Algorithmic Ecologies Laboratory
Interactive models of emergence, allocation, adaptation, and collective behavior.
Updated September, 2026
AE Labs is an evolving collection of interactive computational experiments exploring how simple rules, local interactions, constraints, and feedback produce complex collective behavior. The project began with models of computational fairness and now extends across diffusion, networks, stochastic systems, allocation, ecological dynamics, game theory, and autonomous agents.
Fairness Systems remains the origin and first major research thread: early and ongoing experiments exploring equality, distribution, parity, bias, and corrective dynamics. The original model names are kept as a record of that genealogy.
Early and ongoing experiments exploring equality, distribution, parity, bias, and corrective dynamics.
Standard fairness coefficient with full metrics and entropy tracking
Emphasizes weighted fairness dynamics with adjustable weight distribution
Resource exchange analog with trading dynamics and wealth distribution
Memory-based model with glowing orbs and fading trails showing fairness memory
Coalition formation with Shapley values and Nash bargaining solutions
How local preferences, influence, and interaction produce group-level structure.
Schelling-like model showing local segregation patterns with fairness overlays
Opinion dynamics in 2D space with color blending toward consensus
Living terrain grid with resource-population feedback and sustainability
Chain of agents fold to minimize unfairness energy like proteins finding native structure
Flows, noise, and correction across space and time.
Continuous color field with ripple propagation showing fairness potential
Particles flow through sinusoidal waves with Brownian motion jitter
Forward and reverse SDEs showing bias diffusion and fairness correction equilibrium
Two competitive particle populations with coupled SDEs pushing each other apart
Two-stage fairness: coarse prior captures large-scale structure; diffusion refines the residual toward target (arXiv:2512.21593v1)
Influence, conduits, and the routing of signals through structure.
Dynamic graph network where node brightness and edge thickness show influence
Compare a minimum-biomass tree with loop-forming anastomosis, transport bias, and damage resilience
Directed graph conduits constrain resource diffusion with adaptive fairness control
Fair signal flow through fixed networks with organic circuitry visualization
Physical metaphors for redistribution, equilibrium, and symmetry.
Biophysical growth coupled to a reduced radical-pair response, with thermal and vacuum channels kept distinct.
AE Labs began as a series of interactive models exploring computational fairness. As the project developed, those experiments increasingly intersected with broader questions in dynamical systems, agent behavior, diffusion, networks, game theory, ecology, and collective decision-making.
Algorithmic Ecologies describes this expanded territory: computational environments in which agents, resources, information, constraints, and rules interact over time to produce emergent outcomes.
Fairness remains an important research thread within AE Labs, but it now sits alongside questions of equilibrium, resilience, cooperation, competition, adaptation, and systemic change. The laboratory is built so later work — including allocation, multi-agent learning, causal systems, flow matching, social choice, and artificial life — can join the same collection without replacing the original models.
Special thanks to Shuubham Ojha, PhD candidate in the A. James Clark School of Engineering, Speech Communication Laboratory, for his feedback on the models and guidance on differential equations, diffusion, and their relationship to optimization.