Our research carries one question across every scale, from a single person's ties to a whole institution: what does structure make impossible, and for whom? We study how behaviors and norms diffuse or stall, how closure turns a network into an echo chamber, how one-way dependence sets the terms of professional and economic relationships, how care systems fragment around the people they serve, and how groups coordinate, lock in, or act against their own interest. We describe these patterns as social ripple behaviors, actions that travel along ties and return to the people and institutions that started them.
The method comes from brain network research, where connectivity is reconstructed from partial signal and read through graph theory and persistent homology. The institute applies those tools, alongside network models, multi-agent simulation, and field data, to social, professional, and institutional networks.
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A system can have competent parts and still fail, because the ties between them are missing, one-way, or carry nothing. The cost of each gap lands on whoever bridges it: a family member, a patient, a frontline worker, an informal carer. We measure what that position depends on and which alternatives it still has.
Look at the gaps between nodes, not the nodes. The same person acts differently in a different position.
Direction, weight, reciprocity, independent paths. Constraint is a number, not a feeling.
Signals, norms, and strain travel along ties and echo back. Repair is a second path or a restored channel.
One person's ties or a whole care system: the object is a graph and what its topology allows. Methods: graph theory, network models, simulation, topological data analysis, field data. Output: papers, software tools, runnable models.
Behaviors that need reinforcement from several contacts spread through clustered networks and stall in sparse ones. We map which topologies carry which contagion, and which trends are artifacts of the graph.
An echo chamber is measurable: high constraint, redundant ties, few independent paths to new information. One source arrives many times and sounds like many. We treat closure as a topological state with a number attached.
Care systems are networks of people who share a person but not information. Wellbeing depends partly on the structure of a person's own ties: reciprocity, redundancy, independent paths. We map both, and where load collects in the gaps.
Power belongs to the relation, not the parties. Where one side has no alternatives, structure sets the price. We measure one-way dependence in professional and economic networks, and the options it closes off.
Agents that see only their neighbors still align, split, or stall as one body. We model which coupling structures synchronize, how density changes the outcome, how a small informed minority steers the whole, and where a group locks in.
A general account of how network structure decides what a position depends on, which alternatives it has, and how much loss it can absorb. The paper in preparation measures dependence and alternatives in networks.
The programs above describe structure. The specimens below put it in motion. Each is a multi-agent simulation that runs on the GPU: particles, vertices, or agents that follow local rules, and a collective pattern that emerges from them through consensus, coupling, and phase-space dynamics. They run live in their frames and fullscreen on request. They are models of the vocabulary the institute uses, built to make a mechanism watchable, and they make no claim beyond that.
A fabric mesh with collective intelligence: position-based dynamics on a grid of agents, each carrying height, velocity, synchrony, and energy, with a consensus layer, a multi-species layer, and an environment layer read every frame. Each vertex is pulled toward its neighbors' average by a spring whose stiffness is the coupling constant, and emergent pattern formation follows from evolutionary consensus among agents that see only their neighbors. Raise the coupling and the sheet moves as one body; the individual node still moves, but it no longer decides.
Position dynamics · Multi-species evolution · Consensus mechanisms · Emergence. WebGL 2.
A particle system exploring the phase space of several strange attractors at once, Clifford, De Jong, Hopalong, and hybrid systems, and transitioning between them over time. Every trajectory ends up in a basin. The point of the model is the return: a path that keeps arriving at the same region of the space did not choose it. Basin, attractor, and repeller are the terms the Basin Profiler uses, and this is what they look like.
Strange attractors · Chaos theory · Phase space · GPU transform feedback. WebGL 2.
A living fractal surface that morphs through the complex plane over time. Height at every vertex is the number of escape-time iterations a point survives, computed each frame and drawn as a dynamic mesh. Two starting points a hair apart, one bounded and one gone, sit side by side on the boundary. That is the cascade question restated: the outcome is decided by where on the boundary a signal starts, and the boundary is where prediction fails.
Complex dynamics · Escape-time · Fractal geometry · GPU height field. WebGL 2.
Motion is reduced by your system preference, so the models are shown as still figures.
Models run fullscreen. Press Escape or the close control to return. Tap once to pause, tap again to resume. Color can be changed while a model runs.
Papers will be listed here as they are published.
The HILI Archive, the Personal Network Audit, and 1:1 consulting sessions are listed in one place.
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Conversations with the researchers, investors, entrepreneurs, and artists shaping culture, mapped as a network of the ties between them. Hosted by Dr. Anna Maria Matziorinis.
Each node is a guest, each edge a shared idea or relationship. Explore the graph, tap any node, and listen to the episode there.
Listen to the podcastDr. Matziorinis founded Hidden Information Labs Institute after years of research reconstructing brain networks from diffusion imaging and analyzing their topology. Studying connectivity through graph theory gave her a direct view of what happens when any network degrades: the nodes remain, the edges thin, information stops arriving where it is needed, and the system loses coherence long before anyone can name what went wrong. That neuroimaging work continues separately, as a visiting scholar at the University of Bergen.
The same structural logic holds at other scales. The institute extends that work to social, professional, and institutional networks, publishing research and teaching the method to the people whose systems depend on it.
