Scenarios for urban water management futures: A systematic review.

Constantly changing and evolving social, economic, political, and environmental landscapes create new uncertainties in urban water supplies. These uncertainties surrounding urban water management have been captured using various scenario analysis techniques, which have been developed to envision plausible futures. Although past review papers have conducted broad reviews on water-related issues and water management generally, there has been a lack of attention to urban water management specifically. The growing uncertainty surrounding urban water management systems necessitates a focused review specifically aimed at the use of scenarios in urban water management. Using a comprehensive typology, a systematic review is presented to empirically investigate the necessary dimensions of urban water management scenario assessment. Urban water management scenario studies that exclusively employ qualitative methods, as well as urban water management studies that employ qualitative methods with quantitative techniques, are reviewed against the comprehensive typology. By aligning the reviewed scenarios with the dimensions in the typology, some important gaps in the current literature were identified. The need for: (i) transparency in scenario development and analysis processes, (ii) inclusion of surprises and extreme events, (iii) validation efforts and (iv) considering the impact phase of a scenario process. Recommendations are proposed to address the above gaps in current urban water scenarios literature, providing a path for future scenario analysis in urban water management.

A model of symbiomemesis: machine education and communication as pillars for human-autonomy symbiosis.

Symbiosis is a physiological phenomenon where organisms of different species develop social interdependencies through partnerships. Artificial agents need mechanisms to build their capacity to develop symbiotic relationships. In this paper, we discuss two pillars for these mechanisms: machine education (ME) and bi-directional communication. ME is a new revolution in artificial intelligence (AI) which aims at structuring the learning journey of AI-enabled autonomous systems. In addition to the design of a systematic curriculum, ME embeds the body of knowledge necessary for the social integration of AI, such as ethics, moral values and trust, into the evolutionary design and learning of the AI. ME promises to equip AI with skills to be ready to develop logic-based symbiosis with humans and in a manner that leads to a trustworthy and effective steady-state through the mental interaction between humans and autonomy; a state we name symbiomemesis to differentiate it from ecological symbiosis. The second pillar, bi-directional communication as a discourse enables information to flow between the AI systems and humans. We combine machine education and communication theory as the two prerequisites for symbiosis of AI agents and present a formal computational model of symbiomemesis to enable symbiotic human-autonomy teaming. This article is part of the theme issue 'Towards symbiotic autonomous systems'.

Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach.

System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems.

Scenario processes for socio-environmental systems analysis of futures: A review of recent efforts and a salient research agenda for supporting decision making.

This paper reviews the latest research on scenarios including the processes and products for socio-environmental systems (SES) analysis, modeling and decision making. A group of scenario researchers and practitioners participated in a workshop to discuss consolidation of existing research on the development and use of scenario analysis in exploring and understanding the interplay between human and environmental systems. This paper presents an extended overview of the workshop discussions and follow-up review work. It is structured around the essential challenges that are crucial to progress support of decision making and learning with respect to our highly uncertain socio-environmental futures. It identifies a practical research agenda where challenges are grouped according to the process stage at which they are most significant: before, during, and after the creation of the scenarios as products. These challenges for SES include: enhancing the role of stakeholder and public engagement in the co-development of scenarios, linking scenarios across multiple geographical, sectoral and temporal scales, improving the links between the qualitative and quantitative aspects of scenario analysis, addressing uncertainties especially surprise, addressing scenario diversity and their consistency together, communicating scenarios including visualization methods, and linking scenarios to decision making.

The reliability and transparency bases of trust in human-swarm interaction: principles and implications.

Automation reliability and transparency are key factors for trust calibration and as such can have distinct effects on human reliance behaviour and mission performance. One question that remains unexplored is: what are the implications of reliability and transparency on trust calibration for human-swarm interaction? We investigate this research question in the context of human-swarm interaction, as swarm systems are becoming more popular for their robustness and versatility. Thirty-two participants performed swarm-based tasks under different reliability and transparency conditions. The results indicate that trust, whether it is reliability- or transparency-based, indicates high reliance rates and shorter response times. Reliability-based trust is negatively correlated with correct rejection rates while transparency-based trust is positively correlated with these rates. We conclude that reliability and transparency have distinct effects on trust calibration. Practitioner Summary: Reliability and transparency have distinct effects on trust calibration. Findings from our human experiments suggest that transparency is a necessary design requirement if and when humans need to be involved in the decision-loop of human-swarm systems, especially when swarm reliability is high. Abbreviations: HRI: human-robot interaction; IOS: inter-organisational systems; LMM: liner mixed models; MANOVA: multivariate analysis of variance; UxV: heterogeneous unmanned vehicles; UAV: unmanned aerial vehicle.

Trust Mediating Reliability-Reliance Relationship in Supervisory Control of Human-Swarm Interactions.

OBJECTIVE: This work aims to further test the theory that trust mediates the interdependency between automation reliability and the rate of human reliance on automation. BACKGROUND: Human trust in automation has been the focus of many research studies. Theoretically, trust has been proposed to impact human reliance on automation by mediating the relationship between automation reliability and the rate of human reliance. Experimentally, however, the results are contradicting as some confirm the mediating role of trust, whereas others deny it. Hence, it is important to experimentally reinvestigate this role of trust and understand how the results should be interpreted in the light of existing theory. METHOD: Thirty-two subjects supervised a swarm of unmanned aerial vehicles (UAVs) in foraging missions in which the swarm provided recommendations on whether or not to collect potential targets, based on the information sensed by the UAVs. By manipulating the reliability of the recommendations, we observed changes in participants' trust and their behavioral responses. RESULTS: A within-subject mediation analysis revealed a significant mediation role of trust in the relationship between swarm reliability and reliance rate. High swarm reliability increased the rate of correct acceptances, but decreased the rate of correct rejections. No significant effect of reliability was found on response time. CONCLUSION: Trust is not a mere by-product of the interaction; it possesses a predictive power to estimate the level of reliance on automation. APPLICATION: The mediation role of trust confirms the significance of trust calibration in determining the appropriate level of reliance on swarm automation.

Improved integrated water resource modelling by combining DPSIR and system dynamics conceptual modelling techniques.

Similar to any modelling technique, system dynamics (SD) modelling should start with the essential step of scoping and identifying the problem of interest before further analysis and modelling. In practice, this first step is a challenging task, especially when wicked issues such as water management are being addressed. There is still a vital need for modelling methods and tools that can support modellers to identify and assemble essential data to inform problem scoping and boundary setting. This article aims to narrow this gap by presenting a methodology for combining a series of conceptual modelling techniques (extending the usually linear Driver-Pressure-State-Impact-Response framework with causal loop diagrams, system archetypes, stock and flow diagrams) towards the development of a quantitative SD model. A case study of the Gorganroud-Gharesu Basin, in Iran, is used to illustrate the benefits of the methodology. Our experience shows that combining multiple conceptual models provides complementary insights into the problem boundaries and model structure, as a basis for developing the SD model.

A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: from cognitive maps to agent-based models.

This paper aims to contribute to developing better ways for incorporating essential human elements in decision making processes for modelling of complex socio-ecological systems. It presents a step-wise methodology for integrating perceptions of stakeholders (qualitative) into formal simulation models (quantitative) with the ultimate goal of improving understanding and communication about decision making in complex socio-ecological systems. The methodology integrates cognitive mapping and agent based modelling. It cascades through a sequence of qualitative/soft and numerical methods comprising: (1) Interviews to elicit mental models; (2) Cognitive maps to represent and analyse individual and group mental models; (3) Time-sequence diagrams to chronologically structure the decision making process; (4) All-encompassing conceptual model of decision making, and (5) computational (in this case agent-based) Model. We apply the proposed methodology (labelled ICTAM) in a case study of viticulture irrigation in South Australia. Finally, we use strengths-weakness-opportunities-threats (SWOT) analysis to reflect on the methodology. Results show that the methodology leverages the use of cognitive mapping to capture the richness of decision making and mental models, and provides a combination of divergent and convergent analysis methods leading to the construction of an Agent Based Model.