Avalanche sandpile. The Bak–Tang–Wiesenfeld model of self-organized criticality: a system that drives itself to the edge of instability, where one more grain can do nothing or set off an avalanche of any size.
- Each cell holds up to three grains, shaded light to dark.
- A cell that reaches four topples, passing one grain to each neighbor. Grains off the edge are lost.
- Neighbors that reach four topple in turn, flashing in color, until everything is stable again.
Try it: hover to pour grains, click to dump a handful. Avalanche sizes follow a power law, which is why one rule makes both ripples and banner-wide slides.
Bak, Tang & Wiesenfeld (1987). Self-organized criticality. Physical Review Letters.
Independent cascade. A standard model of how information and behavior spread through a social network.
- Each cell knows its four neighbors. A few also have a long-range tie to someone far away, drawn as a faint arc.
- When a person first hears something, they get one chance to pass it to each contact with probability p. Then they are done.
- People fade back to light after a few seconds and can hear it again. When nothing is spreading, a new seed drops.
Try it: click to seed a cascade and slide p under settings. The tipping point here is p = 0.5: below it cascades fizzle, above it they take over. The long-range ties let an outbreak leap across the banner.
Goldenberg, Libai & Muller (2001). Talk of the network. Marketing Letters. Kempe, Kleinberg & Tardos (2003). Maximizing the spread of influence through a social network. KDD.
Particle monogram. My initials as dots that scatter from the cursor and spring back. Click for a pulse, or explode (Shift+E) to blow them apart and watch them reassemble. Now and then a bomb drops in and goes off on its own. Not a model of anything, just a bit of fun.
Matthew R. DeVerna
I'm a computational social scientist studying the societal impacts of artificial intelligence within the information ecosystem, with the goal of improving how people encounter, interpret, and act on digital information. I take a multi-level approach, pairing system-level evaluations of AI platforms with individual-level experiments and observational analyses of digital trace data. I apply this approach across two connected tracks: evaluating the quality of AI-generated information on topics of societal importance, such as news, politics, and health; and understanding how AI and online platforms shape people's beliefs and decisions. Ultimately, my work aims to guide society's adoption of AI so that its capabilities make our digital spaces more trustworthy and resilient.
I am currently a postdoctoral researcher at Stanford's Tech Impact and Policy Center, working with Dr. Jeffrey Hancock in the Social Media Lab. I have a PhD in Informatics from Indiana University, where I was advised by Dr. Filippo Menczer at the Observatory on Social Media. Before that, I earned an MA in Psychology from New York University, where I collaborated with Dr. Josh Tucker at the Center for Social Media, AI, and Politics and was advised by Dr. John Jost.
Ongoing projects
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If you'd like to chat about my work, feel free to reach out.
[firstinitial][lastname]@stanford.edu