" (Seitz et al, p. 27772). 

If this is a major observation from a field some fifty years in the making, I think it is time to move on.


The very model of a postmodern pandemic: Why technology is the other virus changing all our lives

The very model of a postmodern pandemic:

Why technology is the other virus changing all our lives


Brian Castellani and Tim Fowler


We believe there is sufficient evidence that digital technology, more so than COVID-19, is the viral agent ultimately changing our lives. Digital technology helped us survive and is getting us out of pandemic. The pandemic provided the catalyst for the current spread of digital technology, which even may be moving us into the next wave of globalisation. The contagions are four major technology-driven shifts in western society: smart science, gig services and the platform economy, work-at-home employment, and Zoom culture. 

CITE AS FOLLOWS: Castellani, B and Fowler, T 2021. “The very model of a postmodern pandemic: Why technology is the other virus changing all our lives.” Sociology and Complexity Science Blog, 6 August 2021. https://sacswebsite.blogspot.com/2021/08/the-very-model-of-postmodern-pandemic.html 



Over the last 18 months, COVID-19 has clearly dramatically changed our lives in the global north, but what exactly has changed and what is the cause? Is it the virus or is it something else?

A Day in the life… COVID-19 Style


“I read the news today, oh boy…”


The doorbell rings, it’s Amazon. Again. It is the third delivery of the day. The first was Tesco and the second was your COVID-19 lateral flow kit – you just got your second mNRA-based vaccination, but to protect others you are tested twice a week. There’s a Teams meeting in half an hour to review the latest COVID-19 simulations to assess the resilience of your company. You grab a minute to check on your kids for the fifth time, who are pretending to listen to their online school lecture, while engrossed in WhatsApp. Or perhaps it’s TikTok, you lose track. Your partner, who works in healthcare, texts you a gift certificate for Deliveroo. You’ll eat your delivered treat while celebrating your birthday on ZOOM with extended family and friends, most of whom you haven’t seen face-to-face in over a year. 


If you don’t see yourself in all aspects of this vignette, no worries. There are endless variants on this basic form across western countries in the global north. Amazon, the gig economy, working from home via Zoom or Teams or the latest advances in scientific modelling and vaccinations we see sharpy defined the new constant of global life – digital technology. 

During the pandemic, digital technology has been go-to, suitable-enough, instant fix. It did not shut down businesses, increase mortality rates or force people to work at home, COVID-19 did. Digital technology kept the world rotating, albeit often in diminished form. The pandemic has often shown a new set of qualities inherent in pre-existing technologies. In western countries it has worked well with conventional and new approaches to government, public health, environment, economy, and social life in general. We were pre-infected for these new functions through digital technology’s complex contagion, the global social-cybernetic network.

So, which viral agent will ultimately change us more? The biological or the digital?


Despite the profound changes to daily life, the pandemic does not appear to have transformed civic or governmental responsibilities or provided the catalyst for addressing global social problems. However, it has catalysed the ever-accelerating spread of digital technology, moving us into a new phase of globalisation. This historical shift in technology is not a good or bad thing, nor is it deterministic. It simply is how the history of technology tends to work -- technology and humans co-evolve through a complex interrelationship that cuts across economy, politics, culture, social institutions and organisations and so forth.


What has not changed?


A great deal has changed in the global north due to COVID-19, but if the pandemic were to be eradicated tomorrow, life in western society would strongly resemble December 2019. This is particularly true of civic and governmental institutions. 


Wall Street profit and the global economy are still the number one concern. Indeed, there has been a noticeable dichotomy in political discourse between the offsetting concerns of public health versus economic survival and future growth. The idea that “the cure cannot be worse than the disease” was voiced by President Trump and numerous others.


Health inequalities still abound, particularly for the working poor, minorities, and immigrants. The environment continues to face ruin. Public health efforts are often underfunded, under debated and misused for political and ideological ends.


Social media permits the sowing of division through the spread of misinformation, mistrust, cruelty, and fear. 


Most people sought to do the right thing during the initial lockdown, but Successive lockdowns saw individualism, flippancy, and privilege overwhelming social commitments and our care for others. Should it really have been so necessary to reinforce the idea of care for others, from wearing masks and social distancing to doing a small part to honour the sacrifices of healthcare providers and key workers and the lives lost to COVID-19?


Governments also failed. Politicians rebuffed scientific facts, and health experts were regularly treated with contempt or used as political props to add seriousness where it was lacking. Governments also adopted a reactive approach, to the point where the cycle was predictable. Scientists and public health experts raise alarm; the public worries and asks for guidance; government consults and waits; misinformation, conflict, anxiety and confusion emerge. Government finally responds but later rather than sooner; the working classes, minorities and poor, due to various practicalities, particularly in urban environments, were left to bend the rules to survive; the affluent would take care of themselves; morbidity and mortality rates would rise; and the cycle repeats.


Perhaps this was inevitable in western democracies, with their premium on economy, individualism, and political differences and debate. The exponential growth of viruses like COVID-19 require near total commitment for their control and eradication. Western societies did not respond in such a manner. Hence the need for digital technology in the form of vaccinations, big data, public health modelling, communication platforms, and biomedical advance. Point to a western country succeeding otherwise.


COVID-19, the very model of a postmodern pandemic


As Frank Snowden states in Epidemics and Society:

[E]pidemic diseases are not random events that afflict societies capriciously and without warning. On the contrary, every society produces its own specific vulnerabilities. To study them is to understand that society’s structure, its standard of living, and its political priorities. Epidemic diseases, in that sense, have always been signifiers, and the challenge of medical history is to decipher the meanings embedded in them. (2020, p.7)


COVID-19 represents a massive stress test on our society and shows us a postmodern, globalised world where western countries are highly dependent upon universal digital technologies to solve public health problems, including pandemic. These technologies – be it biomedical, smart machines, computational science, communication platforms, or global cyber-infrastructure – are really the virus changing all our lives, not COVID-19. This change, which may be moving us into a new phase of globalisation, is happening along four major forms of digital transformation, each involving a complex interplay between humans and a particular arena of digital technology, which the pandemic has catalysed into new emergent forms of self-organising social arrangement:


Smart science


The first is smart science -- a term that, to the best of our knowledge, we are first using. The rise of big data and concurrent advances in computational modelling – the use of high-speed computation and algorithms to search for nonobvious patterns in data and simulate various aspects of life – have changed our world irreversibly. Smart science is the usage of smart technology, big data, and computational modelling methods

What has been accomplished in a span of only eighteen months is unparalleled historically, including the rollout of mass vaccinations, the exposure of public rhetoric and post-truth propaganda, the sharing of health data, the simulation of various public health scenarios, and the ability to impact the policy decisions of governments the world over. 


A superb example of smart science is mRNA vaccines. Developed through the ground-breaking work of Katalin Karikó and her global network of colleagues, these vaccines have high potency, capacity for rapid development and potential for low-cost manufacture and safe administration. Another is public health simulation. The number and variety of scientific simulations of COVID-19 during pandemic not only provided governments and public health officials key insights into how the virus was spreading, but they also forced many governments to respond faster than they otherwise might have, including moving into lockdown, enacting social distancing measures, and figuring out useful vaccination approaches and exit solutions. 


(For more on simulations, see my six-part series -- 123456 -- on this blog. See also our recent JASSS article on one of the COVID-19 models we developed.) 

Given the high likelihood of us facing another pandemic soon, as well as the environmental and other global health challenges we face, smart science will continue to radically improve the health of our world. 


Gig services and the platform economy


The second is gig services and platform economy. The platform economy uses digital platforms to link businesses and provide goods and services the world-over. Amazon.com has already posted an $8.1 billion profit during the pandemic. Gig services involve independent contractors, online platform workers, and temporary workers who provide on-demand services such as Deliveroo and Uber. Both approaches come with serious consequences – but can we imagine life now without them?


One of the major social problems of globalisation in the late 20th century was the outsourcing of work (particularly to countries in the global south) and the exploitation of workers that often comes with it. Gig workers and the platform economy represent another form of this social problem. Gig services is another form of global outsourcing, and the platform economy is really just a more efficient workbench by which to do it, making neither particularly new in principle, and only really new in form (See Freidman 2014).

The advantages of gig services and the platform economy for workers are high levels of flexibility, autonomy, task variety and complexity and the ability to work from home or while mobile. The disadvantages range from health and safety issues to employer-provided benefits and workplace protections to low pay and social isolation. For companies, the primary advantage is the reduction in overhead and regulations, from office space and inventory management to worker retention and healthcare costs, as well as the ability to compete globally and survive without an established workforce, office front, or face-to-face interaction. 


During the pandemic, given the dangers of proximity, the immediate enticement of gig services and the platform economy for workers and companies and we, the consumers, was too powerful. When COVID-19 hit, stores and restaurants were closed, travel was illegal. Home schooling became the norm. You put your life at risk going to or working in the office, grocery store or gas station. Those with health vulnerabilities were told to shield themselves, some for months on end, and COVID-19 swept through hospitals and care homes like wildfire. Supplies were suddenly in high demand, a rush on things took place. Toilet paper became an odd obsession. In all, the complex infrastructure of western life basically came to a screeching halt.


Nature abhors a vacuum, and the global economy could not be allowed to crash. Work life and the provision of goods and services needed to somehow continue. Same with medical care, social services, and education. Thanks to digital technology it all survived. Sort of. During the pandemic, small businesses took a major hit. Online education was variable. Store fronts and newly constructed buildings and downtowns sat empty, and it is unclear if or how they will ever reopen. Meanwhile Amazon and other major online corporations became global monsters, often putting the health and safety of employees at risk and undermining local business. 


While it is unclear how exactly this shift will play out over the next several years, what is clear is that we are not going back to the way things were. The opportunities that gig services and the platform economy provide us during the pandemic are too powerful to go back in the box.


Work-at-home employment


The third is work-at-home employment (technically called telecommuting). While statistics vary across western countries, the number of people working at home in the first year of the pandemic more than doubled from most 2019 figures (1). In the UK, roughly 46% of workers did some or all their job at home during the first wave, with higher percentages in urban environments and amongst professional occupations (2 3). As the pandemic unfolded and we moved in and out of lockdown, the numbers varied and, as of summer 2021, they have yet to settle.


One of the ideas most clearly discredited during the pandemic was that home working was not practical for most businesses and negatively impacted productivity and efficiency. Most employers were forced to acknowledge that, in terms of productivity, teamwork, and communication not only did the work generally get done; it also reduced the costs of a full-time workplace. Workers could be hired anywhere in the world: eliminating commute time allowed companies to improve their environmental impact, and organisations could more easily collaborate globally through the usage of communications technologies.


For workers, it could mean long hours, more meetings, a sense of increased surveillance, increased mental health issues, stress, and a general blurring of the boundaries between personal and private life, all of which made work-at-home employment a challenge for a significant percentage of people (4). Employers likewise struggled to inculcate new employees into office culture, manage burnout and employee distractions, and cultivate community (5) While many people will want to return to office life, the percentage of employees who will continue to work at home will most likely stay far above 2019 figures. Work-at-home employment presents too many options, a shift has taken place (6).


Zoom Culture


Online life saved us, didn’t it? Isolation is used as a form of torture. COVID-19 was social anguish for many of us, particularly those left isolated, heartbroken, and alone from friends and family. The elderly isolated in care homes, the vulnerable shielding. Key workers and healthcare providers staying in hotels to protect the ones they love. It was – let’s not understate this – terrible.


ZOOM, FaceTime, Twitter, Instagram, WhatsApp, they became our lifeline. There were others, however, that found online life a different form of saving grace – the socially anxious, those who struggle with face-to-face interactions, the introverts. 


However, social media has its major limitation. Teaching or speaking to a screen with everyone’s cameras off, for example, and no sound other than one’s voice heard is like being on Mars and communicating with folks back on earth, each text a challenge to decipher its emotional and social content. There is no substitute for human contact and being physically present to other human beings. 


We will return to life in each other’s physical presence and travel will resume. But the ecological footprint and economic costs that online life helped to reduce, as well as the strong online bonds people were able to form through digital technology the worldover, will be a strong incentive to rethink how we come back into each other’s’ lives. Hopefully for the better.



So, what can we conclude from this brief essay? The global problems and inequalities and inequities revealed by the COVID-19 pandemic are not new, neither are most of the solutions. Life post-covid is also shaping up to be rather like life before it, with most folks wanting to relegate the pandemic to the past and get on with things. 


We would like to suggest that COVID has acted as a magnifier of existing trends and technological possibilities. The most novel thing about the pandemic is the COVID virus itself – the technologies deployed to combat it all pre-dated it. Indeed, it’s tempting to extend this to individuals and societal groups: have the kind become kinder, the angry more enraged, the dysfunctions more exposed?


We believe there is sufficient evidence, then, that digital technology, more so than COVID-19, is the viral agent that humans are using to change their lives. Digital technology helped us survive and is getting us out of pandemic. The pandemic provided the catalyst for the current spread of digital technology, which we may be engaging with sufficient to move us into the next wave of globalisation. The contagions are four major technology-driven shifts in western society: smart science, gig services and the platform economy, work-at-home employment, and Zoom culture. As with any venture in social forecasting, it is impossible to envisage the extent of change these contagions will bring about. Global warming, environmental pressures, the instability of global capitalism, the exponential growth of metropolitan areas, a potential reactionary movement against digital life, and the possibility of another all-too-soon pandemic all constitute unpredictable factors in the equation.


Change nonetheless has taken place and, at least along these avenues, we are not presently going back to the way things were.