Oracles in the Machine

Curated on Posted on by Stefaan Verhulst

Essay by Zora Che: “…In sociologist Charles Cooley’s theory of the “looking glass of self,” we understand ourselves through the perceptions of others. Online, models perceive us, responding to and reinforcing the versions of ourselves which they glean from our behaviors. They sense my finger lingering, my invisible gaze apparent by the gap of my movements. My understanding of my digital self and my digital reality becomes a feedback loop churned by models I cannot see. Moreover, the model only “sees” me as data that can be optimized for objectives that I cannot uncover. That objective is something closer to optimizing my time spent on the digital product than to holding my deepest needs; the latter perhaps was never a mathematical question to begin with.

Divination and algorithmic opacity both appear to bring us what we cannot see. Diviners see through what is obscure and beyond our comprehension: it may be incomprehensible pain and grief, vertiginous lack of control, and/or the unwarranted future. The opacity of divination comes from the limitations of our own knowledge. But the opacity of algorithms comes from both the algorithm itself and the socio-technical infrastructure that it was built around. Jenna Burrell writes of three layers of opacity in models: “(1) opacity as intentional corporate or state secrecy, (2) opacity as technical illiteracy, and (3) an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully.” As consumers of models, we interact with the first and third layer of the opacity―that of platforms hiding models from us, and that of the gap between what the model is optimizing for and what may be explainable. The black-box model is an alluring oracle, interacting with us in inexplicable ways: no explanation for the daily laconic message Co-Star pushes to its users, no logic behind why you received this tarot reading while scrolling, no insight into the models behind these oracles and their objectives…(More)”.

Real Chaos, Today! Are Randomized Controlled Trials a good way to do economics?

Curated on Posted on by Stefaan Verhulst

Article by Maia Mindel: “A few weeks back, there was much social media drama about this a paper titled: “Social Media and Job Market Success: A Field Experiment on Twitter” (2024) by Jingyi Qiu, Yan Chen, Alain Cohn, and Alvin Roth (recipient of the 2012 Nobel Prize in Economics). The study posted job market papers by economics PhDs, and then assigned prominent economists (who had volunteered) to randomly promote half of them on their profiles(more detail on this paper in a bit).

The “drama” in question was generally: “it is immoral to throw dice around on the most important aspect of a young economist’s career”, versus “no it’s not”. This, of course, awakened interest in a broader subject: Randomized Controlled Trials, or RCTs.

R.C.T. T.O. G.O.

Let’s go back to the 1600s – bloodletting was a common way to cure diseases. Did it work? Well, doctor Joan Baptista van Helmont had an idea: randomly divvy up a few hundred invalids into two groups, one of which got bloodletting applied, and another one that didn’t.

While it’s not clear this experiment ever happened, it sets up the basic principle of the randomized control trial: the idea here is that, to study the effects of a treatment, (in a medical context, a medicine; in an economics context, a policy), a sample group is divided between two: the control group, which does not receive any treatment, and the treatment group, which does. The modern randomized controlled (or control) trial has three “legs”: it’s randomized because who’s in each group gets chosen at random, it’s controlled because there’s a group that doesn’t get the treatment to serve as a counterfactual, and it’s a trial because you’re not developing “at scale” just yet.

Why could it be important to randomly select people for economic studies? Well, you want the only difference, on average, between the two groups to be whether or not they get the treatment. Consider military service: it’s regularly trotted out that drafting kids would reduce crime rates. Is this true? Well, the average person who is exempted from the draft could be, systematically, different than the average person who isn’t – for example, people who volunteer could be from wealthier families who are more patriotic, or poorer families who need certain benefits; or they could have physical disabilities that impede their labor market participation, or wealthier university students who get a deferral. But because many countries use lotteries to allocate draftees versus non draftees, you can get a group of people who are randomly assigned to the draft, and who on average should be similar enough to each other. One study in particular, about Argentina’s mandatory military service in pretty much all of the 20th century, finds that being conscripted raises the crime rate relative to people who didn’t get drafted through the lottery. This doesn’t mean that soldiers have higher crime rates than non soldiers, because of selection issues – but it does provide pretty good evidence that getting drafted is not good for your non-criminal prospects…(More)”.

Assembling Tomorrow

Curated on Posted on by Stefaan Verhulst

Book by Stanford d.school: “…explores how to use readily accessible tools of design to both mend the mistakes of our past and shape our future for the better. It explores the intangibles, the mysterious forces that contribute to the off-kilter feelings of today, and follows up with actionables to help you alter your perspective and find opportunities in these turbulent times. Mixed throughout are histories of the future, short pieces of speculative fiction that illustrate how things go haywire and what might be in store if we don’t set them straight…(More)”.

Top 10 Emerging Technologies to Address Global Challenges

Curated on Posted on by Stefaan Verhulst

World Economic Forum: “The Top 10 Emerging Technologies of 2024 are:

  • 1. AI for scientific discovery: While artificial intelligence (AI) has been used in research for many years, advances in deep learning, generative AI and foundation models are revolutionizing the scientific discovery process. AI will enable researchers to make unprecedented connections and advancements in understanding diseases, proposing new materials, and enhancing knowledge of the human body and mind​​.
  • 2. Privacy-enhancing technologies: Protecting personal privacy while providing new opportunities for global data sharing and collaboration, “synthetic data” is set to transform how information is handled with powerful applications in health-related research.
  • 3. Reconfigurable intelligent surfaces: These innovative surfaces turn ordinary walls and surfaces into intelligent components for wireless communication while enhancing energy efficiency in wireless networks. They hold promise for numerous applications, from smart factories to vehicular networks​​.
  • 4. High-altitude platform stations: Using aircraft, blimps and balloons, these systems can extend mobile network access to remote regions, helping bridge the digital divide for over 2.6 billion people worldwide​​.
  • 5. Integrated sensing and communication: The advent of 6G networks facilitates simultaneous data collection (sensing) and transmission (communication). This enables environmental monitoring systems that help in smart agriculture, environmental conservation and urban planning. Integrated sensing and communication devices also promise to reduce energy and silicon consumption.
  • 6. Immersive technology for the built world: Combining computing power with virtual and augmented reality, these technologies promise rapid improvements in infrastructure and daily systems​. This technology allows designers and construction professionals to check for correspondence between physical and digital models, ensuring accuracy and safety and advancing sustainability.
  • 7. Elastocalorics: As global temperatures rise, the need for cooling solutions is set to soar. Offering higher efficiency and lower energy use, elastocalorics release and absorb heat under mechanical stress, presenting a sustainable alternative to current technologies.
  • 8. Carbon-capturing microbes: Engineered organisms convert emissions into valuable products like biofuels, providing a promising approach to mitigating climate change.
  • 9. Alternative livestock feeds: protein feeds for livestock sourced from single-cell proteins, algae and food waste could offer a sustainable solution for the agricultural industry.
  • 10. Genomics for transplants: The successful implantation of genetically engineered organs into a human marks a significant advancement in healthcare, offering hope to millions awaiting transplants​​…(More)”.

Mission Driven Bureaucrats: Empowering People To Help Government Do Better

Curated on Posted on by Stefaan Verhulst

Book by Dan Honig: “…argues that the performance of our governments can be transformed by managing bureaucrats for their empowerment rather than for compliance. Aimed at public sector workers, leaders, academics, and citizens alike, it contends that public sectors too often rely on a managerial approach that seeks to tightly monitor and control employees, and thus demotivates and repels the mission-motivated. The book suggests that better performance can in many cases come from a more empowerment-oriented managerial approach—which allows autonomy, cultivates feelings of competence, and creates connection to peers and purpose—which allows the mission-motivated to thrive. Arguing against conventional wisdom, the volume argues that compliance often thwarts, rather than enhances, public value—and that we can often get less corruption and malfeasance with less monitoring. It provides a handbook of strategies for managers to introduce empowerment-oriented strategies into their agency. It also describes what everyday citizens can do to support the empowerment of bureaucrats in their governments. Interspersed throughout this book are featured profiles of real-life Mission Driven Bureaucrats, who exemplify the dedication and motivation that is typical of many civil servants. Drawing on original empirical data from a number of countries and the prior work of other scholars from around the globe, the volume argues that empowerment-oriented management and how to cultivate, support, attract, and retain Mission Driven Bureaucrats should have a larger place in our thinking and practice…(More)”.

Governance in silico: Experimental sandbox for policymaking over AI Agents

Curated on Posted on by Stefaan Verhulst

Paper by Denisa Reshef Keraa, Eilat Navonb and Galit Well: “The concept of ‘governance in silico’ summarizes and questions the various design and policy experiments with synthetic data and content in public policy, such as synthetic data simulations, AI agents, and digital twins. While it acknowledges the risks of AI-generated hallucinations, errors, and biases, often reflected in the parameters and weights of the ML models, it focuses on the prompts. Prompts enable stakeholder negotiation and representation of diverse agendas and perspectives that support experimental and inclusive policymaking. To explore the prompts’ engagement qualities, we conducted a pilot study on co-designing AI agents for negotiating contested aspects of the EU Artificial Intelligence Act (EU AI Act). The experiments highlight the value of an ‘exploratory sandbox’ approach, which fosters political agency through direct representation over AI agent simulations. We conclude that such ‘governance in silico’ exploratory approach enhances public consultation and engagement and presents a valuable alternative to the frequently overstated promises of evidence-based policy…(More)”.

This free app is the experts’ choice for wildfire information

Curated on Posted on by Stefaan Verhulst

Article by Shira Ovide: “One of the most trusted sources of information about wildfires is an app that’s mostly run by volunteers and on a shoestring budget.

It’s called Watch Duty, and it started in 2021 as a passion project of a Silicon Valley start-up founder, John Mills. He moved to a wildfire-prone area in Northern California and felt terrified by how difficult it was to find reliable information about fire dangers.

One expert after another said Watch Duty is their go-to resource for information, including maps of wildfires, the activities of firefighting crews, air-quality alerts and official evacuation orders…

More than a decade ago, Mills started a software company that helped chain restaurants with tasks such as food safety checklists. In 2019, Mills bought property north of San Francisco that he expected to be a future home. He stayed there when the pandemic hit in 2020.

During wildfires that year, Mills said he didn’t have enough information about what was happening and what to do. He found himself glued to social media posts from hobbyists who compiled wildfire information from public safety communications that are streamed online.

Mills said the idea for Watch Duty came from his experiences, his discussions with community groups and local officials — and watching an emergency services center struggle with clunky software for dispatching help.

He put in $1 million of his money to start Watch Duty and persuaded people he knew in Silicon Valley to help him write the app’s computer code. Mills also recruited some of the people who had built social media followings for their wildfire posts.

In the first week that Watch Duty was available in three California counties, Mills said, the app had tens of thousands of users. In the past month, he said, Watch Duty has hadroughly 1.1 million users.

Watch Duty is a nonprofit. Members who pay $25 a year have access to extra features such as flight tracking for firefighting aircraft.

Mills wants to expand Watch Duty to cover other types of natural disasters. “I can’t think of anything better I can do with my life than this,” he said…(More)”.

Is Software Eating the World?

Curated on Posted on by Stefaan Verhulst

Paper by Sangmin Aum & Yongseok Shin: “When explaining the declining labor income share in advanced economies, the macro literature finds that the elasticity of substitution between capital and labor is greater than one. However, the vast majority of micro-level estimates shows that capital and labor are complements (elasticity less than one). Using firm- and establishment-level data from Korea, we divide capital into equipment and software, as they may interact with labor in different ways. Our estimation shows that equipment and labor are complements (elasticity 0.6), consistent with other micro-level estimates, but software and labor are substitutes (1.6), a novel finding that helps reconcile the macro vs. micro-literature elasticity discord. As the quality of software improves, labor shares fall within firms because of factor substitution and endogenously rising markups. In addition, production reallocates toward firms that use software more intensively, as they become effectively more productive. Because in the data these firms have higher markups and lower labor shares, the reallocation further raises the aggregate markup and reduces the aggregate labor share. The rise of software accounts for two-thirds of the labor share decline in Korea between 1990 and 2018. The factor substitution and the markup channels are equally important. On the other hand, the falling equipment price plays a minor role, because the factor substitution and the markup channels offset each other…(More)”.

An Anatomy of Algorithm Aversion

Curated on Posted on by Stefaan Verhulst

Paper by Cass R. Sunstein and Jared Gaffe: “People are said to show “algorithm aversion” when (1) they prefer human forecasters or decision-makers to algorithms even though (2) algorithms generally outperform people (in forecasting accuracy and/or optimal decision-making in furtherance of a specified goal). Algorithm aversion also has “softer” forms, as when people prefer human forecasters or decision-makers to algorithms in the abstract, without having clear evidence about comparative performance. Algorithm aversion is a product of diverse mechanisms, including (1) a desire for agency; (2) a negative moral or emotional reaction to judgment by algorithms; (3) a belief that certain human experts have unique knowledge, unlikely to be held or used by algorithms; (4) ignorance about why algorithms perform well; and (5) asymmetrical forgiveness, or a larger negative reaction to algorithmic error than to human error. An understanding of the various mechanisms provides some clues about how to overcome algorithm aversion, and also of its boundary conditions…(More)”.

The Behavioral Scientists Working Toward a More Peaceful World

Curated on Posted on by Stefaan Verhulst

Interview by Heather Graci: “…Nation-level data doesn’t help us understand community-level conflict. Without understanding community-level conflict, it becomes much harder to design policies to prevent it.

Cikara: “So much of the data that we have is at the level of the nation, when our effects are all happening at very local levels. You see these reports that say, “In Germany, 14 percent of the population is immigrants.” It doesn’t matter at the national level, because they’re not distributed evenly across the geography. That means that some communities are going to be at greater risk for conflict than others. But that sort of local variation and sensitivity to it, at least heretofore, has really been missing from the conversation on the research side. Even when you’re in the same place, in the same country within the same state, the same canton, there can still be a ton of variation from neighborhood to neighborhood. 

“The other thing that we know matters a lot is not just the diversity of these neighborhoods but the segregation of them. It turns out that these kinds of prejudices and violence are less likely to break out in those places where it’s both diverse and people are interdigitated with how they live. So it’s not just the numbers, it’s also the spatial organization. 

“For example, in Singapore, because so much of the real estate is state-owned, they make it so that people who are coming from different countries can’t cluster together because they assign them to live separate from one another in order to prevent these sorts of enclaves. All these structural and meta-level organizational features have really, really important inputs for intergroup dynamics and psychology.”..(More)”.