Construct · 2 fields · 2 citations

Where does Monte Carlo collide?

One name. Different field. Different construct. One map.

● jingle · one name, two constructs Nearest construct by meaning·not indexed
Monte Carlo2 CONSTRUCTSMonte Carlo methodMETROPOLIS & ULAM · 1949Monte Carlo tree searchBROWNE ET AL. · 2012

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1949Monte Carlo method2012Monte Carlo tree search
2026

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Computational statistics

Estimate by random sampling

The Monte Carlo method is a computational technique that estimates a quantity by drawing many random samples and averaging the results, trading analytic intractability for repeated simulation. Metropolis and Ulam's 1949 The Monte Carlo method named and set out the approach, using random sampling to approximate solutions to problems in physics and mathematics too complex to solve in closed form. The referent is a family of sampling-based estimation algorithms, assessed by convergence and sampling error, and it is named for the casino only by analogy to games of chance; it is a wholly different construct from the gambler's fallacy or a game-playing search algorithm.[1]

Unit
The estimate from averaging random samples, with its sampling error
Recovery means
Not applicable
Measured by
Judged by the convergence of the sample average and the shrinking of Monte Carlo (sampling) error as draws increase

Artificial intelligence

Plan by simulating many random playouts

Monte Carlo tree search (MCTS) is a decision algorithm that chooses a move by building a search tree incrementally, using many random simulated playouts to estimate the value of each candidate action and steering search toward the most promising branches. Browne and colleagues' 2012 A survey of Monte Carlo tree search methods consolidated the family, including the UCT variant that balances exploring untried moves against exploiting those that have scored well. The referent is a sequential-decision search algorithm, measured by the quality of the move it returns under a sampling budget; it borrows the sampling idea from the Monte Carlo method but is a distinct construct from both that method and the gambler's fallacy.[2]

Unit
The move selected, given a budget of random simulated playouts
Recovery means
Not applicable
Measured by
Evaluated by playing strength and move quality achieved for a given number of simulations

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Nearby collisions

gambler's ruin3 fields · 3 citationsinvariant · one construct, three systemsgambler's fallacy2 fields · 3 citationsinvariant · one bias, two systemsaction3 fields · 6 citationsjingle · one word, three constructs

References

  1. Metropolis, N., & Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association, 44(247), 335–341. https://doi.org/10.1080/01621459.1949.10483310
  2. Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., … Colton, S. (2012). A survey of Monte Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1), 1–43. https://doi.org/10.1109/TCIAIG.2012.2186810

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