Broadening public input into science policy decision making: can the democratic model for science communication influence scientific, innovation and technological trajectories?

This PhD thesis investigates the potential use of science communication models to engage a broader swathe of actors in decision making in relation to scientific and technological innovation in order to address possible democratic deficits in science and technology policy-making. A four-pronged research approach has been employed to examine different representations of the public(s) and different modes of engagement. The first case study investigates whether patient-groups could represent an alternative needs-driven approach to biomedical and health sciences R & D. This is followed by enquiry into the potential for Science Shops to represent a bottom-up approach to promote research and development of local relevance. The barriers and opportunities for the involvement of scientific researchers in science communication are next investigated via a national survey which is comparable to a similar survey conducted in the UK. The final case study investigates to what extent opposition or support regarding nanotechnology (as an emerging technology) is reflected amongst the YouTube user community and the findings are considered in the context of how support or opposition to new or emerging technologies can be addressed using conflict resolution based approaches to manage potential conflict trajectories. The research indicates that the majority of communication exercises of relevance to science policy and planning take the form of a one-way flow of information with little or no facility for public feedback. This thesis proposes that a more bottom-up approach to research and technology would help broaden acceptability and accountability for decisions made relating to new or existing technological trajectories. This approach could be better integrated with and complementary to government, institutional, e.g. university, and research funding agencies activities and help ensure that public needs and issues are better addressed directly by the research community. Such approaches could also facilitate empowerment of societal stakeholders regarding scientific literacy and agenda-setting. One-way information relays could be adapted to facilitate feedback from representative groups e.g. Non-governmental organisations or Civil Society Organisations (such as patient groups) in order to enhance the functioning and socio-economic relevance of knowledge-based societies to the betterment of human livelihoods.

Real-Time Decision Making and Aggressive Behavior in Youth: A Heuristic Model of Response Evaluation and Decision (RED).

Considerable scientific and intervention attention has been paid to judgment and decision-making systems associated with aggressive behavior in youth. However, most empirical studies have investigated social-cognitive correlates of stable child and adolescent aggressiveness, and less is known about real-time decision making to engage in aggressive behavior. A model of real-time decision making must incorporate both impulsive actions and rational thought. The present paper advances a process model (response evaluation and decision; RED) of real-time behavioral judgments and decision making in aggressive youths with mathematic representations that may be used to quantify response strength. These components are a heuristic to describe decision making, though it is doubtful that individuals always mentally complete these steps. RED represents an organization of social-cognitive operations believed to be active during the response decision step of social information processing. The model posits that RED processes can be circumvented through impulsive responding. This article provides a description and integration of thoughtful, rational decision making and nonrational impulsivity in aggressive behavioral interactions.

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.

A system's model for maintenance support and decision making in building services systems

Modern buildings are designed to enhance the match between environment, spaces and the people carrying out work, so that the well-being and the performance of the occupants are all in harmony. Building services are systems that facilitate a healthy working environment within which workers productivity can be optimised in the buildings. However, the maintenance of these services is fraught with problems that may contribute to up to 50% of the total life cycle cost of the building. Maintenance support is one area which is not usually designed into the system as this is not common practice in the services industry. The other areas of shortfall for future designs are; client requirements, commissioning, facilities management data and post occupancy evaluation feedback which needs to be adequately planned to capture and document this information for use in future designs. At the University of Reading an integrated approach has been developed to assemble the multitude of aspects inherent in this field. The means records required and measured achievements for the benefit of both building owners and practitioners. This integrated approach can be represented in a Through Life Business Model (TLBM) format using the concept of Integrated Logistic Support (ILS). The prototype TLBM developed utilises the tailored tools and techniques of ILS for building services. This TLBM approach will facilitate the successful development of a databank that would be invaluable in capturing essential data (e.g. reliability of components) for enhancing future building services designs, life cycle costing and decision making by practitioners, in particular facilities managers.

A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.

The relation of secondary student's career choice readiness to a six-phase model of career decision-making

Based on common aspects of recent models of career decision-making (CDM) a sixphase model of CDM for secondary students is presented and empirically evaluated. The study tested the hypothesis that students who are in later phases possess more career choice readiness and consider different numbers of career alternatives. 266 Swiss secondary students completed measures tapping phase of CDM, career choice readiness, and number of considered career options. Career choice readiness showed an increase with phase of CDM. Later phases were generally associated with a larger increase in career choice readiness. Number of considered career options showed a curve-linear development with fewer options considered at the beginning and at the end of the process. Male students showed a larger variability in their distribution among the process with more male than female students in the first and last phase of the process. Implications for theory and practice are presented.

Emotions, Not Just Decision-Making Processes, Are Critical to an Evolutionary Model of Human Behavior

An evolutionary model of human behavior should privilege emotions: essential, phylogenetically ancient behaviors that learning and decision making only subserve. Infants and non-mammals lack advanced cognitive powers but still survive. Decision making is only a means to emotional ends, which organize and prioritize behavior. The emotion of pride/shame, or dominance striving, bridges the social and biological sciences via internalization of cultural norms.

Behavioral decision making and the levels of change: An application of the transtheoretical model to prenatal smoking in low -income women

Despite continued research and public health efforts to reduce smoking during pregnancy, prenatal cessation rates in the United States have decreased and the incidence of low birth weight has increased from 1985 to 1991. Lower socioeconomic status women who are at increased risk for poor pregnancy outcomes may be resistant to current intervention efforts during pregnancy. The purpose of this dissertation was to investigate the determinants of continued smoking and quitting among low-income pregnant women.^ Using data from cross-sectional surveys of 323 low-income pregnant smokers, the first study developed and tested measures of the pros and cons of smoking during pregnancy. The original decisional balance measure for smoking was compared with a new measure that added items thought to be more salient to the target population. Confirmatory factor analysis using structural equation modeling showed neither the original nor new measure fit the data adequately. Using behavioral science theory, content from interviews with the population, and statistical evidence, two 7-item scales representing the pros and cons were developed from a portion (n = 215) of the sample and successfully cross-validated on the remainder of the sample (n = 108). Logistic regression found only pros were significantly associated with continued smoking. In a discriminant function analysis, stage of change was significantly associated with pros and cons of smoking.^ The second study examined the structural relationships between psychosocial constructs representing some of the levels of and the pros and cons of smoking. The cross-sectional design mandates that statements made regarding prediction do not prove causation or directionality from the data or methods analysis. Structural equation modeling found the following: more stressors and family criticism were significantly more predictive of negative affect than social support; a bi-directional relationship was found between negative affect and current nicotine addiction; and negative affect, addiction, stressors, and family criticism were significant predictors of pros of smoking.^ The findings imply reversing the trend of decreasing smoking cessation during pregnancy may require supplementing current interventions for this population of pregnant smokers with programs addressing nicotine addiction, negative affect, and other psychosocial factors such as family functioning and stressors. ^

Supporting decision making under uncertainty: Development of a participatory integrated model for water management in the middle Guadiana river basin.

Following the Integrated Water Resources Management approach, the European Water Framework Directive demands Member States to develop water management plans at the catchment level. Those plans have to integrate the different interests and must be developed with stakeholder participation. To face these requirements, managers need tools to assess the impacts of possible management alternatives on natural and socio-economic systems. These tools should ideally be able to address the complexity and uncertainties of the water system, while serving as a platform for stakeholder participation. The objective of our research was to develop a participatory integrated assessment model, based on the combination of a crop model, an economic model and a participatory Bayesian network, with an application in the middle Guadiana sub-basin, in Spain. The methodology is intended to capture the complexity of water management problems, incorporating the relevant sectors, as well as the relevant scales involved in water management decision making. The integrated model has allowed us testing different management, market and climate change scenarios and assessing the impacts of such scenarios on the natural system (crops), on the socio-economic system (farms) and on the environment (water resources). Finally, this integrated assessment modelling process has allowed stakeholder participation, complying with the main requirements of current European water laws.

Analysing the complexity of the model-based decision making processes within the industrial management context

PURPOSE The decision-making process plays a key role in organizations. Every decision-making process produces a final choice that may or may not prompt action. Recurrently, decision makers find themselves in the dichotomous question of following a traditional sequence decision-making process where the output of a decision is used as the input of the next stage of the decision, or following a joint decision-making approach where several decisions are taken simultaneously. The implication of the decision-making process will impact different players of the organization. The choice of the decision- making approach becomes difficult to find, even with the current literature and practitioners’ knowledge. The pursuit of better ways for making decisions has been a common goal for academics and practitioners. Management scientists use different techniques and approaches to improve different types of decisions. The purpose of this decision is to use the available resources as well as possible (data and techniques) to achieve the objectives of the organization. The developing and applying of models and concepts may be helpful to solve managerial problems faced every day in different companies. As a result of this research different decision models are presented to contribute to the body of knowledge of management science. The first models are focused on the manufacturing industry and the second part of the models on the health care industry. Despite these models being case specific, they serve the purpose of exemplifying that different approaches to the problems and could provide interesting results. Unfortunately, there is no universal recipe that could be applied to all the problems. Furthermore, the same model could deliver good results with certain data and bad results for other data. A framework to analyse the data before selecting the model to be used is presented and tested in the models developed to exemplify the ideas. METHODOLOGY As the first step of the research a systematic literature review on the joint decision is presented, as are the different opinions and suggestions of different scholars. For the next stage of the thesis, the decision-making process of more than 50 companies was analysed in companies from different sectors in the production planning area at the Job Shop level. The data was obtained using surveys and face-to-face interviews. The following part of the research into the decision-making process was held in two application fields that are highly relevant for our society; manufacturing and health care. The first step was to study the interactions and develop a mathematical model for the replenishment of the car assembly where the problem of “Vehicle routing problem and Inventory” were combined. The next step was to add the scheduling or car production (car sequencing) decision and use some metaheuristics such as ant colony and genetic algorithms to measure if the behaviour is kept up with different case size problems. A similar approach is presented in a production of semiconductors and aviation parts, where a hoist has to change from one station to another to deal with the work, and a jobs schedule has to be done. However, for this problem simulation was used for experimentation. In parallel, the scheduling of operating rooms was studied. Surgeries were allocated to surgeons and the scheduling of operating rooms was analysed. The first part of the research was done in a Teaching hospital, and for the second part the interaction of uncertainty was added. Once the previous problem had been analysed a general framework to characterize the instance was built. In the final chapter a general conclusion is presented. FINDINGS AND PRACTICAL IMPLICATIONS The first part of the contributions is an update of the decision-making literature review. Also an analysis of the possible savings resulting from a change in the decision process is made. Then, the results of the survey, which present a lack of consistency between what the managers believe and the reality of the integration of their decisions. In the next stage of the thesis, a contribution to the body of knowledge of the operation research, with the joint solution of the replenishment, sequencing and inventory problem in the assembly line is made, together with a parallel work with the operating rooms scheduling where different solutions approaches are presented. In addition to the contribution of the solving methods, with the use of different techniques, the main contribution is the framework that is proposed to pre-evaluate the problem before thinking of the techniques to solve it. However, there is no straightforward answer as to whether it is better to have joint or sequential solutions. Following the proposed framework with the evaluation of factors such as the flexibility of the answer, the number of actors, and the tightness of the data, give us important hints as to the most suitable direction to take to tackle the problem. RESEARCH LIMITATIONS AND AVENUES FOR FUTURE RESEARCH In the first part of the work it was really complicated to calculate the possible savings of different projects, since in many papers these quantities are not reported or the impact is based on non-quantifiable benefits. The other issue is the confidentiality of many projects where the data cannot be presented. For the car assembly line problem more computational power would allow us to solve bigger instances. For the operation research problem there was a lack of historical data to perform a parallel analysis in the teaching hospital. In order to keep testing the decision framework it is necessary to keep applying more case studies in order to generalize the results and make them more evident and less ambiguous. The health care field offers great opportunities since despite the recent awareness of the need to improve the decision-making process there are many opportunities to improve. Another big difference with the automotive industry is that the last improvements are not spread among all the actors. Therefore, in the future this research will focus more on the collaboration between academia and the health care sector.

Political Decision-Making in Switzerland. The Consensus Model under Pressure

This in-depth study of the decision-making processes of the early 2000s shows that the Swiss consensus democracy has changed considerably. Power relations have transformed, conflict has increased, coalitions have become more unstable and outputs less predictable. Yet these challenges to consensus politics provide opportunities for innovation.