1,785 research outputs found
Incentive and stability in the Rock-Paper-Scissors game: an experimental investigation
In a two-person Rock-Paper-Scissors (RPS) game, if we set a loss worth
nothing and a tie worth 1, and the payoff of winning (the incentive a) as a
variable, this game is called as generalized RPS game. The generalized RPS game
is a representative mathematical model to illustrate the game dynamics,
appearing widely in textbook. However, how actual motions in these games depend
on the incentive has never been reported quantitatively. Using the data from 7
games with different incentives, including 84 groups of 6 subjects playing the
game in 300-round, with random-pair tournaments and local information recorded,
we find that, both on social and individual level, the actual motions are
changing continuously with the incentive. More expressively, some
representative findings are, (1) in social collective strategy transit views,
the forward transition vector field is more and more centripetal as the
stability of the system increasing; (2) In the individual behavior of strategy
transit view, there exists a phase transformation as the stability of the
systems increasing, and the phase transformation point being near the standard
RPS; (3) Conditional response behaviors are structurally changing accompanied
by the controlled incentive. As a whole, the best response behavior increases
and the win-stay lose-shift (WSLS) behavior declines with the incentive.
Further, the outcome of win, tie, and lose influence the best response behavior
and WSLS behavior. Both as the best response behavior, the win-stay behavior
declines with the incentive while the lose-left-shift behavior increase with
the incentive. And both as the WSLS behavior, the lose-left-shift behavior
increase with the incentive, but the lose-right-shift behaviors declines with
the incentive. We hope to learn which one in tens of learning models can
interpret the empirical observation above.Comment: 19 pages, 14 figures, Keywords: experimental economics, conditional
response, best response, win-stay-lose-shift, evolutionary game theory,
behavior economic
Learning neural trans-dimensional random field language models with noise-contrastive estimation
Trans-dimensional random field language models (TRF LMs) where sentences are
modeled as a collection of random fields, have shown close performance with
LSTM LMs in speech recognition and are computationally more efficient in
inference. However, the training efficiency of neural TRF LMs is not
satisfactory, which limits the scalability of TRF LMs on large training corpus.
In this paper, several techniques on both model formulation and parameter
estimation are proposed to improve the training efficiency and the performance
of neural TRF LMs. First, TRFs are reformulated in the form of exponential
tilting of a reference distribution. Second, noise-contrastive estimation (NCE)
is introduced to jointly estimate the model parameters and normalization
constants. Third, we extend the neural TRF LMs by marrying the deep
convolutional neural network (CNN) and the bidirectional LSTM into the
potential function to extract the deep hierarchical features and
bidirectionally sequential features. Utilizing all the above techniques enables
the successful and efficient training of neural TRF LMs on a 40x larger
training set with only 1/3 training time and further reduces the WER with
relative reduction of 4.7% on top of a strong LSTM LM baseline.Comment: 5 pages and 2 figure
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