Poisson Approval studies the Poisson Game of Approval Voting.
Project description
Poisson Approval
Poisson Approval studies the Poisson Game of Approval Voting.
 Free software: GNU General Public License v3
 Documentation: https://poissonapproval.readthedocs.io.
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 tauvector.
 Explore automatically a grid of ordinal profiles or a grid of tauvectors.
 Perform MonteCarlo experiments on profiles or tauvectors.
Credits
This package was created with Cookiecutter and the audreyr/cookiecutterpypackage project template.
History
0.7.0 (20200130)
 Add ProfileDiscrete: a profile with a discrete distribution of voters.
 Subclasses of Profile: better handling of the additional parameters like well_informed_voters or ratio_sincere. In the conversions to string (str or repr), they are now mentioned. They are also used in the equality tests between two profiles.
0.6.0 (20200129)
 Implement ProfileCardinal.fictitious_play, where the update ratios of the perceived tauvector and the actual tauvector 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 tauvector, 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 (20200118)
 Configure Codecov.
 Reach 100% coverage for this version.
0.5.0 (20200111)
 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 tauvectors 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 tauvector 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 tauvector is close to another tauvector (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 (20200108)
 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 (20200108)
 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 (20200105)
 Relaunch deployment.
0.2.0 (20200105)
 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 (20191224)
 Convert all the documentation to NumPy format, making it more readable in plain text.
0.1.0 (20191220)
 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 tauvector.
 Explore automatically a grid of ordinal profiles or a grid of tauvectors.
 Perform MonteCarlo experiments on profiles or tauvectors.
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