Abstract:We study Dice Bingo, a game in which players fill a $3\times3$ bingo board whose entries are possible sums of two fair dice. After each roll, a player marks one matching square, and the goal is to complete a row, column, or diagonal. We model optimal play for a fixed board as a finite Markov decision process and derive Bellman equations that compute the exact expected number of rolls required to obtain a bingo. Using this framework, we identify a unique optimal board up to natural symmetries and determine its exact expected completion time.
We then investigate head-to-head competition in which two players observe the same sequence of dice rolls. By analyzing a joint Markov chain that tracks both boards simultaneously, we compute (in exact arithmetic) win, loss, and tie probabilities. Surprisingly, a board with a worse expected completion time can nevertheless be favored in head-to-head competition. Motivated by this phenomenon, we exhibit nontransitive triples of bingo boards: board $A$ is favored against board $B$, board $B$ is favored against board $C$, and board $C$ is favored against board $A$.
Finally, we consider strategic play in which players adapt their choices to their opponent's board rather than merely minimizing their own completion time. In this setting, optimal decisions depend on the opponent's state, leading naturally to game-theoretic analysis. We present a position with no pure Nash equilibrium and compute an explicit mixed Nash equilibrium.
From: Benjamin Ong [view email]
[v1]
Wed, 17 Jun 2026 21:44:49 UTC (20 KB)
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