Maximal Rules Research Articles

Evacuation travel behavior in regret minimization or utility maximization rules? Evidence from emergency context

The aim of this paper is to compare Random Regret Minimization (RRM) and Random Utility Maximization (RUM) paradigms in emergency evacuation decision application. Three models including utility model, regret model, and hybrid utility-regret model are formulated, respectively. A Stated Preference (SP) survey on evacuation route choice under emergency was conducted to obtain the evacuees' trade-off among average travel time, travel time uncertainty, perceived road damage probability, and perceived service level. The estimation reveals that regret-based model performs better than utility-based model. The differences between RRM and RUM in emergency context are substantial in model fit when compared with previous empirical results under non-emergency state. Specifically, the model also explains the influence of determining factors related to evacuees' socio-demographics on their route decisions. Further, considering the road damage probability as a predominant factor, Value of Damage (VOD) is proposed to reveal the unobserved regret aversion psychology. Direct elasticity and cross elasticity are calculated for the hybrid utility-regret model. Regret increases in the emergency scenario, supporting that the advantage of RRM paradigm to explain evacuation route choices. The study is expected to give a better understanding of evacuation route choice behavior for emergency actions.

An evacuation route choice model based on multi-agent simulation in order to prepare Tsunami disasters

ABSTRACTMinimising the damage caused by natural disasters such as tsunamis in vulnerable coastal areas requires the development of an emergency action plan. An evacuation plan can reduce the damaging impact of an unexpected tsunami. One of the most important factors in developing an action plan is a guideline for choosing proper evacuation routes in order to minimise loss of life and property. In this study, an evacuation network is designed to develop an evacuation plan based on a hierarchical network design problem used in communication networks, public transport, and social organisation network designs. Experimental analyses have been carried out in order to find effective evacuation plans at Haeundae Beach, Busan, Korea, using multi-agent transportation simulation (MATSim). In order to design an effective hierarchical evacuation network, a hierarchical evacuation network design is developed using the concepts of hub location, clustering, and network design. Further, we consider contraflow in the evacuation network, as well as determination of corridor lines by the minimum distance rule, maximal flow rule, maximum accessible rule, and minimum travel time rule. As a result of running each scenario using MATSim, a criterion for establishing the corridor line is evaluated. An optimal evacuation route is found to minimise overcrowded queues of evacuees, when the total evacuation time from the area of the tsunami is approximately 3 hours and 30 minutes.

Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering.

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.

Investigating Impacts of Alternative Crop Market Scenarios on Land Use Change with an Agent-Based Model

We developed an agent-based model (ABM) to simulate farmers’ decisions on crop type and fertilizer application in response to commodity and biofuel crop prices. Farm profit maximization constrained by farmers’ profit expectations for land committed to biofuel crop production was used as the decision rule. Empirical parameters characterizing farmers’ profit expectations were derived from an agricultural landowners and operators survey and integrated in the ABM. The integration of crop production cost models and the survey information in the ABM is critical to producing simulations that can provide realistic insights into agricultural land use planning and policy making. Model simulations were run with historical market prices and alternative market scenarios for corn price, soybean to corn price ratio, switchgrass price, and switchgrass to corn stover ratio. The results of the comparison between simulated cropland percentage and crop rotations with satellite-based land cover data suggest that farmers may be underestimating the effects that continuous corn production has on yields. The simulation results for alternative market scenarios based on a survey of agricultural land owners and operators in the Clear Creek Watershed in eastern Iowa show that farmers see cellulosic biofuel feedstock production in the form of perennial grasses or corn stover as a more risky enterprise than their current crop production systems, likely because of market and production risks and lock in effects. As a result farmers do not follow a simple farm-profit maximization rule.

COMPARING CARCASS END-POINT AND PROFIT MAXIMIZATION DECISION RULES USING DYNAMIC NONLINEAR GROWTH FUNCTIONS

AbstractThis article develops a market timing decision rule for cattle feeders based on profit maximization. We then compare it with the “status quo” strategy of feeding cattle to a targeted carcass end point. We estimate individual nonlinear dynamic growth functions to derive each animal's value of the marginal product in relation to days on feed. Given individual marginal factor costs, our results indicate that the use of a profit maximization rule could have increased average profits by $16.56 to $21.09 per head for the cattle of known age, and $7.67 to $11.32 per head if age was unknown.

PMCR-Miner: parallel maximal confident association rules miner algorithm for microarray data set.

The MCR-Miner algorithm is aimed to mine all maximal high confident association rules form the microarray up/down-expressed genes data set. This paper introduces two new algorithms: IMCR-Miner and PMCR-Miner. The IMCR-Miner algorithm is an extension of the MCR-Miner algorithm with some improvements. These improvements implement a novel way to store the samples of each gene into a list of unsigned integers in order to benefit using the bitwise operations. In addition, the IMCR-Miner algorithm overcomes the drawbacks faced by the MCR-Miner algorithm by setting some restrictions to ignore repeated comparisons. The PMCR-Miner algorithm is a parallel version of the new proposed IMCR-Miner algorithm. The PMCR-Miner algorithm is based on shared-memory systems and task parallelism, where no time is needed in the process of sharing and combining data between processors. The experimental results on real microarray data sets show that the PMCR-Miner algorithm is more efficient and scalable than the counterparts.

Attribute reduction based on maximal rules in decision formal context

AbstractOne of the key issues in the theory of concept lattices is to extract the useful rules from the decision formal context. The maximal rules implicate the others, thus people are interested in them. This paper proposes two new kinds of attribute reduction in the decision formal context based on maximal rules. The reducts preserve all the condition extensions and the decision extensions related to the original maximal rules. The internal relationship between the original maximal rules and the maximal rules in the reduced decision formal context is derived. The reducts can make the maximal rules more concise and accurate. The mathematical property of the proposed attribute reduction is investigated and we construct the discernibility matrix and function to compute all the reducts. Finally, all the attributes are classified into three types based on the maximal rules. The characteristics of these types of attributes are also analyzed.

Using Whole Disease Modeling to Inform Resource Allocation Decisions: Economic Evaluation of a Clinical Guideline for Colorectal Cancer Using a Single Model

ObjectiveTo assess the feasibility and value of simulating whole disease and treatment pathways within a single model to provide a common economic basis for informing resource allocation decisions. MethodsA patient-level simulation model was developed with the intention of being capable of evaluating multiple topics within National Institute for Health and Clinical Excellence’s colorectal cancer clinical guideline. The model simulates disease and treatment pathways from preclinical disease through to detection, diagnosis, adjuvant/neoadjuvant treatments, follow-up, curative/palliative treatments for metastases, supportive care, and eventual death. The model parameters were informed by meta-analyses, randomized trials, observational studies, health utility studies, audit data, costing sources, and expert opinion. Unobservable natural history parameters were calibrated against external data using Bayesian Markov chain Monte Carlo methods. Economic analysis was undertaken using conventional cost-utility decision rules within each guideline topic and constrained maximization rules across multiple topics. ResultsUnder usual processes for guideline development, piecewise economic modeling would have been used to evaluate between one and three topics. The Whole Disease Model was capable of evaluating 11 of 15 guideline topics, ranging from alternative diagnostic technologies through to treatments for metastatic disease. The constrained maximization analysis identified a configuration of colorectal services that is expected to maximize quality-adjusted life-year gains without exceeding current expenditure levels. ConclusionsThis study indicates that Whole Disease Model development is feasible and can allow for the economic analysis of most interventions across a disease service within a consistent conceptual and mathematical infrastructure. This disease-level modeling approach may be of particular value in providing an economic basis to support other clinical guidelines.

Improving the Medical Diagnosis Process Using Maximal Association Rules

In this paper, methods based on regular association rules and improved by maximal association rules are developed for discovering interesting associations between medical images and diagnoses. The proposed methods have important characteristics that make them different from other computer assisted diagnosis methods: the process is automatic, having the possibility to define a great number of diagnoses; the methods could be applied to any medical domain, because the visual features, the semantic indicators remain unchangeable, and the semantic rules are generated by learning from labeled images-examples; the selection of the visual characteristics set is based on their retrieval accuracy; the spatial information of the regions is considered, offering important medical information of the relationships between sick regions. Although we present the results achieved in endoscopic images analysis, our methods can be used to analyze other types of medical images.

Spatial Prisoner′s Dilemma and Laws of Imitation in Social Psychology

It is designed a game based on the spatial prisoner introducing the three laws of imitation defined by Tarde. The French author described (1) the law of close contact, (2) the law of imitation of superiors by inferiors and (3) the law of insertion. The first law is defined like a Conformist rule; the second law is defined like a Maximization rule and the third law is defined like a Fashion rule or, alternating, a Snob rule. The method used to design the simulation in Java language programming is based on interactive evolutionary game rules which are constantly updated and is inspired by the paradigm of Chavalarias´ metamimetic games. These games introduce very suggestive notions of Nonlinear Dynamics such as mimetic attractor or mimetic equilibrium. It is obtained the formation of little “clusters” of cooperators supporting these laws of imitation

Formal concept analysis based on fuzzy granularity base for different granulations

This paper introduces granular computing (GrC) into formal concept analysis (FCA). It provides a unified model for concept lattice building and rule extraction on a fuzzy granularity base for different granulations. One of the strengths of GrC is that larger granulations help to hide some specific details, whereas FCA in a GrC context can prevent losses due to concept lattice complexity. However, the number of superfluous rules increases exponentially with the scale of the decision context. To overcome this we present some inference rules and maximal rules and prove that the set of all these maximal rules is complete and nonredundant. Thus, users who want to obtain decision rules should generate maximal rules. Examples demonstrate that application of the method is valid and practicable. In summary, this approach utilizes FCA in a GrC context and provides a practical basis for data analysis and processing.

Analysis and implementation of MIMO FULMS algorithm for active vibration control

This correspondence focuses on the analysis and implementation of multi-input multi-output (MIMO) filtered-u least mean square (FULMS) algorithm for active vibration suppression of a cantilever smart beam with surface bonded lead zirconate titanate patches. By analysing a single-input single-output FULMS algorithm, the MIMO FULMS controller structure is given. Then an active vibration control experimental platform is established, with optimal placement of the actuators and sensors based on the maximal modal force rule. Simulation contrast analysis of FULMS algorithm and the most famous filtered-x least mean square (FXLMS) algorithm is performed while the reference signal is extracted from the exciter as well as directly from the controlled structure. Simulation results show that if the feedback information reflects the reference signal collected by the reference transducers, the FXLMS controller could hardly suppress the vibration while the FULMS controller is still effective. Then the actual control experiment is performed, and the result confirms the simulation results. The designed MIMO FULMS vibration controller has a good control performance, suppressing the vibration significantly with rapid convergence.

Low Complexity Multiuser Scheduling in Time-Varying MIMO Broadcast Channels

The sum-rate maximization rule can find an optimal user set that maximizes the sum capacity in multiple input multiple output (MIMO) broadcast channels (BCs), but the search space for finding the optimal user set becomes prohibitively large as the number of users increases. The proposed algorithm selects a user set of the largest effective channel norms based on statistical channel state information (CSI) for reducing the computational complexity, and uses Tomlinson-Harashima precoding (THP) for minimizing the interference between selected users in time-varying MIMO BCs.

Emotions and decision rules in discrete choice experiments for valuing health care programmes for the elderly

The evaluation of health care programmes is commonly approached with stated preference methods such as contingent valuation or discrete choice experiments. These methods provide useful information for policy decisions involving health regulations and infrastructures for health care. However, econometric modelling of these data usually relies on a number of maintained assumptions, such as the use of the compensatory or random utility maximization rule. On the other hand, health policy issues can raise emotional concerns among individuals, which might induce other types of choice behaviour. In this paper we consider potential deviations from the general compensatory rule, and how these deviations might be explained by the emotional state of the subject. We utilized a mixture econometric model which allows for various potential decisions rules within the sample, such as the complete ignorance, conjunctive rule and satisfactory rules. The results show that deviations from the full linear compensatory decision rule are predominant, but they are significantly less observed for those subjects with a medium emotional state about the issue of caring for the health state of the elderly. The implication is that the emotional impact of health policy issues should be taken into account when making assumptions of individual choice behaviour in health valuation methods.

The Limitations of Quantitative Methods for Analyzing Gerrymanders: Indicia, Algorithms, Statistics and Revealed Preference

Simply put, all current quantitative methods are deeply flawed: Threshold rules based on indicia that are hypothesized to be correlated with gerrymanders, such as compactness, margins of competition, and estimated electoral responsive, are at best effective only locally and at worst literally impossible to satisfy. Automatic maximization rules using these indicia or other automatable algorithms universally ignore the political context in which they are applied and thus yield politically biased results despite the appearance of neutrality. The most sophisticated methods, which use computationally-intensive sampling from real districting populations, avoid these problems, but suffer from intractable computational issues and (often) from implausible formulation of the null hypothesis. We place evaluating intent as a motive behind a redistricting plan into a formal quantitative micro-economic framework to evaluate existing and emerging methods, and find that these methods are statistically flawed. In place of classical statistical tests, we formalize a method of revealed preferences to probe intent by comparing aspects of plans that were feasible, but not selected. This method has been used in an informal, ad-hoc, manner in redistricting cases, but is not well documented and has never been rigorously analyzed. Our method has five advantages. First, it is easily interpretable. Second, it can be applied using only the data available to the original planners and does not require estimating the outcomes of hypothetical elections. Third, lacking sophisticated optimization technology, the basic method can be applied using hand drawn maps. Fourth, it is more consistent with the knowledge that distracters had than statistical methods because it does not implicitly assume that a districting authority was aware of all possible plans. Finally, it is the only quantitative method for determining intent, so far proposed, that is statistically sound.

On the Characterization of Efficient Production Vectors

In this paper we study the efficient points of a closed production set with free disposal. We first provide a condition on the boundary of the production set, which is equivalent to the fact that all boundary points are efficient. When the production set is convex, we also give an alternative characterization of efficiency around a given production vector in terms of the profit maximization rule. In the non-convex case, this condition expressed with the marginal pricing rule is sufficient for efficiency. Then we study the Luenberger’s shortage function. We first provide basic properties on it. Then, we prove that the above necessary condition at a production vector implies that the shortage function is locally Lipschitz continuous and the efficient points in a neighborhood are the zeros of it and conversely.

An individual-based simulation model for mottled sculpin ( Cottus bairdi) in a southern Appalachian stream

We describe and analyze a spatially explicit, individual-based model for the local population dynamics of mottled sculpin ( Cottus bairdi). The model simulated daily growth, mortality, movement and spawning of individuals within a reach of stream. Juvenile and adult growth was based on consumption bioenergetics of benthic macroinvertebrate prey; benthic macroinvertebrate densities were a function of flow, season, and habitat quality. We based mortality rates of individual sculpin on their condition. Fish movement was determined by a growth maximization rule. We adjusted selected parameters to calibrate the model for a sculpin population in a southern Appalachian stream, in terms of adult and juvenile abundance and mean adult weight and length. Sensitivity and correlation analysis of the calibrated model suggested that this population was regulated by overwinter density-dependence among juveniles and adults.

The Theory of the Revenue Maximizing Firm

An endogenous growth model of the revenue maximizing firm is here presented. It is demonstrated that, in a static analysis, a revenue maximizing firm in equilibrium equates the average product of labor to the wage rate. In a dynamic analysis, the maximization rule becomes the balance between the rate of marginal substitution - between labor and capital - and the ratio of the wage rate over the discount rate. When the firm satisfies this rule, it grows endogenously at the rate of return on capital. The firm may also have multiple stationary equilibria, which are very similar to the static equilibrium. JEL classification: D21, O41.

Feature selection based on mutual information and redundancy-synergy coefficient

Mutual information is an important information measure for feature subset. In this paper, a hashing mechanism is proposed to calculate the mutual information on the feature subset. Redundancy-synergy coefficient, a novel redundancy and synergy measure of features to express the class feature, is defined by mutual information. The information maximization rule was applied to derive the heuristic feature subset selection method based on mutual information and redundancy-synergy coefficient. Our experiment results showed the good performance of the new feature selection method.

FRIwE: Fuzzy Rule Identification With Exceptions

In this paper, the FRIwE method is proposed to identify fuzzy models from examples. Such a method has been developed trying to achieve a double goal:accuracy and interpretability. In order to do that, maximal structure fuzzy rules are firstly obtained based on a method proposed by Castro et al. In a second stage, the conflicts generated by the maximal rules are solved, thus increasing the model accuracy. The resolution of conflicts are carried out by including exceptions in the rules. This strategy has been identified by psychologists with the learning mechanism employed by the human being, thus improving the model interpretability. Besides, in order to improve the interpretability even more, several methods are presented based on reducing and merging rules and exceptions in the model. The exhaustive use of the training examples gives the method a special suitability for problems with small training sets or high dimensionality. Finally, the method is applied to an example in order to analyze the achievement of the goals.