Sustainable development, as defined by the Brundtland Commission, is “development meeting the needs of the present without compromising the ability of future generations to meet their needs”. Since 2015, this agenda has been captured and encapsulated by 17 interlinked Sustainable Development Goals (SDGs) and accompanying targets to be achieved by 2030. These cover a range of societal challenges, including the eradication of poverty and hunger, access to clean energy and water, and climate action.
The sustainability challenges we face cannot be understood in isolation from one another. Siloed or disconnected approaches are insufficient in addressing root causes of long-standing problems. There is a growing movement to understand and solution from the “systems” perspective that recognizes and works with complex interrelationships between issues of concern. Energy, food and water in particular form a “nexus” of interwoven problems where vulnerabilities, risks and leverage points for change emerge from the linkage between systems.
I am therefore excited to facilitate a panel discussion at Indo Data Week exploring challenges and opportunities in this nexus in regard to artificial intelligence and data science. The panelists are eminent experts, including a serial entrepreneur in the sustainability/clean-tech space (Mr. Ganesh Shankar), a senior Government strategic and operational leader working in food & agriculture (Mr. Bhubesh Kumar), and a leading AI researcher and innovator (Ms. Tanuja Ganu) with more than 13 years of industry experience in building large scale system design and innovation projects.
We’ll be looking at where data and AI are being applied to create environmental, social and economic value addressing critical challenges in the food-energy-water nexus. What are the use-cases for AI? What kinds of AI models are being built and for what purposes? What are the benefits of leveraging AI? How does AI enable new business models for sustainable development? What challenges might occur with respect to equity, inclusion and privacy by applying AI in this nexus? Can we solve not only for India but for other emerging/Global South markets as well? And what vision do our panelists have for the further application of AI and data science in this nexus?
It promises to be an interesting learning experience for me! I look forward to seeing you at the discussion on Thursday 19 November, at 11:30 AM IST. I’ll further be at the Indo Data Week Hackathon For Good as an enabler and mentor to teams where we are addressing a problem in this nexus- so hope to see you there as well!
Greetings from approximately 3,650 days in the future! I am happy that you and your generation made it through relatively unscathed, despite climate chaos, fascist authoritarianism, the near-collapse of social and ecological systems, and the failures of late stage capitalism.
The world is different now. The almost-mad wannabe-dictators who insisted on screwing up the world “their way or the highway” are now dead and gone. But not forgotten, no. They remain frozen in time on the Internet tubes for us to study and criticize and learn from – learn how not to be, that is. You did well by biding your time.
Business is different too. The “social responsibility of business” is no longer “to increase its profits”. We dance on the grave of that idea. The Invisible Hand turned out to be more or less immaterial when it came to rolling up our sleeves, solving climate change, air pollution, water security, energy access, the eradication of poverty, etc etc etc etc. Business leaders shuffled around in an uncomfortable tango for several years before finally embracing the truth – they were responsible, they would pay, they would change, they must change, or die.
Work is therefore different, as well as play. Culture has changed. A man’s worth is no longer measured by his purse, and it’s no longer a Man’s World. Material Girls no longer program our consumption habits with their Fair and Lovely skin, and Biblical imperatives to “go forth and multiply” are replaced by suggestions to “make love, not babies”. It is a time of healing, learning, loving, trying, experimenting, succeeding, falling flat on our faces, undertaking the generally heroic, mundane task of living simply and quietly in harmony with the world.
We figured it out, we’re figuring it out, we were too clever by half, we will learn, we are learning, we have turned the corner, we will face this and every challenge, we have pivoted, we survived. Still alive, seeking harmony in a connected system of systems of systems.
Life around the world has been disrupted by the COVID-19 pandemic. The situation is no different in India, where Government authorities, hospitals, businesses, and citizens struggle to deal with the fallout of a public health emergency, the magnitude of which has not been seen in recent memory.
With the objective of not only recovering from the crisis but rebuilding more resilient societies, the World Bank and partners have launched the Hack4Resilience challenge in which our team (The Hotspotters) participated. Under this program, teams from around the world work on the unique challenges faced by the Government and other stakeholders in managing situations arising out of the COVID-19 crisis.
Hyderabad, the capital of the Indian State of Telangana, covers an area of 625 square kilometers and is the fourth most populous city in the country. With almost 10 million people in the greater metropolitan region, the area is seeing a rising number of COVID-19 cases which the authorities have been trying to control by locking down selected “containment zones”.
Our team’s challenge, which was posed by the Emerging Technologies Wing of the Government of Telangana, was “forecasting new hotspots and rate of transmission.. using mobility data or other proxies.. before the cases are confirmed by tests”. How may we use computer models and datasets including mobility data from Facebook, network providers, etc. to predict emerging hotspots in Hyderabad that can then be confirmed by targeted testing?
We were provided excellent online design tools and mentorship from Hack4Resilience partner WorldStartup, based on a synthesis of design thinking, the lean startup approach, and others. The design process enabled a detailed examination of the problem-space, from which we realized that it is not just the Department of Health that would be under pressure from the COVID-19 situation. Other entities, including the Department of Administration, the Department of Labour, the Department of Police, and various stakeholders like private businesses, factories, schools, and others would all need coordination and support to adapt to the new normal. In other words, in building resilience in response to COVID-19, we must avoid siloed strategies, and consider the systems perspective.
The “pain points” common to many of these stakeholders were found to include incomplete information, and difficulties in coordination (due to the structure of institutions, for example).
These manifested in various ways depending on the stakeholder. For example, the police might be required to check and enforce lockdown in various containment zones but might lack the specific information or procedures on how to execute this effectively. Interestingly, we found that value propositions can be crafted across stakeholders, in a sort of platform model fulfilling these and other needs, and using a technological approach underpinned by computer modeling and deployed through mobile and web tools.
We then completed the design process, narrowing down to the exact constraints of the challenge statement. We decided to create a lo-fi prototype (mockup below) augmented by a functional backend computer model to demonstrate proof-of-concept. These would be used to get feedback from potential users to drive further design stages, determine data requirements, etc. The intended users of the solution are officialsfrom theDepartment of Health in Hyderabad city.
In this dashboard, 4 “heatmaps” of the city’s hotspots are available.
“Live” shows where the model predicts COVID-19 hotspots to have emerged, on the basis of the latest available data in conjunction with the underlying computer model.
“Where to test” helps prioritize testing based on the new hotspots and available resources. Support will also be provided in terms of “how many” people to test, using representative random sample sizes calculated for the cluster.
“Current lock protocol” is a display where officials can input their preferred protocol for lockdown in a granular fashion across zones in a city.
“+7 days” shows the computer model’s estimation of cases in the city 7 days later, if the currently selected lockdown protocol is applied from now onwards. Of course, the actual time delay could be selectable by the user.
(It was decided to leave other ideas, such as area-wise estimation of resources required to deal with cases, etc, out of the current proposal as it is out of the scope of the challenge definition.)
The Heart of the Solution: Building a Network SIR Model
In order to generate the displays in Figure 1, it is obvious that some type of computer modeling is required. One of the basic building blocks available is the SIR Model. Here S is the susceptible population; I is the Infected population, and R is the Removed population (either through death or immunity, they no longer contribute to the immediate spread of infection.)
One of our team members has developed and published a simple network SIR model– where several individual SIR nodes are connected together, say by transport linkages such as air travel or roads. This allows us to model the case of a city like Hyderabad, where each cluster in the city can be represented by a SIR node. If the “connectivity” (i.e. travel) between the nodes is known or can be estimated, the model can be used to predict the spread of COVID-19 in the city.
Figure 3 shows a network that is created by combining SIR nodes 1, 2 & 3. Now, the time-evolution of Susceptible, Infected, and Removed populations in each node is no longer independent of the other nodes. There is a “connectivity factor”alpha that has been included between each pair of nodes in the system. This factor is defined to vary between 0 & 1 and is meant to represent the “mobility between nodes”. For example, alpha_23=0 means that there is no mobility from node 2 to node 3 — and hence no conversion of node 3’s susceptible by contact with node 2’s infected. Setting alpha_23=1 would imply complete transport of the population of node 2 to node 3.
Deriving the Connectivity Factors alpha_ij
The factor alpha_ij represents the possibility for the susceptible population in node i to become infected by traveling to node j and coming into contact with the infected population there. This factor varies between 0 and 1. If alpha_ij is 0, it means that there is no infection of node 1’s (susceptible) population by mixing through mobility with node 2’s (infected) population. If alpha_ij is 1, it means that there is complete mixing between node 1 and node 2’s population –the susceptible population of node 1 will be completely exposed to node 2’s infected population as a result of mobility. Of course, the actual disease spread will depend on the dynamics of COVID-19 (i.e. the beta factor in the node(s) — see technical note).
There is no way of knowing the alpha_ijs’ for sure. However, we can form a daily estimate by using mobility data (such as that provided by mobile operators or from platforms like Google or Facebook). For example, the Quadrant Asia-Pacific Data Alliance provides a dataset where 3 fields are of interest — Latitude-Longitude; Unique ID for each mobile device; Timestamp. For a particular mobile device, transport is determined if, within a timeframe, the change in Latitude-Longitude between nodes (clusters) is significant.
We then sum up the number of all such smart-phone devices which have commuted between different areas in order to compute the adjacency matrixof the model, which can be updated in real-time as new data comes in. This information will be utilized to construct the alpha_ij which our model uses to predict hotspots, but it has other potential uses as well — such as controlling traffic to/from certain areas under lockdown conditions.
There are various possible approaches to making these alpha_ij more accurate. The basic method, as mentioned, is to compute these factors from mobility data. However, a more refined model could also incorporate the geographical connectedness of different wards; factoring in the presence of roads; incorporate multiple estimates from different datasets, etc. Finally, we aim to use machine learning in our full solution in order to correctly ground-truth these factors using a range of datasets.
Building the Full Solution for an Indian City
We have built a prototype of the solution for the Hack4Resilience hackathon, with various components implemented to the degree possible. These include a multinode network SIR model (coded in MATLAB), code for computing connectivity factors alpha_ij from mobility data, and client-side code for rendering the main components of the main dashboard.
The challenge definition was to predict emerging hotspots in Hyderabad city which could then be confirmed by testing. In order for us to move from our current prototype to a system that is capable of doing this, a more complete model reflecting actual areas/clusters in Hyderabad needs to be built.
This requires more spatially disaggregated COVID-19 data, along with time history to be provided. Ideally, the data — on number of cases, with timestamps — would be provided disaggregated at the level of ward or less. (There are up to 200 wards in the Greater Hyderabad Metropolitan region). This would allow a model with up to 200 or more nodes (population clusters) to be set up. The displays in Figure 1 would be granular to the same level as the model structure.
Machine Learning to Ground-truth the Model
With such a complex model with hundreds of nodes running, it is natural to enquire after the accuracy or validity of the model. This can be framed as a Machine Learning (ML) challenge as outlined in Figure 5.
In the setup of Figure 5, there are now hundreds of nodes representing different areas/clusters comprising Hyderabad city. (There can also be special nodes to represent migrant populations). The connectivity factors, alpha, as before are derived from mobility data. Further, in Figure 5, a more fine-grained approach has been used, with each node having its own beta_i. (For example, in a densely populated residential area, the beta might be different than in an industrial zone).
The system is faced with the following challenge — as new data comes in regarding occurrence of cases, results of testing, etc — can the model “compare” its predictions with the actual data, and then adjust itself by learning closer approximations to the true, underlying parameters so that there is a better fit with reality?
There are a number of ways to approach this problem, including using techniques of feedback learning (from computer science) and techniques derived from the theory of optimal control. One of the team members (Ashutosh) has already demonstrated “learning of parameters” in the single-node model.
If supplied with appropriate data (including time-series COVID-19 data with appropriate spatial resolution), then we can extend this Machine Learning approach to cover the whole network of connected clusters representing Hyderabad city. The machine learning algorithm must also be provided data high-resolution historical data of when, and where, the lockdown was applied precisely.
1. Commented MATLAB Code is present in our team’s GitHub for the single node, two-node, and multi-node (network) SIR models. A stub has been created for “trivial” machine learning. This can be expanded in the next version of the model provided adequate data is provided.
2. Each SIR node in the network model possesses an intrinsic parameter beta_i, representing the growth rate of the infection. It is a “lumped parameter” which depends on the “exposure factor” in the node (cluster), among other things. Different clusters can certainly have different beta values, and this is illustrated in Figure 5. For the sake of convenience, we have currently modeled a homogenous beta into our simulations, based on India data. The beta factor would also factor in the “degree of lockdown” currently present in the area represented by the node.
3. In order for the adjacency matrix computation to return a non-trivial (i.e. non-Identity Matrix) result, city-level mobility data of sufficient spatial disaggregation (e.g. at the ward level or lower) needs to be provided. We were not able to find this in the provided datasets or on Quadrant. Therefore, we have currently implemented only the rendering of a fictitious adjacency matrix for proof-of-concept. Of course, this could be upgraded given appropriate data availability.
Simha, Ashutosh, R. Venkatesha Prasad, and Sujay Narayana. “A simple stochastic SIR model for COVID-19 infection dynamics for Karnataka: Learning from Europe.” arXiv preprint arXiv:2003.11920 (2020) —
“There’s so much talk about the system. And so little understanding.”
Robert M. Pirsig, Zen and the Art of Motorcycle Maintenance
In a 2012 paper, Prof. John Sterman of MIT raises the intriguing question: Is the sustainability movement itself sustainable? Do current approaches to sustainability make any real difference to the long-term sustainability of human society?
Most efforts by ﬁrms, individuals and governments- like reducing waste, cutting energy & material footprints, reducing GHG emissions etc.- are directed at the symptoms of unsustainability. While necessary, these measures are insufficient where they fail to consider and address underlying causes. Further, sustainability is often presented as a “competition” between competing dimensions. For example – “environment vs. economy”, or even “polar bears vs. Arctic drilling”.
Sterman notes that “economy, society, and environment are not separate domains to be traded off against one another” and that framing sustainability in terms of these false dichotomies or “invisible fences of the mind” reflects a “narrow and deeply dysfunctional mental model”. In other words, the interests of business, society and the environment are fundamentally aligned.
This mindset is echoed by Dr. Peter Senge, systems thinker and author of The Necessary Revolution. Senge offers the example of Coca Cola – from an integrated perspective, Coke’s business is “really about water”. Without “more effective, long-term, integrative management of watersheds in the world” the company will literally be out of business. This hasn’t figured in Coke’s strategy until relatively recently, though- indicative of the mental models of its decision makers.
In moving towards a more holistic understanding of our sustainability problems, we are confronted with some annoying features of real-world complexity. Principal among these is the presence of feedback – which can be intuitively thought of as the propagation of “information” about an “action” in some part of the system through various channels so that it eventually “returns” to the point of origin, possibly influencing future behaviour.
There are only 2 types of feedback – reinforcing and balancing. An interesting example of reinforcing feedback is provided by Walters & Neely (2016).
more people running► more panic► more people running►… etc.
As is evident from this news report, false alarms trigger stampedes when reinforcing dynamics are present.
Balancing feedback, on the other hand, works as showed in the example below.
more highways ► less traffic jams ► less need for new highways► less (new) highways► etc.
A “converse” reading of the loop also exists – i.e. less highways►more traffic jams ► etc.
These loops “balance” opposing effects and stabilize the outcomes of systems. For this reason, they are also called “goal seeking loops” – the “goal” being the stable outcome(s) generated.
Of course, balancing and feedback loops can be combined (sometimes to dizzying levels of complexity) in the quest to understand dynamic phenomena. It turns out, though, that certain “systems archetypes” exist – patterns of loop structure that repeat across problems, therefore creating similar “stories” in terms of the system’s dynamics. We can even see these patterns embedded in the political events of the day – if we know how to look!
Wicked Problems, Policy Resistance & Systems Thinking
In principle, the dynamics of a system can be extrapolated if a sufficiently accurate loop structure is known. It would be great if human beings were naturally gifted at this! Unfortunately, research suggests otherwise – as Sterman notes, the mental models of most people don’t contain even the simplest notions of feedback or exponential growth, let alone higher-order archetypes like ‘the tragedy of the commons’ or ‘limits to growth’.
This is a challenge for sustainability problem-solving.
As Lönngren & Svanström note, the “systems-thinking competence” is a key component of education for sustainable development. These are the skills we need to equip solvers with, for them to deal with the challenges of the world they are inheriting. This is missing from our education system!
The solvers of today (and tomorrow) need tools to deal with climate change, poverty, gender inequality and other challenges. Often ill-formulated and complex, characterized by finite resource constraints and value conflicts, these poetically-named ‘wicked’ problems need to be explored and processed through approaches like systems thinking for us to have the best chance of creating a better world.
Failure to do so results in effects like policy resistanceand “unintended consequences”– where a well-intentioned intervention strangely backfires, with the system in question seeming to “resist” the change.
The application of systems thinking to sustainability is a profoundly deep and important topic, with much to reflect on. We’ve barely scratched the surface! Still, I hope you have enjoyed reading this as much as I enjoyed writing it. We’ll explore more aspects in follow-on articles. In the meantime, I leave you with a quote from one of my favourite systems thinkers…
“God grant us the serenity to exercise our bounded rationality freely in the systems that are structured appropriately, the courage to restructure the systems that aren’t, and the wisdom to know the difference!”
I didn’t realize for the longest time how privileged I was. Born into a scholarly middle-class family from South India, I grew up in a home where computers, books and thinking in English were taken for granted. I received three years of brilliant British education (as my father pursued his doctorate in Wales), went to good schools, and studied at some of the best universities in India.
It’s strange, then, that I “failed” – time and again – academically. (Or was it the Indian academic system that failed me?). I started losing interest in the curriculum by the time I reached 10th grade, preferring to spend my study hours fooling around with advanced computer programs, and math problems I solved in weirdly elliptical ways. The high school years were no different – I deeply explored Feynman’s wonderful Lectures on Physics, but barely knew which class I was attending. I then proceeded to become the only person I knew who failed the firstsemester of engineering college – after a lifetime of never having failed a single test (I managed to leave in due course with a respectable degree.) I also “failed” my way out of a doctoral programme in a prestigious Indian university, leaving with a Masters’ degree. (I lost all interest in the topic I was studying- an excellent decision in retrospect, but that’s a story for another time!)
I have since managed to find the work I care for and the field I love – and in which I hope to keep learning, growing and contributing, hopefully forever. I’ve also managed to work on many remarkable projects, with wonderful people from all around the world- so things haven’t turned out too badly for this “sustainability problemsolver”. Still, when an academic friend recently canvassed opinion on how education might change to meet the challenges of the post-COVID world, you can be sure I had some strong opinions.. read on..
Q: What would be the requirements of future platforms, pedagogy, and the education system as a whole to meet the challenges posed by the post COVID world?
A (especially in the Indian context): Be outcome-oriented from the start- equip learners to handle the problems of the world they are inheriting – like climate change, societal inequality and resource constraints. Focus on whybefore what and then how. Learn by doing, not by rote. Incorporate systems thinking and design thinking from an early age. Encourage entrepreneurial thinking and problem solving. Move away from the mindset of “I earn credit for giving the right answer” to “I earn credit for asking the right question.” Incorporate the Socratic method into the process of education. Emphasize the scientific method— of proceeding closer to truth through empirical means, with doubt as the touchstone.
The existing system of Indian education is derived from what the British left behind. They didn’t educate us to be critical thinkers who would create knowledge – they educated us to be clerks (babus) – intermediaries between rulers and ruled. So – stop trying to adapt the existing education system to the post-Covid world. Design and create a new system which creates critical thinking problem solvers – not more babus.
Towards this end, knock educators off their pedestals. Reward dissent and a different point of view. Don’t focus on facts, focus on insight. Use technology to accelerate learning. Use simulations and systems models (like this) to help learners interact with concepts from an early age. Design education for digital natives, and focus on building digital access and literacy as rapidly as possible in marginalised sections. Strengthen data literacyat a societal level.
There should be no lower societal goal than to have universal, lifelong and free access to state-of-the-art education (how? is an interesting conundrum). New platforms should provide customised learning journeys based on psychological profile, preferences and interests, and the needs of society. Disrupt. Democratize. Demolish the obsolete!
In 1972, a group of top scientists from MIT published a ground-breaking book called The Limits to Growth. The researchers, lead by Dennis Meadows, applied the newly created field of systems dynamics to explore what they called the world problematique – the complex of problems affecting the globe including poverty and inequity, environmental degradation, institutional failure and economic disruption.
The scientists had created a systems model called World3 based on five interacting subsystems – population, food production, industrial production, pollution, and the consumption of non-renewable natural resources. The model was created using historical data going back to 1900. World3 broadly reproduced the state of the world in 1970, and generated predictions for key parameters over a 200-year timeframe upto 2100.
They ran World3 simulations of several types – including the Standard Run, Comprehensive Technology and Stabilized World scenarios, encompassing different possible responses to the “world-problem”. Of the 12 scenarios mentioned in the book, 7 ended in “collapse”, with the authors mentioning that “without major change in the present system, population and industrial growth will certainly stop within the next century.”
Limits to Growth, and its two updated sequels, stimulated vigorous critical debate that continues to this day. Despite genuine shortcomings and scathing attacks from powerful interests, the central message was credible and based on rigorous methodology.
The story doesn’t end there, though! Dr. Graham Turner of the University of Melbourne compared the Limits to Growth with 30 years of reality – and found that indeed, we seem to be on course with the “standard run” business-as-usual scenario which sees a global system collapse in the mid 21st-century – and all this without climate change modelled in!
This sounds depressing – but I’ve learned to hold on to a certain optimism that we can learn, we can solve our problems and march towards creating a better world. And that in the process, marvellous oracles like World3 and its descendents will allow us to see, dimly, visions of futures that might be, or not – and which are perhaps in our power to choose.
[If you want to play around with World3, you can find Brian Hayes’ recreation here. You can also find the 1972-edition Limits To Growthfree to download here.Have fun! ]
Having pondered long & hard, stared for hours at a blank computer screen, scratching my head not a little- I have decided to take the plunge and simply start writing about all the things I think about & care for, as far as my journey in sustainability goes! I really do think it’s the most important topic in the world, so I’m hoping to get tons of feedback & encouragement from you, dear reader.
What am I thinking about nowadays?
Growing up, I had my own “favourite” scientists of all time. Archimedes, Kepler, Galileo, Newton, Leibniz, Einstein… and many others. I yearned for a “teleportation device” to take me across time and space, so I could escape my homework (and the incredibly boring adults around me), and indulge to my heart’s content in high-flying discussion of vital importance to the progress of humanity and science…
Fast forward 25 years.. I’m currently in a deep-dive into the thought and world of Donella Meadows and her collaborators. Scientist & systems thinker, author, teacher and farmer – Donella (or Dana) died almost 20 years ago, in 2001. Her papers and articles brim with ideas and vitality, inventiveness, different perspectives and things to think about. Curious, powerful and playful, her ideas were of her time and ahead of her time.. now, perhaps more than ever, could we use some of them to transition towards a more sustainable, just and inclusive society and world?
She’s definitely up there on the “genius list” with my other scientific heroes. Until I learn how to teleport, then, I’ll do the next best thing and explore her ideas through her writings and legacy..