Incomplete Decision Research Articles

Incomplete Decisions on Reward-Based Crowdfunding Platforms: Exploring Motivations from Temporal and Social Perspectives

Incomplete Decisions on Reward-Based Crowdfunding Platforms: Exploring Motivations from Temporal and Social Perspectives

Information entropy based attribute reduction for incomplete heterogeneous data

Attribute reduction in an information system (IS) is an important research topic in rough set theory (RST). This paper investigates attribute reduction for incomplete heterogeneous data based on information entropy. Information entropy in an incomplete IS with heterogeneous data (IISH) is first defined. Then, some derived notions of information entropy, such as joint information entropy, conditional information entropy, mutual information entropy, gain and gain ratio in an incomplete decision IS with heterogeneous data (IDISH), are presented. Next, information entropy is applied to perform attribute reduction in an IDISH. Two attribute reduction algorithms, based on gain and gain ratio, respectively, are proposed. Finally, in order to illustrate the feasibility and efficiency of the proposed algorithms, experimental analysis is carried out and comparisons are done. It is worth mentioning that the incomplete rate is used to deal with incomplete heterogeneous data.

Freedom Made Manifest: Rahner's Fundamental Option and Theological Aesthetics, Peter JosephFritz, The Catholic University of America Press, 2019 (ISBN 978‐0‐8132‐3119‐8), xx + 278 pp., hb $65

Freedom Made Manifest: Rahner's Fundamental Option and Theological Aesthetics, Peter JosephFritz, The Catholic University of America Press, 2019 (ISBN 978‐0‐8132‐3119‐8), xx + 278 pp., hb $65

ОСОБЛИВОСТІ ПСИХОЛОГО- ПЕДАГОГІЧНОЇ ГОТОВНОСТІ ПЕДАГОГІВ ДО РОБОТИ З ОБДАРОВАНИМИ ДІТЬМИ

У статті проаналізовано психолого-педагогічна готовність педагогів до роботи з обдарованими дітьми. Висвітлюються сучасні концептуальні підходи до проблеми обдарованості, прийняті у вітчизняній психології та педагогіці; наводяться прийоми і методи ідентифікації як явної, так і прихованої обдарованості. Охарактеризовані сприятливі соціальні й психологічні умови для розвитку творчої обдарованості студентів. Звернено увагу на категорії обдарованих; сфери діяльності обдарованого студента; характерні особливості обдарованих студентів. Зроблений комплексний аналіз поняття «інтелектуально-творчий потенціал» студента. Представлена специфіка роботи з обдарованими студентами у закладі вищої освіти. Розкриті особливості підготовки роботи з обдарованими студентами: проблемні запитання; побудова гіпотез; «виділяти найкраще»; «запитання – відповідь; «інформаційно – пізнавальна суперечність»; «незавершене рішення». Запропоновані шляхи формування готовності педагогів до роботи з обдарованими дітьми: заняття у наукових товариствах; участь у тематичних масових заходах (вечорах любителів літератури, історії, фізики, хімії та ін.); огляди-конкурси художньої, технічної та інших видів творчості, зустрічі з ученими тощо.

Granularity Selections in Generalized Incomplete Multi-Granular Labeled Decision Systems

Granularity Selections in Generalized Incomplete Multi-Granular Labeled Decision Systems

Influence of the Environmental Factors on the Coastal Zones Formation in the Siberian Urban Territories

The coastal zones of Siberian large cities are characterized by high demand at various historical stages. Coastal territories are considered as potential reserves of the urban environment, their functions have great dynamics and high natural potential. The objective of the research is identifying the influence of environmental factors on the zones’ landscape and town planning reorganization in the structure of the Siberian large cities' coastal territories. Research methods are an integrated approach which involves considering the research object as an element of a single town-planning structure, excluding the possibility of making limited and incomplete decisions. The study results are to build a green river facade as proposing solutions to environmental problems of “buffr zones”. Significance of the study is to preserve the environmental balance of the large Siberian Rivers’ territories. This comprehensive approach involves consideration of the research object as an element of a single town-planning structure. First of all it requires the identification of optimal territories for placing point objects in a complex terrain territory and choosing the most cost-effective and efficient reconstruction and revitalization methods, within the development of methods for their application in local conditions.

Local Optimal Granularity Selections in Incomplete Multi-Granular Decision Systems

Local Optimal Granularity Selections in Incomplete Multi-Granular Decision Systems

Analyzing Critical Decision-Based Processes

Decision-based processes are composed of tasks whose application may depend on explicit decisions relying on the state of the process environment. In specific domains such as healthcare, decision-based processes are often complex and critical in terms of timing and resources. The paper presents a variety of tool-supported techniques for analyzing models of such processes. The analyses allow a variety of errors to be detected early and incrementally on partial models, notably: inadequate decisions resulting from inaccurate or outdated information about the environment state; incomplete decisions; non-deterministic task selections; unreachable tasks along process paths; and violations of non-functional process requirements involving time, resources or costs. The proposed techniques are based on different instantiations of the same generic algorithm that propagates decorations iteratively through the process model. This algorithm in particular allows event-based models to be automatically decorated with state-based invariants. A formal language supporting both event-based and state-based specifications is introduced as a process modeling language to enable such analyses. This language mimics the informal flowcharts commonly used by process stakeholders. It extends High-Level Message Sequence Charts with guards on task-related and environment-related variables. The language provides constructs for specifying task compositions, task refinements, decision trees, multi-agent communication scenarios, and time and resource constraints. The proposed techniques are demonstrated on the incremental building and analysis of a complex model of a real protocol for cancer therapy.

Value Reduction Algorithm Based on Incomplete Grey Decision Table

Value Reduction Algorithm Based on Incomplete Grey Decision Table

Incomplete fuzzy information system based on total order dominance relation and its attribute reduction

Concerning the incomplete fuzzy decision information system with a dominance relation,according to the analyses on the advantages and disadvantages of two dominance relations,a new total order dominance relation was built by using the probability distribution principle of the attribute value.This relation was used to reasonably improve the extended dominance relation and overcome the shortcoming that the limited dominance relation cannot be used to compare all the objects in the domain.And further,based on the new relation,the expression of the lower and upper approximate,the definition of relative reduction and specific calculation method were given.Finally,an example verifies the effectiveness of the proposed method.

Reduction Algorithms of Incomplete Covering Decision Systems

This paper discusses the reduction algorithms of an incomplete covering decision system. Firstly, we give the definition of a new pair of upper and lower approximations in an incomplete covering information system and give their axiomatic characterizations. Then, we introduce the special conditional entropy and the limitary conditional entropy of a covering decision system with multi-coverings and study the reduction of coverings by means of special conditional entropy (limitary conditional entropy) in -consistent (inconsistent) covering decision systems. Two algorithms are designed to compute reductions of the -consistent and inconsistent covering decision systems, respectively.

A Positive-Approximation Based Accelerated Algorithm to Feature Selection from Incomplete Decision Tables

Positive approximation is an effective approach to characterizing the structure of a target concept in information systems.To overcome the limitation of time-consuming of all existing feature selection algorithms in incomplete decision tables.This paper provides a general accelerated algorithm based on the positive approximation.This modified algorithm both possesses the rank preservation of attributes and reduces the time consumption through reducing the scale of data,which effectively accelerates the process of feature selection in incomplete decision tables.Experimental analyses verify the validity and efficiency of the accelerated algorithm.It is deserved to point out that the performance of these modified algorithms are getting better in time reduction with the data set becoming larger.

Consensus Arbitration: A Negotiation‐Based Decision‐Making Process for Arbitrators

In reaching their decisions, arbitrators are currently expected to act like judges by listening fully to both sides and then withdrawing to write the final and complete decision. But because of some key differences between their roles, I argue, arbitrators and judges should exercise completely different styles of decision making. Unlike judges, who make decisive rulings in order to enforce the law, arbitrators are empowered and chosen by the parties themselves to handle specific disputes or govern continuing relationships. Instead of shifting a negotiated process into an authoritative one, arbitrators have the capacity to solicit input from parties as they craft the award. Under a new model of arbitration that I call “consensus arbitration,” arbitrators would facilitate negotiation between the parties but retain the power to break impasses with partial, incomplete decisions, behaving more like facilitators than judges.

Decision fusion of a multi-sensing embedded system for occupant safety measures

The need for an embedded system that can fuse incomplete, inconsistent, and imprecise decisions from several sensing systems is a crucial step in achieving an effective decision for occupant safety measures. This paper deals with the decision fusion strategies of a multi-sensing embedded system to achieve significant enhancement in the reliability of occupant safety through the fused decisions. Multi-sensing approaches to determine weight, vision, and crash sensing are developed for occupant detection, classification, position calculation, and crash detection. A rule-based decision fusion algorithm is then developed to fuse the multi-sensing decisions. The developed sensing systems are incorporated into an embedded device. To execute the embedded system, a system interface between the software and hardware is developed using Lab Window/CVI with the C programming language. The experimental results demonstrated that the real time operation of the embedded system validate the effectiveness of the decision fusion algorithm, characterize the safety measures and monitor the decision application. Several events were tested that prove the performance of the embedded system is robust towards occupant safety measures.

Abstract C41: Phase II pilot study of induction erlotinib therapy in patients with resectable non-small cell lung cancer to select postoperative adjuvant therapy

Abstract Background: Adjuvant chemotherapy has been widely accepted as the standard therapy for stage I-IIIA NSCLC, but resulted in significant toxicity and short-term deterioration of quality of life. In addition, not all patients can receive cytotoxic adjuvant chemotherapy. On the other hand, erlotinib demonstrated to significantly improve survival outcome for previously treated NSCLC patients regardless of their clinical characteristics. Based on those findings, we conducted a prospective pilot study of short-term induction erlotinib to select responders who might benefit from adjuvant erlotinib therapy in patients who were scheduled to undergo surgical resection. Methods: Patients had previously untreated, potentially surgical resectable, stage IB-IIIA NSCLC with ECOG PS of 0–1 and adequate organ functions. Induction erlotinib 150 mg once a day was administered for 28 days. Subsequently patients underwent restaging and planned surgery. Erlotinib responders, defined as their tumor shrinkage of more than 10%, would receive adjuvant erlotinib for 12 weeks while the others would receive any kind of adjuvant platinum doublets. Postoperative radiotherapy could be given in case of incomplete resection or clinician's decision. Results: Between October 2006 and March 2009, 16 patients participated in this study; median age was 59 (41–71); 13 were male; 6 were never-smoker; and 13 had adenocarcinoma. Preoperative clinical stage was as follows; IB 4, IIA 1, IIB 5, IIIA 6. All patients received induction erlotinib monotherapy and all but one, who declined surgery but received curative radiotherapy, underwent planned surgical resection. Out of 16 patients, 5 patients (3 PRs and 2 SDs) received adjuvant erlotinib therapy, while an adjuvant platinum doublet was given in 6, concurrent chemoradiation in 3 due to incomplete resection in 3 and no adjuvant therapy in 1 due to his pathologic stage of IA. There was no complete response. With a median follow-up of 24 months, 8 patients progressed and 3 patient died of progressive disease. No relationships were observed between the responsiveness and biomarkers, such expression levels of VEGF, EGFR, Ki67 and P53. Additional biomarker studies are now ongoing. Conclusions: Induction erlotinib is tolerable and modestly active in patients with resectable NSCLC. The role of adjuvant erlotinib is still not defined yet, but short-term erlotinib before curative surgical resection could predict patients who might benefit from adjuvant erlotinib therapy and avoid toxic adjuvant chemotherapy. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):C41.

DRSA and reductions in incomplete fuzzy information system

Although many extended rough set models have been successfully applied into the incomplete information system, most of them do not take the incomplete information system with initial fuzzy data into account. This paper thus presents a general framework for the study of dominance-based rough set approach to the incomplete fuzzy information systems. First, the traditional dominance relation is expanded in the incomplete fuzzy information system by assuming the condition attributes, which are not only positively but also negatively related with classification analysis. We then present the dominance-based rough approximations by the rough fuzzy technique. Finally, we propose two types of knowledge reductions, relative lower and upper approximate reducts, which can be used to induce simplified decision rules from the incomplete fuzzy decision table. We also present the judgement theorems and discernibility functions, which describe how relative lower and upper approximate reducts can be calculated. We employ some numerical examples in this paper to substantiate the conceptual arguments.

Clustering Based Approximation Procedure for Semi-Markov Decision Processes with Incomplete State Information

It is well-known that in practice it is rather difficult to calculate the minimal value function and optimal policies for decision processes with countable state space. Thus in general, approximation procedures are investigated. In this paper we show how the concepts and use of clustering techniques for large data sets, by adequate grouping of objects, can be useful in decision making processes. Using a linear-time clustering algorithm for the state space decomposition, we introduce an approximation procedure for a numerical solution of a finite horizon incomplete state-information semi-Markov decision processes with countable state space, finite action and signal spaces. We introduce the concept of admissibility for the decomposition of the state space, and for the new semi-Markov decision processes obtained, we calculate the minimal value functions and the optimal policies by dynamic programming. Finally, the convergence theorems are also proved.

Behavioral uncertainty and investments in cooperative relationships

AbstractWe study a cooperative relationship between two economic agents, in which one agent can make investments in the relationship to reduce the probability that the other agent defects. The probability of defection is derived from an incomplete information decision model of the transaction partner. We show that the relationship between investment and probability of defection depends on the type of uncertainty considered and analyze the effects of parameter changes on the optimal level of investment. Copyright © 2004 John Wiley & Sons, Ltd.

A geometric approach to incomplete soft decision block decoding

Two soft-decision incomplete decoding rules based on geometric considerations are proposed and analyzed for binary antipodal signaling on an additive white Gaussian channel. It is seen that for high signal to noise ratios, low decoder error probabilities are attainable requiring approximately 2 dB less power than equivalent hard-decision decoding schemes without a significant loss in performance due to rejections. The incomplete decoding rules allow the implementation of a new reduced complexity decoding algorithm based on a processing loop constructed around an encoder. Initial results indicate the performance of this new decoder compares favorably with that of the ideal decoder. >