Conjunctive Rule Research Articles

Assessment of Measures for Tax Regulation of Transfer Pricing from the Standpoint of National Welfare

This article is devoted to development of mathematical models for resolving an actual scientific challenge in the field of corporate finance. This involves substantiating taxation policies for the counter-acting tax planning of multinational companies (MNC), and then devising and articulating the appropriate international taxation scheme, as evaluated from the position of national welfare policy. Based on an analysis of existing models of international taxation, and on the peculiarities of the actual mechanism of capital movement tax regulation, new models with equilibrium postulated have been developed.
 The primary mechanisms of this research involve the following considerations: (1) examination of an approach targeted at the determination of the final outcomes of international taxation from the perspective of national economies; (2) measures of tax planning on the part of MNCs, and corresponding counter-acting measures to the tax planning applied by governments, are taken as a complex.
 Our results indicate that because a government uses rules of controlled transactions, in order to counter-act MNCs’ tax planning, for the government the final outcome from an application of these rules may be negative. This is due to a possibility of MNCs’ development in convenient and offshore jurisdictions. This finding is illustrated by means of an approbation of models with a case study involving a three-tier structure.Further to this point, instead of additional revenues, a government is at a risk of a shrinking tax base and a reduction in budget revenues; and moreover from the perspective of national welfare, the additional loss of revenues and capital of MNCs. Therefore there is a significant importance in forming rules for MNC taxation policies which would focus not on taxes as such, but would focus on trying to keep capital within the territory and/or would facilitate the return of earlier divested income. This could be attempted, for example, by using the secondary adjustment rule in conjunction with a minimum tax on return.
 The novelty of this research resides in the specificity of our investigation and the applicability of our conclusions to the practical challenges of international taxation and national revenue policies. The peculiarities of this economic moment and the crucial challenges for national governments in dealing with MNCs and the digital economy underline the significance of this study. Our results expand and develop the existing literature in this ever-crucial area be of immediate use to policymakers, academics and administrators involved in national and international taxation, finance, economics, and analysis.

CHIRPS: Explaining random forest classification

Modern machine learning methods typically produce “black box” models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation, we propose Collection of High Importance Random Path Snippets (CHIRPS); a novel algorithm for explaining random forest classification per data instance. CHIRPS extracts a decision path from each tree in the forest that contributes to the majority classification, and then uses frequent pattern mining to identify the most commonly occurring split conditions. Then a simple, conjunctive form rule is constructed where the antecedent terms are derived from the attributes that had the most influence on the classification. This rule is returned alongside estimates of the rule’s precision and coverage on the training data along with counter-factual details. An experimental study involving nine data sets shows that classification rules returned by CHIRPS have a precision at least as high as the state of the art when evaluated on unseen data (0.91–0.99) and offer a much greater coverage (0.04–0.54). Furthermore, CHIRPS uniquely controls against under- and over-fitting solutions by maximising novel objective functions that are better suited to the local (per instance) explanation setting.

ON OBDD-BASED ALGORITHMS AND PROOF SYSTEMS THAT DYNAMICALLY CHANGE THE ORDER OF VARIABLES

AbstractIn 2004 Atserias, Kolaitis, and Vardi proposed $\text {OBDD}$ -based propositional proof systems that prove unsatisfiability of a CNF formula by deduction of an identically false $\text {OBDD}$ from $\text {OBDD}$ s representing clauses of the initial formula. All $\text {OBDD}$ s in such proofs have the same order of variables. We initiate the study of $\text {OBDD}$ based proof systems that additionally contain a rule that allows changing the order in $\text {OBDD}$ s. At first we consider a proof system $\text {OBDD}(\land , \text{reordering})$ that uses the conjunction (join) rule and the rule that allows changing the order. We exponentially separate this proof system from $\text {OBDD}(\land )$ proof system that uses only the conjunction rule. We prove exponential lower bounds on the size of $\text {OBDD}(\land , \text{reordering})$ refutations of Tseitin formulas and the pigeonhole principle. The first lower bound was previously unknown even for $\text {OBDD}(\land )$ proofs and the second one extends the result of Tveretina et al. from $\text {OBDD}(\land )$ to $\text {OBDD}(\land , \text{reordering})$ .In 2001 Aguirre and Vardi proposed an approach to the propositional satisfiability problem based on $\text {OBDD}$ s and symbolic quantifier elimination (we denote algorithms based on this approach as $\text {OBDD}(\land , \exists )$ algorithms). We augment these algorithms with the operation of reordering of variables and call the new scheme $\text {OBDD}(\land , \exists , \text{reordering})$ algorithms. We notice that there exists an $\text {OBDD}(\land , \exists )$ algorithm that solves satisfiable and unsatisfiable Tseitin formulas in polynomial time (a standard example of a hard system of linear equations over $\mathbb {F}_2$ ), but we show that there are formulas representing systems of linear equations over $\mathbb {F}_2$ that are hard for $\text {OBDD}(\land , \exists , \text{reordering})$ algorithms. Our hard instances are satisfiable formulas representing systems of linear equations over $\mathbb {F}_2$ that correspond to checksum matrices of error correcting codes.

Study on the Location of Private Clinics Based on K-Means Clustering Method and an Integrated Evaluation Model

Appropriate location is an important prerequisite for the long-term survival and development of private medical institutions. However, in both theory and practice, the issue of location decision-making for private clinics has not been fully studied. We therefore aimed to provide a feasible scheme for the location of new private clinics. This paper combines the k-means clustering method and an integrated 2DULVs (two-dimensional uncertain language variables)-TOPSIS (technique for order preference by similarity to ideal solution)-DSCCR (Dempster-Shafer conjunctive combination rule) model to screen and evaluate all of the areas in the target region for an Internet medical company to set up offline clinics. We first created geographic grids using GIS and collected point of interest (POI) data. We then used the k-means clustering method to obtain 10 suitable grids as alternatives. Last, we established an evaluation index system and used the proposed model to rank them. The results show that grids 178, 179 and 202 are more suitable for the company to establish offline clinics in the expansion of business. The results of this study are also consistent with those of the other three fusion methods. This paper provides a beneficial attempt for private clinics to make location decisions and can be extended to the strategic decision-making of other industries or other issues.

Rule Interaction Conversion Operations

Different types of interactions between pairs of phonological rules can be converted into one another using three formal operations that we discuss in this article. One of these conversion operations, rule re-ordering (here called swapping), is well-known; another, flipping, is a more recent finding (Hein et al., 2014). We introduce a third conversion operation that we call cropping. Formal relationships among the members of the set of rule interactions, expanded by cropping beyond the classical four (feeding, bleeding, counterfeeding, and counterbleeding) to include four more (mutual bleeding, seeding, counterseeding, and merger), are identified and clarified. We show that these conversion operations exhaustively delimit the set of possible pairwise rule interactions predicted by conjunctive rule ordering (Chomsky & Halle, 1968), and that each interaction is related to each of the others by the application of at most two conversion operations.

Ensemble belief rule base modeling with diverse attribute selection and cautious conjunctive rule for classification problems

Belief rule-based systems have demonstrated its advantages in solving complicated problems with uncertain information. However, the rule combinatorial explosion problem is still a great challenge for belief rule bases (BRBs) when a problem involves a large number of attributes, because existing attempts have not addressed this challenge adequately, e.g., utilization of single attribute selection method to downsize BRBs without considering its inherent weakness, or adjustment of referential values to optimize BRBs without attribute selection. Thus, inspired by ensemble learning, the objective of this paper is to propose an ensemble BRB modeling method to deal with classification problems. First, six attribute selection methods that have different advantages are introduced to select diverse sets of antecedent attributes for constructing multiple BRBs, and all of these BRBs are further trained by parameter learning for diverse belief rule-based systems. Second, due to the fact that each belief rule-based system has different importance and hardly satisfies the assumption of independence, a weight learning method is proposed to determine the weight of each belief rule-based system, and a new analytical cautious conjunctive rule (CCR) is deduced from the recursive CCR, that is suitable for the combination of non- independent individuals, to combine the outputs of all belief rule-based systems. Eight classification datasets from the well- known UCI database are adopted to verify the effectiveness of the proposed BRB modeling method in comparison with the belief rule-based systems constructed by single attribute selection, conventional fuzzy rule-based classifiers, and machine learning-based classifiers.

Spy game: verifying a local generic solver in Iris

We verify the partial correctness of a "local generic solver", that is, an on-demand, incremental, memoizing least fixed point computation algorithm. The verification is carried out in Iris, a modern breed of concurrent separation logic. The specification is simple: the solver computes the optimal least fixed point of a system of monotone equations. Although the solver relies on mutable internal state for memoization and for "spying", a form of dynamic dependency discovery, it is apparently pure: no side effects are mentioned in its specification. As auxiliary contributions, we provide several illustrations of the use of prophecy variables, a novel feature of Iris; we establish a restricted form of the infinitary conjunction rule; and we provide a specification and proof of Longley's modulus function, an archetypical example of spying.

A Numerical Calculation of Arbitrary Integrals of Functions

This paper presents a numerical technique for solving fractional integrals of functions by employing the trapezoidal rule in conjunction with the finite difference scheme. The proposed scheme is only a simple modification of the trapezoidal rule, in which it is treated as an algorithm in a sequence of small intervals for finding accurate approximate solutions to the corresponding problems. This method was applied to solve fractional integral of arbitrary order α > 0 for various values of alpha. The fractional integrals are described in the Riemann-Liouville sense. Figurative comparisons and error analysis between the exact value, two-point and three-point central difference formulae reveal that this modified method is active and convenient.

About Linda Again: How Narratives and Group Reasoning Can Influence Conjunction Fallacy

Conjunction fallacy (together with other systematic reasoning errors) is usually explained in terms of the dual process theory of reasoning: Biases should be ascribed to fast and automatic processes, whereas slow and deliberative processes are responsible of producing answers that are correct with respect of normative criterion. The dual process theory is related to Bruner’s distinction between narrative and paradigmatic thought: Both modes of thought can be characterized by the two different processes of reasoning. In this paper, we explore the role of Bruner’s mode of thought manipulating also the difference between group vs individual reasoning. We observed that the narrative strategy of response induces more wrong answers. However, narrative-based strategies have higher effectiveness in the case of group reasoning. Our results suggest that narrative reasoning and group reasoning may induce violations of the conjunction rule when acceptable by the verisimilitude of the story. Five models are also presented in order to predict answer correctness and strategy of reasoning using a text analysis software.

Diagnosis of Coronary Artery Diseases using Classification Algorithms based on Wavelet Transforms

One of the primary drivers of the death in the world is Coronary Artery Diseases (CAD) which is a major threat in developing and developed countries. The fundamental drivers in CAD leads to blockage of the coronary lumen subsequently blood clot and that prompts to damage of heart muscles or unexpected heart attack which causes death. It is difficult to ascertain that a certain person has been affected by CAD, since there are bunch of parameters has been involved to ascertain the conclusion. Classification has been done using wavelet transform to classify the certain parameters. We analyzed following methods such as NB, Logistic, SMO, RBF Network, K-star, Multiclass Classifier, Conjunctive rule, Decision table, LMT, NB Tree, DTNB, LAD Tree, Random Tree and Random Forest calculations has been associated with extensive fragment of the surveys. This database has been generated from UCI machine learning database. In this paper, we used k-fold cross validation with k values as 10, with 14 properties and calculations of Accuracy, Precision, TPR, FPR, Recall, F-measure and ROC are analyzed practically. The experimental evaluation shows the improvement in accuracy rate of 77.0%, by using the Logistic, SMO and LMT algorithms than the traditional method.

The contradiction between belief functions: Its description, measurement, and correction based on generalized credal sets

In the paper, we formalize the notion of contradiction between belief functions: we argue that belief functions are not contradictory if they provide non-contradictory models for decision-making. To elaborate on this idea, we take the decision rule from imprecise probabilities and show that sources of information described by belief functions are not contradictory iff the intersection of corresponding credal sets is not empty. We demonstrate that evidential conjunctive and disjunctive rules fit with this idea and they are justified in a probabilistic setting. In the case of contradictory sources of information, we analyze possible conjunctions and show how the result can be described by generalized credal sets. Based on generalized credal sets, we propose a measure of contradiction between information sources and find its axiomatics. We show how the contradiction correction can be produced based on generalized credal sets and how it can be done on sets of surely desirable gambles.

A Smart Decision System for Digital Farming

New technologies have the potential to transform agriculture and to reduce environmental impact through a green revolution. Internet of Things (IoT)-based application development platforms have the potential to run farm management tools capable of monitoring real-time events when integrated into interactive innovation models for fertirrigation. Their capabilities must extend to flexible reconfiguration of programmed actions. IoT platforms require complex smart decision-making systems based on data-analysis and data mining of big data sets. In this paper, the advantages are demonstrated of a powerful tool that applies real-time decisions from data such as variable rate irrigation, and selected parameters from field and weather conditions. The field parameters, the index vegetation (estimated using aerial images), and the irrigation events, such as flow level, pressure level, and wind speed, are periodically sampled. Data is processed in a decision-making system based on learning prediction rules in conjunction with the Drools rule engine. The multimedia platform can be remotely controlled, and offers a smart farming open data network with shared restriction levels for information exchange oriented to farmers, the fertilizer provider, and agricultural technicians that should provide the farmer with added value in the form of better decision making or more efficient exploitation operations and management.

Indirect disjunctive belief rule base modeling using limited conjunctive rules: Two possible means

A traditional Belief Rule Base (BRB) is constructed under the conjunctive assumption (conjunctive BRB), which requires covering the traversal combinations of the referenced values for the attributes. Consequentially, a traditional conjunctive BRB may have to face the combinatorial explosion problem when there are too many attributes and/or referenced values for the attributes. It is difficult or at least expensive to construct a complete conjunctive BRB, while it is easy to derive one or several conjunctive rules. Comparatively, a BRB under the disjunctive assumption (disjunctive BRB) requires only covering the referenced values for the attributes instead of the traversal combination of them. Thus, the combinatorial explosion problem can be avoided. However, it is difficult to directly obtain a disjunctive BRB from either historical data or experts' knowledge. To combine the advantages of both conjunctive and disjunctive BRBs, a new approach is proposed to construct a disjunctive BRB using a limited number of conjunctive rules (insufficient to construct a complete conjunctive BRB). In the new disjunctive BRB modeling approach, each disjunctive rule is derived by quantifying its correlation with one or multiple conjunctive rules. To do so, two means for belief generation are proposed, namely, equal probability and self-organizing mapping (SOM). Two cases are studied for validating the efficiency of the proposed approach. The results by the disjunctive BRB show consistency with those derived by the conjunctive BRB as well as other approaches, which validates the efficiency of the proposed approach considering that the disjunctive BRB is constructed with only a limited number of conjunctive rules.

Consideration-Constrained Engineering Design for Strategic Insights

Consumer behavior can be modeled using a decision-making process termed “consideration” in which consumers form requirements, “consideration rules,” in order to narrow their options for further evaluation. One type of consideration rule is the conjunctive rule, where a consumer makes a list of requirements and a product must meet all of the requirements in order to be considered for purchase, such as “the vehicle must get 25 miles per gallon or more”; “it must be priced at $22,000 or less”; and “it must be a standard-sized sedan.” This paper offers a design framework for linking these consideration rules with design. We demonstrate the use of our framework with a case study, namely the Volkswagen (VW) “clean diesel” scandal, which investigates the design strategies used in response to the scandal by capturing considerations within the marketing product planning subproblem and assuring engineering feasibility within the engineering design subproblem.

Heuristic decision-making in foster care matching: Evidence from a think-aloud study.

Complex decisions are often based on heuristics, which are shortcuts or simple 'rules of thumb'. Since the matching decision in family foster care is often made in a less-than-ideal setting and riddled with uncertainty, heuristics are expected to be applied in that field of child and youth care on a daily basis. However, the use of heuristics in the matching decision has not been studied empirically until now. This research explores how decision-making heuristics are used by practitioners to determine which foster family is the best fit for a child. A number of 20 matching practitioners from the Netherlands were interviewed using vignettes and a 'think-aloud' methodology to generate an understanding of their reasoning. Two types of vignettes were created: hypothetical children and hypothetical foster families. The interviews were analyzed using a qualitative deductive content analysis focusing on key indicators of three classes of heuristics: recognition heuristics, one-reason heuristics, and trade-off heuristics. The results show that recognition heuristics did not play a decisive role in the matching process; practitioners considered more than one family before making a final decision. The findings for the one-reason heuristics reveal conjunctive decision-making rules; families were rejected based on one negative premise. The analysis of the trade-off heuristics demonstrates that the number of positive premises and the ratio between positive and negative premises predicted the matching decision. However, the total number of premises also predicted the matching decision, which might indicate confirmation bias.

A belief combination rule for a large number of sources

The theory of belief functions is widely used for data from multiple sources. Different evidence combination rules have been proposed in this framework according to the properties of the sources to combine. However, most of these combination rules are not efficient when there are a large number of sources. This is due to either the complexity or the existence of an absorbing element such as the total conflict mass function for the conjunctive based rules when applied on unreliable evidence. In this paper, based on the assumption that the majority of sources are reliable, a combination rule for a large number of sources is proposed using a simple idea: the more common ideas the sources share, the more reliable these sources are supposed to be. This rule is adaptable for aggregating a large number of sources which may not all be reliable. It will keep the spirit of the conjunctive rule to reinforce the belief on the focal elements with which the sources are in agreement. The mass on the empty set will be kept as an indicator of the conflict. The proposed rule, called LNS-CR (Conjunctive combination Rule for a Large Number of Sources), is evaluated on synthetic mass functions. The experimental results verify that the rule can be effectively used to combine a large number of mass functions and to elicit the major opinion.

Extraction of Conjunctive Hedging Rules for the Operation Management of Surface and Groundwater Resources

Extraction of Conjunctive Hedging Rules for the Operation Management of Surface and Groundwater Resources

Prevalence of alternative processing rules in the formation of daily travel satisfaction in the context multi-trip, multi-stage, multi-attribute travel experiences

Requesting respondents to provide satisfaction ratings for multi-stage trips or daily travel experiences implies they have to value each stage, respectively trip, based on memory recall and then cognitively integrate these judgments into the requested satisfaction rating. Our knowledge about the prevalence of alternate processing rules that may be used to arrive at trip satisfaction ratings is very limited. Research on this topic in travel behavior analysis is very scarce indeed. In contributing to the research on travel satisfaction, we therefore compare the performance of different processing rules using data on satisfaction with public transport trips from Xi’an, China. Based on the results of this study, we found the peak-end rule, except for the disjunctive rule, consistently had the lowest explained variance, also after controlling for socio-demographics, mood and personality traits. Rather, for all estimated models, the conjunctive processing rule had the highest associated explained variance. It suggests that the trip stage, respectively trip, with the lowest satisfaction dominates overall satisfaction. Also, we did not find much evidence of a recency effect. Rather, the satisfaction of the first trip or stage has higher marginal effects on overall satisfaction than more recent trips or stages.

Akaike Information Criterion-based conjunctive belief rule base learning for complex system modeling

Nonlinear complex system modeling has become the basis of many theoretical and practical problems, which requires balancing the correlations between the modeling accuracy and the modeling complexity. However, the two objectives may not be consistent with each other under many practical conditions, especially for complex systems with multiple influential factors. The belief rule base (BRB) has shown advantages in nonlinear complex system modeling under uncertainty. However, most of current works on BRB has focused only on the modeling accuracy. As such, an Akaike Information Criterion (AIC)-based objective, AICBRB, is deduced to represent both the modeling accuracy (denoted by the Mean Square Error (MSE)) and the modeling complexity (denoted by the number of the parameters). Based on the proposed AICBRB, a bi-level optimization model and a corresponding bi-level optimization algorithm are developed. Moreover, an empirical optimization path search strategy is proposed for upper-level optimization. The optimization path is comprised of multiple solutions with optimal performance. After the BRB learning process, both the structure and the parameters of BRB are optimized, which identifies the best decision structure of BRB. A numerical multi-extreme function case and a practical pipeline leak detection case are studied. The results show that an optimization path could be identified with a series of optimal solutions. With AICBRB as the objective, the jointly optimized BRB in the best decision structure can be obtained with an improved modeling accuracy as well as reduced modeling complexity.

Why the Conjunction Effect Is Rarely a Fallacy: How Learning Influences Uncertainty and the Conjunction Rule.

In this article we explore the relationship between learning and the conjunction fallacy. The interpretation of the conjunction effect as a fallacy assumes that all observers share the same knowledge, and that nobody has access to privileged information. Such situations are actually quite rare in everyday life. Building on an existing model of surprise, we prove formally that in the more typical scenarios, where observers are alert to the possibility of learning from event outcomes, the conjunction rule does not apply. Scenarios which have been engineered to produce the so-called conjunction “fallacy” (e.g., Tverksy and Kahneman, 1983) often imply subjective uncertainty and hence the possibility of learning. In Experiment 1 we demonstrate that when these scenarios are rephrased so as to eliminate subjective uncertainty, the effect is mitigated. In Experiment 2 we demonstrate that when subjective uncertainty is reduced by allowing participants to learn about the mechanism behind a conjunction-inducing scenario, the conjunction effect again diminishes. We conclude that the conjunction effect arises due to the unnaturalness of interpreting verbal descriptions in terms of a situation in which all observers share the same knowledge. Instead, when people hear descriptions of real world situations, they are likely to assume that learning is possible, and that subjective rather than objective uncertainty applies.