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Poisson Approval studies the Poisson Game of Approval Voting.

Project description

Poisson Approval

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Poisson Approval studies the Poisson Game of Approval Voting.

Features

  • Implement only the case of 3 candidates.
  • Deal with ordinal or cardinal profiles.
  • Compute the asymptotic developments of the probability of pivot events when the number of players tends to infinity.
  • Compute the best response to a given tau-vector.
  • Explore automatically a grid of ordinal profiles or a grid of tau-vectors.
  • Perform Monte-Carlo experiments on profiles or tau-vectors.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.6.0 (2020-01-29)

  • Implement ProfileCardinal.fictitious_play, where the update ratios of the perceived tau-vector and the actual tau-vector can be functions of the time. It is also faster that ProfileCardinal.iterated_voting, but can not detect cycles (only convergence).
  • ProfileCardinal.iterated_voting_taus is renamed to ProfileCardinal.iterated_voting. It has been generalized by implementing a notion of perceived tau-vector, like for ProfileCardinal.fictitious_play. The syntax has been modified in consequence.
  • ProfileCardinal.iterated_voting_strategies is deprecated and suppressed.
  • Iterated voting and fictitious play do not need a StrategyThreshold as initial strategy, but any strategy that is consistent with the profile subclass. For example, with ProfileTwelve, you can use a StrategyTwelve.
  • Strategy.profile is now a property that can be reassigned after the creation of the object.
  • Add Strategy.deepcopy_with_attached_profile: make a deep copy and attach a given profile.
  • Add the utility to_callable: convert an object to a callable (making it a constant function if it is not callable already).

0.5.1 (2020-01-18)

  • Configure Codecov.
  • Reach 100% coverage for this version.

0.5.0 (2020-01-11)

  • In iterated voting, implement the possibility to move only progressively towards the best response:
    • Add ProfileCardinal.iterated_voting_taus: at each iteration, a given ratio of voters update their ballot.
    • Replace the former method ProfileCardinal.iterated_voting by ProfileCardinal.iterated_voting_strategies: as in former versions, at each iteration, the threshold utility of each ranking’s strategy is moved in the direction of the best response’s threshold utility. The method now returns a cycle of tau-vectors and the corresponding cycle of best response strategies, in order to be consistent with ProfileCardinal.iterated_voting_taus.
    • Add the utility barycenter: compute a barycenter while respecting the type of one input if the other input has weight 0.
    • Accelerate the algorithm used in iterated voting.
  • In ProfileCardinal, add the possibility of partial sincere voting:
    • Add parameter ratio_sincere: ratio of sincere voters.
    • Add property tau_sincere: the tau-vector if all voters vote sincerely.
    • The former method tau is renamed tau_strategic: the tau_vector if all voters vote strategically.
    • The new method tau takes both sincere and strategic voting into account.
    • The method is_equilibrium has a new implementation to take this feature into account.
  • Add TauVector.isclose: whether the tau-vector is close to another tau-vector (in the sense of math.isclose). This method is used by the new version of ProfileCardinal.is_equilibrium.
  • Add Profile.best_responses_to_strategy: convert a dictionary of best responses to a StrategyThreshold that mentions only the rankings that are present in the profile.
  • In random generators of profiles (GeneratorProfileOrdinalUniform, GeneratorProfileOrdinalGridUniform, GeneratorProfileOrdinalVariations, GeneratorProfileHistogramUniform): instead of having explicit arguments like well_informed_voters or ratio_sincere, there are **kwargs that are directly passed to the __init__ of the relevant Profile subclass.
  • Update the tutorials with these new features.

0.4.0 (2020-01-08)

  • Add image_distribution: estimate the distribution of f(something) for a random something.
  • Update the tutorial on mass simulations with this new feature.

0.3.0 (2020-01-08)

  • Add new random generators:
    • GeneratorExamples: run another generator until the generated object meets a given test.
    • GeneratorStrategyOrdinalUniform: draw a StrategyOrdinal uniformly.
    • GeneratorProfileOrdinalGridUniform: draw a ProfileOrdinal uniformly on a grid of rational numbers.
    • GeneratorTauVectorGridUniform: draw a TauVector uniformly on a grid of rational numbers.
  • Utilities:
    • Add rand_integers_fixed_sum: draw an array of integers with a given sum.
    • Add rand_simplex_grid: draw a random point in the simplex, with rational coordinates of a given denominator.
    • Update probability: allow for a tuple of generators.
  • Tutorials:
    • Add a tutorial on asymptotic developments.
    • Update the tutorial on mass simulations with the new features.

0.2.1 (2020-01-05)

  • Relaunch deployment.

0.2.0 (2020-01-05)

  • Add GeneratorProfileStrategyThreshold.
  • Add ProfileHistogram.plot_cdf.
  • Modify masks_distribution: remove the trailing zeros. This has the same impact on ProfileOrdinal.distribution_equilibria.
  • Modify NiceStatsProfileOrdinal.plot_cutoff: center the textual indications.
  • Replace all notations r with profile and sigma with strategy.
  • Add tutorials.

0.1.1 (2019-12-24)

  • Convert all the documentation to NumPy format, making it more readable in plain text.

0.1.0 (2019-12-20)

  • First release on PyPI.
  • Implement only the case of 3 candidates.
  • Deal with ordinal or cardinal profiles.
  • Compute the asymptotic developments of the probability of pivot events when the number of players tends to infinity.
  • Compute the best response to a given tau-vector.
  • Explore automatically a grid of ordinal profiles or a grid of tau-vectors.
  • Perform Monte-Carlo experiments on profiles or tau-vectors.

Project details


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