Stefaan Verhulst

Joseph D. Harrison at AMA Journal of Ethics: “Nudges are subtle changes to the design of the environment or the framing of information that can influence our behaviors. There is significant potential to use nudges in health care to improve patient outcomes and transform health care delivery. However, these interventions must be tested and implemented using a systematic approach. In this article, we describe several ways to design nudges for success by focusing on optimizing and fitting them into the clinical workflow, engaging the right stakeholders, and rapid experimentation….(More)”.

Guidance by Rainer Schnell: “Linking existing administrative data sets on the same units is used increasingly as a research strategy in many different fields. Depending on the academic field, this kind of operation has been given different names, but in application areas, this approach is mostly denoted as record linkage. Although linking data on organisations or economic entities is common, the most interesting applications of record linkage concern data on persons. Starting in medicine, this approach is now also being used in the social sciences and official statistics. Furthermore, the joint use of survey data with administrative data is now standard practice. For example, victimisation surveys are linked to police records, labour force surveys are linked to social security databases, and censuses are linked to surveys.

Merging different databases containing information on the same unit is technically trivial if all involved databases have a common identification number, such as a social security number or, as in the Scandinavian countries, a permanent personal identification number. Most of the modern identification numbers contain checksum mechanisms so that errors in these identifiers can be easily detected and corrected. Due to the many advantages of permanent personal identification numbers, similar systems have been introduced or discussed in some European countries outside Scandinavia.

In many jurisdictions, no permanent personal identification number is available for linkage. Examples are New Zealand, Australia, the UK, and Germany. Here, the linkage is most often based on alphanumeric identifiers such as surname, first name, address, and place of birth. In the literature, such identifiers are most often denoted as indirect or quasi-identifiers. Such identifiers are prone to error, for example, due to typographical errors, memory faults (previous addresses), different recordings of the same identifier (for example, swapping of substrings: reversal of first name and last name), deliberately false information (for example, year of birth) or changes of values over time (for example name changes due to marriages). Linking on exact matching information, therefore, yields only a non-randomly selected subset of records.

Furthermore, the quality of identifying information in databases containing only indirect identifiers is much lower than usually expected. Error rates in excess of 20% and more records containing incomplete or erroneous identifiers are encountered in practice….(More)”.

Robert H. Frank at the New York Times: “…Why, then, hasn’t the United States adopted a carbon tax? One hurdle is the fear that emissions would fall too slowly in response to a carbon tax, that more direct measures are needed. Another difficulty is that political leaders have reason to fear voter opposition to taxation of any kind. But there are persuasive rejoinders to both objections.

Regarding the first, critics are correct that a carbon tax alone won’t parry the climate threat. It is also true that as creatures of habit, humans tend to change their behavior only slowly, even in the face of significant financial incentives. But even small changes in behavior are greatly amplified by behavioral contagion — the social scientist’s term for how ideas and behaviors spread from person to person like infectious diseases. And if a carbon tax were to shift the behavior of some individuals now, those changes would quickly spread more widely.

Smoking rates, for example, changed little in the short run even as cigarette taxes rose sharply, but that wasn’t the end of the story. The most powerful predictor of whether someone will smoke is the percentage of her friends who smoke. Most smokers stick with their habit in the face of higher taxes, but a small minority quit, and still others refrain from starting.

Every peer group that includes those people thus contains a smaller proportion of smokers, which influences still others to quit or refrain, and so on. This contagion process explains why the percentage of American adults who smoke has fallen by two-thirds since the mid-1960s.

Behavioral contagion would similarly amplify the effects of a carbon tax. By making solar power cheaper in comparison with fossil fuels, for example, the tax would initially encourage a small number of families to install solar panels on their rooftops. But as with cigarette taxes, it’s the indirect effects that really matter….(More)”.

Report by Deloitte: “…When experts try to explain why so many transformation initiatives fail, both in the commercial world and at government agencies, they rarely talk about habit or other human factors. It’s much easier to focus on a project’s most expensive, visible, or contentious elements, such as new technologies, process redesign, or changes to the organizational chart. But the human element can be crucial to the success of organizational transformation.

This is true when you’re aiming for transformation with a capital T, such as designing and implementing an entirely new operating model. It’s also true when you’re trying to make some aspects of an existing model work better, or when you’re trying to build a new capability. No matter what kind of change an organization wants to implement, it’s crucial to understand how the proposed changes will affect people as individuals, and what it might take—psychologically, emotionally, or politically—to make them change their behavior. After all, if people keep doing the same things in the same way, what has really been transformed?

Even when leaders understand that they need to encourage behavioral change, they often aim for that goal using strategies without a proven track record. For example, they might offer financial incentives to employees who meet certain performance targets, without clear evidence that this kind of reinforcement works in that context. What if the motivating factors that can overcome inertia in a certain situation have more to do with how employees feel about their work, or how they cohere as a community?

Fortunately, thanks to the work of behavioral psychologists and economists, as well as neuroscientists, we understand much more than we used to about human behavior and its drivers. This work has demonstrated, for example:

• What typically motivates people—a sense of purpose, a sense of autonomy, and the ability to grow in one’s job¹
• Why people engage in “predictably irrational” behavior, and how we can harness this understanding to shape and nudge behavior²
• How to use customer analytics and customer experience techniques such as human-centered design to drive workforce experience³

We also know that transparency can help a transformation initiative succeed. By keeping stakeholders informed about all aspects of the effort and inviting feedback, project leaders can gain insights into human motivation and behavior, and how their employees are experiencing the change, as those factors pertain to their particular project. This can help leaders understand which aspects of the transformation are working and which are not, and it helps to build trust among everyone involved.

“Behavior-first change” applies insights from a range of disciplines including anthropology, behavioral economics, neuroscience, and psychology to better understand and influence human behavior—and governments are beginning to apply it to their transformation initiatives. In fact, with more than 200 behavioral insights units in government worldwide, the public sector has been at the forefront of this movement. So, we’re not proposing a behavioral change approach as something radically new. Instead, we’re suggesting government agencies that might already be applying these techniques externally can systematically apply these insights to their own transformations—whether “capital T” programs or more focused efforts—to improve the chances that these programs will realize their intended outcomes….(More)”.

Blog by Actionable Intelligence for Social Policy (AISP): “…State and local leaders are called upon to respond to the immediate harms of COVID-19. Yet with a looming recession threatening to undo gains among marginalized groups — particularly the Black middle class — tools to understand and disrupt long-term impacts on economic mobility and well-being are also urgently needed.

Administrative data[3] — the information collected during the course of routine service delivery, program administration, and business operations — provide an essential tool to help policymakers, community leaders, and researchers understand short- and long-term impacts of the pandemic. Several jurisdictions now have the capacity to link administrative data across programs in order to better understand how individuals interact with multiple systems, study longitudinal outcomes, and identify vulnerable subpopulations. As the COVID-19 crisis reveals weaknesses in the U.S. social safety net, states and localities with integrated administrative data infrastructure can use their capacity to identify populations and needs otherwise overlooked. Youth who “age out” of the child welfare system or individuals experiencing chronic homelessness often remain invisible when using traditional methods, aggregate data, or administrative records from a single source.

This blogpost demonstrates how nimble state and local data integration efforts have leveraged their capacity to quickly respond to and understand the impacts of COVID-19, while also reflecting on what can be done to mitigate harm and shift thinking about social welfare and the safety net….(More)”.

Book by Cory Doctorow: “…Today, there is a widespread belief that machine learning and commercial surveillance can turn even the most fumble-tongued conspiracy theorist into a svengali who can warp your perceptions and win your belief by locating vulnerable people and then pitching them with A.I.-refined arguments that bypass their rational faculties and turn everyday people into flat Earthers, anti-vaxxers, or even Nazis. When the RAND Corporation blames Facebook for “radicalization” and when Facebook’s role in spreading coronavirus misinformation is blamed on its algorithm, the implicit message is that machine learning and surveillance are causing the changes in our consensus about what’s true.

After all, in a world where sprawling and incoherent conspiracy theories like Pizzagate and its successor, QAnon, have widespread followings, something must be afoot.

But what if there’s another explanation? What if it’s the material circumstances, and not the arguments, that are making the difference for these conspiracy pitchmen? What if the trauma of living through real conspiracies all around us — conspiracies among wealthy people, their lobbyists, and lawmakers to bury inconvenient facts and evidence of wrongdoing (these conspiracies are commonly known as “corruption”) — is making people vulnerable to conspiracy theories?

If it’s trauma and not contagion — material conditions and not ideology — that is making the difference today and enabling a rise of repulsive misinformation in the face of easily observed facts, that doesn’t mean our computer networks are blameless. They’re still doing the heavy work of locating vulnerable people and guiding them through a series of ever-more-extreme ideas and communities.

Belief in conspiracy is a raging fire that has done real damage and poses real danger to our planet and species, from epidemics kicked off by vaccine denial to genocides kicked off by racist conspiracies to planetary meltdown caused by denial-inspired climate inaction. Our world is on fire, and so we have to put the fires out — to figure out how to help people see the truth of the world through the conspiracies they’ve been confused by.

But firefighting is reactive. We need fire prevention. We need to strike at the traumatic material conditions that make people vulnerable to the contagion of conspiracy. Here, too, tech has a role to play.

There’s no shortage of proposals to address this. From the EU’s Terrorist Content Regulation, which requires platforms to police and remove “extremist” content, to the U.S. proposals to force tech companies to spy on their users and hold them liable for their users’ bad speech, there’s a lot of energy to force tech companies to solve the problems they created.

There’s a critical piece missing from the debate, though. All these solutions assume that tech companies are a fixture, that their dominance over the internet is a permanent fact. Proposals to replace Big Tech with a more diffused, pluralistic internet are nowhere to be found. Worse: The “solutions” on the table today require Big Tech to stay big because only the very largest companies can afford to implement the systems these laws demand….(More)”.

Lessons from Survey Research by National Academies of Sciences, Engineering, and Medicine: “Contact tracing shares important features with the collection of survey data, as well as the taking of the U.S. Census. This rapid expert consultation suggests proven strategies from survey research that decision makers can use to encourage participation in and cooperation with contact tracing efforts along two fronts: encouraging individuals to respond to outreach from health department officials regarding participation in contact tracing and case investigation, and encouraging those who do participate to share information about people whom they may have exposed to COVID-19.

Encouraging Participation and Cooperation in Contact Tracing is intended to help decision makers in local public health departments and local governments increase participation and cooperation in contact tracing related to COVID-19. This publication focuses on contact tracing methods that involve phone, text, or email interviews with people who have tested positive and with others they may have exposed to the virus…(More)”.

Aaron Gordon at Vice: “…The Louisville highway project is hardly the first time travel demand models have missed the mark. Despite them being a legally required portion of any transportation infrastructure project that gets federal dollars, it is one of urban planning’s worst kept secrets that these models are error-prone at best and fundamentally flawed at worst.

Recently, I asked Renn how important those initial, rosy traffic forecasts of double-digit growth were to the boondoggle actually getting built.

“I think it was very important,” Renn said. “Because I don’t believe they could have gotten approval to build the project if they had not had traffic forecasts that said traffic across the river is going to increase substantially. If there isn’t going to be an increase in traffic, how do you justify building two bridges?”

ravel demand models come in different shapes and sizes. They can cover entire metro regions spanning across state lines or tackle a small stretch of a suburban roadway. And they have gotten more complicated over time. But they are rooted in what’s called the Four Step process, a rough approximation of how humans make decisions about getting from A to B. At the end, the model spits out numbers estimating how many trips there will be along certain routes.

As befits its name, the model goes through four steps in order to arrive at that number. First, it generates a kind of algorithmic map based on expected land use patterns (businesses will generate more trips than homes) and socio-economic factors (for example, high rates of employment will generate more trips than lower ones). Then it will estimate where people will generally be coming from and going to. The third step is to guess how they will get there, and the fourth is to then plot their actual routes, based mostly on travel time. The end result is a number of how many trips there will be in the project area and how long it will take to get around. Engineers and planners will then add a new highway, transit line, bridge, or other travel infrastructure to the model and see how things change. Or they will change the numbers in the first step to account for expected population or employment growth into the future. Often, these numbers are then used by policymakers to justify a given project, whether it’s a highway expansion or a light rail line…(More)”.

Report by the Open Data Institute: “The outbreak of the coronavirus (Covid-19) has amplified and accelerated the need for an effective technology ecosystem that benefits everyone’s health. This report explores models of ‘data stewardship’ (the collection, maintenance and sharing of data) required to enable better evaluation

The pandemic has been accompanied by a marked increase in the use of digital technology, including introduction of remote consultation in general practice, new data flows to support the distribution of food and other essentials, and applications to support digital contact tracing.

This report explores models of ‘data stewardship’ (the collection, maintenance and sharing of data) required to enable better evaluation. It argues everybody involved in technology has a shared responsibility to enable evaluation, whether that means innovators sharing data for evaluation purposes, or healthcare providers being clearer, from the outset, about what data is needed to support effective evaluation.

This report re-envisages the role of evaluators as data stewards, who could use their positions as intermediaries to encourage stakeholders to share data, and help increase access to data for public benefit…(More)”.

Paper by Khaled Moustafa in Cities: “The ongoing COVID-19 pandemic should teach us some lessons at health, environmental and human levels toward more fairness, human cohesion and environmental sustainability. At a health level, the pandemic raises the importance of housing for everyone particularly vulnerable and homeless people to protect them from the disease and against other similar airborne pandemics. Here, I propose to make good use of big data along with 3D construction printers to construct houses and solve some major and pressing housing needs worldwide. Big data can be used to determine how many people do need accommodation and 3D construction printers to build houses accordingly and swiftly. The combination of such facilities- big data and 3D printers- can help solve global housing crises more efficiently than traditional and unguided construction plans, particularly under environmental and major health crises where health and housing are tightly interrelated….(More)”.