Models Of Inductive Reasoning Research Articles

Implementing community policing practices to manage conflict resolution in Galeshewe, South Africa

Community policing has emerged as a vital instrument for combatting crime and enhancing public safety in South Africa. As a result, it has the capacity to go beyond traditional law enforcement functions as a mediator in disputes, fostering improved relationships between the police and the communities where they work. This paper analyses the implementation of community policing strategies by the South African police with the purpose of resolving conflicts. This study aims to address social crime prevention-related concerns through community policing methods in the Galeshewe police area within the Francis Baard policing regions of the Sol Plaatje Municipality, South Africa. The paper examines the tactics that community police employ to enforce the law, avoid social issues, and manage conflict resolution in the communities. A qualitative method and descriptive design were employed. Comprehensive document analysis, semi-structured interviews, and observations were employed as data collection strategies. An inductive reasoning model was used to analysis data. The findings of the study demonstrated that community policing plays an important role in optimizing problem mapping and it increases public knowledge of the importance of upholding security and order in the different police operations that support the community policing program.

Inductive Reasoning Model

We introduce the Inductive Reasoning Model (IRM) as a comprehensive platform for the study of several phenomena central to self- and social perception. Going beyond the traditional phenomenon-focused research strategy, the model shows how to generate point-specific hypotheses about the size of individual effects and how to predict the interrelations among phenomena of interest. The model points to additional psychological processes at play when outputs cannot be accounted for within the confines of the IRM alone. The IRM is parsimonious in its assumptions and generative in its predictions. Using two empirically-based inputs, namely, the positivity of a person’s self-image and the strength of social projection, the model predicts the direction and extent of four higher-order phenomena: intergroup accentuation, self-enhancement, ingroup favoritism, and differential accuracy. The model affords precise predictions pertaining to the relationships among these phenomena. Critically, alternative conceptions of social perceptions are not rendered irrelevant. Researchers can ask if the IRM over- or underpredicts social-perceptual phenomena in contexts of interest, and if alternative models can explain the differences.

KARAKTERISTIK AKSARA PAKPAK BERDASARKAN SUMBER TERTULIS DI DAIRI DAN PAKPAK BHARAT, SUMATERA UTARA

The Pakpak ethnic group occupy two areas in North Sumatra, namely Dairi and Pakpak Bharat, and speak a language with its own set of characters called the Pakpak script. This study discusses the Pakpak script characteristics and writing materials, and provides a compiled description of the Pakpak community in the past based on written sources. The purpose of this study is to determine the characteristics of the Pakpak script and provide a historical description of the ethnic group based on written sources. This study uses the exploratory inductive reasoning model. Results show that the Pakpak and Batak scripts share similar characteristics. Stones and tree barks were the two main media on which the script was written. Based on historical records, the Pakpak people practiced animism with a unique burial system in which corpses were burned and the ashes were then placed in stone grave containers called perabuen. The livelihood system of the Pakpak community was dominated by rice farming. There was also a social system consisting of permangmang (the “oldest” clan) and persinabul(the “younger” clan), each of which had to respect each other and obey the existing rules.

Policy Implementation of Recognition and Protection of Ulaya Rights in Indigenous communities in the West Papua Region

Ulayat rights are rights attached to indigenous peoples in West Papua, and are recognized and protected by national and international law. This right refers to the traditional rights held by indigenous peoples to the land, territories and natural resources in the areas they inhabit and manage for generations. This study focuses on identifying the government's recognition and protection of customary rights as the rights of indigenous peoples in West Papua. The research method used is qualitative with a qualitative descriptive approach, aiming to reveal events or facts, circumstances, phenomena, variables and circumstances that occur during the research by presenting what actually happened. Data collection techniques were carried out through observation, interviews and document and literature studies. At the data analysis stage, the inductive reasoning model was carried out. The results of the study show that the government's policy of recognizing and protecting ulayat rights as the rights of indigenous peoples in West Papua is due to the low knowledge, low public awareness, community culture, facilities and facilities that are still limited, so that the carrying capacity of the government's recognition and protection for the community is not yet optimal. The policy of recognizing and protecting customary rights in indigenous communities in the West Papua region requires a collaborative, participatory and sensitive approach to cultural diversity and the aspirations of indigenous peoples.

Acquiring Indonesian for 3-5 Year Olds in the Children Study Group of Wolosoko Village, Wolowaru District, Ende Regency

This study aims to objectively find and describe the acquisition of Indonesian for children aged 3-5 years in the Wolosoko Village Children Study Group, Wolowaru District, Ende Regency (abbreviated as WVCSG). This paper elaborates on the acquisition and the factors that influence the acquisition of Indonesian for children aged 3-5 years at the WVCSG. This research uses a qualitative approach. An approach that emphasizes the originality of the data natural, the researcher acts as a key instrument, the data is collected using the technique of engaging, face-to-face, record and documentation. The data were analyzed using an inductive reasoning model. The theoretical basis of this paper uses Psycholinguistic theory. The results of the study prove that phonologically, children aged 3-5 years are not able to pronounce the sounds [r], [ng], [ny], [y] and [k] perfectly, while children aged 5 years are already able to pronounce these two sounds. perfectly. Morphologically, children aged 3-5 years are able to use basic words and rephrases, while children aged 5 years are able to use three word forms (basic words, rephrases and affix words). Syntactically, children aged 3-5 are able to communicate using 2-3 words while children aged 5 years are able to use 2-5 words in communicating.

Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs

Inductive link prediction---where entities during training and inference stages can be different---has been shown to be promising for completing continuously evolving knowledge graphs. Existing models of inductive reasoning mainly focus on predicting missing links by learning logical rules. However, many existing approaches do not take into account semantic correlations between relations, which are commonly seen in real-world knowledge graphs. To address this challenge, we propose a novel inductive reasoning approach, namely TACT, which can effectively exploit Topology-Aware CorrelaTions between relations in an entity-independent manner. TACT is inspired by the observation that the semantic correlation between two relations is highly correlated to their topological structure in knowledge graphs. Specifically, we categorize all relation pairs into several topological patterns, and then propose a Relational Correlation Network (RCN) to learn the importance of the different patterns for inductive link prediction. Experiments demonstrate that TACT can effectively model semantic correlations between relations, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the inductive link prediction task.

How to Pivot or Persevere? Unpacking the Role of Reasoning Models in Entrepreneurial Strategy Formation

Despite the emphasis placed on the importance of pivoting, perspectives seem to diverge on an important dimension: the process by which founders and venture leadership teams reason on how to pivot. Drawing upon the conceptual lenses developed by the literature on organizational search, we explore the type of reasoning models that guide pivoting decisions, in the context of early stage ventures. We observe two key reasoning models at play: an inductive reasoning model and a deductive reasoning model. The type of reasoning model that enables early stage ventures to progress forward differs depending on the level of complexity and ambiguity faced by ventures, at different stages of entrepreneurial growth. Our findings shed light on to the tension between the two approaches of strategy formation identified in the literature, namely thinking and doing. Moreover, the comparison of pivoting decisions across the different stages allows us to highlight the possible tools that benefit venture founders while engaging in decision making. In that regard, we identify boundaries to the often advocated “build-measure-learn” approach.

The Effects of an Inductive Reasoning Learning Strategy Assisted by the GeoGebra Software on Students’ Motivation for the Functional Graph II Topic

Motivation is an aspect heavily emphasized in a mathematics class to produce a quality human capital. However, students' motivation towards mathematics is still low while students' performance in mathematics is declining. The students' lack of motivation toward mathematics could be attributed to ineffective learning strategies in mathematics classrooms. In addition, many previous studies have only focused on an inductive reasoning strategy and the separate use of GeoGebra. Therefore, this study aims to identify the effects of learning through an inductive reasoning strategy assisted by GeoGebra on students' motivation for the Functional Graph II topic. The research design was quasi-experimental which involved 94 Form 4 students from a secondary school in Johor. The research sample was divided into three groups: (1) Study Group 1 (an inductive reasoning strategy assisted by GeoGebra); (2) Study Group 2 (an inductive reasoning strategy without GeoGebra); and (3) Control Group (a conventional strategy). The research instruments consisted of a motivational questionnaire set and an inductive reasoning strategy assisted by GeoGebra and without GeoGebra. The inductive reasoning strategy was guided by the Marzano's Inductive Reasoning Model consisting of an observation process on specific examples and patterns that determined if a generalization was true. The MANOVA test results show that the overall motivation level for Study Group 1 is high in terms of attention and relevance. With regard to confidence, the results indicate that Control Group and Study Group 1 show the same motivation level. As for satisfaction, the motivation level for Control Group is the highest compared to other groups. In conclusion, learning through an inductive reasoning strategy assisted by GeoGebra can increase the students' motivation in mathematics specifically for the Functional Graph II topic.

Do modals identify better models? A comparison of signal detection and probabilistic models of inductive reasoning

The nature of the relationship between deductive and inductive reasoning is a hotly debated topic. A key question is whether there is a single dimension of evidence underlying both deductive and inductive judgments. Following Rips (2001), Rotello and Heit (2009) and Heit and Rotello (2010) implemented one- and two-dimensional models grounded in signal detection theory to assess predictions for receiver operating characteristic data (ROCs), and concluded in favor of the two-dimensional model. Recently, Lassiter and Goodman (2015) proposed a different type of one-dimensional model, the Probability Threshold Model (PTM), that they concluded offered a good account of data collected over a range of decision modals (e.g., How likely, possible, or necessary is the argument conclusion?). Here, we apply the PTM and the signal detection models to ROCs from 3 large experiments in which participants made judgments about arguments varying in terms of modals introduced by Lassiter and Goodman (2015). Two independent variables that are theoretically important for the study of inductive reasoning, namely premise-conclusion similarity (as utilized in Heit & Rotello, 2010) and number of premises (as utilized in Rotello & Heit, 2009), are also varied in Experiments 1 and 2, respectively. In all cases, the PTM provides the poorest fit both quantitatively and qualitatively; the two-dimensional signal detection model is preferred.

The translation and introduction of Mo Yan's works based on neurolinguistics

As an advanced function of the cognitive neural mechanism of human brain, inductive reasoning is an important skill in language communication. Under the background of the development of information intelligence, it is a new research field to effectively display the cognitive neural function of inductive reasoning with the advantage of the logic operation of artificial intelligence algorithm. Therefore, in this paper, based on the neurolinguistics, the translation and introduction of Mo Yan's works were studied. And on the basis of the analysis of the characteristics of the cognitive neural mechanism of sentence inductive reasoning, by using fMR and ERP techniques, the narrowing characteristics of the semantic integrated components in the induction were investigated, and the dual processing model of inductive reasoning was discussed. After that, artificial intelligence particle swarm optimization (PSO) algorithm was introduced, and the problem of alignment in the translation of English and Chinese sentences in Mo Yan's works was transformed into the problem of finding the optimal solution for the corresponding fitness function in Chinese and English sentences in bilingual space. Thus, a scientific mathematical model was used to improve the accuracy of translation. The simulation experiments show that this study can effectively improve the accuracy of the translation and introduction of Mo Yan's works.

Using a Model to Describe Students’ Inductive Reasoning in Problem Solving

Introducción. Presentamos algunos aspectos de una investigación más amplia (Cañadas, 2007), cuyo principal objetivo es describir y caracterizar el razonamiento inductive utilizado por estudiantes españoles de 3º y 4º de Educación Secundaria Obligatoria cuando resuelven problemas que involucran sucesiones lineales y cuadráticas.Método. Propusimos un cuestionario de seis problemas de diferentes características relacionados con sucesiones a 359 estudiantes de Secundaria. Estos problemas podían ser resueltos mediante el razonamiento inductivo. Utilizamos un modelo de razonamiento inductivo compuesto por siete pasos (Cañadas and Castro, 2007) para analizar las respuestas de los estudiantes.Resultados. Mostramos algunos resultados relacionados son: (a) las frecuencias de los pasos que emplean los estudiantes, (b) las relaciones entre las frecuencias de los pasos según las características del problema y (c) el estudio de las relaciones de (in)dependencia entre los diferentes estados del modelo de razonamiento inductivo.Discusión. Podemos concluir que el modelo de razonamiento inductive fue útil para describir la actuación de los estudiantes. En este artículo ponemos de manifiesto que este modelo no es linear. Como ejemplo, los estudiantes lograron la generalización sin haber realizado algunos pasos previos en algunos problemas. Describir cómo los estudiantes llegan a pasos más avanzados sin haber realizado los considerados previos y analizar si la realización de los pasos intermedios puede ser útil para los estudiantes son tareas pendientes.

Comparisons in research and reasoning: Toward an integrative theory of social induction

Strong scientific theories give coherence to a body of research findings, make precise predictions about key phenomena, and guide the search for new discoveries. In social psychology, some contemporary theories fall short of this ideal. Mini-theories are prevalent (cf. Van Lange, Higgins, & Kruglanski, 2011), many predictions are merely directional (like this one!) and theorizing post-hoc. Guided by experimental reasoning, many researchers emphasize—and reify—empirical differences. Taking the experimental method as an epistemological gold standard, they regard comparative thinking as a criterion of rational thinking. Using examples from social judgment and decision making, we show how comparative reasoning can constrain theoretical development and bias assessments of human rationality. To encourage movement toward stronger theory, we describe a model of inductive reasoning in social contexts.

End Effect Processing for Empirical Mode Decomposition Using Fuzzy Inductive Reasoning

This paper focuses on the end effect problem of the empirical mode decomposition (EMD) algorithm, which results in a serious distortion in the EMD sifting process. A new method based on fuzzy inductive reasoning (FIR) is proposed to overcome the end effect. Fuzzy inductive reasoning method has simple inferring rules and strong predictive capability. The fuzzy inductive reasoning based method uses the sequence near the end as the input signal of fuzzy inductive reasoning model. This predictive value can be obtained after fuzzification, qualitative modeling ,qualitative simulation and debluring. The simulation results have shown that the fuzzy inductive reasoning based method has equivalent performance to the neural network based method.

A Study on the Inductive Reasoning Model as an Instructional Model in Art Lessons

A Study on the Inductive Reasoning Model as an Instructional Model in Art Lessons

Modeling the effects of argument length and validity on inductive and deductive reasoning.

In an effort to assess models of inductive reasoning and deductive reasoning, the authors, in 3 experiments, examined the effects of argument length and logical validity on evaluation of arguments. In Experiments 1a and 1b, participants were given either induction or deduction instructions for a common set of stimuli. Two distinct effects were observed: Induction judgments were more affected by argument length, and deduction judgments were more affected by validity. In Experiment 2, fluency was manipulated by displaying the materials in a low-contrast font, leading to increased sensitivity to logical validity. Several variants of 1-process and 2-process models of reasoning were assessed against the results. A 1-process model that assumed the same scale of argument strength underlies induction and deduction was not successful. A 2-process model that assumed separate, continuous informational dimensions of apparent deductive validity and associative strength gave the more successful account.

“Structured statistical models of inductive reasoning”: Correction.

“Structured statistical models of inductive reasoning”: Correction.

Structured statistical models of inductive reasoning.

Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describes [corrected] 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.

Computational models of inductive reasoning using a statistical analysis of a Japanese corpus

Existing computational models of human inductive reasoning have been constructed based on psychological evaluations concerning the similarities or relationships between entities. However, the costs involved in collecting psychological evaluations for the sheer number of entities that exist mean that they are prohibitively impractical. In order to avoid this problem, the present article examines three types of models: a category-based neural network model, a category-based Bayesian model, and a feature-based neural network model. These models utilize the results of a statistical analysis of a Japanese corpus computing co-occurrence probabilities for word pairs, rather than using psychological evaluations. Argument strength ratings collected by a psychological experiment were found to correlate well with simulations for the category-based neural network model.

Causal relations and feature similarity in children's inductive reasoning.

Four experiments examined the development of property induction on the basis of causal relations. In the first 2 studies, 5-year-olds, 8-year-olds, and adults were presented with triads in which a target instance was equally similar to 2 inductive bases but shared a causal antecedent feature with 1 of them. All 3 age groups used causal relations as a basis for property induction, although the proportion of causal inferences increased with age. Subsequent experiments pitted causal relations against featural similarity in induction. It was found that adults and 8-year-olds, but not 5-year-olds, preferred shared causal relations over strong featural similarity as a basis for induction. The implications for models of inductive reasoning and development are discussed.

Relations among categorization, induction, recognition, and similarity: Comment on Sloutsky and Fisher (2004).

V. M. Sloutsky and A. V. Fisher reported 5 experiments documenting relations among categorization, induction, recognition, and similarity in children as well as adults and proposed a new model of induction, SINC (similarity, induction, categorization). Those authors concluded that induction depends on perceptual similarity rather than conceptual knowledge. Despite the useful contributions of this work, there are some important limitations. The experimental designs examined a limited range of phenomena that are not the most revealing about the use of nonperceptual information. The main results involved a simple triad task, for which the SINC model's predictions are equivalent to the predictions of previous models of inductive reasoning. It is also unclear whether the SINC model can account for the observed relations between similarity and recognition. Implications for future work on induction and related cognitive activities are discussed.