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Chien-Wei Lin, Kuan-Ta Chen, Ling-Jyh Chen
              Academia Sinica

        Irwin King, and Jimmy Lee
   The Chinese University of Hong Kong
Motivation
ESP games annotate images on the web.



Can we optimize the goodput of the game?



Service provider can utilize our model to improve
their system.
Idea
Model the performance of the game and optimize it.

A more generalized ESP game.
  The number of players can be more than 2.

  The consensus threshold can be any positive integer, but not
  larger than the number of players.

  The stopping condition can be more than 1.
3 Collaborative Quantity
Efficiency
  the rate that labels are matched.


Quality
  the proportion of good labels among all matched labels.


Utility
  the throughput rate of good labels.
  Utility = Efficiency × Quality
Assumption of Model
Round-based play
  Make only one guess in each round.
Independent guess
  Current guess is not affected by previous guesses.
Good and bad words
  The sizes of good and bad words are both limited.
  Players will do their best to guess good words.
Uniform guess
  The guess is made uniformly.
Parameters in the Model
Number of players
  denoted as n.
Consensus threshold
  denoted as m.
Size of good vocabulary
  denoted as vgood
                 .
Probability of guessing good words
  denoted as probgood
                    .
Stopping condition is our main variable.
Model Validation
Trade-off between       Validate the model by
efficiency & quality.   simulations.
Optimal Stopping Conditions
Optimal stopping condition changes under different
parameter settings.
Benefit of Optimization
We provide twice as much utility as a non-optimized
game.
Contribution
Model for generalized games.

Propose an optimal termination condition to optimize
the system.

Game providers can utilize our model to maximize the
outcome of games.
Thank You!

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