Version 16.3.0

General

• Dropped support for Python 3.8.

Added

• Implemented maximum-likelihood estimation for agent logit QRE, to parallel existing support
  for strategic logit QRE. Strategic logit QRE function names have been modified to provide
  parallel naming. Estimation using the correspondence now supports an option to stop at the
  first interior local maximizer found (if one exists).
• Maximum-likelihood estimation for logit QRE using empirical payoffs has an improved internal
  calculation of log-likelihood, and returns the estimated profile instead of just a list of
  probabilities.
• Reorganized naming conventions in pygambit for functions for computing QRE in both strategic
  and agent versions, and added a corresponding section in the user guide.
• enumpoly_solve has been returned to being fully supported from temporarily being experimental;
  now available in pygambit.
• enumpoly_solve for strategic games now uses the Porter, Nudelman, and Shoham (2004) ordering
  of supports to search.
• to_arrays converts a Game to a list of numpy arrays containing its reduced strategic form.
  (#461)
• Integrated support (in Python) for using PHCpack to solve systems of polynomial equations in
  enumpoly_solve based on an implementation formerly in the contrib section of the
  repository. (#165)
• New format-specific functions pygambit.read_* and pygambit.Game.to_* functions have been
  added to (de-)serialise games. The existing Game.read_game and Game.write functions have
  been deprecated and will be removed in 16.4. (#357)

Changed

• The built-in implementation of lrslib (dating from 2016) has been removed. Instead, access to
  lrsnash is provided as an external tool via the enummixed_solve function, in parallel to
  PHCpack for enumpoly_solve.

Fixed

• When parsing .nfg files, check that the number of outcomes or payoffs is the expected number,
  and raise an exception if not. (#119)

Removed

• Game.write() no longer supports generation of the XML-format files for Game Theory
  Explorer, as GTE no longer reads files in this format.

Version 16.2.1

Fixed

• Corrected an internal implementation error in Game.reveal() in resolving references to
  information sets and players (#453)
• Reading .efg and .nfg game files which did not have whitespace at the end would lead to
  an infinite loop (#457)
• Attempting to call the default constructor on Game objects (rather than one of the factory
  functions) now raises a more informative exception (#463)

Version 16.2.0

Fixed

• gnm_solve/gambit-gnm now correctly handles the degenerate case of a game where all
  payoffs are the same (#405), and checks that the perturbation vector specified has at least
  one non-zero component (#194)
• ipa_solve/gambit-ipa ensures the use of a generic perturbation vector; this resolves a
  problem where the method could return non-Nash output (#406)
• gambit-enumpoly could get stuck in an infinite loop, and/or fail to report some equilibria,
  due to floating-point rounding/tolerance issues; this has been fixed on known cases (#198)
• gambit-logit now uses perturbations to attempt to resolve correspondences that have
  bifurcations, and instead tries always to follow a curve that has the same orientation.
  This should eliminate cases in which tracing gets stuck in a loop or reverses itself
  when encountering bifurcations (#3)

Added

• MixedStrategyProfile and MixedBehaviorProfile objects in pygambit can now be iterated in
  various dict-like ways
• gnm_solve/gambit-gnm now exposes several parameters which control the behavior of the
  path-following procedure
• The MixedBehaviorProfile object can now be initialized on creation by a given distribution.
• append_move/append_infoset now resolves either a singleton node reference or any
  iterable set of node references
• Additional regret-related functions added to MixedBehaviorProfile and MixedStrategyProfile
  in both C++ and Python
• Some caching added to payoff/strategy value calculations in MixedStrategyProfile
• gambit-simpdiv now supports expressing output as floating-point with a specified number of
  digits (#296)
• Parameters first_step and max_accel added to gambit_logit for finer control of
  numerical continuation process

Changed

• Gambit now requires a compiler that supports C++17.
• Functions to compute Nash equilibria now return a NashComputationResult object instead of a bare
  list of profiles (#190)
• liap_solve/gambit-liap has been reimplemented to scale payoffs uniformly across games,
  to always take an explicit starting point (in liap_solve), and to specify a regret-based
  acceptance criterion (#330)
• simpdiv_solve/gambit-simpdiv now accepts a regret-based acceptance criterion (#439)
• simpdiv_solve now takes an explicit starting point (#445)
• Converted test suite for mixed behavior profiles to pytest style; added parametrizations for
  test_realiz_prob; added test_martingale_property_of_node_value (#375)
• Improved test suite for mixed strategy profiles (#374)
• Test suite for pygambit moved from src/pygambit/tests/ to tests/
• Improved repr methods in pygambit for game-related classes
• Further extension of test suite for mixed behavior profiles to cover new indexing and profile
  order consistency for payoff-related calculations
• Overhaul of caching in MixedBehaviorProfile to use maps (std::map)
• Creation of random mixed profiles in pygambit is done with new Game.random_strategy_profile and
  Game.random_behavior_profile methods; these accept numpy.random.Generator objects for
  reproducible state.
  Creation of random mixed profiles in C++ is done with new Game::NewRandomStrategyProfile and
  Game::NewRandomBehaviorProfile methods; these accept STL Generator objects for reproducible state.
  The Python implementation is no longer just a wrapper around the C++ one.
• Graphical interface now uses simplicial subdivision as the recommended method for finding some
  equilibria in games with more than two players, instead of Lyapunov function minimisation

Version 16.1.1

The stable release of version 16.1.1.

Fixed

• In gambit-logit, if there are chance actions with zero probability, information sets may be reached
  with zero probability. In this event, gambit-logit treats beliefs at those information sets as being
  uniform across nodes (#63)
• Corrected outdated code in fit_fixedpoint and fit_empirical, and added extended documentation
  of both methods (#1)
• Fixed bug in gambit-lp which would return non-Nash output on extensive games if the game had chance nodes
  other than the root node (#134)
• In pygambit, fixed indexing in mixed behavior and mixed strategy profiles, which could result
  in strategies or actions belonging to other players or information sets being referenced when
  indexing by string label

Changed

• In pygambit, resolving game objects with ambiguous or duplicated labels results in a ValueError,
  instead of silently returning the first matching object found.

Version 16.1.0

The stable release of version 16.1.0. See ChangeLog for what's new.

Version 16.1.0b1

The first beta release in preparation for Version 16.1.0. See ChangeLog for what's new.

Version 16.1.0a4

The fourth alpha release in preparation for Version 16.1.0. See ChangeLog for what's new.

Version 16.1.0a3

The third alpha release in preparation for Version 16.1.0. See ChangeLog for what's new.

Version 16.1.0a2

The second alpha release in preparation for Version 16.1.0. See ChangeLog for what's new.

Version 16.1.0a1

The first alpha release for 16.1.0.