📡 On our radar

EPISTEMIC STIGMERGY: NATURAL VS. ARTIFICIAL INTELLIGENCE

The article\(^{1}\) defends the thesis that intelligent behavior might require not internal complexity but complex interaction. This is demonstrated by the various forms of stigmergy that can be observed both in social insects and in humans. The exposition is structured as follows: (§0) explains how the term “intelligence” is interpreted in the following text; (§1) clarifies the relation between intelligence and complexity; (§2) shows that intelligent behavior does not require internal complexity; (§3) introduces the concept of stigmergy; (§4) presents the mechanisms that give rise to this phenomenon; (§5) distinguishes several types of stigmergic interaction; (§6) briefly discusses the evolutionary mechanisms that could have produced them; (§7) sketches the possible ways in which the concept of stigmergy is used outside biology; (§8) examines collaborative stigmergy in humans; (§9) points to its epistemic projections; (§10) outlines some conclusions concerning the role of artificial intelligence systems and their place in human society.

Lea: research-oriented social media

Introducing a beta-version of our new research-focused social media platform

The Least Agentic People Alive

The arms race of deferring agency and the dark acquiescence to robotic bureaucracy.

The Ground Truth Institute

Could we engineer scientific revolutions, then spin out radical improvements to everyday life?

Reinventing Discovery: The New Era of Networked Science

In Reinventing Discovery, Michael Nielsen argues that w…

A budding discipline around how science gets read and judged - Aris

Scholarly publishing spent thirty years arguing about access. It has barely begun arguing about what readers do with the paper once they have it. At Aris we call that second argument scholarly interface design. Mike Morrison's community calls it ScienceUX. Either way it is becoming a field, and here is why it is worth your attention.

supercritical | shishyko!

Supercritical is a newsletter about what happens when systems produce faster than they can coordinate. AI is making the generation of knowledge abundant, but generation was never the whole system. Validation, dissemination, credit, trust: these were bundled into institutions designed for scarcity, and this legacy architecture is now the primary bottleneck. If we fail to redesign it, progress will be sawtooth rather than smooth. Using the crisis in modern science as an early warning, this newsletter imagines the capabilities and infrastructure that come next, including provenance, trust-graded disclosure, and new protocols of coordination.

Holdfast 0: Situating

This post introduces the written version of some optimistic and constructive thinking on knowledge in networks that I’ve been wrestling with since the end of last year.

In an era where research evaluation methods are evolving, the Research Contribution Claim Network makes trustworthy tracking of non-traditional research output easy!

In this whitepaper, Patrick Hochstenbach (Ghent University Library), Thomas van Himbergen (SURF), Laurents Sesink (SURF) and Herbert Van de Sompel (DANS) introduce the...

Mastodon pilot for research and education

SURF and Universities of the Netherlands are jointly exploring Mastodon as an open source platform for education and research in the Netherlands.

Holdfast 0: Situating

This post introduces the written version of some optimistic and constructive thinking on knowledge in networks that I’ve been wrestling with since the end of last year.

Coordination Tech in Science: Letters, Journals, and Whatever Comes Next | shishyko!

To modernize our scientific infrastructure, we need new contextualization and coordination technologies that decouple trust from legacy branding — shifting from gatekeeping on write to algorithmic contextualization on read.

In an era where research evaluation methods are evolving, the Research Contribution Claim Network makes trustworthy tracking of non-traditional research output easy!

In this whitepaper, Patrick Hochstenbach (Ghent University Library), Thomas van Himbergen (SURF), Laurents Sesink (SURF) and Herbert Van de Sompel (DANS) introduce the...

Science as Collective Sensemaking

What is Science?

AI for Science Curation

Knowing exactly who to talk to or what to read

Creative Reading: Scaffolding Reading for Transformation

Reading augmentation systems increasingly help readers process text at scale. While these tools address real constraints of time and cognitive load, they often implicitly frame reading as...

Publish and Perish: How AI-Accelerated Writing Without Proportional Verification Investment Degrades Scientific Knowledge

Artificial intelligence tools are accelerating manuscript production far faster than peer review capacity can expand. Applying the theory of constraints from manufacturing science, we formalize this asymmetry through a minimal two-variable ordinary differential equation model coupling review queue evolution and verification quality degradation via an endogenous, queue-pressure-driven review AI adoption mechanism. The causal chain is: writing AI adoption increases submissions, growing the review queue, which drives reviewer AI adoption under pressure, degrading verification quality and reducing net knowledge output. Under empirically informed parameters (writing acceleration γ = 2.0, review acceleration δ = 0.5), the model predicts a deceptive honeymoon where knowledge output peaks at 1.10K0 (circa 2026), followed by paradox onset at t = 6 years (2028) and long-term degradation to 0.68K0 (32% loss), approaching a steady state of 0.60K0 (40% loss). The critical condition for net benefit is δ > γ; the current operating point lies deep in the paradox regime. Empirical validation against NeurIPS, ICLR, arXiv, and bioRxiv submission data shows qualitative consistency with observed post-ChatGPT acceleration patterns. Policy analysis reveals that only combined interventions such as review infrastructure investment paired with institutional quality standards can restore positive knowledge production.

On-protocol Organizing

Fighting bad networks with good networks.

Science Needs Scientists

And Scientists Need Science