Zollman Lab — original model

Bandit networks & transient diversity (Zollman 2007, 2010), his assumptions only. Old theory pays at exactly 0.5; the new one at 0.5 + ε. Will the community find out?  ·  ← tutorial  ·  the LLM extension →

Presets

Community

who sees whose results — Zollman's headline: sparser networks are slower but likelier to find the truth
community size — bigger communities are harder to tip into premature consensus

The problem

how much better the new theory really is (its payoff rate is 0.5 + ε) — small ε = a hard problem, easy to get wrong
how much data each agent generates per round — the size of each "study" before everyone updates

The scientists

Zollman's "extreme beliefs": how much contrary evidence it takes to move an agent off its starting opinion

Simulation

how many independent communities to simulate — more runs, steadier percentages
the horizon: runs still divided at this point count as "unsettled" (permanent diversity)
random seed — same seed + same settings reproduces the exact same result
Log sliders: ε = 10^(−3+x), trials = 10^x, prior strength = 10^x. Everything reruns client-side; nothing leaves this page.
Press Run.

The community

Outcomes

Diversity trajectories (fraction working on the new theory; 40 sample runs)

When is the question settled? (round of unanimity)

Sweep: P(settle on truth) by stubbornness × network (the 2010 paper's core interaction; uses current ε, trials, agents)