Merge pull request #163 from wesholliday/main

Added "marginal clone" option to independence of clones

Author: epacuit

pref_voting/c1_methods.py

@@ -179,7 +179,7 @@ def copeland_ranking(edata, curr_cands=None, local=True, tie_breaking=None):
 @vm(name = "Llull",
     input_types = [ElectionTypes.PROFILE, ElectionTypes.PROFILE_WITH_TIES, ElectionTypes.MAJORITY_GRAPH, ElectionTypes.MARGIN_GRAPH])
 def llull(edata, curr_cands = None):
-    """The Llull score for a candidate :math:`c` is the number of candidates that :math:`c` is weakly majority preferred to.  This is equivalent to calculating the Copeland scores for a candidate :math:`c` with 1 point for each candidate that :math:`c` is majority preferred to, 1/2 point for each candidate that :math:`c` is tied with, and 0 points for each candidate that is majority preferred to :math:`c`.  The Llull winners are the candidates with the maximum Llull score in the profile restricted to ``curr_cands``. 
+    """The Llull score for a candidate :math:`c` is the number of candidates that :math:`c` is weakly majority preferred to.  This is equivalent to calculating the Copeland scores for a candidate :math:`c` with 1 point for each candidate that :math:`c` is majority preferred to, 1 point for each candidate that :math:`c` is tied with (instead of 1/2 a point as for Copeland), and 0 points for each candidate that is majority preferred to :math:`c`.  The Llull winners are the candidates with the maximum Llull score in the profile restricted to ``curr_cands``. 
 
     Args:
         edata (Profile, ProfileWithTies, MajorityGraph, MarginGraph): Any election data that has a `copeland_scores` method. 

pref_voting/helper.py

@@ -1,11 +1,10 @@
-
 from pref_voting.profiles import Profile
 from pref_voting.profiles_with_ties import ProfileWithTies
 from pref_voting.weighted_majority_graphs import MajorityGraph
 from pref_voting.rankings import Ranking
 from pref_voting.social_welfare_function import *
 from pref_voting.voting_method import *
-from itertools import combinations
+from itertools import combinations, chain
 import random
 
 import networkx as nx
@@ -363,4 +362,9 @@ def convex_lexicographic_sublists(l):
             if idx == len(l) - 1:
                 cl_sublists.append(current_list)
 
-    return cl_sublists
+    return cl_sublists
+
+def powerset(iterable):
+    """powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"""
+    s = list(iterable)
+    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

pref_voting/variable_candidate_axioms.py

@@ -2,6 +2,7 @@
     File: variable_candidate_axioms.py
     Author: Wes Holliday ([email protected]) and Eric Pacuit ([email protected])
     Date: July 7, 2024
+    Updated: August 20, 2025
     
     Variable candidate axioms 
 """
@@ -10,6 +11,7 @@
 from pref_voting.axiom_helpers import *
 from pref_voting.c1_methods import top_cycle
 import numpy as np
+from itertools import combinations
 
 def has_stability_for_winners_violation(edata, vm, verbose=False, strong_stability=False):
     """
@@ -425,13 +427,46 @@ def tideman_clone_sets(prof):
 
     return clone_sets   
 
-def has_independence_of_clones_violation(prof, vm, clone_def = "Tideman", conditions_to_check = "all", verbose = False):
+def marginal_clone_sets(edata, epsilon=0):
+    """Given a profile or margin graph, returns all sets of "marginal clones": a set C of candidates is a set of marginal clones if for any c,d in C and x not in C, |margin(c,x) - margin(d,x)| <= epsilon."""
+    
+    mc_sets = []
+
+    cands = edata.candidates
+
+    for subset in powerset(cands):
+
+        if len(subset) <=1 or len(subset) == len(cands):
+            continue
+
+        is_marginal_clone_set = True
+
+        for c, d in combinations(subset, 2):
+            are_clones = True
+
+            for x in cands:
+                if not x in subset:
+                    if abs(edata.margin(c,x) - edata.margin(d,x)) > epsilon:
+                        are_clones = False
+                        break
+
+            if not are_clones:
+                is_marginal_clone_set = False
+                break
+    
+        if is_marginal_clone_set:
+            mc_sets.append(subset)
+                
+    return mc_sets   
+
+def has_independence_of_clones_violation(prof, vm, clone_def = "Tideman", epsilon = 0, conditions_to_check = "all", verbose = False):
     """Independence of Clones: returns True if there is a set C of clones and a clone c in C such that removing c either (i) changes which non-clones (candidates not in C) win or (ii) changes whether any clone in C wins. We call (i) a violation of "non-clone choice is independent of clones" (NCIC) and call (ii) a violation of "clone choice is independent of clones" (CIC).
 
     Args:
         prof (Profile): the election data.
         vm (VotingMethod): A voting method to test.
-        clone_def (str, default="Tideman"): The definition of clones. Currently only "Tideman" is supported.
+        clone_def (str, default="Tideman"): The definition of clones. Options are "Tideman" and "Marginal".
+        epsilon (float, default=0): If clone_def is "Marginal", then for C to be a marginal clone set, it must but that for any c,c' in C and x not in C, |margin(c,x) - margin(c',x)| <= epsilon.
         conditions_to_check (str, default="all"): The conditions to check. If "all", then both NCIC and CIC are checked. If "NCIC", then only NCIC is checked. If "CIC", then only CIC is checked.
         verbose (bool, default=False): If a violation is found, display the violation.
     
@@ -440,6 +475,9 @@ def has_independence_of_clones_violation(prof, vm, clone_def = "Tideman", condit
     if clone_def == "Tideman":
         clone_sets = tideman_clone_sets(prof)
 
+    if clone_def == "Marginal":
+        clone_sets = marginal_clone_sets(prof, epsilon)
+
     for clone_set in clone_sets:
 
         non_clones = [n for n in prof.candidates if n not in clone_set]
@@ -500,13 +538,14 @@ def has_independence_of_clones_violation(prof, vm, clone_def = "Tideman", condit
 
     return False
 
-def find_all_independence_of_clones_violations(prof, vm, clone_def = "Tideman", conditions_to_check = "all", verbose = False):
+def find_all_independence_of_clones_violations(prof, vm, clone_def = "Tideman", epsilon = 0, conditions_to_check = "all", verbose = False):
         """Returns all violations of Independence of Clones for the given election data and voting method.
         
         Args:
             prof (Profile): the election data.
             vm (VotingMethod): A voting method to test.
-            clone_def (str, default="Tideman"): The definition of clones. Currently only "Tideman" is supported.
+            clone_def (str, default="Tideman"): The definition of clones. Options are "Tideman" and "Marginal".
+            epsilon (float, default=0): If clone_def is "Marginal", then for C to be a marginal clone set, it must but that for any c,c' in C and x not in C, |margin(c,x) - margin(c',x)| <= epsilon.
             conditions_to_check (str, default="all"): The conditions to check. If "all", then both NCIC and CIC are checked. If "NCIC", then only NCIC is checked. If "CIC", then only CIC is checked.
             verbose (bool, default=False): If a violation is found, display the violation.
         
@@ -516,6 +555,9 @@ def find_all_independence_of_clones_violations(prof, vm, clone_def = "Tideman",
 
         if clone_def == "Tideman":
             clone_sets = tideman_clone_sets(prof)
+
+        if clone_def == "Marginal":
+            clone_sets = marginal_clone_sets(prof, epsilon)
 
         violations = list()