Computability of simple games: A complete investigation of the sixty-four possibilities

Classify simple games into sixteen "types" in terms of the four conventional axioms: monotonicity, properness, strongness, and nonweakness. Further classify them into sixty-four classes in terms of finiteness (existence of a finite carrier) and algorithmic computability. For each such class, we either show that it is empty or give an example of a game belonging to it. We observe that if a type contains an infinite game, then it contains both computable ones and noncomputable ones. This strongly suggests that computability is logically, as well as conceptually, unrelated to the conventional axioms.Comment: 25 page

Computability of simple games: A characterization and application to the core

It was shown earlier that the class of algorithmically computable simple games (i) includes the class of games that have finite carriers and (ii) is included in the class of games that have finite winning coalitions. This paper characterizes computable games, strengthens the earlier result that computable games violate anonymity, and gives examples showing that the above inclusions are strict. It also extends Nakamura’s theorem about the nonemptyness of the core and shows that computable simple games have a finite Nakamura number, implying that the number of alternatives that the players can deal with rationally is restricted

Interval Query Problem on Cube-Free Median Graphs

In this paper, we introduce the \emph{interval query problem} on cube-free median graphs. Let $G$ be a cube-free median graph and $\mathcal{S}$ be a commutative semigroup. For each vertex $v$ in $G$, we are given an element $p(v)$ in $\mathcal{S}$. For each query, we are given two vertices $u,v$ in $G$ and asked to calculate the sum of $p(z)$ over all vertices $z$ belonging to a $u-v$ shortest path. This is a common generalization of range query problems on trees and grids. In this paper, we provide an algorithm to answer each interval query in $O(\log^2 n)$ time. The required data structure is constructed in $O(n\log^3 n)$ time and $O(n\log^2 n)$ space. To obtain our algorithm, we introduce a new technique, named the \emph{stairs decomposition}, to decompose an interval of cube-free median graphs into simpler substructures.Comment: ISAAC'21, 21 page

Lipschitz Continuous Algorithms for Graph Problems

It has been widely observed in the machine learning community that a small perturbation to the input can cause a large change in the prediction of a trained model, and such phenomena have been intensively studied in the machine learning community under the name of adversarial attacks. Because graph algorithms also are widely used for decision making and knowledge discovery, it is important to design graph algorithms that are robust against adversarial attacks. In this study, we consider the Lipschitz continuity of algorithms as a robustness measure and initiate a systematic study of the Lipschitz continuity of algorithms for (weighted) graph problems. Depending on how we embed the output solution to a metric space, we can think of several Lipschitzness notions. We mainly consider the one that is invariant under scaling of weights, and we provide Lipschitz continuous algorithms and lower bounds for the minimum spanning tree problem, the shortest path problem, and the maximum weight matching problem. In particular, our shortest path algorithm is obtained by first designing an algorithm for unweighted graphs that are robust against edge contractions and then applying it to the unweighted graph constructed from the original weighted graph. Then, we consider another Lipschitzness notion induced by a natural mapping that maps the output solution to its characteristic vector. It turns out that no Lipschitz continuous algorithm exists for this Lipschitz notion, and we instead design algorithms with bounded pointwise Lipschitz constants for the minimum spanning tree problem and the maximum weight bipartite matching problem. Our algorithm for the latter problem is based on an LP relaxation with entropy regularization