Are there laws of history?

Curated on Posted on by Stefaan Verhulst

Amanda Rees at AEON: “…If big data could enable us to turn big history into mathematics rather than narratives, would that make it easier to operationalise our past? Some scientists certainly think so.

In February 2010, Peter Turchin, an ecologist from the University of Connecticut, predicted that 2020 would see a sharp increase in political volatility for Western democracies. Turchin was responding critically to the optimistic speculations of scientific progress in the journal Nature: the United States, he said, was coming to the peak of another instability spike (regularly occurring every 50 years or so), while the world economy was reaching the point of a ‘Kondratiev wave’ dip, that is, a steep downturn in a growth-driven supercycle. Along with a number of ‘seemingly disparate’ social pointers, all indications were that serious problems were looming. In the decade since that prediction, the entrenched, often vicious, social, economic and political divisions that have increasingly characterised North American and European society, have made Turchin’s ‘quantitative historical analysis’ seem remarkably prophetic.

A couple of years earlier, in July 2008, Turchin had made a series of trenchant claims about the nature and future of history. Totting up in excess of ‘200 explanations’ proposed to account for the fall of the Roman empire, he was appalled that historians were unable to agree ‘which explanations are plausible and which should be rejected’. The situation, he maintained, was ‘as risible as if, in physics, phlogiston theory and thermodynamics coexisted on equal terms’. Why, Turchin wanted to know, were the efforts in medicine and environmental science to produce healthy bodies and ecologies not mirrored by interventions to create stable societies? Surely it was time ‘for history to become an analytical, and even a predictive, science’. Knowing that historians were themselves unlikely to adopt such analytical approaches to the past, he proposed a new discipline: ‘theoretical historical social science’ or ‘cliodynamics’ – the science of history.

Like C P Snow 60 years before him, Turchin wanted to challenge the boundary between the sciences and humanities – even as periodic attempts to apply the theories of natural science to human behaviour (sociobiology, for example) or to subject natural sciences to the methodological scrutiny of the social sciences (science wars, anyone?) have frequently resulted in hostile turf wars. So what are the prospects for Turchin’s efforts to create a more desirable future society by developing a science of history?…

In 2010, Cliodynamics, the flagship journal for this new discipline, appeared, with its very first article (by the American sociologist Randall Collins) focusing on modelling victory and defeat in battle in relation to material resources and organisational morale. In a move that paralleled Comte’s earlier argument regarding the successive stages of scientific complexity (from physics, through chemistry and biology, to sociology), Turchin passionately rejected the idea that complexity made human societies unsuitable for quantitative analysis, arguing that it was precisely that complexity which made mathematics essential. Weather predictions were once considered unreliable because of the sheer complexity of managing the necessary data. But improvements in technology (satellites, computers) mean that it’s now possible to describe mathematically, and therefore to model, interactions between the system’s various parts – and therefore to know when it’s wise to carry an umbrella. With equal force, Turchin insisted that the cliodynamic approach was not deterministic. It would not predict the future, but instead lay out for governments and political leaders the likely consequences of competing policy choices.

Crucially, and again on the back of the abundantly available and cheap computer power, cliodynamics benefited from the surge in interest in the digital humanities. Existing archives were being digitised, uploaded and made searchable: every day, it seemed, more data were being presented in a format that encouraged quantification and enabled mathematical analysis – including the Old Bailey’s online database, of which Wolf had fallen foul. At the same time, cliodynamicists were repositioning themselves. Four years after its initial launch, the subtitle of their flagship journal was renamed, from The Journal of Theoretical and Mathematical History to The Journal of Quantitative History and Cultural Evolution. As Turchin’s editorial stated, this move was intended to position cliodynamics within a broader evolutionary analysis; paraphrasing the Russian-American geneticist Theodosius Dobzhansky, he claimed that ‘nothing in human history makes sense except in the light of cultural evolution’. Given Turchin’s ecological background, this evolutionary approach to history is unsurprising. But given the historical outcomes of making politics biological, it is potentially worrying….

Mathematical, data-driven, quantitative models of human experience that aim at detachment, objectivity and the capacity to develop and test hypotheses need to be balanced by explicitly fictional, qualitative and imaginary efforts to create and project a lived future that enable their audiences to empathically ground themselves in the hopes and fears of what might be to come. Both, after all, are unequivocally doing the same thing: using history and historical experience to anticipate the global future so that we might – should we so wish – avoid civilisation’s collapse. That said, the question of who ‘we’ are does, always, remain open….(More)”.

The Coronavirus Is Rewriting Our Imaginations

Curated on Posted on by Stefaan Verhulst

Kim Stanley Robinson at the New Yorker: “…We are individuals first, yes, just as bees are, but we exist in a larger social body. Society is not only real; it’s fundamental. We can’t live without it. And now we’re beginning to understand that this “we” includes many other creatures and societies in our biosphere and even in ourselves. Even as an individual, you are a biome, an ecosystem, much like a forest or a swamp or a coral reef. Your skin holds inside it all kinds of unlikely coöperations, and to survive you depend on any number of interspecies operations going on within you all at once. We are societies made of societies; there are nothing but societies. This is shocking news—it demands a whole new world view. And now, when those of us who are sheltering in place venture out and see everyone in masks, sharing looks with strangers is a different thing. It’s eye to eye, this knowledge that, although we are practicing social distancing as we need to, we want to be social—we not only want to be social, we’ve got to be social, if we are to survive. It’s a new feeling, this alienation and solidarity at once. It’s the reality of the social; it’s seeing the tangible existence of a society of strangers, all of whom depend on one another to survive. It’s as if the reality of citizenship has smacked us in the face.

As for government: it’s government that listens to science and responds by taking action to save us. Stop to ponder what is now obstructing the performance of that government. Who opposes it?…

There will be enormous pressure to forget this spring and go back to the old ways of experiencing life. And yet forgetting something this big never works. We’ll remember this even if we pretend not to. History is happening now, and it will have happened. So what will we do with that?

A structure of feeling is not a free-floating thing. It’s tightly coupled with its corresponding political economy. How we feel is shaped by what we value, and vice versa. Food, water, shelter, clothing, education, health care: maybe now we value these things more, along with the people whose work creates them. To survive the next century, we need to start valuing the planet more, too, since it’s our only home.

It will be hard to make these values durable. Valuing the right things and wanting to keep on valuing them—maybe that’s also part of our new structure of feeling. As is knowing how much work there is to be done. But the spring of 2020 is suggestive of how much, and how quickly, we can change. It’s like a bell ringing to start a race. Off we go—into a new time….(More)”.

Viruses Cross Borders. To Fight Them, Countries Must Let Medical Data Flow, Too

Curated on Posted on by Stefaan Verhulst

Nigel Cory at ITIF: “If nations could regulate viruses the way many regulate data, there would be no global pandemics. But the sad reality is that, in the midst of the worst global pandemic in living memory, many nations make it unnecessarily complicated and costly, if not illegal, for health data to cross their borders. In so doing, they are hindering critically needed medical progress.

In the COVID-19 crisis, data analytics powered by artificial intelligence (AI) is critical to identifying the exact nature of the pandemic and developing effective treatments. The technology can produce powerful insights and innovations, but only if researchers can aggregate and analyze data from populations around the globe. And that requires data to move across borders as part of international research efforts by private firms, universities, and other research institutions. Yet, some countries, most notably China, are stopping health and genomic data at their borders.

Indeed, despite the significant benefits to companies, citizens, and economies that arise from the ability to easily share data across borders, dozens of countries—across every stage of development—have erected barriers to cross-border data flows. These data-residency requirements strictly confine data within a country’s borders, a concept known as “data localization,” and many countries have especially strict requirements for health data.

China is a noteworthy offender, having created a new digital iron curtain that requires data localization for a range of data types, including health data, as part of its so-called “cyber sovereignty” strategy. A May 2019 State Council regulation required genomic data to be stored and processed locally by Chinese firms—and foreign organizations are prohibited. This is in service of China’s mercantilist strategy to advance its domestic life sciences industry. While there has been collaboration between U.S. and Chinese medical researchers on COVID-19, including on clinical trials for potential treatments, these restrictions mean that it won’t involve the transfer, aggregation, and analysis of Chinese personal data, which otherwise might help find a treatment or vaccine. If China truly wanted to make amends for blocking critical information during the early stages of the outbreak in Wuhan, then it should abolish this restriction and allow genomic and other health data to cross its borders.

But China is not alone in limiting data flows. Russia requires all personal data, health-related or not, to be stored locally. India’s draft data protection bill permits the government to classify any sensitive personal data as critical personal data and mandate that it be stored and processed only within the country. This would be consistent with recent debates and decisions to require localization for payments data and other types of data. And despite its leading role in pushing for the free flow of data as part of new digital trade agreements, Australia requires genomic and other data attached to personal electronic health records to be only stored and processed within its borders.

Countries also enact de facto barriers to health and genomic data transfers by making it harder and more expensive, if not impractical, for firms to transfer it overseas than to store it locally. For example, South Korea and Turkey require firms to get explicit consent from people to transfer sensitive data like genomic data overseas. Doing this for hundreds or thousands of people adds considerable costs and complexity.

And the European Union’s General Data Protection Regulation encourages data localization as firms feel pressured to store and process personal data within the EU given the restrictions it places on data transfers to many countries. This is in addition to the renewed push for local data storage and processing under the EU’s new data strategy.

Countries rationalize these steps on the basis that health data, particularly genomic data, is sensitive. But requiring health data to be stored locally does little to increase privacy or data security. The confidentiality of data does not depend on which country the information is stored in, only on the measures used to store it securely, such as via encryption, and the policies and procedures the firms follow in storing or analyzing the data. For example, if a nation has limits on the use of genomics data, then domestic organizations using that data face the same restrictions, whether they store the data in the country or outside of it. And if they share the data with other organizations, they must require those organizations, regardless of where they are located, to abide by the home government’s rules.

As such, policymakers need to stop treating health data differently when it comes to cross-border movement, and instead build technical, legal, and ethical protections into both domestic and international data-governance mechanisms, which together allow the responsible sharing and transfer of health and genomic data.

This is clearly possible—and needed. In February 2020, leading health researchers called for an international code of conduct for genomic data following the end of their first-of-its-kind international data-driven research project. The project used a purpose-built cloud service that stored 800 terabytes of genomic data on 2,658 cancer genomes across 13 data centers on three continents. The collaboration and use of cloud computing were transformational in enabling large-scale genomic analysis….(More)”.

Models v. Evidence

Curated on Posted on by Stefaan Verhulst

Jonathan Fuller at the Boston Review: “COVID-19 has revealed a contest between two competing philosophies of scientific knowledge. To manage the crisis, we must draw on both….The lasting icon of the COVID-19 pandemic will likely be the graphic associated with “flattening the curve.” The image is now familiar: a skewed bell curve measuring coronavirus cases that towers above a horizontal line—the health system’s capacity—only to be flattened by an invisible force representing “non-pharmaceutical interventions” such as school closures, social distancing, and full-on lockdowns.

How do the coronavirus models generating these hypothetical curves square with the evidence? What roles do models and evidence play in a pandemic? Answering these questions requires reconciling two competing philosophies in the science of COVID-19.

To some extent, public health epidemiology and clinical epidemiology are distinct traditions in health care, competing philosophies of scientific knowledge.

In one camp are infectious disease epidemiologists, who work very closely with institutions of public health. They have used a multitude of models to create virtual worlds in which sim viruses wash over sim populations—sometimes unabated, sometimes held back by a virtual dam of social interventions. This deluge of simulated outcomes played a significant role in leading government actors to shut borders as well as doors to schools and businesses. But the hypothetical curves are smooth, while real-world data are rough. Some detractors have questioned whether we have good evidence for the assumptions the models rely on, and even the necessity of the dramatic steps taken to curb the pandemic. Among this camp are several clinical epidemiologists, who typically provide guidance for clinical practice—regarding, for example, the effectiveness of medical interventions—rather than public health.

The latter camp has won significant media attention in recent weeks. Bill Gates—whose foundation funds the research behind the most visible outbreak model in the United States, developed by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington—worries that COVID-19 might be a “once-in-a-century pandemic.” A notable detractor from this view is Stanford’s John Ioannidis, a clinical epidemiologist, meta-researcher, and reliable skeptic who has openly wondered whether the coronavirus pandemic might rather be a “once-in-a-century evidence fiasco.” He argues that better data are needed to justify the drastic measures undertaken to contain the pandemic in the United States and elsewhere.

Ioannidis claims, in particular, that our data about the pandemic are unreliable, leading to exaggerated estimates of risk. He also points to a systematic review published in 2011 of the evidence regarding physical interventions that aim to reduce the spread of respiratory viruses, worrying that the available evidence is nonrandomized and prone to bias. (A systematic review specific to COVID-19 has now been published; it concurs that the quality of evidence is “low” to “very low” but nonetheless supports the use of quarantine and other public health measures.) According to Ioannidis, the current steps we are taking are “non-evidence-based.”…(More)”.

Which Covid-19 Data Can You Trust?

Curated on Posted on by Stefaan Verhulst

Article by Satchit Balsari, Caroline Buckee and Tarun Khanna: “The Covid-19 pandemic has created a tidal wave of data. As countries and cities struggle to grab hold of the scope and scale of the problem, tech corporations and data aggregators have stepped up, filling the gap with dashboards scoring social distancing based on location data from mobile phone apps and cell towers, contact-tracing apps using geolocation services and Bluetooth, and modeling efforts to predict epidemic burden and hospital needs. In the face of uncertainty, these data can provide comfort — tangible facts in the face of many unknowns.

In a crisis situation like the one we are in, data can be an essential tool for crafting responses, allocating resources, measuring the effectiveness of interventions, such as social distancing, and telling us when we might reopen economies. However, incomplete or incorrect data can also muddy the waters, obscuring important nuances within communities, ignoring important factors such as socioeconomic realities, and creating false senses of panic or safety, not to mention other harms such as needlessly exposing private information. Right now, bad data could produce serious missteps with consequences for millions.

Unfortunately, many of these technological solutions — however well intended — do not provide the clear picture they purport to. In many cases, there is insufficient engagement with subject-matter experts, such as epidemiologists who specialize in modeling the spread of infectious diseases or front-line clinicians who can help prioritize needs. But because technology and telecom companies have greater access to mobile device data, enormous financial resources, and larger teams of data scientists, than academic researchers do, their data products are being rolled out at a higher volume than high quality studies.

Whether you’re a CEO, a consultant, a policymaker, or just someone who is trying to make sense of what’s going on, it’s essential to be able to sort the good data from the misleading — or even misguided.

Common Pitfalls

While you may not be qualified to evaluate the particulars of every dashboard, chart, and study you see, there are common red flags to let you know data might not be reliable. Here’s what to look out for:

Data products that are too broad, too specific, or lack context. Over-aggregated data — such as national metrics of physical distancing that some of our largest data aggregators in the world are putting out — obscure important local and regional variation, are not actionable, and mean little if used for inter-nation comparisons given the massive social, demographic, and economic disparities in the world….(More)”.

10 transformative data questions related to gender

Curated on Posted on by Stefaan Verhulst

Press Release: “As part of efforts to identify priorities across sectors in which data and data science could make a difference, The Governance Lab (The GovLab) at the New York University Tandon School of Engineering has partnered with Data2X, the gender data alliance housed at the United Nations Foundation, to release ten pressing questions on gender that experts have determined can be answered using data. Members of the public are invited to share their views and vote to help develop a data agenda on gender.

The questions are part of the 100 Questions Initiative, an effort to identify the most important societal questions that can be answered by data. The project relies on an innovative process of sourcing “bilinguals,” individuals with both subject-matter and data expertise, who in this instance provided questions related to gender they considered to be urgent and answerable. The results span issues of labor, health, climate change, and gender-based violence.

Through the initiative’s new online platform, anyone can now vote on what they consider to be the most pressing, data-related questions about gender that researchers and institutions should prioritize. Through voting, the public can steer the conversation and determine which topics should be the subject of data collaboratives, an emerging form of collaboration that allows organizations from different sectors to exchange data to create public value.

The GovLab has conducted significant research on the value and practice of data collaboratives, and its research shows that inter-sectoral collaboration can both increase access to data as well as unleash the potential of that data to serve the public good.

Data2X supported the 100 Questions Initiative by providing expertise and connecting The GovLab with relevant communities, events, and resources. The initiative helped inform Data2X’s “Big Data, Big Impact? Towards Gender-Sensitive Data Systems” report, which identifies gaps of information on gender equality across key policy domains.

“Asking the right questions is a critical first step in fostering data production and encouraging data use to truly meet the unique experiences and needs of women and girls,” said Emily Courey Pryor, executive director of Data2X. “Obtaining public feedback is a crucial way to identify the most urgent questions — and to ultimately incentivize investment in gender data collection and use to find the answers.”Said Stefaan Verhulst, co-founder and chief research and development officer at The GovLab, “Sourcing and prioritizing questions related to gender can inform resource and funding allocation to address gender data gaps and support projects with the greatest potential impact. This way, we can be confident about solutions that address the challenges facing women and girls.”…(More)”.

Assessing the feasibility of real-world data

Curated on Posted on by Stefaan Verhulst

Blog Post by Manuela Di Fusco: “Real-world data (RWD) and real-world evidence (RWE) are playing an increasing role in healthcare decision making.

The conduct of RWD studies involves many interconnected stages, ranging from the definition of research questions of high scientific interest, to the design of a study protocol and statistical plan, and the conduct of the analyses, quality reviews, publication and presentation to the scientific community. Every stage requires extensive knowledge, expertise and efforts from the multidisciplinary research team.

There are a number of well-accepted guidelines for good procedural practices in RWD . Despite their stress on the importance of data reliability, relevance and studies being fit for purpose, their recommendations generally focus on methods/analyses and transparent reporting of results. There often is little focus on feasibility concerns at the early stages of a study; ongoing RWD initiatives, too, focus on improving standards and practices for data collection and analyses.

RWD and RWE are playing an increasing role in healthcare decision making.”

The availability and use of new data sources, which have the ability to store health-related data, have been growing globally, and include mobile technologies, electronic patient-reported outcome tools and wearables [1]. 

As data sources exist in various formats, and are often created for non-research purposes, they have inherent associated limitations – such as missing data. Determining the best approach for collecting complete and quality data is of critical importance. At study conception, it is not always clear if it is reasonable to expect that the research question of interest could be fully answered and all analyses carried out. Numerous methodological and data collection challenges can emerge during study execution. However, some of these downstream study challenges could be proactively addressed through an early feasibility study, concurrent to protocol development. For example, during this exploratory study, datasets may be explored carefully to ensure data points deemed relevant for the study are routinely ascertained and captured sufficiently, despite potential missing data and/or other data source limitations.

Determining the best approach for collecting complete and quality data is of critical importance.”

This feasibility assessment serves primarily as a first step to gain knowledge of the data and ensure realistic assumptions are included in the protocol; relevant sensitivity analyses can test those assumptions, hence setting the basis for successful study development.  

Below is a list of key feasibility questions which may guide the technical exploration and conceptualization of a retrospective RWD study. The list is based on experience supporting observational studies on a global scale and is not intended to be exhaustive and representative of all preparatory activities. This technical feasibility analysis should be carried out while considering other relevant aspects, including the novelty and strategic value of the study versus the existing evidence – in the form of randomized controlled trial data and other RWE –, the intended audience, data access/protection, reporting requirements and external validity aspects.

This feasibility assessment serves primarily as a first step to gain knowledge of the data and ensure realistic assumptions are included in the protocol…”

The list may support early discussions among study team members during the preparation and determination of a RWD study.

  • Can the population be accurately identified in the data source?

Diagnosis and procedures can be identified through International Classification of Diseases codes; published code validation studies on the population of interest can be a useful guide.

  • How generalizable is the population of the data source?

Generalizability issues should be recognized upfront. For example, the patient population for which data is available in the data source might be restricted to a specific geographic region, health insurance plan (e.g. Medicare or commercial), system (hospital/inpatient and ambulatory) or group (e.g. age, gender)…(More)”.

10 Tips for Making Sense of COVID-19 Models for Decision-Making

Curated on Posted on by Stefaan Verhulst

Elizabeth Stuart et al at John Hopkins School of Public Health: “Models can be enormously useful in the context of an epidemic if they synthesize evidence and scientific knowledge. The COVID-19 pandemic is a complex phenomenon and in such a dynamic setting it is nearly impossible to make informed decisions without the assistance models can provide. However, models don’t perfectly capture reality: They simplify reality to help answer specific questions.

Below are 10 tips for making sense of COVID-19 models for decision-making such as directing health care resources to certain areas or identifying how long social distancing policies may need to be in effect.

Flattening the Curve for COVIX-19
  1. Make sure the model fits the question you are trying to answer.
    There are many different types of models and a wide variety of questions that models can be used to address. There are three that can be helpful for COVID-19:
    1. Models that simplify how complex systems work, such as disease transmission. This is often done by putting people into compartments related to how a disease spreads, like “susceptible,” “infected,” and “recovered.” While these can be overly simplistic with few data inputs and don’t allow for the uncertainty that exists in a pandemic, they can be useful in the short term to understand basic structures. But these models generally cannot be implemented in ways that account for complex systems or when there is ongoing system or individual behavioral change.
    2. Forecasting models try to predict what will actually happen. They work by using existing data to project out conclusions over a relatively short time horizon. But these models are challenging to use for mid-term assessment—like a few months out—because of the evolving nature of pandemics.
    3. Strategic models show multiple scenarios to consider the potential implications of different interventions and contexts. These models try to capture some of the uncertainty about the underlying disease processes and behaviors. They might take a few values of such as the case fatality ratio or the effectiveness of social distancing measures, and play out different scenarios for disease spread over time. These kinds of models can be particularly useful for decision-making.
  2. Be mindful that forecast models are often built with the goal of change, which affects their shelf life.
    The irony of many COVID-19 modeling purposes is that in some cases, especially for forecasting, a key purpose in building and disseminating the model is to invoke behavior change at individual or system levels—e.g., to reinforce the need for physical distancing.

    This makes it difficult to assess the performance of forecasting models since the results of the model itself (and reactions to it) become part of the system. In these cases, a forecasting model may look like it was inaccurate, but it may have been accurate for an unmitigated scenario with no behavior change. In fact, a public health success may be when the forecasts do not come to be!
  3. Look for models (and underlying collaborations) that include diverse aspects and expertise.
    One of the challenges in modeling COVID-19 is the multitude of factors involved: infectious disease dynamics, social and behavioral factors such as how frequently individuals interact, economic factors such as employment and safety net policies, and more.

    One benefit is that we do know that COVID-19 is an infectious disease and we have a good understanding about how related diseases spread. Likewise, health economists and public health experts have years of experience understanding complex social systems. Look for models, and their underlying collaborations, that take advantage of that breadth of existing knowledge….(More)”.

Behavioural Insights Teams (BITs) and Policy Change: An Exploration of Impact, Location, and Temporality of Policy Advice

Curated on Posted on by Stefaan Verhulst

Paper by Ishani Mukherjee and Sarah Giest: “Behavioural Insights Teams (BITs) have gained prominence in government as policy advisors and are increasingly linked to the way policy instruments are designed. Despite the rise of BITs as unique knowledge brokers mediating the use of behavioral insights for policymaking, they remain underexplored in the growing literature on policy advice and advisory systems. The article emphasizes that the visible impact that BITs have on the content of policy instruments, the level of political support they garner and their structural diversity in different political departments, all set them apart from typical policy brokers in policy advisory systems connecting the science-policy divide…(More)”.

Collaborative Society

Curated on Posted on by Stefaan Verhulst

Book by Dariusz Jemielniak and Aleksandra Przegalinska: “Humans are hard-wired for collaboration, and new technologies of communication act as a super-amplifier of our natural collaborative mindset. This volume in the MIT Press Essential Knowledge series examines the emergence of a new kind of social collaboration enabled by networked technologies. This new collaborative society might be characterized as a series of services and startups that enable peer-to-peer exchanges and interactions though technology. Some believe that the economic aspects of the new collaboration have the potential to make society more equitable; others see collaborative communities based on sharing as a cover for social injustice and user exploitation.

The book covers the “sharing economy,” and the hijacking of the term by corporations; different models of peer production, and motivations to participate; collaborative media production and consumption, the definitions of “amateur” and “professional,” and the power of memes; hactivism and social movements, including Anonymous and anti-ACTA protest; collaborative knowledge creation, including citizen science; collaborative self-tracking; and internet-mediated social relations, as seen in the use of Instagram, Snapchat, and Tinder. Finally, the book considers the future of these collaborative tendencies and the disruptions caused by fake news, bots, and other challenges….(More)”.