Inferior Candidates Research Articles

Lying for votes

To what extent do pivotal nonpartisan voters believe and act upon potentially deceptive messages from partisans who privately observe candidates' ex-ante fitness-for-office attributes? How do nonpartisan voters' responses to messages vary with changing odds of candidates' fitness-for-office attributes? This paper derives contrasting predictions regarding pivotal nonpartisans' reactions to potential lies across two-candidate voting competitions and tests them in the laboratory. We find that the partisan voters lie substantially more when candidates possess unequal than equal ex-ante odds. The inferior (superior) candidate's base transmits more unfavorable (favorable) lies about their opponent (own) candidate. Facing candidates with different odds, the nonpartisan voters largely ignore the message and vote for the ex-ante superior candidate. Yet, lying lowers efficiency compared to when the message is always factual, and the efficiency loss is marginally higher when the candidates are ex-ante unequal than equal. We develop a behavioral framework to explain key features of our data.

Detecting ulterior motives from verbal cues in group deliberations.

Forensic interviewing entails practitioners interviewing suspects to secure valid information and elicit confessions. Such interviews are often conducted in police stations but may also occur in field settings such as border crossings, security checkpoints, bus terminals, and sports venues. Because these real-world interviews often lack experimental control and ground truth, this investigation explored whether results of non-forensic interviews generalize to forensic ones. Organizational espionage was simulated to determine (1) what verbal signals distinguish truth from deception, (2) whether deception in groups aligns with deception in dyads, and (3) whether non-forensic venues can be generalized to forensic ones. Engaging in a mock hiring deliberation, participants (4-5 strangers) reviewed and discussed resumes of potential candidates. Surreptitiously, two group members assigned the role of "organizational spies" attempted to persuade the group to hire an inferior candidate. Each group member presented notes from an interview of "their" candidate, followed by a discussion of all candidates. Spies were to use any means possible, including deception, to persuade others to select their candidate. A financial incentive was offered for having one's candidate chosen. The interview reports and discussions were transcribed and analyzed with SPLICE, an automated text analysis program. Deceivers were perceived as less trustworthy than truth-tellers, especially when the naïve players won but overall, deceivers were difficult for non-spies to detect even though they were seen as less trustworthy than the naïve participants. Deceivers' language was more complex and exhibited an "echoing" strategy of repeating others' opinions. This collusion evolved naturally, without pre-planning. No other verbal differences were evident, which suggests that the difference between spies and non-spies was subtle and difficult for truth-tellers to spot. Whether deception can be successfully detected hinges on a variety of factors including the deceiver's skill to disguise and the detector's ability to sense and process information. Furthermore, the group dynamics and communication context subtly moderate how deception manifests and influence the accuracy of detecting ulterior motives. Our future investigations could encompass non-verbal communication channels and verbal patterns rooted in content, thereby providing a more comprehensive understanding of deception detection.

A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism

Scatter search (SS) is a population-based metaheuristic algorithm, which has been proved high efficiency and effective optimizer for complex continuous real value problems. A two-stage cooperative SS guided with the multi-population hierarchical learning mechanism (TCSSMH) to overcome the slow convergence speed of the original SS is proposed. Three strategies are applied to the original SS. Firstly, TCSSMH adopts an adaptive two-way selection search strategy based on the elite reference set (RefSet), which is elite-oriented and ensures the quality of the population. Secondly, the multi-group hierarchical learning mechanism is embedded in the updating process of the RefSet, and the population of the candidates is divided into three levels including excellent candidates, medium candidates, and inferior candidates according to the fitness value of the function. These three subpopulations cooperate to balance the exploration and exploitation ability of the algorithm in the process of evolution. Finally, each subpopulation adopts an interactive learning strategy to increase the diversity of the population and avoid premature convergence of solutions. The optimum of each subpopulation with high accuracy is obtained by the pattern search (PS) optimization. The stronger search ability and higher search efficiency of these additional proposed strategies are verified by extensive experiments. The TCSSMH algorithm is tested on the CEC2017 benchmark test suite and practical engineering problems. The experimental results show that the TCSSMH algorithm is superior to other state-of-the-art algorithms in global search ability and convergence on the benchmark problems.

Top-k Competitive Location Selection over Moving Objects

The location selection (LS) problem identifies an optimal site to place a new facility such that its influence on given objects can be maximized. With the proliferation of GPS-enabled mobile devices, LS studies have made progress for moving objects. However, the state-of-the-art LS techniques over moving objects assume the new facility has no competitor, which is too restrictive and unrealistic for real-world business. In this paper we study Competitive Location Selection over Moving objects (CLS-M), which takes into account competition against existing facilities in mobile scenarios. We present a competition-based influence score model to evaluate the influence of a candidate. To solve the problem, we propose an influence pruning algorithm to prune objects who are either influenced by inferior candidates or affected by no candidate. Experimental study over two real-world datasets demonstrates that the proposed algorithm outperforms state-of-the-art LS techniques in terms of efficiency.

Mechanistic Understanding of Efficient Photocatalytic H2 Evolution on Two‐Dimensional Layered Lead Iodide Hybrid Perovskites

Three-dimensional (3D) organic-inorganic hybrid perovskites have demonstrated excellent capability in solar fuel production, while the two-dimensional (2D) counterparts are generally considered inferior candidates due to the high exciton binding energy and weak light absorption. Herein, contrary to our common understanding, we find that 2D perovskites can perform photocatalytic H2 production from HI splitting more efficiently than their 3D counterparts. We observed sharp difference between 2D perovskites crystals with organic phenylalkylammonium cations of different lengths and the 3D counterparts in their stabilization behavior in aqueous solution. Moreover, we show that the organic cations length of the 2D perovskites affects the nanostructures, optoelectronic properties, and the charge transfer process significantly, which determines the photocatalytic activity of the 2D perovskites. Among the 2D perovskites under investigation, phenylmethylammonium lead iodide with the shortest organic cations achieved the best solar-to-chemical conversion efficiency of ca. 1.57 %, which is the highest value ever reported for hybrid perovskites.

Mechanistic Understanding of Efficient Photocatalytic H2 Evolution on Two‐Dimensional Layered Lead Iodide Hybrid Perovskites

AbstractThree‐dimensional (3D) organic–inorganic hybrid perovskites have demonstrated excellent capability in solar fuel production, while the two‐dimensional (2D) counterparts are generally considered inferior candidates due to the high exciton binding energy and weak light absorption. Herein, contrary to our common understanding, we find that 2D perovskites can perform photocatalytic H2 production from HI splitting more efficiently than their 3D counterparts. We observed sharp difference between 2D perovskites crystals with organic phenylalkylammonium cations of different lengths and the 3D counterparts in their stabilization behavior in aqueous solution. Moreover, we show that the organic cations length of the 2D perovskites affects the nanostructures, optoelectronic properties, and the charge transfer process significantly, which determines the photocatalytic activity of the 2D perovskites. Among the 2D perovskites under investigation, phenylmethylammonium lead iodide with the shortest organic cations achieved the best solar‐to‐chemical conversion efficiency of ca. 1.57 %, which is the highest value ever reported for hybrid perovskites.

Saliency Integration: An Arbitrator Model

Saliency integration has attracted much attention on unifying saliency maps from multiple saliency models. Previous offline integration methods usually face two challenges: 1) if most of the candidate saliency models misjudge the saliency on an image, the integration result will lean heavily on those inferior candidate models; and 2) an unawareness of the ground truth saliency labels brings difficulty in estimating the expertise of each candidate model. To address these problems, in this paper, we propose an arbitrator model (AM) for saliency integration. First, we incorporate the consensus of multiple saliency models and the external knowledge into a reference map to effectively rectify the misleading by candidate models. Second, our quest for ways of estimating the expertise of the saliency models without ground truth labels gives rise to two distinct online model-expertise estimation methods. Finally, we derive a Bayesian integration framework to reconcile the saliency models of varying expertise and the reference map. To extensively evaluate the proposed AM model, we test 27 state-of-the-art saliency models, covering both traditional and deep learning ones, on various combinations over four datasets. The evaluation results show that the AM model improves the performance substantially compared to the existing state-of-the-art integration methods, regardless of the chosen candidate saliency models.

Solid rocket motor design employing an efficient performance matching approach

Solid rocket motor design matching the thrust requirement of the overall vehicle has become an exigent task. In the present research, an efficient performance matching optimization approach is proposed and applied to the solid rocket motor design. To obtain reliable solid rocket motor designs, refined performance evaluation is accomplished successfully based on the coupling of the level set grain burnback analysis and one-dimensional ballistic simulation by the time-stepping algorithm. Due to the heavy computing burden of the coupled performance solver, a novel progressive matching optimization approach is proposed. This approach depends on partial but not full-burn thrust profile to eliminate inferior candidates, predict superior candidates, advance the simulation and drive the candidates converging to the specified performance. Validation of the proposed solid rocket motor design approach is carried out by two test cases possessing different thrust requirements, and results indicate that the proposed approach is practical and efficient.

PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects

The location selection ( ls ) problem, which aims to mine the optimal location from a set of candidates to place a new facility such that a score (i.e., benefit or influence on some given objects) can be maximized, has drawn significant research attention in recent years. State-of-the-art ls techniques assume each object is static and can only be influenced by a single facility. However, in reality, objects (e.g., people, vehicles) are mobile and are influenced by multiple facilities, which prevents classical ls solutions from selecting accurate results. In this paper, we introduce a generalized ls problem called Prime-ls which takes mobility and probability factors into consideration to address the aforementioned limitations. Specifically, given a set of candidate locations, Prime-ls aims to mine the optimal location which can influence the most number of moving objects . Also, to address the problem we propose an efficient algorithm called Pinocchio that leverages two pruning rules based on a novel distance measure. These rules enable us to prune many inferior candidate locations prior to influence computation, paving the way to efficient and accurate solution. Furthermore, we extend Pinocchio ( Pinocchio-vo ) by incorporating two optimization strategies during candidate validation phase, which further reduce unnecessary computations. Experimental study over two real-world datasets demonstrates superiority of our framework in comparison to state-of-the-art ls techniques.

On lattice event probabilities for Levin-Robbins-Leu subset selection procedures

ABSTRACTThe Levin-Robbins-Leu (LRL) family of sequential subset selectionprocedures admits of a simple formula that provides a lower bound for the probability of various types of subset selection events. Here we demonstrate that a corresponding lower bound formula holds for lattice events when using the nonadaptive family member with binary outcomes. Lattice events are more general than the type of events that we previously considered in demonstrating that the nonadaptive LRL procedure selects “acceptable” subsets with an arbitrarily large prespecified probability irrespective of the true population parameters. Interestingly, the proof of the lower bound formula for lattice events that we present here simplifies the methods previously used and sheds additional light on why the lower bound formula holds. As for acceptable subset selection, we conjecture that the other LRL family members, which allow for adaptive elimination of inferior candidates and/or recruitment of superior candidates, obey the same lower bound formulas for lattice events.

A Low-Error, Cost-Efficient Design Procedure for Evaluating Logarithms to Be Used in a Logarithmic Arithmetic Processor

Based on an error-flattened, non-uniform-region linear-approximation algorithm, this brief proposes a low-error and a cost-efficient design procedure for realizing an optimized shift-and-add Logarithmic Unit (LU), which uses minimum hardware to meet the desired error constraint for embedded graphics systems. Mathematically, this brief first derives two solutions of the error-flattened algorithm. Subsequently, for an error constraint, the minimum number of approximation regions, n , the corresponding i th interpolation coefficients ( ai , bi ), and the regional endpoints ( xi-1 , xi ), 1 ≤ i ≤ n , are obtained accordingly. Using the unique properties of the logarithmic function, spacing of xi is non-uniform to make errors in each region consistent. Carefully examining the cost of the applied add/sub network, a low-cost candidate for a shift-and-add LU is determined. Next, a cost exploration process, which gradually increases n , is performed. A large number of regions results in a more accurate conversion algorithm that might tolerate more implementation errors by using simple hardware whose cost is lower than that of the inferior candidate. After exploring the cost based on error tolerance, the proposed design procedure finally generates a hardware to meet the desired error constraint. Slightly modifying the regional endpoint xi increases hardware efficiency at the cost of increased error. Proposed circuits were synthesized in UMC 65RVT CMOS technology. Compared to state-of-the-art shift-and-add logarithmic converters, simulation results reveal that the proposed design saves approximately 12.7-51.1 percent area of polynomial approximation and improves approximately 1-14-dB SNR gain while achieving a tighter error constraint.

One Person, Many Votes: Divided Majority and Information Aggregation

This paper shows that information imperfections and common values can solve coordination problems in multicandidate elections. We analyze an election in which (i) the majority is divided between two alternatives and (ii) the minority backs a third alternative, which the majority views as strictly inferior. Standard analyses assume voters have a fixed preference ordering over candidates. Coordination problems cannot be overcome in such a case, and it is possible that inferior candidates win. In our setup the majority is also divided as a result of information imperfections. The majority thus faces two problems: aggregating information and coordinating to defeat the minority candidate. We show that when the common value component is strong enough, approval voting produces full information and coordination equivalence: the equilibrium is unique and solves both problems. Thus, the need for information aggregation helps resolve the majority's coordination problem under approval voting. This is not the case under standard electoral systems.

The decoy effect as a covert influence tactic

AbstractThe purpose of this research was to determine whether individuals could use the decoy effect to influence others' choices. In study 1, undergraduates (n = 50) and executive master's of business administration (EMBA) students (n = 24) read an employee selection scenario in which they were randomly assigned to prefer one of two candidates that were equal in overall attractiveness, but that had different strengths and weaknesses. They were then asked to choose one of three inferior candidates to add to the choice set that would make their preferred candidate more likely to be chosen by other decision makers. The “correct” inferior candidate was asymmetrically dominated—dominated by one of the two existing candidates, but not the other. Participants chose the “correct” decoy candidate at better than chance levels. In study 2, undergraduates and EMBA students (total n = 66) completed a set of four decision tasks, in which they were asked to choose from potential decoy alternatives that would highlight their preferred job candidate or the product they preferred to sell to a customer. Participants again chose the correct option at better than chance levels. When participants provided free‐response reasons for their choices, these responses indicated a fairly strong recognition of the influential nature of creating a dominating relationship. Implications for understanding this effect and how it may be used by hiring managers, sales personnel, and others who attempt to influence others people's decisions at work, are discussed. Copyright © 2010 John Wiley & Sons, Ltd.

Conditions Under Which Index Models are Useful: Reply to Bio-Index Commentaries

This paper summarizes the key conditions under which the index method is valuable for forecasting and describes the procedures one should use when developing index models. The paper also addresses the specific concern of selecting inferior candidates when using the bio-index as a nomination helper. Political decision-makers should not use the bio-index as a stand-alone method but should combine forecasts from a variety of different methods that draw upon different information..

Conditions under which index models are useful: Reply to bio-index commentaries

This paper summarizes the key conditions under which the index method is valuable for forecasting and describes the procedures one should use when developing index models. The paper also addresses the specific concern of selecting inferior candidates when using the bio-index as a nomination helper. Political decision-makers should not use the bio-index as a stand-alone method but should combine forecasts from a variety of different methods that draw upon different information.

Information Exchange in Group Decision Making: The Hidden Profile Problem Reconsidered

Group decision making provides a mechanism for channeling individual members' knowledge into productive organizational outcomes. However, in hidden profile experiments in which group members have common information favoring an inferior choice, with private information favoring a superior choice, groups typically choose an inferior alternative. We report a hidden profile experiment where we induce homogenous preferences over choice characteristics and provide financial incentives so that the common purpose assumptions of the model hold more completely than in past experiments. Nevertheless, groups continue to choose an inferior alternative most of the time. These failures primarily result from mistakes in recalling information. Mistakes in recalling common information (which favors an inferior candidate) are typically corrected, whereas mistakes in recalling the private information needed to uncover the hidden profile cannot be corrected. Therefore, the dismal performance of groups in pooling the information needed to identify the superior option primarily result from the structure of the problem rather than deficiencies in how groups share and process information. The discussions necessary to resolve mistakes in recalling common information also help to explain the often noted fact that groups spend a disproportionate amount of time discussing common information.

When the inferior candidate is offered the job: The selection interview as a political and power game

The article seeks to advance the view that the selection interview frequently serves as a political arena for various power networks in the organization whose interests may be conflicting. Members of the interview panel try to advance the interests of the power networks to which they belong by lobbying for the candidates whose background and values concur most with those interests. The notion of the interview as a political and power game is illustrated with a case from the academic environment. It is concluded that there is a need for systematic investigation to establish the prevalence of the phenomenon, develop taxonomies, and examine its relationship with variables and outcomes of importance to organizations.

Engineering Application of Washington State’s Pavement Management System

Previous investigation into the Washington State Department of Transportation’s (WSDOT) Pavement Management System (WSPMS) revealed pavement sections on the state route system that were outperforming or underperforming other pavement sections constructed of similar materials and subjected to similar traffic and environmental conditions. Reasons were not clear. The WSPMS was used to identify superior and inferior candidate pavements for further investigation. All state route pavements were stratified into 18 distinct analysis groups, and population statistics were generated for each group providing WSDOT with a snapshot of the current “state of the state route system” and providing the basis for selecting candidate pavements. The five performance measures considered included: age of the surface course, a distress-based pavement structural condition score, annual design-lane equivalent single axle loads, roughness (in terms of International Roughness Index), and rutting. Results of the analysis suggest that WSDOT is properly designing layer thicknesses at appropriate reliability levels. Also, in 6 of 10 analysis group comparisons, inferior pavements were actually thicker than superior pavements. Both findings suggest that thickness design factors are not the primary cause of inferior performance in Washington State; construction, material and site specific factors are likely the cause. Additionally, in an international effort to exchange information on pavement performance and construction practices, highlights of a comparative study of pavement performance with South Africa’s Gauteng Department of Transportation are presented.

Information Acquisition in Promotion Decisions

When organizations publicly post notices of promotional opportunities, they often must justify the rejection of unsuccessful applicants, and may therefore decide to rate all applicants. When the process is less public, however, selectors are not required to assign ratings to inferior candidates. We hypothesized that selectors would gather less information on inferior candidates when they were not required to rate them than when they were so required. Results of a study of 157 managers using an information display board methodology confirmed our hypothesis. Contrary to previous research in consumer behavior, individual proxies for “product familiarity,” such as number of years of previous work experience, were not related to information-gathering behavior.

A two-phase methodology for technology selection and system design

A two-phase methodology to guide research and development managers in the evaluation and selection of competing technologies is presented. Deterministic multi-attribute utility theory is used in the first phase to rank the technological alternatives and to eliminate inferior candidates. The procedure is illustrated with an application drawn from a study centering on the evaluation of electric and hybrid passenger vehicles. Thirty-nine individuals were interviewed to assess their risk preferences and determine their attitudes toward the vehicle design. In the second phase, it is assumed that a particular technology has been chosen for further development. The decision-maker must then allocate a fixed amount of resources to different projects, some of which may be undertaken in parallel, to maximize a given measure of performance. The problem is formulated as a probabilistic network and solved heuristically using Monte Carlo simulation. Results are presented for the most preferred vehicle identified in phase one for two representative decision-makers and three budget options. In each case, the heuristic finds a solution that corresponds to the optimal allocation of funds.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>