Advancing psychotherapy and evidence-based psychological interventions

Psychological models of mental disorders guide research into psychological and environmental factors that elicit and maintain mental disorders as well as interventions to reduce them. This paper addresses four areas. (1) Psychological models of mental disorders have become increasingly transdiagnostic, focusing on core cognitive endophenotypes of psychopathology from an integrative cognitive psychology perspective rather than offering explanations for unitary mental disorders. It is argued that psychological interventions for mental disorders will increasingly target specific cognitive dysfunctions rather than symptom-based mental disorders as a result. (2) Psychotherapy research still lacks a comprehensive conceptual framework that brings together the wide variety of findings, models and perspectives. Analysing the state-of-the-art in psychotherapy treatment research, “component analyses” aiming at an optimal identification of core ingredients and the mechanisms of change is highlighted as the core need towards improved efficacy and effectiveness of psychotherapy, and improved translation to routine care. (3) In order to provide more effective psychological interventions to children and adolescents, there is a need to develop new and/or improved psychotherapeutic interventions on the basis of developmental psychopathology research taking into account knowledge of mediators and moderators. Developmental neuroscience research might be instrumental to uncover associated aberrant brain processes in children and adolescents with mental health problems and to better examine mechanisms of their correction by means of psychotherapy and psychological interventions. (4) Psychotherapy research needs to broaden in terms of adoption of large-scale public health strategies and treatments that can be applied to more patients in a simpler and cost-effective way. Increased research on efficacy and moderators of Internet-based treatments and e-mental health tools (e.g. to support “real time” clinical decision-making to prevent treatment failure or relapse) might be one promising way forward.

Structural and Institutional Determinants of Influence Reputation: A Comparison of Collaborative and Adversarial Policy Networks in Decision Making and Implementation

The central assumption in the literature on collaborative networks and policy networks is that political outcomes are affected by a variety of state and nonstate actors. Some of these actors are more powerful than others and can therefore have a considerable effect on decision making. In this article, we seek to provide a structural and institutional explanation for these power differentials in policy networks and support the explanation with empirical evidence. We use a dyadic measure of influence reputation as a proxy for power, and posit that influence reputation over the political outcome is related to vertical integration into the political system by means of formal decision-making authority, and to horizontal integration by means of being well embedded into the policy network. Hence, we argue that actors are perceived as influential because of two complementary factors: (a) their institutional roles and (b) their structural positions in the policy network. Based on temporal and cross-sectional exponential random graph models, we compare five cases about climate, telecommunications, flood prevention, and toxic chemicals politics in Switzerland and Germany. The five networks cover national and local networks at different stages of the policy cycle. The results confirm that institutional and structural drivers seem to have a crucial impact on how an actor is perceived in decision making and implementation and, therefore, their ability to significantly shape outputs and service delivery.

Greenfield shopping center development. The integration between environmental features, building design and investment analysis at the decision making stage

The Shopping centre is a long term investment in which Greenfield development decisions are often taken based on risks analysis regarding construction costs, location, competition, market and an expected DCF. Furthermore, integration between the building design, project planning, operational costs and investment analysis is not entirely considered by the investor at the decision making stage. The absence of such information tends to produce certain negative impacts on the future running costs and annual maintenance of the building, especially on energy demand and other occupancy expenses paid by the tenants to the landlord. From the investor´s point of view, this blind spot in strategy development will possibly decrease their profit margin as changes in the occupancy expenses[ ] have a direct outcome on the profit margin. In order to try to reduce some higher operating cost components such as energy use and other utility savings as well as their CO2 emissions, quite a few income properties worldwide have some type of environmental label such as BREEAM and LEED. The drawback identified in this labelling is that usually the investments required to get an ecolabel are high and the investor finds no direct evidence that it increases market value. However there is research on certified commercial properties (especially offices) that shows better performance in terms of occupancy rate and rental cost (Warren-Myers, 2012). Additionally, Sayce (2013) says that the certification only provides a quick reference point i.e. the lack of a certificate does not indicate that a building is not sustainable or efficient. Based on the issues described above, this research compares important components of the development stages such as investments costs, concept/ strategy development as well as the current investor income and property value. The subjects for this analysis are a shopping centre designed with passive cooling/bioclimatic strategies evaluated at the decision making stage, a certified regional shopping centre and a non-certified standard regional shopping centre. Moreover, the proposal intends to provide decision makers with some tools for linking green design features to the investment analysis in order to optimize the decision making process when looking into cost savings and design quality.

Case-based reasoning to support decision making for managing drinking water quality events in distribution systems

Peer reviewed

GRADE Evidence to Decision (EtD) frameworks : A systematic and transparent approach to making well-informed healthcare choices. 1. Introduction

Funding: Work on this article has been partially funded by the European Commission FP7 Program (grant agreement 258583) as part of the DECIDE project. Sole responsibility lies with the authors; the European Commission is not responsible for any use that may be made of the information contained therein.

GRADE Evidence to Decision (EtD) frameworks : a systematic and transparent approach to making well informed healthcare choices. 2: Clinical practice guidelines

Funding: Work on this article has been partially funded by the European Commission FP7 Program (grant agreement 258583) as part of the DECIDE project. Sole responsibility lies with the authors; the European Commission is not responsible for any use that may be made of the information contained therein.

Precautionary principles: a jurisdiction-free framework for decision-making under risk

Fundamental principles of precaution are legal maxims that ask for preventive actions, perhaps as contingent interim measures while relevant information about causality and harm remains unavailable, to minimize the societal impact of potentially severe or irreversible outcomes. Such principles do not explain how to make choices or how to identify what is protective when incomplete and inconsistent scientific evidence of causation characterizes the potential hazards. Rather, they entrust lower jurisdictions, such as agencies or authorities, to make current decisions while recognizing that future information can contradict the scientific basis that supported the initial decision. After reviewing and synthesizing national and international legal aspects of precautionary principles, this paper addresses the key question: How can society manage potentially severe, irreversible or serious environmental outcomes when variability, uncertainty, and limited causal knowledge characterize their decision-making? A decision-analytic solution is outlined that focuses on risky decisions and accounts for prior states of information and scientific beliefs that can be updated as subsequent information becomes available. As a practical and established approach to causal reasoning and decision-making under risk, inherent to precautionary decision-making, these (Bayesian) methods help decision-makers and stakeholders because they formally account for probabilistic outcomes, new information, and are consistent and replicable. Rational choice of an action from among various alternatives-defined as a choice that makes preferred consequences more likely-requires accounting for costs, benefits and the change in risks associated with each candidate action. Decisions under any form of the precautionary principle reviewed must account for the contingent nature of scientific information, creating a link to the decision-analytic principle of expected value of information (VOI), to show the relevance of new information, relative to the initial ( and smaller) set of data on which the decision was based. We exemplify this seemingly simple situation using risk management of BSE. As an integral aspect of causal analysis under risk, the methods developed in this paper permit the addition of non-linear, hormetic dose-response models to the current set of regulatory defaults such as the linear, non-threshold models. This increase in the number of defaults is an important improvement because most of the variants of the precautionary principle require cost-benefit balancing. Specifically, increasing the set of causal defaults accounts for beneficial effects at very low doses. We also show and conclude that quantitative risk assessment dominates qualitative risk assessment, supporting the extension of the set of default causal models.

Knowledge based improvement:simulation and artificial intelligence for identifying and improving human decision-making in an operations systems

The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as 'knowledge-based improvement' (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant.

Multi-criteria decision making approaches for supplier evaluation and selection:a literature review

Supplier evaluation and selection problem has been studied extensively. Various decision making approaches have been proposed to tackle the problem. In contemporary supply chain management, the performance of potential suppliers is evaluated against multiple criteria rather than considering a single factor-cost. This paper reviews the literature of the multi-criteria decision making approaches for supplier evaluation and selection. Related articles appearing in the international journals from 2000 to 2008 are gathered and analyzed so that the following three questions can be answered: (i) Which approaches were prevalently applied? (ii) Which evaluating criteria were paid more attention to? (iii) Is there any inadequacy of the approaches? Based on the inadequacy, if any, some improvements and possible future work are recommended. This research not only provides evidence that the multi-criteria decision making approaches are better than the traditional cost-based approach, but also aids the researchers and decision makers in applying the approaches effectively.

ICT and internet use at the household levels:a melting pot of sub-optimum decision making in the case of E-Grocery shopping

Research shows that consumers are readily embracing the Internet to buy products. This paper proposes that, in the case of grocery shopping, this may lead to sub-optimal decisions at the household level. Decisions online on what, where and from who to buy are normally taken by one individual. In the case of grocery shopping, decisions, however, need to be ‘vetted’ by ‘other’ individuals within the household. The ‘household wide related’ decisions influence how information technologies and systems for commerce should be designed and managed for optimum decision making. This paper argues, unlike previous research, that e-grocery retailing is failing to grow to its full potential not solely because of the ‘classical’ hazards and perceived risks associated with doing grocery shopping online but because e-grocery retailing strategy has failed to acknowledge the micro-household level specificities that affect decision making. Our exploratory research is based on empirical evidence which were collected through telephone interviews. We offer an insight into how e-grocery ‘fits’ and is ‘disrupted’ by the reality of day to day consumption decision making at the household level. Our main finding is to advocate a more role-neutral, multi-user and multi-technology approach to e-grocery shopping which re-defines the concept of the main shopper/decision maker thereby reconceptualising the ‘shopping logic’ for grocery products.

Supporting decision-making for self-adaptive systems:from goal models to dynamic decision networks

Context/Motivation - Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total fulfilment of functional (goals) and non-functional requirements (softgoals). Different goalrealization strategies can have different effects on softgoals which are specified with weighted contribution-links. The final decision about what strategy to use is based, among other reasons, on a utility function that takes into account the weighted sum of the different effects on softgoals. Questions/Problems - One of the main challenges about decisionmaking in self-adaptive systems is to deal with uncertainty during runtime. New techniques are needed to systematically revise the current model when empirical evidence becomes available from the deployment. Principal ideas/results - In this paper we enrich the decision-making supported by goal models by using Dynamic Decision Networks (DDNs). Goal realization strategies and their impact on softgoals have a correspondence with decision alternatives and conditional probabilities and expected utilities in the DDNs respectively. Our novel approach allows the specification of preferences over the softgoals and supports reasoning about partial satisfaction of softgoals using probabilities. We report results of the application of the approach on two different cases. Our early results suggest the decision-making process of SASs can be improved by using DDNs. © 2013 Springer-Verlag.