Stefaan Verhulst
Report by Laura Maher and Stefaan Verhulst: “Any of the defining challenges of our time, from climate change and democratic resilience to public health, AI governance, and systemic inequity, defy linear solutions. They are interconnected, constantly evolving, and often contested in how they are understood and framed.
Yet philanthropy, despite its ambition to address these challenges, remains largely organized around linear theories of change, predefined deliverables, and evaluation frameworks which often assume that problems can be sufficiently understood in advance and progress measured against predetermined outcomes. While essential for accountability, these approaches can leave little room for questioning assumptions, exploring uncertainty, or adapting as new insights emerge.
This tension becomes particularly striking in the age of AI. As our capacity to generate “answers” expands exponentially, the real constraint may no longer be access to information or solutions, but our ability to determine which questions matter, whose perspectives inform them, and when they need to change.
Questions are not neutral. They shape what gets funded, which communities are heard, what evidence is collected, and which possibilities are explored. They are agenda-setting devices that influence not only how problems are addressed, but which problems receive attention in the first place.
For philanthropy, this suggests a shift: from treating questions as a preliminary step toward funding answers to recognizing inquiry as an essential investment in its own right. Supporting the capacity to formulate, test, and revisit questions could make philanthropy more responsive to complexity, more inclusive in setting priorities, and ultimately more effective in generating lasting change…(More)”.
Report by Australian Human Rights Commission: “…finds synthetic media has become a low-cost and highly scalable tool for influencing public debate. It can be used to discredit political candidates, mislead voters and undermine confidence in elections. Its speed and reach make harmful content difficult to identify and stop before damage is done.
When people can no longer trust what they see and hear online, it becomes harder to make informed decisions, participate in public discussions and hold institutions to account.
‘Democracy depends on people having access to reliable information and being able to participate in public life free from manipulation and intimidation,’ Commissioner Finlay said.
‘Synthetic media can distort public debate and erode the shared understanding of facts that democratic decision-making relies on.’
The report also highlights how such content can amplify existing prejudice and discrimination. Women, LGBTIQA+ people, culturally and linguistically diverse communities and others have been targeted through harassment, humiliation and disinformation campaigns.
As synthetic media becomes easier to produce and distribute, its influence is increasingly shaped by online platforms and algorithms that determine what content people see…(More)”.
Article by Julian Norris: “A lot of organizational systems thinking has become overly attached to its own representational devices. Causal loop diagrams, systems maps, stakeholder networks, iceberg models, adaptive cycles, and so on. These are useful instruments, but they can become reified. People start treating the map as the territory, and the analytical representation as an adequate substitute for the experience of the phenomenon.
When we ‘map’ or otherwise describe complex reality, we use language, symbols and metaphors. We might begin with mathematical representation, but as soon as we start using words like ‘order’, ‘instability’, ‘complexity’ or ‘attractor’ – they enter a domain of social description and start to carry all kinds of additional cultural meaning and linguistic associations.
And every map rests on a mapping platform that is much more than just a background grid. It’s a crystallized worldview. It decides what counts as a unit, which directions matter, which cycles are primary. Such platforms tend to encode our understanding of the natural world and the nature of our relationship with it. They make some questions answerable and others un-askable. For example, as David Krakauer reminds us, highly symmetrical platforms don’t just struggle to represent complexity – they struggle to detect it in the first place.
If every map is a worldview, we need a way to think about how maps make meaning in the first place. Not just what they show, but how they help us see at all. For that I’m going to lean on the work of anthropologist Roy Wagner and use his ideas as a handrail.
Wagner spent years working with the Daribi people in Papua New Guinea and in The Invention of Culture (1975) and Symbols That Stand for Themselves (1986) he elaborated a theory of metaphor. Most of us were taught that a metaphor is decoration – a colourful way of saying something that could be said plainly. Wagner argued the reverse. A metaphor – he preferred the word trope – is a form of perception.
When you use the classic iceberg model to describe some organizational dynamic, you aren’t just describing the phenomenon. You’re collapsing two worlds into a single image, and the image lets you see something you couldn’t see before. Wagner called a good trope “too definite for words.” It isn’t vague. It’s more precise than any paraphrase, which is why the iceberg needs no explanation in a room. People just get it.
I find three of Wagner’s ideas useful to hold here.
Tropes come in orders. A first-order trope is a single image: the iceberg, the tree. A second-order trope is metaphor spread out – a sequence in which each image transforms the one before. Wagner called this process obviation. It’s a subtle process of contextualization and transformation in which the earlier image doesn’t simply disappear; its meaning is altered by the subsequent image. A third-order trope draws in the perceiver…the image through which we see ourselves seeing…(More)”.
Article by Samuel Greengard: “The generosity of strangers has long powered online communities. Spaces like Stack Overflow, GitHub, curl, OpenSSL, Wikipedia, and OpenStreetMap have a rich history of volunteers sharing knowledge and maintaining code, encyclopedias, and maps.
Now, AI is having a say. Trained on content from these sites, it is changing how volunteers contribute and how users consume content. Stack Overflow, for example, has seen monthly questions fall from a peak of roughly 200,000 in 2014 to about 1,400 in July 2026. Meanwhile, a deluge of contributions—often fake and misleading—has overrun these projects.
This has created unease about the future of online knowledge sharing. “We are starting to replace social communities and their underlying processes with a piece of software,” said Gordon Burtch, Allen and Kelli Questrom Professor in Information Systems at Boston University. “As fewer people go to the community with questions, the incentive for experts to be there falls in turn.”
He isn’t the only one sounding alarms. “There is a huge fear, with growing evidence, that AI companies are kicking the legs out from underneath the table they are eating at,” said Benjamin Mako Hill, an associate professor of communication at the University of Washington and a longtime contributor to peer-production projects.
The stakes extend beyond site traffic and human connections. Fundamental questions arise about the quality of AI-provided information, who owns and profits from it, and what role humans play in vetting it. The cycle can also be recursive: models trained on public knowledge inhibit the production of new content. The question now is: will these communities evolve or disappear?..(More)”.
Book by Steven Strogatz and Alex Townsend: “It’s in every photo you post to Instagram, every Google search you make, and every answer you get back from ChatGPT. It routes our packages and planes, maps social networks, and predicts the weather. This is Big Math, and it is quietly running our world—while also reshaping it in ways we can’t yet comprehend. A toolkit of powerful ideas such as matrices and vectors, networks and optimization, Big Math allows humanity to tackle problems too vast to hold in our heads. Yet the tools themselves are not especially new or mysterious. We created them, and for most of their history we understood them and controlled them. But once we began supercharging them with computers to explore truly enormous systems, things changed.
Now, as celebrated mathematicians Steven Strogatz and Alex Townsend reveal in this dazzling and unsettling book, Big Math is beginning to drift beyond our grasp, even as it delivers greater gifts and power through machine learning and, soon, quantum computing. What happens when Big Math outgrows its creators—when it becomes something we no longer understand, but only witness? And how can we learn to live wisely with tools that grow stronger and stranger every day?..(More)”.
Article by Barbara Wheeler: “…As the Trump administration has taken down or altered hundreds of federal datasets that don’t align with their political or ideological priorities, it has become clear that the old order of our federal data system has effectively collapsed. The data systems that state and local government agencies, researchers, and community organizations once considered trusted, fixed features of the landscape are no longer reliable, have been weaponized against communities, and in some cases no longer exist.
The ongoing federal data collapse now threatens one of the nation’s most essential data sources—the US Census. Conducted every 10 years, census data is used to allocate federal funding to communities, determine political representation in Congress, and inform programs related to housing, transportation, emergency preparedness, and more. But in September 2026, the Trump administration proposed unprecedented changes to the US Census, including a prohibition on collecting data related to race, ethnicity, and sexual orientation, as well as the exclusion of many noncitizen residents. These changes, if adopted, would infuse discrimination, exclusion, and inaccuracy into a critical national data source. Excluding noncitizens would also likely violate the 14th Amendment’s requirement that census results include “the whole number of persons in each State.”
While the destruction of so many federal data sources must be grieved, it also reveals a moment ripe for reimagination. We are living in a Phoenix moment: a volatile, high-potential period for transformation where we can create new and better approaches to federal data. This moment offers us an opportunity to change, and ultimately improve, how our country produces, protects, and uses civic data—information a society gathers about itself to influence governance and determine where resources should flow. Like the mythical bird of the same name, we must first contend with the burning ashes of the old data order and assess the cost of its limitations and its loss. Only then can we rise anew from the ashes with better approaches to federal data—approaches that are more inclusive, more insightful, and more actionable for communities across the nation…(More)”.
Paper by Terence Chau et al: “Program impact estimates that are not statistically significant are often treated as providing little guidance for policy choice, even when economically important effects remain plausible. In such cases, the decision-relevant task is not simply to test whether the effect differs from zero, but to estimate the probability distribution of programmatic effects. The importance of going beyond hypothesis testing is especially clear when evaluations are underpowered relative to the effect needed for benefits to exceed costs. We illustrate this point using Chicago’s contract for ShotSpotter, an acoustical gunshot detection system that reduces police response times and may therefore have improved survival among shooting victims. Using a geographic RD design, we estimate that ShotSpotter reduced the shooting fatality rate by 2.5 percentage points, equivalent to roughly 49 fewer deaths per year. Although the estimate is not statistically significant, its implied benefit far exceeds the cost of the service. To have enough statistical power under the hypothesis testing approach to detect an effect of this size would require 87 years’ worth of Chicago shooting data. We instead implement a decision-relevant perspective using Bayesian analysis, treating the life-saving effect as a random variable. We find positive expected net benefits, with posterior probabilities that benefits exceed costs ranging from 70% under a flat prior and a $0.5 million value of statistical life to 99.6% under a no-harm prior and a $13.6 million value. The results illustrate how reliance on statistical significance can discard decision-relevant evidence when economically meaningful effects are difficult to detect precisely…(More)”.
Paper by Riel Miller: “War recurs not because leaders fail but because the human capacity to imagine the future has been systematically narrowed to a single mode: anticipation-for-the-future (AfF), the colonising drive to design, control, and win tomorrow. This article argues that this narrowing – what the Futures Literacy Framework calls futures illiteracy – is not a deficit correctable by better planning or wider access to foresight tools, but an ontological condition: a way of being in time that reproduces the delusional agency at the root of war. Drawing on the Theory of Anticipation, Whitehead’s concept of concrescence, Bergson’s proposition that the real creates the possible, and feminist critiques of the master’s tools from Lorde and Feukeu, the article develops the distinction between AfF and anticipation-for-emergence (AfE) as a practical way of distinguishing the simultaneity of the closed and open present. It proposes that cultivating futures literacy – understood as the meta-cognitive capacity to hold both anticipatory frames simultaneously – could change the conditions of change: not a remedy for war, but as fundamental shift in the anticipatory contexts from which war draws its so far inescapable necessity…(More)”.
Book edited by Abbey K. Elder, Shannon M. Smith and Bryan Ricupero: “Open movements all center public access and broad representation and work to prevent the concentration of power governing information and its monetization. As general discussion of openness has narrowed to focus on areas like open science, data, access, and education, these individual communities have grown distanced from each other and, occasionally, their shared values.
In five sections, Open Movements: Recognizing Challenges and Building Connections aims to surface the challenges in each of these movements and how we might more effectively collaborate for sustainable change:
- Forging and Maintaining Open Communities
- Exploring the Structures that Support Open
- Improving Access Points for Open
- Navigating Choice and Engagement in Open
- Reflecting on Our Past to Build an Open Future
Open is a continually evolving environment, but the values that guide our systems and practices will be critical well into the future. Open Movements will help practitioners engage more thoughtfully in work that supports not just broad access to information but also public empowerment. This book is also available in an open access edition…(More)”.
Book by George Rzevski: “The book describes, using clear and concise language, the next generation of Artificial Intelligence (AI), which consists of swarms of hundreds of thousands of intelligent digital agents that exhibit collective intelligence greater than the sum of the intelligence of individual agents. These agents have access to domain knowledge stored in the attached ontology and do not need training to operate. Swarms of agents self-organise to adapt to environmental changes, resist hacking, and coevolve with the systems they interact with.
The author explains the basics of Swarm AI and its uses, including adaptive schedulers, pattern recognisers, semantic processors, variable geometry engineering products, and teams of space robots. They show how swarms of agents can handle operational tasks in businesses and protect nations from pandemics, financial crises, and military attacks.
The book is written for curious readers who want to understand the simple principles behind the power of the next generation of AI. It is also aimed at business executives, administrators, politicians, government leaders, regulators and policy makers, offering them insights into the new tools that will transform the organisations they lead and the world they inhabit…(More)”.