Quantitative Measures of Regret and Trust in Human-Robot Collaboration Systems

Human-robot collaboration (HRC) systems integrate the strengths of both humans and robots to improve the joint system performance. In this thesis, we focus on social human-robot interaction (sHRI) factors and in particular regret and trust. Humans experience regret during decision-making under uncertainty when they feel that a better result could be obtained if chosen differently. A framework to quantitatively measure regret is proposed in this thesis. We embed quantitative regret analysis into Bayesian sequential decision-making (BSD) algorithms for HRC shared vision tasks in both domain search and assembly tasks. The BSD method has been used for robot decision-making tasks, which however is proved to be very different from human decision-making patterns. Instead, regret theory qualitatively models human\u27s rational decision-making behaviors under uncertainty. Moreover, it has been shown that joint performance of a team will improve if all members share the same decision-making logic. Trust plays a critical role in determining the level of a human\u27s acceptance and hence utilization of a robot. A dynamic network based trust model combing the time series trust model is first implemented in a multi-robot motion planning task with a human-in-the-loop. However, in this model, the trust estimates for each robot is independent, which fails to model the correlative trust in multi-robot collaboration. To address this issue, the above model is extended to interdependent multi-robot Dynamic Bayesian Networks

Decision-making under uncertainty: A Brehmerian approach

This article discusses the contributions of the late Professor Berndt Brehmer with an emphasis on dynamic decision making under uncertainty. This concept has a long history as ambiguity implied in selective attention, later emphasised by prospect theory, which incorporates a time dimension. Time may be a solution to problems of uncertainty, not least the timing of decisions with each other and with environmental developments. This approach sees  decision making, from a process perspective, ultimately asking whether it makes sense to frame decisions as specific events or as an expression of an ongoing design process where the possibility spaces are expanded rather than limited to decision making among pre-existing alternatives. A dynamic view of the time dimension also encourages decision making as learning through probing actions and negotiation and collaboration, as well as with the environment. As much as this may sound like a recipe for managing second-track processes, it is also a recipe for managing through direct interaction, albeit a less-than-objective one understood through the biased perception of boundedly rational actors

Operationalising Analytics for Action: A Conceptual Framework Linking Embedded Analytics with Decision-Making Agility

Organisations are increasingly practising Business analytics (BA) to make data-driven business decisions amidst environmental complexities and fierce global competition. However, organisations find it challenging to operationalise BA outputs (such as analytical models, reports, and visualization) primarily due to a lack of (a) integrated technology, (b) collaboration and (c) governance. These factors inhibit organisations’ ability to make data-driven decisions in an agile manner. Embedded analytics, an emerging BA practice, has the potential to address these issues by integrating BA outputs into business applications and workflows, thereby promoting the culture of data-driven decision-making. In this research-in-progress paper, we integrate the diverse areas of literature on BA, embedded analytics, and dynamic capabilities theory and propose a research model that links embedded analytics to decision-making agility through the development of dynamic capabilities. The details of the framework highlight how organisations can get maximum value from data and analytics initiatives through operationalisation of BA outputs

The challenge of return to work in workers with cancer : employer priorities despite variation in social policies related to work and health

This study explored employer's perspectives on (1) their experience of good practice related to workers diagnosed with cancer and their return to work (RTW), and (2) their perceived needs necessary to achieve good practice as reported by employers from nine separate countries. Twenty-five semi-structured interviews were held in eight European countries and Israel with two to three employers typically including HR managers or line managers from both profit and non-profit organisations of different sizes and sectors. Interviews were recorded and transcribed verbatim. A grounded theory/thematic analysis approach was completed. Employers' experience with RTW assistance for workers with cancer appears to be a dynamic process. Results indicate that good practice includes six phases: (1) reacting to disclosure, (2) collecting information, (3) decision-making related to initial actions, (4) remaining in touch, (5) decision-making on RTW, and (6) follow-up. The exact details of the process are shaped by country, employer type, and worker characteristics; however, there was consistency related to the need for (1) structured procedures, (2) collaboration, (3) communication skills training, (4) information on cancer, and (5) financial resources for realizing RTW support measures. Notwithstanding variations at country, employer, and worker levels, the employers from all nine countries reported that good practice regarding RTW assistance in workers with a history of cancer consists of the six phases above. Employers indicate that they would benefit from shared collaboration and resources that support good practice for this human resource matter. Further research and development based on the six phases of employer support as a framework for a tool or strategy to support workers with a history of cancer across countries and organisations is warranted

Innovative Approaches To Nursing Administration Education; A Systematic Review Based Study

Background: Nursing administration education plays a crucial role in the development of skilled leaders in the ever-evolving healthcare industry. To meet the current challenges in healthcare, it is crucial to employ innovative pedagogical approaches. These approaches include the integration of virtual reality simulations, fostering interdisciplinary collaboration, utilizing real-world case studies, implementing telehealth platforms, and establishing mentorship programs. Addressing this need for forward-thinking nursing administrators is imperative. Aim: This study comprehensively examines the impact of these innovative strategies on nursing administration education. It assesses how their integration enhances decision-making, communication, strategic thinking, technological proficiency, and leadership skills among students. The goal is to illuminate the potential of these approaches in molding skilled healthcare leaders. Method: A mixed-methods approach is utilized. Qualitative interviews with nursing administration students exposed to innovative approaches provide insights. Thematic analysis is employed to extract meaningful patterns, capturing both subjective experiences and skill development outcomes. Results: Findings highlight the transformative potential of innovative approaches in nursing administration education. Virtual reality simulations enhance decision-making, interdisciplinary collaboration fosters effective communication and teamwork, real-world case studies cultivate strategic thinking, telehealth platforms enhance remote service proficiency, and mentorship programs empower leadership competencies. Conclusion: This study underscores the pivotal role of innovation in shaping adept nursing administrators. Integration of innovative approaches equips healthcare leaders with holistic perspectives, adaptable skills, and technological readiness. As healthcare systems evolve, these approaches offer promise for addressing challenges effectively. Innovative Contribution: By delving into cutting-edge nursing administration education, this study offers insights that reshape healthcare leadership. It bridges theory and practice, equipping future administrators to navigate the dynamic healthcare landscape

Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models

Developing effective Multi-Agent Systems (MAS) is critical for many applications requiring collaboration and coordination with humans. Despite the rapid advance of Multi-Agent Deep Reinforcement Learning (MADRL) in cooperative MAS, one major challenge is the simultaneous learning and interaction of independent agents in dynamic environments in the presence of stochastic rewards. State-of-the-art MADRL models struggle to perform well in Coordinated Multi-agent Object Transportation Problems (CMOTPs), wherein agents must coordinate with each other and learn from stochastic rewards. In contrast, humans often learn rapidly to adapt to nonstationary environments that require coordination among people. In this paper, motivated by the demonstrated ability of cognitive models based on Instance-Based Learning Theory (IBLT) to capture human decisions in many dynamic decision making tasks, we propose three variants of Multi-Agent IBL models (MAIBL). The idea of these MAIBL algorithms is to combine the cognitive mechanisms of IBLT and the techniques of MADRL models to deal with coordination MAS in stochastic environments from the perspective of independent learners. We demonstrate that the MAIBL models exhibit faster learning and achieve better coordination in a dynamic CMOTP task with various settings of stochastic rewards compared to current MADRL models. We discuss the benefits of integrating cognitive insights into MADRL models.Comment: 22 pages, 5 figures, 2 table

A Conceptual Framework of Reverse Logistics Impact on Firm Performance

This study aims to examine the reverse logistics factors that impact upon firm performance. We review reverse logistics factors under three research streams: (a) resource-based view of the firm, including: Firm strategy, Operations management, and Customer loyalty (b) relational theory, including: Supply chain efficiency, Supply chain collaboration, and institutional theory, including: Government support and Cultural alignment. We measured firm performance with 5 measures: profitability, cost, innovativeness, perceived competitive advantage, and perceived customer satisfaction. We discuss implications for research, policy and practice

Understanding Leadership in Public Collaborative Context

The paper aims to contribute to theory on collaboration through problematizing and exploring the complexity that characterizes leadership in collaborative contexts and to provide some generative conceptualizations to inform future empirical and conceptual development. It draws on a substantial review of leadership theory relevant to the context of public sector collaboration and provides examples from empirical research on collaboration over the last two decades in the UK and US respectively. It does so by identifying and developing key characteristics of the context, the nature of leadership in relation to context, and leadership agency pertaining to actors who actually make a difference in the process and outcomes of collaboration

Evolution of Supply Chain Collaboration: Implications for the Role of Knowledge

Increasingly, research across many disciplines has recognized the shortcomings of the traditional “integration prescription” for inter-organizational knowledge management. This research conducts several simulation experiments to study the effects of different rates of product change, different demand environments, and different economies of scale on the level of integration between firms at different levels in the supply chain. The underlying paradigm shifts from a static, steady state view to a dynamic, complex adaptive systems and knowledge-based view of supply chain networks. Several research propositions are presented that use the role of knowledge in the supply chain to provide predictive power for how supply chain collaborations or integration should evolve. Suggestions and implications are suggested for managerial and research purposes