What you're seeing
Each tank runs an independent Bayesian agentAn agent that updates its beliefs using Bayes' theorem — combining prior beliefs with new evidence to form a posterior probability. The more evidence, the more confident the belief. simulation. Agents (nodes) run experiments to determine which of two treatments is better, share results with their network neighbors, and update beliefs accordingly.
Green particles carry honest evidence between agents. Red particles are false testimony from the agnotologistA "manufacturer of ignorance" — an agent who never updates their beliefs and always reports misleading evidence to their neighbors, attempting to prevent the network from discovering the truth. (the red node). Compare hub-biased tanks against peripheral ones — the hub agent poisons beliefs far more effectively.
Node size reflects degree centralityA measure of how many connections a node has relative to the maximum possible. High centrality = hub position = more influence over the network's collective beliefs.. Color shifts from blue (misled) through neutral to green (approaching truth).
Real-world parallels
Tobacco industry (1950s–90s): Industry-funded scientists at hub positions in citation networks published doubt about the link between smoking and cancer, delaying public consensus by decades.
Pharmaceutical lobbying: Key opinion leaders paid by companies to endorse drugs occupy central positions in medical information networks, shaping prescribing behavior across entire communities.
Emergence on non-standard grids
Traditional cellular automata run on a rigid 2D grid where every cell has exactly 8 neighbors. Here, we've mapped these simple rules (like Conway's Game of LifeA node is born if exactly 3 neighbors are alive, survives if 2 or 3 are alive, and dies otherwise.) onto complex network topologies.
By using Fractional Rules (e.g., a node comes to life if 25%–40% of its neighbors are alive), we reveal something profound about structures: on scale-free networks, massive hubs frequently die of "overpopulation" because they have too many alive neighbors, while peripheral chains die of "underpopulation".
Evolutionary Game Theory
These agents don't update beliefs — they choose strategiesA complete plan of action: Cooperate (always help), Defect (always exploit), Tit-for-Tat (copy your opponent's last move), or Pavlov (win-stay, lose-switch). Strategies compete for survival.. Each round, every agent plays a game with each neighbor. After all games are played, agents imitate the most successful neighbor's strategy — a process of evolutionary selectionStrategies that earn higher payoffs spread through the population, while unsuccessful strategies die out. This mirrors natural selection but for behavioral rules rather than genes..
In the Prisoner's DilemmaTwo players simultaneously choose to Cooperate or Defect. Mutual cooperation pays 3 each. Mutual defection pays 1 each. But if one defects while the other cooperates, the defector gets 5 and the cooperator gets 0. The temptation to defect is the core tension., cooperation is fragile but can survive in spatial networks where cooperators form clusters. Network topology determines whether cooperation or defection dominates — a finding with implications for institutional design.
Why topology matters
Grid/lattice: Cooperators survive by forming protective clusters. Defectors dominate edges but can't penetrate dense cooperative cores.
Scale-free networks: Hubs amplify whatever strategy they adopt. A cooperative hub can sustain cooperation network-wide; a defecting hub can collapse it.
Well-mixed (complete): No spatial structure means no shelter for cooperators. Defection typically dominates — the classic "tragedy of the commons."
Global Consensus Nodes in Epistemic Networks
What happens when we introduce an aggregator nodeA global signal that observes the average belief of the community and broadcasts it back. Agents periodically consult this signal and adjust their own beliefs toward it. Can function as a prediction market, polling aggregator, or any consensus mechanism. into a vulnerable network? The Purple Diamond represents a global consensus signal — it could act as a prediction market, a polling aggregator, or any mechanism that distills collective belief into a single reference point.
Agents intermittently check this signal and pull their beliefs toward it. The core tension: Does the aggregator protect honest agents from isolated bad actors by providing a solid global baseline, or does it accidentally amplify the bad actor's influence by broadcasting a corrupted consensus to agents who were otherwise safe?
Experiment with how aggregator nodes interact with epistemic networks under adversarial conditions.
About
Explore interactive simulations of computational agents on networks. Watch, configure, and experiment with four types of agent dynamics: Bayesian agents that update beliefs via evidence sharing, network automata that demonstrate emergent complexity from simple rules on graphs, strategic agents that model evolutionary game theory (cooperation and defection), and aggregator markets that explore how prediction-market-style consensus mechanisms interact with epistemic networks.
Research Context
These simulations grew out of research into agnotology — the study of manufactured ignorance. That work examines how a biased agent's network position amplifies its ability to suppress truth. The toolkit expanded from there into a broader set of experiments for exploring agent-based dynamics across multiple paradigms — each new simulation type offering a different lens on emergent behavior in networks.
Further reading
About the author
Created by Rouzbeh Rezaei Sanjabi as part of ongoing research in epistemic network dynamics. For full papers, more on these simulations, or to get in touch, visit rouzbehrezaei.com.