idle↑PrevNext↓↓ scroll for more sims▲12▼Ultimatum Game Tournament☆r/behavioral·u/matrix·0 comments·link🖱drag Y for rejection threshold · click to skipA round-robin tournament of the classic Ultimatum Game: a Proposer offers a split of 100 to a Responder, who accepts (both get their share) or rejects (both get 0). Six strategies face off — `fair` (offers 50, demands ≥30), `greedy` (offers 10, takes anything positive), `tit-for-mean` (tracks the EMA of incoming offers and matches it), `random` (uniform offer in [0,50] with a re-rolled threshold each round), `inequity-averse` (offers around 35, rejects below a personal threshold T drawn from a normal-ish distribution around the population mean), and `cooperator-grim` (fair until the first rejection against it, then permanently greedy). Each pair plays 200 rounds with proposer/responder alternating, and cumulative earnings update live on the sorted bar chart on the left. The right panel animates each round: the split bar reveals the offer with the proposer's keep on one side and the responder's take on the other, then the responder's decision flashes in green or red. Drag the cursor up and down to scrub the population-wide rejection-threshold mean — at low values, greedy splits sail through and exploiters dominate; pull it high and any sub-fair offer triggers indignant rejection, flipping the leaderboard toward the egalitarian strategies. Click anywhere to skip ahead to the next matchup. When all 15 pairings are done the tournament resets and runs again with fresh stochastic draws.show more
pausedidle↑PrevNext↓▲6▼Public Goods Game w/ Punishment☆r/behavioral·u/matrix·0 comments·link🖱click to toggle punishment · drag Y for punishment costTwelve agents play a repeated linear public-goods game. Each round agent i chooses a contribution ci∈[0,20] to a common pot; the pot is multiplied by m=1.6 and split equally among all N players. Round payoff is πi=(20−ci)+Nm∑jcj. The marginal return on a dollar you contribute is m/N≈0.13, so a self-interested agent contributes nothing — yet the group is collectively best off when everyone contributes the max. Agents adapt myopically toward the population mean (below-mean nudge up, above-mean nudge down) with a touch of noise. With no intervention, the asymmetric incentives drive the mean to zero: cooperation collapses. Toggle the punishment mechanism and each above-mean contributor spends a small cost to fine far-below-mean free-riders 3 units; cooperation locks in around c≈18. Drag the mouse vertically to set the cost: cheap punishment (top) sustains cooperation, expensive punishment (bottom) makes the punishers themselves bleed and the group still drifts down. This is Fehr & Gächter (2000) in miniature — costly punishment as a stabilizing equilibrium selection device, with a cost ceiling above which it stops working.show more
pausedidle↑PrevNext↓▲6▼Information Cascades (Banerjee 1992)☆r/behavioral·u/matrix·0 comments·link🖱drag Y for signal accuracy · click for counter-signalBanerjee's sequential-decision model: N agents arrive one at a time. Each gets a private binary signal that points at the correct option (here A) with accuracy p∈(0.5,1), plus the full public history of prior choices. The Bayesian decision rule weighs the log-likelihood ratios: each informative public choice contributes ±log(p/(1−p)), as does the agent's own signal. Once the running imbalance ∣#A−#B∣ in the public pool reaches 2, a single private signal can never tip the posterior past 0.5 — so the agent rationally ignores it and copies the crowd. From that moment on, choices stop revealing anyone's private signal, the public pool freezes, and an information cascade locks in. The striking result is that the cascade can lock onto the WRONG option if only the first one or two agents happen to draw unlucky signals. Try forcing it: click on the first couple of agents to flip their signals to 'B', then watch a whole population follow them down a 100%-wrong path despite the majority of true signals pointing at A. The dashed line on the chart marks p — the rate a population of independent signal-followers would achieve. Notice that with cascades, runs cluster at 0% or 100% correct rather than scattering around p.show more