Exploring the No-Show Paradox for Condorcet Extensions Using Ehrhart Theory and Computer Simulations

The (group) no-show paradox refers to the undesirable situation where a group of agents has the incentive to abstain from voting to get a more favorable winner. We examine the computational complexity of verifying whether the group no-show paradox exists given agents' preferences and the voting rule. We prove that the verification problem is NP-hard to compute for commonly studied voting rules such as Copeland, maximin, single transferable vote, and Black's rule. We propose integer linear programming-based algorithms and a breadth-first search algorithm for the verification problem. Experimental results illustrate that the former work better for a small number of alternatives, and the latter work better for a small number of agents. Using these algorithms, we observe that the group no-show paradoxes rarely occur in real-world data.

Voting rules are powerful tools that allow multiple agents to aggregate their preferences in order to reach joint decisions. A common flaw of some voting rules, known as the no-show paradox, is that agents may obtain a more preferred outcome by abstaining an election. Whenever a rule does not suffer from this paradox, it is said to satisfy participation. In this paper, we initiate the study of participation in probabilistic social choice, i.e., for voting rules that yield probability distributions over alternatives. We consider three degrees of participation based on expected utility, the strongest of which even requires that an agent is strictly better off by participating at an election. While the latter condition is prohibitive in non-probabilistic social choice, we show that it can be met by reasonable probabilistic functions. More generally, we study to which extent participation and Pareto efficiency are compatible. To the best of our knowledge, this is the first work in this direction.

This paper addresses the collective action problem of nonpoint-source pollution control in a small agricultural watershed. At issue is the stability of cooperative behavior among a group of farmers, who have voluntarily agreed to discontinue their use of the herbicide atrazine due to high concentrations of the herbicide in a local water supply. Continued cooperation among the group is threatened by the unexpected cancellation of cyanazine, an inexpensive and widely used alternative to atrazine. With cyanazine no longer available, the farmers will face a significant increase in weed control costs if they continue to use products that do not contain atrazine. Is cooperation among the farmers still possible despite the increased cost of cooperating? This research explores the economic and behavioral factors that influence the collective outcome of this social dilemma. The collective action is modeled as a recurrent coordination problem. The producers (farmers) are engaged in a repeated assurance game with imperfect public information, where producers' choices are driven by the desire to coordinate their actions with the others in the group. A producer's decision to cooperate or defect is based on a threshold approach; if the number of others believed to be cooperating exceeds the level of cooperation required to make cooperation beneficial, then the producer will choose to cooperate. Otherwise, the producer will defect. Since producers are unable to directly observe the choices of the others in the group, each producer must rely on a subjective assessment of the group's behavior based on the realization of the public outcome, the concentration of atrazine in the lake. Producers use a naive Bayesian learning process to update their beliefs about the joint actions of the group. The formal learning process is modeled using a sequential quasi-Bayesian procedure that is consistent with the fictitious play model of learning. The interaction between the producers and the impact of their collective behavior on the levels of atrazine in the lake is formulated as a computational multi-agent system (MAS). The MAS is an artificial representation of the collective action problem that integrates the economic, behavioral and environmental factors that influence the decision-making process of producers. The MAS is used to simulate the evolution of collective behavior among the group and to evaluate the effectiveness of selected incentive mechanisms in preventing the collapse of joint cooperation. The results suggest that without additional incentives, farmers are likely to abandon their voluntary agreement and resume their use of atrazine within the watershed. It is then demonstrated how a combination of policy instruments can be used to alter the underlying game configuration of the collective action problem, resulting in cooperative outcomes. An ambient-based penalty, when used in conjunction with a subsidy payment, is shown to produce divergent incentive structures that shift the classification of the collective action away from a coordination problem with two equilibria to a mixed configuration containing several different game structures and many possible equilibria. This result has important consequences in terms of the evolution of producer behavior and the set of possible collective outcomes. The analysis concludes with an example, which demonstrates that when a mixture of game structures characterizes the collective action, joint cooperation is not a prerequisite to the realization of socially desirable outcomes. By carefully selecting the combination of subsidy payment and ambient penalty, a policy maker can manipulate the underlying structure of the collective action, whereby producers with the smallest impact on water quality choose to defect while all others cooperate.

When we try to accomplish a collaborative task, e.g., playing football or carrying large tables, we have to share a goal and a way of achieving the goal. Although people accomplish such tasks, achieveing such cooperation is not so easy in the context of a computational multi-agent learning system because participating agents cannot observe another person’s intention directly. We cannot know directly what other participants intend to do and how they intend to achieve that. Therefore, we have to notice another participant’s intention by utilizing other hints or information. In other words, we have to estimate another’s intention to accomplish collaborative tasks. In particular, in multi-agent reinforcement learning tasks, when another’s intention is unobservable the learning process is fatally harmed. When a participating agent of a collaborative task changes its intention and switches or modifies its controller, system dynamics for each agent will inevitably change. If other agents learn on the basis of simple reinforcement learning architecture, they cannot keep up with changes in the task environment because most reinforcement learning architectures assume that environmental dynamics are fixed. To overcome the problem, each agent must have a simple reinforcement learning architecture and some additional capability, which solves the problem. We take the capability of “estimation of another’s intention” as an example of such a capability. Human beings can perform several kinds of collaborative tasks. This means that we have some computational skills, which enable us to estimate another’s intention to some extent even if we cannot observe another’s intention directly. The computational model for implicit communication is described in this chapter on the basis of a framework of modular reinforcement learning. The computational model is called situation-sensitive reinforcement learning (SSRL), which is a type of modular reinforcement learning architecture. We assumed that such a distributed learning architecture would be essential for an autonomous agent to cope with a physically dynamic environment and a socially dynamic environment that included changes in another agent’s intentions. The skill, estimation of another’s intention, seems to be a social skill. However, human adaptability, which we believe our selves to be equipped with to deal with a physically dynamic environment, enables an agent to deal with such a dynamic social environment, including O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Enabled to provide pervasive access to distributed resources in parallel ways, heterogeneous scheduling is extensively applied in large-scaled computing system for high performance. Conventional real-time scheduling algorithms, however, either disregard applications' security needs and thus expose the applications to security threats or run applications at inferior security levels without optimizing security performance. In recognition of high reliability, a security-aware model is firstly presented via quantization of security overheads of heterogeneous systems. Secondly, inspired by multi disciplines, the meta-heuristic is addressed based on the supercomputer hybrid architecture. On the other hand, some technological breakthroughs are achieved, including boundary conditions for different heterogeneous computing and cloud scheduling and descriptions of real-time variation of scheduling indexes (stringent timing and security constraints). Extensive simulator and simulation experiments highlight higher efficacy and better scalability for the proposed approaches compared with the other three meta-heuristics; the overall improvements achieve 8 %, 12 % and 14 % for high-dimension instances, respectively.

We study preference representation models based on partial lexicographic preference trees (PLP-trees). We propose to represent preference relations as forests of small PLP-trees (PLP-forests), and to use voting rules to aggregate orders represented by the individual trees into a single order to be taken as a model of the agent’s preference relation. We show that when learned from examples, PLP-forests have better accuracy than single PLP-trees. We also show that the choice of a voting rule does not have a major effect on the aggregated order, thus rendering the problem of selecting the “right” rule less critical. Next, for the proposed PLP-forest preference models, we develop methods to compute optimal and near-optimal outcomes, the tasks that appear difficult for some other common preference models. Lastly, we compare our models with those based on decision trees, which brings up questions for future research.

In this paper, a stochastic search technique, namely Particle Swarm Optimization (PSO) is used to determine the optimal locations of two Static Var Compensators (SVCs) in an IEEE 14-bus system. Static Var compensators (SVC) are the most widely used shunt FACTS devices within power networks because of their smaller costs and significant system enhancements. Appeared about two decades ago, the SVC is mainly installed for voltage support and furthermore, when installed in a proper location, it can reduce the power loss. Here the problem is framed as an optimization task and the optimal locations of SVC are identified using the novel technique. The efficacy of the new algorithm is tested with extensive computer simulations and further compared with Genetic Algorithm (GA) based approach. The performance analysis is done through extensive simulations and shows that the proposed dispensation is on a par with existing techniques.

Direct-conversion radio receivers can offer highly integrated low-cost hardware solutions for cognitive radio (CR) devices. Such receivers are, however, also very sensitive to various radio frequency (RF) impairments such as IQ imbalance, which can considerably limit the spectrum sensing capabilities. Most of the existing spectrum sensing studies in literature assume an ideal RF receiver and hence neglect the impacts of such practical RF hardware limitations. In this article, we study energy detection (ED) based spectrum sensing in both single-channel and multi-channel direct-conversion receiver scenarios impaired by IQ imbalance. With complex Gaussian primary user (PU) signal models, we first derive the detection and false alarm probabilities in closed-form for both receiver scenarios. The analytical results, confirmed through extensive simulations, show that while the single-channel receiver scenario is fairly robust to IQ imbalance, the wideband multi-channel sensing receiver is very sensitive to the image channel crosstalk induced by IQ imbalance. More specifically, it is shown that the false alarm probability of multi-channel energy detection increases significantly, compared to ideal RF receiver case, and the exact performance depends on the image channel power level and IQ imbalance values. In order to prevent such degradation in the ability to identify free spectrum, a waveform level interference cancellation method is then proposed to mitigate the image channel crosstalk. The optimum cancellation coefficient yielding interference-free signal is first derived, being also complemented with a practical coefficient sample estimator. An explicit condition is also derived for which the proposed cancellation scheme deploying the practical coefficient sample estimator provides performance gain in the sensing decisions compared to uncompensated energy detection. Extensive computer simulations with various signal and imbalance conditions are provided which demonstrate that the proposed enhanced energy detection can suppress the image channel crosstalk efficiently, yielding detection and false alarm probabilities essentially identical to those of an ideal RF receiver.

Interactive and nonparametric modeling of preferences on an ordinal scale using small data

This paper presents the preference ratios in multiattribute evaluation (PRIME) method which supports the analysis of incomplete information in multiattribute weighting models. In PRIME, preference elicitation and synthesis is based on 1) the conversion of possibly imprecise ratio judgments into an imprecisely specified preference model, 2) the use of dominance structures and decision rules in deriving decision recommendations, and 3) the sequencing of the elicitation process into a series of elicitation tasks. This process may be continued until the most preferred alternative is identified or, alternatively, stopped with a decision recommendation if the decision maker is prepared to accept the possibility that the value of some other alternative is higher. An extensive simulation study on the computational properties of PRIME is presented. The method is illustrated with a re-analysis of an earlier case study on international oil tanker negotiations.

The North American Cordillera is generally interpreted as a result of the long-lived, east-dipping subduction at the western margin of the North American plate. However, the east-dipping subduction seems problematic for explaining some of the geological features in the Cordillera such as large volume back-arc magmatism. Recent studies suggested that westward subduction of a now-consumed oceanic plate during the Cretaceous could explain these debated geological features. The evidence includes petrological and geochemical variations in magmatism, the presence of ophiolite that indicates tectonic sutures between the Cordillera and Craton, and seismic tomography images showing high-velocity bodies within the underlying convecting mantle that are interpreted as slab remnants from the westward subduction. Here we use 2-D upper mantle-scale numerical models to investigate the dynamics associated with westward subduction and Cordillera-Craton collision. The models demonstrate the controls on slab breakoff (remnant) following collision including: (1) oceanic and continental mantle lithosphere strength, (2) variations in density (eclogitization of continental lower crust and cratonic mantle lithosphere density), and (3) convergence rate. Our preferred model has a relatively weak mantle lithosphere, eclogitization of the lower continental crust, cratonic mantle lithosphere density of 3250 kg/m3, and a convergence rate of 5 cm/yr. It shows that collision and slab breakoff result in an ~2 km increase in surface elevation of the Cordilleran region west of the suture as the dense oceanic plate detaches. The surface also shows a foreland geometry that extends >1000 km east of the suture with ~4 km of subsidence relative to the adjacent Cordillera.

"ADAPTIVE SPECIATION"?IT IS NOT THAT EASY: REPLY TO DOEBELI ET AL.

Quantum-dot Cellular Automata (QCA) technology presents a promising avenue for the realization of ultralow-power and high-speed digital circuits owing to its inherent quantum properties and nanoscale dimensions. In this study, a novel design and analysis of a binary-coded decimal (BCD) adder implemented using QCA technology is proposed. The BCD adder is a fundamental component of digital arithmetic circuits and is widely applied in various computational systems, including calculators, processors, and digital signal processors. Leveraging the unique features of QCA, such as the absence of current flow and the potential for highly compact layouts, we present a detailed design methodology for the BCD adder circuit, including the implementation of basic QCA gates and construction of the BCD addition logic. Performance metrics, such as area, delay, and power consumption, were evaluated through extensive simulations using QCA Designer, QCA design, and a simulation tool. The Ripple Carry Adder (RCA) is one of the major blocks in the BCD circuit and a prominent factor in determining the area and delay of the circuit. Therefore, an efficient full adder was used to design an RCA with less area and delay. Clocking in a QCA impacts the polarization state of a QCA cell, and this scheme is utilized efficiently to achieve novelty. The proposed design occupies 15.625% less area, and the cell count is reduced by 11.55% compared with the existing state-of-the-art design. Power dissipation reports were obtained using the QCA Pro tool, which briefly describes the average leakage and average dissipation energy of the total circuit, and a spectrum to depict the energy dissipation of each cell in the circuit. The results demonstrate the feasibility and efficiency of the proposed BCD adder design in QCA technology, demonstrating its potential for realizing energy-efficient and high-performance digital arithmetic circuits. This work contributes to the growing body of research on QCA-based digital circuit design and lays the groundwork for the further exploration of QCA technology in practical computing applications.

Computer simulation and analysis techniques for reliable circuit design

Self-adaptive FEM numerical modeling of the mild-slope equation