When What’s Right Is Also Wrong: The Pandemic As A Corporate Social Responsibility Paradox

Curated on Posted on by Stefaan Verhulst

Article by Heidi Reed: “When the COVID-19 pandemic first hit, businesses were faced with difficult decisions where making the ‘right choice’ just wasn’t possible. For example, if a business chose to shut down, it might protect employees from catching COVID, but at the same time, it would leave them without a paycheck. This was particularly true in the U.S. where the government played a more limited role in regulating business behavior, leaving managers and owners to make hard choices.

In this way, the pandemic is a societal paradox in which the social objectives of public health and economic prosperity are both interdependent and contradictory. How does the public judge businesses then when they make decisions favoring one social objective over another? To answer this question, I qualitatively surveyed the American public at the start of the COVID-19 crisis about what they considered to be responsible and irresponsible business behavior in response to the pandemic. Analyzing their answers led me to create the 4R Model of Moral Sensemaking of Competing Social Problems.

The 4R Model relies on two dimensions: the extent to which people prioritize one social problem over another and the extent to which they exhibit psychological discomfort (i.e. cognitive dissonance). In the first mode, Reconcile, people view the problems as compatible. There is no need to prioritize then and no resulting dissonance. These people think, “Businesses can just convert to making masks to help the cause and still make a profit.”

The second mode, Resign, similarly does not prioritize one problem over another; however, the problems are seen as competing, suggesting a high level of cognitive dissonance. These people might say, “It’s dangerous to stay open, but if the business closes, people will lose their jobs. Both decisions are bad.”

In the third mode, Ranking, people use prioritizing to reduce cognitive dissonance. These people say things like, “I understand people will be fired, but it’s more important to stop the virus.”

In the fourth and final mode, Rectify, people start by ranking but show signs of lingering dissonance as they acknowledge the harm created by prioritizing one problem over another. Unlike with the Resign mode, they try to find ways to reduce this harm. A common response in this mode would be, “Businesses should shut down, but they should also try to help employees file for unemployment.”

The 4R model has strong implications for other grand challenges where there may be competing social objectives such as in addressing climate change. To this end, the typology helps corporate social responsibility (CSR) decision-makers understand how they may be judged when businesses are forced to re- or de-prioritize CSR dimensions. In other words, it helps us understand how people make moral sense of business behavior when the right thing to do is paradoxically also the wrong thing…(More)”

Systems Thinking, Big Data and Public Policy

Curated on Posted on by Stefaan Verhulst

Article by Mauricio Covarrubias: “Systems thinking and big data analysis are two fundamental tools in the formulation of public policies due to their potential to provide a more comprehensive and evidence-based understanding of the problems and challenges that a society faces.

Systems thinking is important in the formulation of public policies because it allows for a holistic and integrated approach to addressing the complex challenges and issues that a society faces. According to Ilona Kickbusch and David Gleicher, “Addressing wicked problems requires a high level of systems thinking. If there is a single lesson to be drawn from the first decade of the 21st century, it is that surprise, instability and extraordinary change will continue to be regular features of our lives.”

Public policies often involve multiple stakeholders, interrelated factors and unintended consequences, which require a deep understanding of how the system as a whole operates. Systems thinking enables policymakers to identify the key factors that influence a problem and how they relate to each other, enabling them to develop solutions that more effectively address the issues. Instead of trying to address a problem in isolation, systems thinking considers the problem as part of a whole and seeks solutions that address the root causes.

Additionally, systems thinking helps policymakers anticipate the unintended consequences of their decisions and actions. By understanding how different components of the system interact, they can predict the possible side effects of a policy in other areas. This can help avoid decisions that have unintended consequences…(More)”.

Behavioural Incentive Design for Health Policy

Curated on Posted on by Stefaan Verhulst

Book by Joan Costa-Font, Tony Hockley, Caroline Rudisill: “Behavioural economics has become a popular way of tackling a broad range of issues in public policy. By presenting a more descriptive and possibly accurate representation of human behaviour than traditional economics, Behavioural Incentive Design for Health Policy tries to make sense of decisions that follow a wider conception of welfare, influenced by social norms and narratives, pro-social motivations and choice architectures which were generally neglected by standard economics. The authors show how this model can be applied to tackle a wide range of issues in public health, including smoking, the obesity crisis, exercise uptake, alcoholism, preventive screenings and attitudes towards vaccinations. It shows not only how behavioural economics allows us to better understand such challenges, but also how it can design effective incentives for addressing them. This book is an extensive reassessment of the interaction between behavioural incentives and health….(More)”.

Revisiting the Behavioral Revolution in Economics 

Curated on Posted on by Stefaan Verhulst

Article by Antara Haldar: “But the impact of the behavioral revolution outside of microeconomics remains modest. Many scholars are still skeptical about incorporating psychological insights into economics, a field that often models itself after the natural sciences, particularly physics. This skepticism has been further compounded by the widely publicized crisis of replication in psychology.

Macroeconomists, who study the aggregate functioning of economies and explore the impact of factors such as output, inflation, exchange rates, and monetary and fiscal policy, have, in particular, largely ignored the behavioral trend. Their indifference seems to reflect the belief that individual idiosyncrasies balance out, and that the quirky departures from rationality identified by behavioral economists must offset each other. A direct implication of this approach is that quantitative analyses predicated on value-maximizing behavior, such as the dynamic stochastic general equilibrium models that dominate policymaking, need not be improved.

The validity of these assumptions, however, remains uncertain. During banking crises such as the Great Recession of 2008 or the ongoing crisis triggered by the recent collapse of Silicon Valley Bank, the reactions of economic actors – particularly financial institutions and investors – appear to be driven by herd mentality and what John Maynard Keynes referred to as “animal spirits.”…

The roots of economics’ resistance to the behavioral sciences run deep. Over the past few decades, the field has acknowledged exceptions to the prevailing neoclassical paradigm, such as Elinor Ostrom’s solutions to the tragedy of the commons and Akerlof, Michael Spence, and Joseph E. Stiglitz’s work on asymmetric information (all four won the Nobel Prize). At the same time, economists have refused to update the discipline’s core assumptions.

This state of affairs can be likened to an imperial government that claims to uphold the rule of law in its colonies. By allowing for a limited release of pressure at the periphery of the paradigm, economists have managed to prevent significant changes that might undermine the entire system. Meanwhile, the core principles of the prevailing economic model remain largely unchanged.

For economics to reflect human behavior, much less influence it, the discipline must actively engage with human psychology. But as the list of acknowledged exceptions to the neoclassical framework grows, each subsequent breakthrough becomes a potentially existential challenge to the field’s established paradigm, undermining the seductive parsimony that has been the source of its power.

By limiting their interventions to nudges, behavioral economists hoped to align themselves with the discipline. But in doing so, they delivered a ratings-conscious “made for TV” version of a revolution. As Gil Scott-Heron famously reminded us, the real thing will not be televised….(More)”.

Norm-Nudging: Harnessing Social Expectations for Behavior Change

Curated on Posted on by Stefaan Verhulst

Paper by Cristina Bicchieri and Eugen Dimant: “Nudging is a popular approach to achieving positive behavior change. It involves subtle changes to the decision-making environment designed to steer individuals towards making better choices. Norm-nudging is a type of behavioral nudge that aims to change social expectations about what others do or approve/disapprove of in a similar situation. Norm-nudging can be effective when behaviors are interdependent, meaning that their preferences are influenced by others’ actions and/or beliefs. However, norm-nudging is not a one-size-fits-all solution and there are also risks associated with it, such as the potential to be perceived as manipulative or coercive, or the difficulty to effectively implement interventions. To maximize the benefits and minimize the risks of using social information to achieve behavior change, policymakers should carefully choose what behavior they want to promote, consider the target audience for the social information, and be aware of the potential for unintended consequences…(More)”.

Judging Nudging: Understanding the Welfare Effects of Nudges Versus Taxes

Curated on Posted on by Stefaan Verhulst

Paper by John A. List, Matthias Rodemeier, Sutanuka Roy & Gregory K. Sun: “While behavioral non-price interventions (“nudges”) have grown from academic curiosity to a bona fide policy tool, their relative economic efficiency remains under-researched. We develop a unified framework to estimate welfare effects of both nudges and taxes. We showcase our approach by creating a database of more than 300 carefully hand-coded point estimates of non-price and price interventions in the markets for cigarettes, influenza vaccinations, and household energy. While nudges are effective in changing behavior in all three markets, they are not necessarily the most efficient policy. We find that nudges are more efficient in the market for cigarettes, while taxes are more efficient in the energy market. For influenza vaccinations, optimal subsidies likely outperform nudges. Importantly, two key factors govern the difference in results across markets: i) an elasticity-weighted standard deviation of the behavioral bias, and ii) the magnitude of the average externality. Nudges dominate taxes whenever i) exceeds ii). Combining nudges and taxes does not always provide quantitatively significant improvements to implementing one policy tool alone…(More)”.

Five Enablers for a New Phase of Behavioral Science

Curated on Posted on by Stefaan Verhulst

Article by Michael Hallsworth: “Over recent weeks I’ve been sharing parts of a “manifesto” that tries to give a coherent vision for the future of applied behavioral science. Stepping back, if I had to identify a theme that comes through the various proposals, it would be the need for self-reflective practice.

Behavioral science has seen a tremendous amount of growth and interest over the last decade, largely focused on expanding its uses and methods. My sense is it’s ready for a new phase of maturity. That maturity involves behavioral scientists reflecting on the various ways that their actions are shaped by structural, institutional, environmental, economic, and historical factors.

I’m definitely not exempt from this need for self-reflection. There are times when I’ve focused on a cognitive bias when I should have been spending more time exploring the context and motivations for a decision instead. Sometimes I’ve homed in on a narrow slice of a problem that we can measure, even if that means dispensing with wider systemic effects and challenges. Once I spent a long time trying to apply the language of heuristics and biases to explain why people were failing to use the urgent care alternatives to hospital emergency departments, before realizing that their behavior was completely reasonable.     

The manifesto critiques things like this, but it doesn’t have all the answers. Because it tries to both cover a lot of ground and go into detail, many of the hard knots of implementation go unpicked. The truth is that writing reports and setting goals is the easy part. Turning those goals into practice is much tougher; as behavioral scientists know, there is often a gap between intention and action.

Right now, I and others don’t always realize the ambitions set out in the manifesto. Changing that is going to take time and effort, and it will involve the discomfort of disrupting familiar practices. Some have made public commitments in this direction; my organization is working on upgrading its practices in line with proposals around making predictions prior to implementation, strengthening RCTs to cope with complexity, and enabling people to use behavioral science, among others.

The truth is that writing reports and setting goals is the easy part. Turning those goals into practice is much tougher; as behavioral scientists know, there is often a gap between intention and action.

But changes by individual actors will not be enough. The big issue is that several of the proposals require coordination. For example, one of the key ideas is the need for more multisite studies that are well coordinated and have clear goals. Another prioritizes developing international professional networks to support projects in low- and middle-income countries…(More)”.

Misunderstanding Misinformation

Curated on Posted on by Stefaan Verhulst

Article by Claire Wardle: “In the fall of 2017, Collins Dictionary named fake news word of the year. It was hard to argue with the decision. Journalists were using the phrase to raise awareness of false and misleading information online. Academics had started publishing copiously on the subject and even named conferences after it. And of course, US president Donald Trump regularly used the epithet from the podium to discredit nearly anything he disliked.

By spring of that year, I had already become exasperated by how this term was being used to attack the news media. Worse, it had never captured the problem: most content wasn’t actually fake, but genuine content used out of context—and only rarely did it look like news. I made a rallying cry to stop using fake news and instead use misinformation, disinformation, and malinformation under the umbrella term information disorder. These terms, especially the first two, have caught on, but they represent an overly simple, tidy framework I no longer find useful.

Both disinformation and misinformation describe false or misleading claims, but disinformation is distributed with the intent to cause harm, whereas misinformation is the mistaken sharing of the same content. Analyses of both generally focus on whether a post is accurate and whether it is intended to mislead. The result? We researchers become so obsessed with labeling the dots that we can’t see the larger pattern they show.

By focusing narrowly on problematic content, researchers are failing to understand the increasingly sizable number of people who create and share this content, and also overlooking the larger context of what information people actually need. Academics are not going to effectively strengthen the information ecosystem until we shift our perspective from classifying every post to understanding the social contexts of this information, how it fits into narratives and identities, and its short-term impacts and long-term harms…(More)”.

An Audit Framework for Adopting AI-Nudging on Children

Curated on Posted on by Stefaan Verhulst

Paper by Marianna Ganapini, and Enrico Panai: “This is an audit framework for AI-nudging. Unlike the static form of nudging usually discussed in the literature, we focus here on a type of nudging that uses large amounts of data to provide personalized, dynamic feedback and interfaces. We call this AI-nudging (Lanzing, 2019, p. 549; Yeung, 2017). The ultimate goal of the audit outlined here is to ensure that an AI system that uses nudges will maintain a level of moral inertia and neutrality by complying with the recommendations, requirements, or suggestions of the audit (in other words, the criteria of the audit). In the case of unintended negative consequences, the audit suggests risk mitigation mechanisms that can be put in place. In the case of unintended positive consequences, it suggests some reinforcement mechanisms. Sponsored by the IBM-Notre Dame Tech Ethics Lab…(More)”.

The Untapped Potential of Computing and Cognition in Tackling Climate Change

Curated on Posted on by Stefaan Verhulst

Article by Adiba Proma, Robert Wachter and Ehsan Hoque: “Alongside the search for climate-protecting technologies like EVs, more effort needs to be directed to harnessing technology to promote climate-protecting behavior change. This will take focus, leadership, and cooperation among technologists, investors, business executives, educators, and governments. Unfortunately, such focus, leadership, and cooperation have been lacking.  

Persuading people to change their lifestyles to benefit the next generations is a significant challenge. We argue that simple changes in how technologies are built and deployed can significantly lower society’s carbon footprint. 

While it is challenging to influence human behavior, there are opportunities to offer nudges and just-in-time interventions by tweaking certain aspects of technology. For example, the “Climate Pledge Friendly” tag added to products that meet Amazon’s sustainability standards can help users identify and purchase ecofriendly products while shopping online [3]. Similarly, to help users make more ecofriendly choices while traveling, Google Flights provides information on average carbon dioxide emission for flights and Google Maps tags the “most fuel-efficient” route for vehicles. 

Computer scientists can draw on concepts from psychology, moral dilemma, and human cooperation to build technologies that can encourage people to lead ecofriendly lifestyles. Many mobile health applications have been developed to motivate people to exercise, eat a healthy diet, sleep better, and manage chronic diseases. Some apps designed to improve sleep, mental wellbeing, and calorie intake have as many as 200 million active users. The use of apps and other internet tools can be adapted to promote lifestyle changes for climate change. For example, Google Nest rewards users with a “leaf” when they meet an energy goal…(More)”.