Improve Decision Processes Research Articles

Free Will as Defined by the Constrained Disorder Principle: a Restricted, Mandatory, Personalized, Regulated Process for Decision-Making.

The concept of free will has challenged physicists, biologists, philosophers, and other professionals for decades. The constrained disorder principle (CDP) is a fundamental law that defines systems according to their inherent variability. It provides mechanisms for adapting to dynamic environments. This work examines the CDP's perspective of free will concerning various free will theories. Per the CDP, systems lack intentions, and the "freedom" to select and act is built into their design. The "freedom" is embedded within the response range determined by the boundaries of the systems' variability. This built-in and self-generating mechanism enables systems to cope with perturbations. According to the CDP, neither dualism nor an unknown metaphysical apparatus dictates choices. Brain variability facilitates cognitive adaptation to complex, unpredictable situations across various environments. Human behaviors and decisions reflect an underlying physical variability in the brain and other organs for dealing with unpredictable noises. Choices are not predetermined but reflect the ongoing adaptation processes to dynamic prssu½res. Malfunctions and disease states are characterized by inappropriate variability, reflecting an inability to respond adequately to perturbations. Incorporating CDP-based interventions can overcome malfunctions and disease states and improve decision processes. CDP-based second-generation artificial intelligence platforms improve interventions and are being evaluated to augment personal development, wellness, and health.

What matters most: Randomized controlled trial of breast cancer surgery conversation aids across socioeconomic strata.

BackgroundWomen of lower socioeconomic status (SES) with early‐stage breast cancer are more likely to report poorer physician‐patient communication, lower satisfaction with surgery, lower involvement in decision making, and higher decision regret compared to women of higher SES. The objective of this study was to understand how to support women across socioeconomic strata in making breast cancer surgery choices.MethodsWe conducted a 3‐arm (Option Grid, Picture Option Grid, and usual care), multisite, randomized controlled superiority trial with surgeon‐level randomization. The Option Grid (text only) and Picture Option Grid (pictures plus text) conversation aids were evidence‐based summaries of available breast cancer surgery options on paper. Decision quality (primary outcome), treatment choice, treatment intention, shared decision making (SDM), anxiety, quality of life, decision regret, and coordination of care were measured from T0 (pre‐consultation) to T5 (1‐year after surgery.ResultsSixteen surgeons saw 571 of 622 consented patients. Patients in the Picture Option Grid arm (n = 248) had higher knowledge (immediately after the visit [T2] and 1 week after surgery or within 2 weeks of the first postoperative visit [T3]), an improved decision process (T2 and T3), lower decision regret (T3), and more SDM (observed and self‐reported) compared to usual care (n = 257). Patients in the Option Grid arm (n = 66) had higher decision process scores (T2 and T3), better coordination of care (12 weeks after surgery or within 2 weeks of the second postoperative visit [T4]), and more observed SDM (during the surgical visit [T1]) compared to usual care arm. Subgroup analyses suggested that the Picture Option Grid had more impact among women of lower SES and health literacy. Neither intervention affected concordance, treatment choice, or anxiety.ConclusionsPaper‐based conversation aids improved key outcomes over usual care. The Picture Option Grid had more impact among disadvantaged patients.Lay Summary The objective of this study was to understand how to help women with lower incomes or less formal education to make breast cancer surgery choices.Compared with usual care, a conversation aid with pictures and text led to higher knowledge. It improved the decision process and shared decision making (SDM) and lowered decision regret. A text‐only conversation aid led to an improved decision process, more coordinated care, and higher SDM compared to usual care. The conversation aid with pictures was more helpful for women with lower income or less formal education.Conversation aids with pictures and text helped women make better breast cancer surgery choices.

Corrigendum to: Modelling suppression difficulty: current and future applications

Improving decision processes and the informational basis upon which decisions are made in pursuit of safer and more effective fire response have become key priorities of the fire research community. One area of emphasis is bridging the gap between fire researchers and managers through development of application-focused, operationally relevant decision support tools. In this paper we focus on a family of such tools designed to characterise the difficulty of suppression operations by weighing suppression challenges against suppression opportunities. These tools integrate potential fire behaviour, vegetation cover types, topography, road and trail networks, existing fuel breaks and fireline production potential to map the operational effort necessary for fire suppression. We include case studies from two large fires in the USA and Spain to demonstrate model updates and improvements intended to better capture extreme fire behaviour and present results demonstrating successful fire containment where suppression difficulty index (SDI) values were low and containment only after a moderation of fire weather where SDI values were high. A basic aim of this work is reducing the uncertainty and increasing the efficiency of suppression operations through assessment of landscape conditions and incorporation of expert knowledge into planning.

Modelling suppression difficulty: current and future applications

Improving decision processes and the informational basis upon which decisions are made in pursuit of safer and more effective fire response have become key priorities of the fire research community. One area of emphasis is bridging the gap between fire researchers and managers through development of application-focused, operationally relevant decision support tools. In this paper we focus on a family of such tools designed to characterise the difficulty of suppression operations by weighing suppression challenges against suppression opportunities. These tools integrate potential fire behaviour, vegetation cover types, topography, road and trail networks, existing fuel breaks and fireline production potential to map the operational effort necessary for fire suppression. We include case studies from two large fires in the USA and Spain to demonstrate model updates and improvements intended to better capture extreme fire behaviour and present results demonstrating successful fire containment where suppression difficulty index (SDI) values were low and containment only after a moderation of fire weather where SDI values were high. A basic aim of this work is reducing the uncertainty and increasing the efficiency of suppression operations through assessment of landscape conditions and incorporation of expert knowledge into planning.

A new approach agent-based for distributing association rules by business to improve decision process in ERP systems

Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.

A new approach agent-based for distributing association rules by business to improve decision process in ERP systems

A new approach agent-based for distributing association rules by business to improve decision process in ERP systems

Uncertainty in heart rate complexity metrics caused by R-peak perturbations

Heart rate complexity (HRC) is a proven metric for gaining insight into human stress and physiological deterioration. To calculate HRC, the detection of the exact instance of when the heart beats, the R-peak, is necessary. Electrocardiogram (ECG) signals can often be corrupted by environmental noise (e.g., from electromagnetic interference, movement artifacts), which can potentially alter the HRC measurement, producing erroneous inputs which feed into decision support models. Current literature has only investigated how HRC is affected by noise when R-peak detection errors occur (false positives and false negatives). However, the numerical methods used to calculate HRC are also sensitive to the specific location of the fiducial point of the R-peak. This raises many questions regarding how this fiducial point is altered by noise, the resulting impact on the measured HRC, and how we can account for noisy HRC measures as inputs into our decision models. This work uses Monte Carlo simulations to systematically add white and pink noise at different permutations of signal-to-noise ratios (SNRs), time segments, sampling rates, and HRC measurements to characterize the influence of noise on the HRC measure by altering the fiducial point of the R-peak. Using the generated information from these simulations provides improved decision processes for system design which address key concerns such as permutation entropy being a more precise, reliable, less biased, and more sensitive measurement for HRC than sample and approximate entropy.

The Advantage of Digital Decision Making for Strategic Decisions – Proofed by a Supply Chain Case

This paper will discuss the advantage of decision making supported by a digital system and will provide an overview of an empiric analysis researched on this topic. Decision making in organizations is a significant system implied task of managers and therefore a broad area in scientific research, not only in the disciplines management or business studies – even from technical to humanistic disciplines. Nowadays the trend of digitalization captures all areas of life especially in business, as well as the typical management task of decision making. Triggered by the digitalization trend business will move toward an autonomous decision making of machines or cyber systems. The important step toward autonomous decisions or decision support (cyber systems will prepare a decision, but finally executed by a human) will be the next development step for decision making. Designed algorithmic models for these decisions will use the content of classical decision models to reach maximum utility. Hence, a view on classical decision making will illuminate the basis for these models, from researchers like von Neumann/Morgenstern or Bernoulli. Furthermore, digitalization changes the process of decision making, especially focussing on the behavioral part of decisions, discussed from Simon, Selten or Tversky/Kahneman. Due to reason that today the human aspect of decision making is sustainable in organizations. The hypothesis that the digital set up for decision making in organizations will increase the efficiency of strategic decisions will be proofed by an empiric study. The research method for the empirical part of this paper is a questionnaire. It is online questionnaire which will be answered by professionals and scholars. As a typical example of a strategic decision, the author explains a business case in the supply chain function of organizations. A section of the well-known SCOR model will be tested on digitalization characteristics. The research findings of the questionnaire will illustrate that the digitalization of supply chain processes is in the scope of managers and consultants, to reach higher efficiency by increased turnover or by reducing costs. The result of this paper verifies an improved decision process by usage of digital features, but the capability of the entire digital possibilities is not fully achieved yet

Achieving Well-Performance Optimization Through Work-Flow Automation

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 173578, “Samarang Integrated Operations: Achieving Well-Performance Monitoring, Surveillance, and Optimization Through Data- and Model-Driven Work- Flow Automation,” by M. Zul Izzi Ahmad and Colinus Lajim Sayung, Petronas, and Muzahidin M. Salim, M. Kasim Som, Lee Hin Wong, Shripad Biniwale, Nur Erziyati, Kenneth Soh, Roland Hermann, Vo Tri Nghia, Lau Chong Ee, and Muhammad Firdaus Hassan, Schlumberger, prepared for the 2015 SPE Digital Energy Conference and Exhibition, The Woodlands, Texas, USA, 3–5 March. The paper has not been peer reviewed. The Samarang field is located offshore Sabah, Malaysia. The field is undergoing a major redevelopment project with integrated operations (IO). In order to overcome a variety of challenges and to improve field awareness, several work flows were designed and deployed in order to achieve an early milestone of providing realtime well-performance monitoring, surveillance, and optimization. The paper discusses how these work flows were implemented in an integrated way to provide a modern decision-support system for the Samarang field. Introduction Samarang is an old oil field and thus exhibits the characteristics of many mature fields, including declining production. It has been in production for more than 35 years from multiple, now aging, platforms with insufficient metering and monitoring processes. Given this situation, well status and uptime is unknown for many days, causing delays in mitigation and rectification of production issues. Most of the instrument measurements have been carried out on an ad hoc basis; various facility readings were measured manually, which subsequently deferred field review, causing loss of production. The main technique implemented for production optimization has been artificial lift, with more than 80% of wells and strings being gas lifted. It was necessary to find a better way to manage this production flow stream efficiently with an improved asset-management strategy. Solution Overview For Samarang, the IO solution is designed in such a way that work flows process data into actionable information, improve decision processes by leveraging technology, and equip people with systems and tools enabling enhanced decision support. An IO field creates value through enhanced asset management by focusing on decisions to improve reservoir drainage, production, and operations. As part of Samarang’s overall IO development strategy, the wells and facilities were equipped with the required instrumentation to support approximately 25 work flows. Oil and gas asset activities and decisions span a range of time scales; therefore, the work flows are divided mainly into three main categories: fast-loop, medium-loop, and slowloop decision cycles. These are then sub divided into seven different subdomain categories covering flow-assurance, well- performance, facilities-monitoring, artificial-lift, production-planning, enhanced-oil- recovery (EOR) - surveillance, and optimization work flows.

Technology Readiness Levels at 40: A Study of State-of-the-Art Use, Challenges, and Opportunities

The technology readiness level (TRL) scale was introduced by NASA in the 1970s as a tool for assessing the maturity of technologies during complex system development. TRL data have been used to make multi-million dollar technology management decisions in programs such as NASA's Mars Curiosity Rover. This scale is now a de facto standard used for technology assessment and oversight in many industries, from power systems to consumer electronics. Low TRLs have been associated with significantly reduced timeliness and increased costs across a portfolio of US Department of Defense programs. However, anecdotal evidence raises concerns about many of the practices related to TRLs. We study TRL implementations based on semi-structured interviews with employees from seven different organizations and examine documentation collected from industry standards and organizational guidelines related to technology development and demonstration. Our findings consist of 15 challenges observed in TRL implementations that fall into three different categories: system complexity, planning and review, and validity of assessment. We explore research opportunities for these challenges and posit that addressing these opportunities, either singly or in groups, could improve decision processes and performance outcomes in complex engineering projects.

Investigating active ingredients in a complex intervention: a nested study within the Patient and Decision Aids (PANDAs) randomised controlled trial for people with type 2 diabetes.

BackgroundRandomised trials provide evidence that patient decision aids improve outcomes with respect to patient knowledge, involvement and satisfaction in decision making. It is less clear how these complex interventions are implemented within patient-clinician interactions and which components are active for improving decision processes. To investigate the experiences of using a diabetes treatment decision aid and to explore how components within a complex intervention influenced the decision making process.MethodsA pragmatic mixed methods study nested within the PANDAs cluster randomised trial of a patient decision aid. Themes inductively derived from interviews and observation of consultations with further triangulation with results of decision quality and involvement measurements and case analyses.ResultsThe decision aid intervention was employed flexibly within the consultation with both the patient and clinician active in marshalling elements. The decision aid improved processing and organization of information needed for decision making within the consultation interaction. It also improved decision quality by preparing the patient for active involvement within the clinical consultation.ConclusionThe intervention was acceptable, flexible and readily implemented in primary care consultations. The decision aid was effective in facilitating cognitive processing. The intervention also facilitated rehearsal in preparation for active roles in a shared decision process.Trial registrationTrials Register Number: ISRCTN14842077. Date registered: 24.06.2010.

Preface to the EURO Journal on Decision Processes

The fates of institutions, organizations and even individuals are largely governed by the decisions they make and put into action. In an increasingly interconnected and fast-paced world, these decisions have become more complex while the stakes have grown. For instance, in order to attain competitive advantages, firms may have to reach high-quality decisions more quickly than their rivals; regulators must account for a broader range of impacts to ensure the safety of products and processes; and policy makers are expected to recognise a greater variety of concerns and perspectives when charting pathways for the future. Thus, for example, research councils emphasise objectives that transcend narrower notions of research quality as they urge researchers to generate new scientific knowledge for the benefit of society at large. Against this backdrop, research that seeks to improve decision processes by way of modelling has exceptional leverage: for it is at this very juncture where results from different disciplines are combined with modelling activities to inform decisions that shape the future. Here, the complexity of decision problems and the quest for decision quality make it necessary to draw appropriately on the entire suite of approaches that lie at the heart of operational research. These approaches range from the ‘softer’ methods such as problem structuring, decision analysis and decision aiding to more quantitative methods like performance measurement, costefficiency analysis, simulation, forecasting and optimisation. For all these methods, issues of process design, model validity and communication are crucial: the quality

Emergent Leadership in Extreme Events: A Knowledge Commons for Sustainable Communities

Extreme events create extraordinary demands on public managers, particularly at national and international scales of operation. The rapid escalation of demand in contexts of urgent need, scarce resources, and cascading, interdependent consequences challenges the physical, intellectual, technical, and social skills of public managers in profound ways, with high potential for failure at sobering costs. We examine this process of evolving leadership in two different disaster contexts, Haiti and Japan, and conclude that developing a “knowledge commons” or interactive information infrastructure offers a constructive means of improving decision processes for public managers by engaging a wider exchange of knowledge and skills in situations of deep uncertainty. Timely, valid information becomes the key resource in mobilizing collective action in response to disaster in both developed and developing countries.

The effect of acquisition decision making on security posture

Purpose – The purpose of this paper is to examine the effectiveness of decision making in IT acquisition and security, and the disparity between the two domains. The paper postulates that improving decision processes during acquisition increases decision makers' security consciousness and security posture.Design/methodology/approach – Semi‐structured interviews were conducted with 15 IT decision makers of small‐to‐medium sized organizations using questions derived from previous research in psychology, HCI, and MIS. Questions from the security and acquisition areas were coded based upon a predefined rubric and correlation testing was performed. The author chose to focus on small‐to‐medium sized organizations since they often lack sufficient background and resources to address IT security concerns.Findings – Analysis suggests a significant positive correlation between the effectiveness of acquisition decision making and organizational security posture and attitudes, further suggesting that small improvement...

Are more and better indicators the solution?

Summary We discuss Starbuck's proposal to improve decision processes in scholarly evaluation. While we agree that more variety is needed in evaluation committees, we suggest to enlist scholars from other research fields rather than people from outside academia. We disagree with the proposal that more and better indicators of research effectiveness will improve research. We argue that this even would lead to worse results than what is observed today. Attention would be deviated from the content of research, and intrinsic motivation, which is essential for good research, would be crowded out. We propose that evaluations that are based on indicators need to be pushed back. After a careful selection process, researchers need to be given the opportunity to pursue the research they consider to be fruitful.

River of Change: An Environmental History of Climate and Water Management in the Upper Little Colorado Watershed

Perhaps nowhere in the United States has climate and water resource interaction been more closely observed than in the U.S. Southwest (see, e.g., Austin 1988;Worster 1985;Reisner 1993; Sheridan 2001), and with good reason. Overall, semiarid to arid conditions predominate. These conditions, together with wide variability in temperature and precipitation over time and space, challenge even the most earnest human efforts to sustain livelihoods and communities. Scientific studies and popular literature alike reflect recurrent episodes of overexploitation of natural resources due to inadequate recognition or acceptance of environmental limits such as water availability. Accretion of local knowledge and experience provides a mechanism for anticipating and adapting to the vicissitudes of variability and change. However, factors such as reliance on faulty heuristics (Nicholls 1999), occurrence of events falling outside the range of local memory, policies that restrict decision options, and narrow focus on immediate economic returns can render such knowledge ineffective at best and destructive at worst. Science-based knowledge and predictive capabilities provide avenues for improving decision processes in the face of interacting environmental and societal stresses. To be accepted and used appropriately, such knowledge needs to be framed in ways that mesh well with local values and practices. Environmental histories that create a contextual account of embedded experience, values, and practices provide insights useful to this task.

Intelligent delivery of military lessons learned

Lessons learned systems (LLS) are a common knowledge management (KM) initiative among the American government agencies (e.g., Department of Defense (DOD), Department of Energy (DOE), NASA). An effective lessons learned (LL) process can substantially improve decision processes, thus representing an essential chapter in a knowledge-sharing digital government. Unfortunately, these systems typically fail to deliver lessons when and where they are needed. In this paper, we introduce, describe, and empirically evaluate the monitored distribution (MD) approach for the active delivery of lessons learned. Our results show that this just-in-time information delivery approach, embedded in a decision support system (DSS) for plan authoring, significantly improved plan execution performance measures.

Championship Management for Healthcare Organizations

Stakeholders will put increasing pressure on integrated health systems (IHS) for measured performance, demanding data on quality and patient satisfaction, while simultaneously pressing for lower cost. The changes to Joint Commission on Accreditation of Healthcare Organizations (Joint Commission) and the growing importance of the National Committee on Quality Assurance (NCQA) are simply forerunners of an intensifying trend. Quality of care in particular will face increasing scrutiny. Achieving competitive targets in these areas will also require measures addressing demand and worker satisfaction. "Balanced scorecard" approaches will allow IHS and their accountable work groups to track performance on several dimensions and establish integrated goals or targets. Those with consistently good scores will be labeled "champions." Champions will support the multidimensional measures with improved decision processes. About eight major processes will be central--governance/strategic management, clinical quality, clinical organization, financial planning, planning and marketing, information services, human resources, and plant services. It is possible to map these processes to the criteria of the Joint Commission, NCQA, and Malcolm Baldrige Quality Award. The processes themselves can be measured and common weaknesses identified and corrected. Champions share some common characteristics that seem to arise from the combination of processes and measures. Among these characteristics are service line orientation, extensive partnering with other organizations, and the possibility of outsourcing organizational components.

Data mining: an industrial research perspective

Just what exactly is data mining? At a broad level, it is the process by which accurate and previously unknown information is extracted from large volumes of data. This information should be in a form that can be understood, acted upon, and used for improving decision processes. Obviously, with this definition, data mining encompasses a broad set of technologies, including data warehousing, database management, data analysis algorithms, and visualization. The crux of the appeal for this new technology lies in the data analysis algorithms, since they provide automated mechanisms for sifting through data and extracting useful information. The analysis capability of these algorithms, coupled with today's data warehousing and database management technology, make corporate and industrial data mining possible. The data representation model for such algorithms is quite straightforward. Data is considered to be a collection of records, where each record is a collection of fields. Using this tabular data model, data mining algorithms are designed to operate on the contents, under differing assumptions, and delivering results in differing formats. The data analysis algorithms (or data mining algorithms, as they are more popularly known nowadays) can be divided into three major categories based on the nature of their information extraction: predictive modeling (also called classification or supervised learning), clustering (also called segmentation or unsupervised learning), and frequent pattern extraction.

Superfund Decision Analysis in Presence of Uncertainty

Environmental decision making (EDM) analysis is developing into a recognized specialty of environmental engineering. One of the major challenges of EDM is to accommodate the high levels of uncertainty that exist in many environmental problems. EDM methods based on absolute or relative, probabilistic, multiattribute utility analysis are presented. Example EDM solutions are presented for the selection of remedial action alternatives for a Superfund site using the Environmental Protection Agency's decision criteria and New York's system for quantifying decision criteria. The advantages and limitations of both are discussed. Results illustrate that the major advantages of EDM analysis are in the need to formalize the decision criteria by which a decision is made and the value judgments that are applied to weigh multiple criteria. EDM analysis can also improve decision process documentation and can sharpen the quality of debate about controversial decisions.