In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

Stephen J. Hartley Oxford University Press, New York, 1998, 260 pp. ISBN 0-19-511315-2, $45.00 Concurrent Programming  is a thorough treatment of Java multi-threaded programming for both a stand-alone and distributed environment. Designed mostly for students in concurrent or parallel programming classes, the text is also an excellent reference for the practicing professional developing multi-threaded programs or applets. Hartley first provides a complete explanation of the features of Java necessary to write concurrent programs, including topics such as exception handling, interfaces, and packages. He then gives the reader a solid background to write multi-threaded programs and also presents the problems introduced when writing concurrent programs—namely race conditions, mutual exclusion, and deadlock. Hartley also provides several software solutions that do not require the use of common process and thread mechanisms. Once the groundwork is laid for writing concurrent programs, Hartley then takes a different approach than most Java references. Rather than presenting how Java handles mutual exclusion with the  synchronized  keyword (although it is covered later), he first looks at semaphore-based solutions to classic concurrent problems such as bounded-buffer, readers-writers, and the dining philosophers. Hartley also uses the same approach to develop Java classes for monitors and message passing. This unique approach to introducing concurrency allows the readers to both understand how Java threads are synchronized and how the basic synchronization mechanism can be used to construct more abstract tools such as semaphores. If there is a shortcoming with the text it is with the lack of sufficient coverage of remote method invocation (RMI), although there is a section covering RMI. This is quite understandable as RMI is a fairly recent phenomenon with the Java community. Also, the classes that Hartley provides could easily implement RMI rather than sockets to handle communication. The strengths of the book include its ease in reading, several examples at the end of chapters, a package similar to Xtango that provides algorithm animation, and a supportive web site by the author (see  www.mcs.drexel.edu/~shartley/ConcProgJava/index.html ) including compressed source code. As Java becomes more dominant on the server side of multi-tier applications, writing thread-safe concurrent applications becomes even more important.  Concurrent Programming  is a strong step towards teaching students and professionals such skills. Greg Gagne, Westminster College of Salt Lake City Salt Lake City, Utah

Distributed Computing: Fundamentals, Simulations and Advanced Topics

Computing exact solutions of consensus halving and the Borsuk-Ulam theorem

In the e-Consensus-Halvingproblem, a fundamental problem in fair division, there are n agents with valuations over the interval [0,1], and the goal is to divide the interval into pieces and assign a label "+" or "-" to each piece, such that every agent values the total amount of "+" and the total amount of "-" almost equally. The problem was recently proven by Filos-Ratsikas and Goldberg[18,19] to be the first "natural" complete problem for the computational class PPA, answering a decade-old open question. In this paper, we examine the extent to which the problem becomes easy to solve, if one restricts the class of valuation functions. To this end, we provide the following contributions. First, we obtain a strengthening of the PPA-hardness result of [19], to the case when agents have piecewise uniformvaluations with only two blocks. We obtain this result via a new reduction, which is in fact conceptually much simpler than the corresponding one in [19]. Then, we consider the case of single-block (uniform) valuationsand provide a parameterized polynomial time algorithm for solving e-Consensus-Halvingfor any e, as well as a polynomial-time algorithm for e=1/2; these are the first algorithmic results for the problem. Finally, an important application of our new techniques is the first hardness result for a generalization of Consensus-Halving, the Consensus-1/k-Division problem [33]. In particular, we prove that e-Consensus-1/3-Division is PPAD-hard.

Consensus-Halving: Does It Ever Get Easier?

Consider a network vulnerable to security attacks and equipped with defense mechanisms. How much is the loss in the provided security guarantees due to the selfish nature of attacks and defenses? The Price of Defense was recently introduced in [7] as a worst-case measure, over all associated Nash equilibria, of this loss. In the particular strategic game considered in [7], there are two classes of confronting randomized players on a graph G(V,E): v attackers, each choosing vertices and wishing to minimize the probability of being caught, and a single defender, who chooses edges and gains the expected number of attackers it catches. In this work, we continue the study of the Price of Defense. We obtain the following results: - The Price of Defense is at least |V| 2; this implies that the Perfect Matching Nash equilibria considered in [7] are optimal with respect to the Price of Defense, so that the lower bound is tight. - We define Defense-Optimal graphs as those admitting a Nash equilibrium that attains the (tight) lower bound of |V| 2. We obtain: ○ A graph is Defense-Optimal if and only if it has a Fractional Perfect Matching. Since graphs with a Fractional Perfect Matching are recognizable in polynomial time, the same holds for Defense-Optimal graphs. ○ We identify a very simple graph that is Defense-Optimal but has no Perfect Matching Nash equilibrium. - Inspired by the established connection between Nash equilibria and Fractional Perfect Matchings, we transfer a known bivaluedness result about Fractional Matchings to a certain class of Nash equilibria. So, the connection to Fractional Graph Theory may be the key to revealing the combinatorial structure of Nash equilibria for our network security game.

The price of defense and fractional matchings

The Existential Theory of the Reals (ETR) consists of existentially quantified Boolean formulas over equalities and inequalities of polynomial functions of variables in $\mathbb{R}$. In this paper we propose and study the approximate existential theory of the reals ($\epsilon$-ETR), in which the constraints only need to be satisfied approximately. We first show that when the domain of the variables is $\mathbb{R}$ then $\epsilon$-ETR = ETR under polynomial time reductions, and then study the constrained $\epsilon$-ETR problem when the variables are constrained to lie in a given bounded convex set. Our main theorem is a sampling theorem, similar to those that have been proved for approximate equilibria in normal form games. It discretizes the domain in a grid-like manner whose density depends on various properties of the formula. A consequence of our theorem is that we obtain a quasi-polynomial time approximation scheme (QPTAS) for a fragment of constrained $\epsilon$-ETR. We use our theorem to create several new PTAS and QPTAS algorithms for problems from a variety of fields.

Approximating the Existential Theory of the Reals

We study the problem of finding an exact solution to the consensus halving problem. While recent work has shown that the approximate version of this problem is PPA-complete [Filos-Ratsikas and Goldberg, 2018; Filos-Ratsikas and Goldberg, 2018], we show that the exact version is much harder. Specifically, finding a solution with n agents and n cuts is FIXP-hard, and deciding whether there exists a solution with fewer than n cuts is ETR-complete. We also give a QPTAS for the case where each agent's valuation is a polynomial.
Along the way, we define a new complexity class BU, which captures all problems that can be reduced to solving an instance of the Borsuk-Ulam problem exactly. We show that FIXP subseteq BU subseteq TFETR and that LinearBU = PPA, where LinearBU is the subclass of BU in which the Borsuk-Ulam instance is specified by a linear arithmetic circuit.

https://drops.dagstuhl.de/opus/volltexte/2019/10714/pdf/LIPIcs-ICALP-2019-138.pdf/

Computing Exact Solutions of Consensus Halving and the Borsuk-Ulam Theorem

In the ε-Consensus-Halving problem, we are given n probability measures v1, …, vn on the interval R = [0,1], and the goal is to partition R into two parts R+ and R− using at most n cuts, so that |vi(R+) − vi(R−)| ≤ ε for all i. This fundamental fair division problem was the first natural problem shown to be complete for the class PPA, and all subsequent PPA-completeness results for other natural problems have been obtained by reducing from it. We show that ε-Consensus-Halving is PPA-complete even when the parameter ε is a constant. In fact, we prove that this holds for any constant ε < 1/5. As a result, we obtain constant inapproximability results for all known natural PPA-complete problems, including Necklace-Splitting, the Discrete-Ham-Sandwich problem, two variants of the pizza sharing problem, and for finding fair independent sets in cycles and paths.

/pdf/constant-inapproximability-for-ppa-3w0lkox5.pdf

Constant inapproximability for PPA

We study the problem of finding an exact solution to the consensus halving problem. While recent work has shown that the approximate version of this problem is PPA-complete, we show that the exact version is much harder. Specifically, finding a solution with $n$ cuts is FIXP-hard, and deciding whether there exists a solution with fewer than $n$ cuts is ETR-complete. We also give a QPTAS for the case where each agent's valuation is a polynomial. Along the way, we define a new complexity class BU, which captures all problems that can be reduced to solving an instance of the Borsuk-Ulam problem exactly. We show that FIXP $\subseteq$ BU $\subseteq$ TFETR and that LinearBU $=$ PPA, where LinearBU is the subclass of BU in which the Borsuk-Ulam instance is specified by a linear arithmetic circuit.

Themistoklis Melissourgos

Papers

Computing exact solutions of consensus halving and the Borsuk-Ulam theorem

Approximating the Existential Theory of the Reals

Computing Exact Solutions of Consensus Halving and the Borsuk-Ulam Theorem

Constant inapproximability for PPA

Computing Exact Solutions of Consensus Halving and the Borsuk-Ulam Theorem