Computational dynamic support model for social support assignments around stressed individuals among graduate students

Configuring the best resources for optimal overall performance is one of the challenging topics in Computer Science domains. Within the domain of intelligent social support assignment applications to help individuals with stress, it requires important aspects of configuring a possible set of input and parameters to obtain optimal solutions from both computational support provider and recipient models. However, the existing configuration algorithms are often randomized and static. Thus, their results can vary significantly between multiple runs. In the context of social support perspectives, the assigned support may not sufficient or cause a burden to the providers. Hence, this study aims to develop the dynamic configuration algorithm to provide an optimal support assignment based on information generated from both social support recipient and provision computational models. The computational models that simulate support providers and recipients behaviours were developed to generate several simulated patterns. These models explain the dynamics of support seeking and provision behaviours and were evaluated using equilibria analysis and automatic logical verification approaches for 14 selected empirical cases. Later, the dynamic configuration algorithm was designed to utilize possible support assignments based on support provision requirements. The algorithm complexity analysis was used to measure the execution time in the worst case. Finally, a prototype was developed and validated with 30 graduate students. This study allows to explore computational analysis in explicit comprehension of how seeking and giving support process can be obtained at different case conditions. Also, the study explicitly shows the psychological stress of support recipient can be reduced after the dynamic configuration algorithm process assigned selected social support providers from social support network members. Furthermore, this study provides an alternative method for software engineers in intelligent stress management systems to integrate social support-based concepts as one of the mechanisms in addressing the support of an individual with cognitive related stress

Policy implications of technologies for cognitive enhancement

The Advanced Concepts Group at Sandia National Laboratory and the Consortium for Science, Policy and Outcomes at Arizona State University convened a workshop in May 2006 to explore the potential policy implications of technologies that might enhance human cognitive abilities. The group's deliberations sought to identify core values and concerns raised by the prospect of cognitive enhancement. The workshop focused on the policy implications of various prospective cognitive enhancements and on the technologies/nanotechnology, biotechnology, information technology, and cognitive science--that enable them. The prospect of rapidly emerging technological capabilities to enhance human cognition makes urgent a daunting array of questions, tensions, ambitions, and concerns. The workshop elicited dilemmas and concerns in ten overlapping areas: science and democracy; equity and justice; freedom and control; intergenerational issues; ethics and competition; individual and community rights; speed and deliberations; ethical uncertainty; humanness; and sociocultural risk. We identified four different perspectives to encompass the diverse issues related to emergence of cognitive enhancement technologies: (1) Laissez-faire--emphasizes freedom of individuals to seek and employ enhancement technologies based on their own judgment; (2) Managed technological optimism--believes that while these technologies promise great benefits, such benefits cannot emerge without an active government role; (3) Managed technological skepticism--views that the quality of life arises more out of society's institutions than its technologies; and (4) Human Essentialism--starts with the notion of a human essence (whether God-given or evolutionary in origin) that should not be modified. While the perspectives differ significantly about both human nature and the role of government, each encompasses a belief in the value of transparency and reliable information that can allow public discussion and decisions about cognitive enhancement. The practical question is how to foster productive discussions in a society whose attention is notably fragmented and priorities notably diverse. The question of what to talk about remains central, as each of the four perspectives is concerned about different things. Perhaps the key issue for initial clarification as a condition for productive democratic discussion has to do with the intended goals of cognitive enhancement, and the mechanisms for allowing productive deliberation about these goals

A theoretical and practical approach to a persuasive agent model for change behaviour in oral care and hygiene

There is an increased use of the persuasive agent in behaviour change interventions due to the agent‘s features of sociable, reactive, autonomy, and proactive. However, many interventions have been unsuccessful, particularly in the domain of oral care. The psychological reactance has been identified as one of the major reasons for these unsuccessful behaviour change interventions. This study proposes a formal persuasive agent model that leads to psychological reactance reduction in order to achieve an improved behaviour change intervention in oral care and hygiene. Agent-based simulation methodology is adopted for the development of the proposed model. Evaluation of the model was conducted in two phases that include verification and validation. The verification process involves simulation trace and stability analysis. On the other hand, the validation was carried out using user-centred approach by developing an agent-based application based on belief-desire-intention architecture. This study contributes an agent model which is made up of interrelated cognitive and behavioural factors. Furthermore, the simulation traces provide some insights on the interactions among the identified factors in order to comprehend their roles in behaviour change intervention. The simulation result showed that as time increases, the psychological reactance decreases towards zero. Similarly, the model validation result showed that the percentage of respondents‘ who experienced psychological reactance towards behaviour change in oral care and hygiene was reduced from 100 percent to 3 percent. The contribution made in this thesis would enable agent application and behaviour change intervention designers to make scientific reasoning and predictions. Likewise, it provides a guideline for software designers on the development of agent-based applications that may not have psychological reactance

Extending the Recognition-Primed Decision Model to Support Human-Agent Collaboration

There has been much research investigating team cognition, naturalistic decision making, and collaborative technology as it relates to real world, complex domains of practice. However, there has been limited work in incorporating naturalistic decision making models for supporting distributed team decision making. The aim of this research is to support human decision making teams using cognitive agents empowered by a collaborative Recognition-Primed Decision model. In this paper, we first describe an RPDenabled agent architecture (R-CAST), in which we have implemented an internal mechanism of decision-making adaptation based on collaborative expectancy monitoring, and an information exchange mechanism driven by relevant cue analysis. We have evaluated R-CAST agents in a real-time simulation environment, feeding teams with frequent decisionmaking tasks under di#erent tempo situations. While the result conforms to psychological findings that human team members are extremely sensitive to their workload in hightempo situations, it clearly indicates that human teams, when supported by R-CAST agents, can perform better in the sense that they can maintain team performance at acceptable levels in high time pressure situations

Supporting mega-collaboration: a framework for the dynamic development of team culture

Indiana University-Purdue University Indianapolis (IUPUI)This research project, inspired by the nationwide crisis following Hurricane Katrina, identifies mega-collaboration as an emergent social phenomenon enabled by the Internet. The substantial, original contribution of this research is a mega-collaboration tool (MCT) to enable grassroots individuals and organizations to rapidly form teams, negotiate problem definitions, allocate resources, organize interventions, and mediate their efforts with those of official response organizations. The project demonstrated that a tool that facilitates the exploration of a team’s problem space can support online collaboration. It also determined the basic building blocks required to construct a mega-collaboration tool. In addition, the project demonstrated that it is possible to dynamically build the team data structure through use of the proposed interface, a finding that validates the database design at the core of the MCT. This project has made a unique contribution by proposing a new operational vision of how disaster response, and potentially many other problems, should be managed in the future

A framework for knowledge-based team training

Teamwork is crucial to many disciplines, from activities such as organized sports to economic and military organizations. Team training is difficult and as yet there are few automated tools to assist in the training task. As with the training of individuals, effective training depends upon practice and proper training protocols. In this research, we defined a team training framework for constructing team training systems in domains involving command and control teams. This team training framework provides an underlying model of teamwork and programming interfaces to provide services that ease the construction of team training systems. Also, the framework enables experimentation with training protocols and coaching to be conducted more readily, as team training systems incorporating new protocols or coaching capabilities can be more easily built. For this framework (called CAST-ITT) we developed an underlying intelligent agent architecture known as CAST (Collaborative Agents Simulating Teamwork). CAST provides the underlying model of teamwork and agents to simulate virtual team members. CAST-ITT (Intelligent Team Trainer) uses CAST to also monitor trainees, and support performance assessment and coaching for the purposes of evaluating the performance of a trainee as a member of a team. CAST includes a language for describing teamwork called MALLET (Multi-Agent Logic Language for Encoding Teamwork). MALLET allows us to codify the behaviors of team members (both as virtual agents and as trainees) for use by CAST. In demonstrating CAST-ITT through an implemented team training system called TWP-DDD we have shown that a team training system can be built that uses the framework (CAST-ITT) and has good performance and can be used for achieving real world training objectives