The literature on algorithmic mechanism design is mostly concerned with game-theoretic versions of optimization problems to which standard economic money-based mechanisms cannot be applied efficiently. Recent years have seen the design of various truthful approximation mechanisms that rely on payments. In this article, we advocate the reconsideration of highly structured optimization problems in the context of mechanism design. We explicitly argue for the first time that, in such domains, approximation can be leveraged to obtain truthfulness without resorting to payments. This stands in contrast to previous work where payments are almost ubiquitous and (more often than not) approximation is a necessary evil that is required to circumvent computational complexity.We present a case study in approximate mechanism design without money. In our basic setting, agents are located on the real line and the mechanism must select the location of a public facility; the cost of an agent is its distance to the facility. We establish tight upper and lower bounds for the approximation ratio given by strategyproof mechanisms without payments, with respect to both deterministic and randomized mechanisms, under two objective functions: the social cost and the maximum cost. We then extend our results in two natural directions: a domain where two facilities must be located and a domain where each agent controls multiple locations.

/pdf/approximate-mechanism-design-without-money-2pt91l4prf.pdf

Approximate mechanism design without money

/pdf/alfred-weber-s-theory-of-the-location-of-industries-54x5xzdke4.pdf

Alfred Weber's Theory of the Location of Industries@@@Coastwise Cruising, from Erith to Lowestoft

We consider approval-based committee voting, i.e., the setting where each voter approves a subset of candidates, and these votes are then used to select a fixed-size set of winners (committee). We propose a natural axiom for this setting, which we call justified representation (JR). This axiom requires that if a large enough group of voters exhibits agreement by supporting the same candidate, then at least one voter in this group has an approved candidate in the winning committee. We show that for every list of ballots it is possible to select a committee that provides JR. We then check if this axiom is fulfilled by well-known approval-based voting rules. We show that the answer is negative for most of the rules we consider, with notable exceptions of PAV (Proportional Approval Voting), an extreme version of RAV (Reweighted Approval Voting), and, for a restricted preference domain, MAV (Minimax Approval Voting). We then introduce a stronger version of the JR axiom, which we call extended justified representation (EJR), and show that PAV satisfies EJR, while other rules do not. We also consider several other questions related to JR and EJR, including the relationship between JR/EJR and unanimity, and the complexity of the associated algorithmic problems.

/pdf/justified-representation-in-approval-based-committee-voting-5anu6ay1hv.pdf

Justified representation in approval-based committee voting

We consider approval-based committee voting, i.e. the setting where each voter approves a subset of candidates, and these votes are then used to select a fixed-size set of winners (committee). We propose a natural axiom for this setting, which we call justified representation (JR). This axiom requires that if a large enough group of voters exhibits agreement by supporting the same candidate, then at least one voter in this group has an approved candidate in the winning committee. We show that for every list of ballots it is possible to select a committee that provides JR. However, it turns out that several prominent approval-based voting rules may fail to output such a committee. In particular, while Proportional Approval Voting (PAV) always outputs a committee that provides JR, Reweighted Approval Voting (RAV), a tractable approximation to PAV, does not have this property. We then introduce a stronger version of the JR axiom, which we call extended justified representation (EJR), and show that PAV satisfies EJR, while other rules we consider do not; indeed, EJR can be used to characterize PAV within the class of weighted PAV rules. We also consider several other questions related to JR and EJR, including the relationship between JR/EJR and core stability, and the complexity of the associated algorithmic problems.

Justified Representation in Approval-Based Committee Voting

We consider the following problem: There is a set of items (e.g., movies) and a group of agents (e.g., passengers on a plane); each agent has some intrinsic utility for each of the items. Our goal is to pick a set of K items that maximize the total derived utility of all the agents (i.e., in our example we are to pick K movies that we put on the plane's entertainment system). However, the actual utility that an agent derives from a given item is only a fraction of its intrinsic one, and this fraction depends on how the agent ranks the item among the chosen, available, ones. We provide a formal specification of the model and provide concrete examples and settings where it is applicable. We show that the problem is hard in general, but we show a number of tractability results for its natural special cases.

/pdf/finding-a-collective-set-of-items-from-proportional-43ry27vt4o.pdf

Finding a collective set of items: from proportional multirepresentation to group recommendation

While it is generally accepted that garbage-collected languages offer advantages over languages in which objects must be explicitly deallocated, real-time developers are leery of the adverse effects a garbage collector might have on real-time performance. Semiautomatic approaches based on regions have been proposed, but incorrect usage could cause unbounded storage leaks or program failure. Moreover, correct usage cannot be guaranteed at compile time. Recently, real-time garbage collectors have been developed that provide a guaranteed fraction of the CPU to the application, and the correct operation of those collectors has been proven, subject only to the specification of certain statistics related to the type and rate of objects allocated by the application. However, unless those statistics are provided or estimated appropriately, the collector may fail to collect dead storage at a rate sufficient to pace the application's need for storage. Overspecification of those statistics is safe but leaves the application with less than its possible share of the CPU, which may prevent the application from meeting its deadlines.In this paper we present a static analysis to bound conservatively an application's allocation rate. The analysis is based on a data flow framework that requires interprocedural evaluation. We present the framework and results from analyzing some Java benchmarks. Because static analysis is necessarily conservative, we also present measurements of our benchmarks' actual allocation rates.Our work is a necessary step toward making real-time garbage collectors attractive to the hard-real-time community. By guaranteeing a bound on statistics provided to a real-time collector, we can guarantee the operation of the collector for a given application.

/pdf/static-determination-of-allocation-rates-to-support-real-1xw88vm6xi.pdf

Static determination of allocation rates to support real-time garbage collection

Minimax is a recently proposed procedure for electing committees, but it cannot be applied without an understanding of its computational complexity and its susceptibility to manipulation. We examine two compelling variations of minimax and prove both to be NP-complete. We derive upper and lower bounds on the size of a minimax winner set for a given election. We present a heuristic for calculating a minimax winner set and present experiments based on its use. Finally, we present a general strategy for manipulating a minimax election. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160

/pdf/analysis-of-the-minimax-procedure-47hliq6r91.pdf

Analysis of the Minimax Procedure

Development of a smart insole tracking system is described. Originally designed for healthcare applications, the system has found applications in both physical therapy and athletic training. The entire system is distributed between insole hardware, mobile device applications that interface with the insoles and a central Internet server for data warehousing and analysis. We describe the development of these components so far including a discussion of custom algorithm development required for the system. The athletic version has been commercialized while the more complex healthcare version is still under development.

http://www.cs.angelo.edu/~rlegrand/research/roden2014medhab.pdf

Development of a smart insole tracking system for physical therapy and athletics

A comprehensive undergraduate curriculum in computer game development is described. The program was created as a set of elective courses in the context of a traditional computer science (CS) degree. Primary goals of the program were to increase enrollment in CS and prepare students for careers in the entertainment software industry. In addition, the CS department sought to compete for students with larger state institutions. To do so effectively the department needed to offer a unique program. Results show the new program helped the CS department to nearly triple enrollment over four years and achieve both statewide prominence and national recognition.

https://www.cs.angelo.edu/~rlegrand/research/roden2013gamedev.pdf

Growing a computer science program with a focus on game development

Computational social choice is a relatively new discipline that explores issues at the intersection of social choice theory and computer science. Designing a protocol for collective decision-making is made difficult by the possibility of manipulation through insincere voting. In approval voting systems, voters decide whether to approve or disapprove available alternatives; however, the specific nature of rational approval strategies has not been adequately studied. This research explores aspects of strategy under three different approval systems, from chiefly a computational viewpoint. 
While traditional voting systems elicit only the outcome of a voter's strategic thinking, a Declared-Strategy Voting (DSV) system accepts such strategies directly and applies them according to the voter's preferences over the available alternatives. Ideally, when rational strategies are employed on behalf of the voters, voters are discouraged from expressing insincere preferences. Approval voting is a natural fit for use with DSV, but, unlike for the common plurality voting system, there is no extant theory regarding the most effective approval strategies in a DSV context. We propose such a theory.

Computational aspects of approval voting and declared-strategy voting

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