A decision reconstruction support model

The importance of understanding the reasons for past decisions is not a new subject. However, there seems to be a gap when it comes to verifying the efficiency of tools for understanding past decisions. In this paper we show that the ability to perform decision reconstruction using a Group Support System (GSS) can provide a flexible solution to the problem, but only if the information model underlying it is able to provide bidirectional support to the phases of a decision-making process. For this, we present a general information model to support the decision-making process, as well as the decision reconstruction process. We tested these ideas by setting up a case study where we used a GSS, based on our model, to analyze a simulated public contracting process. We present a discussion of the results.info:eu-repo/semantics/publishedVersio

Understanding the factors that influence breast reconstruction decision making in Australian women

Background Breast reconstruction is safe and improves quality of life. Despite this, many women do not undergo breast reconstruction and the reasons for this are poorly understood. This study aims to identify the factors that influence a woman's decision whether or not to have breast reconstruction and to better understand their attitudes toward reconstruction. Methodology An online survey was distributed to breast cancer patients from Breast Cancer Network Australia. Results were tabulated, described qualitatively and analyzed for significance using a multiple logistic regression model. Results 501 mastectomy patients completed surveys, of which 62% had undergone breast reconstruction. Factors that positively influenced likelihood of reconstruction included lower age, bilateral mastectomy, access to private hospitals, decreased home/work responsibilities, increased level of home support and early discussion of reconstructive options. Most common reasons for avoiding reconstruction included “I don't feel the need” and “I don't want more surgery”. The most commonly sited sources of reconstruction information came from the breast surgeon followed by the plastic surgeon then the breast cancer nurse and the most influential of these was the plastic surgeon. Conclusions A model using factors easily obtained on clinical history can be used to understand likelihood of reconstruction. This knowledge may help identify barriers to reconstruction, ultimately improving the clinicians' ability to appropriately educate mastectomy patients and ensure effective decision making around breast reconstruction

Using GSS For Decision Reconstruction: A Preliminary Study

The importance of understanding the reasons of past decisions is not a new subject. However, there seems to be a gap regarding the verification of the efficiency of tools for understanding past decisions. In this paper we posit that the ability to perform decision reconstruction, using a GSS solution, can provide a flexible solution to the problem, but only if the information model underneath it is able to support/structure both ways the phases of a decision-making process. Based on earlier work, we present a first proposal for a general information model to support the decision-making process, as well as the decision reconstruction process. We tested these ideas by setting a case study where we used a prototype, based on a proposed model, to analyze a simulated public contracting process and present a discussion based on the obtained results

The missing link : theoretical reflections on decision reconstruction

In this paper, we address theoretical considerations on the problem of decision reconstruction, which is defined as the process that allows an individual or group of individuals, whether internal or external to the organization, to understand how a group, using a GSS, reached a previous decision. We also analyze the implications of decision reconstruction with regard to both group support systems (GSS) research and knowledge management We present an information model, whose constituting elements are not only concerned with GSS decision-making, but also towards GSS decision reconstruction. Using a GSS prototype based on the proposed model, we made a preliminary test in order to analyze how different people act when reconstructing decisions. In the process, we have exposed and detected limitations and present a solution proposal to overcome these limitations

Reducing Decisional Conflict and Enhancing Satisfaction with Information among Women Considering Breast Reconstruction following Mastectomy: Results from the BRECONDA Randomized Controlled Trial

Background: Deciding whether or not to have breast reconstruction following breast cancer diagnosis is a complex decision process. This randomized controlled trial assessed the impact of an online decision aid [Breast RECONstruction Decision Aid (BRECONDA)] on breast reconstruction decision-making. Methods: Women (n = 222) diagnosed with breast cancer or ductal carcinoma in situ, and eligible for reconstruction following mastectomy, completed an online baseline questionnaire. They were then assigned randomly to receive either standard online information about breast reconstruction (control) or standard information plus access to BRECONDA (intervention). Participants then completed questionnaires at 1 and 6 months after randomization. The primary outcome was participants' decisional conflict 1 month after exposure to the intervention. Secondary outcomes included decisional conflict at 6 months, satisfaction with information at 1 and 6 months, and 6-month decisional regret. Results: Linear mixed-model analyses revealed that 1-month decisional conflict was significantly lower in the intervention group (27.18) compared with the control group (35.5). This difference was also sustained at the 6-month follow-up. Intervention participants reported greater satisfaction with information at 1- and 6-month follow-up, and there was a nonsignificant trend for lower decisional regret in the intervention group at 6-month follow-up. Intervention participants' ratings for BRECONDA demonstrated high user acceptability and overall satisfaction. Conclusions: Women who accessed BRECONDA benefited by experiencing significantly less decisional conflict and being more satisfied with information regarding the reconstruction decisional process than women receiving standard care alone. These findings support the efficacy of BRECONDA in helping women to arrive at their breast reconstruction decision

Dynamical Enhancement of the Large Scale Remote Sensing Imagery for Decision Support in Environmental Resource Management

In this study, we address a new efficient robust optimization approach to large-scale environmental RSSS reconstruction/enhancement as required for remote sensing imaging with multi-spectral array sensors/SAR. First, the problem- oriented robustification of the previously proposed fused Bayesian-regularization (FBR) enhanced imaging method is performed to alleviate its ill-poseness due to system-level and model-level uncertainties. Second, we incorporate the dynamic filtration paradigm into the overall reconstruction technique to enhance the quality of the imagery as it is required for decision support in environmental resource management with dynamic RSSS behavior.CINVESTA

Investigating the Complexities of Nation-building: A Sub-national Regional Perspective

Stabilization and reconstruction operations are necessary to secure and maintain the peace in the aftermath of conflict. The complexities of nation-building involve many different but interrelated systems and institutions. The basic structure of a country may or may not remain; its political, economic, and judicial systems, cultural, educational, medical, and military institutions, and critical infrastructure all vitally contribute to the overall progression of stability and prosperity. Understanding the significance of the dynamic relationships between the forces in play during stability and reconstruction operations is paramount to the successful conclusion of such missions. The system dynamics model proposed in this research functions as a support tool allowing decision makers and analysts to investigate different sets of decision approaches at a sub-national, regional level. Concentration on the regional level allows for specific identification and investigation of potentially troublesome regions, providing the model-user with more detailed information concerning the internal dynamics prevalent within the area of operations. Construction of two different measures via logistic regression, a probability of stabilization success and a probability of stabilization failure, provide indication as to the successful execution of stabilization and reconstruction operations. The proposed model is a general construct, widely adaptable to a variety of post-conflict nation-building scenarios. The model is notionally demonstrated using Operation Iraqi Freedom as a test case

A Dynamic Knowledge Model of Project Time-Cost Analysis Based on Trend Modelling

This paper investigates the application of trend quantifiers of project time-cost analysis as a tool for decision-making support in the project management. Practical project management-related problems are solved under information shortages. It means that methods of statistical analysis cannot be easily used as they are based on the law of large numbers of observations. Numbers are information intensive quantifiers. The least information intensive quantifier is a trend; its values are increasing, constant, decreasing. If a derivative cannot be quantified by a trend, then nothing is known and therefore nothing can be analyzed/predicted. For this reason, the trend model M was created. The model M is based on a degraded set of differential equations or heuristics. A trend analysis of the model M is an evaluation of the relevant discrete set of solutions/scenarios S. A trend reconstruction is an evaluation of the model M if a (sub)set of scenarios S is given. The paper studies linear reconstruction, i.e. the model M is a set of linear differential equations. The trend reconstruction is partially reverse process to trend analysis. A case study has 7 variables (e.g. Project duration, Direct personnel costs, Indirect personal costs etc.) and the reconstructed set of linear differential equations has 7 equations. The set of 243 scenarios is obtained if this reconstructed set of trend linear equations is solved. Any future or past behavior of the model M can be described by a sequence of obtained scenarios

A Novel Algorithm for Tridimensional Reconstruction using Data from Low-Cost Sensors

A novel algorithm to generate a tridimensional reconstruction of an object using a series of images obtained with sensors within low-cost cameras is proposed. As a matter of particular study, this paper present the methodology employed to set an array of sensors to extract the necessary information from a particular group of acquired images surrounding the sample, the processing schema for its interpretation and its mapping, in order to approximate a tridimensional model with the use of real data. The simulation results can verify the efficiency of the proposed approach, showing an application where it could be a useful tool for decision support or resource management.Consejo Nacional de Ciencia y Tecnologí

Using machine learning to infer reasoning provenance from user interaction log data: based on the data/frame theory of sensemaking

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems