ABSTRACT
Lessons from the COVID-19 pandemic inspire a guide to recognizing the politics of modeling.
ABSTRACT
There is a growing debate amongst academics and practitioners on whether interventions made, thus far, towards Responsible AI have been enough to engage with the root causes of AI problems. Failure to effect meaningful changes in this system could see these initiatives not reach their potential and lead to the concept becoming another buzzword for companies to use in their marketing campaigns. Systems thinking is often touted as a methodology to manage and effect change; however, there is little practical advice available for decision-makers to include systems thinking insights to work towards Responsible AI. Using the notion of 'leverage zones' adapted from the systems thinking literature, we suggest a novel approach to plan for and experiment with potential initiatives and interventions. This paper presents a conceptual framework called the Five Ps to help practitioners construct and identify holistic interventions that may work towards Responsible AI, from lower-order interventions such as short-term fixes, tweaking algorithms and updating parameters, through to higher-order interventions such as redefining the system's foundational structures that govern those parameters, or challenging the underlying purpose upon which those structures are built and developed in the first place. Finally, we reflect on the framework as a scaffold for transdisciplinary question-asking to improve outcomes towards Responsible AI.
ABSTRACT
Water governing systems are twisted with complex interplays among levels and scales which embody their structures. Typically, the mismatch between human-generated and natural systems produces externalities and inefficiencies reflectable in spatial scales. The largely known problem of fit in water governance is investigated to detect the issues of fit between administrative/institutional scales and the hydrological one in a lake basin. To implement the idea, constraining the level of analysis interlinked to the concentrated levels of administration in spatial scales, the fit of the governing system was analyzed by means of statistical mechanics. Modeling the structure of water demand/supply governing system in a given region through the Curie-Weiss Mean Field approximation, the system cost in relation to its structure and fit was appraised and compared with two other conceptual structures in the Urmia Lake Basin in Iran. The methodology articulated an analysis framework for exploring the effectiveness of the formulated water demand/supply governing system and its fit to the relevant hydrological system. The findings of this study may help developing strategies to encourage adaptations, rescaling/reforms for effective watershed management.
Subject(s)
Water Supply , Water , Humans , Hydrology , Iran , LakesABSTRACT
Our digital age is characterized by both a generalized access to data and an increased call for participation of the public and other stakeholders and communities in policy design and decision-making. This context raises new challenges for political decision-makers and analysts in providing these actors with new means and moral duties for decision support, including in the area of environmental policy. The concept of "policy analytics" was introduced in 2013 as an attempt to develop a framework, tools, and methods to address these challenges. This conceptual initiative prompted numerous research teams to develop empirical applications of this framework and to reflect on their own decision-support practice at the science-policy interface in various environmental domains around the world. During a workshop in Paris in 2018, participants shared and discussed their experiences of these applications and practices. In this paper, we present and analyze a set of applications to identify a series of key properties that underpin a policy analytics approach, in order to provide the conceptual foundation for policy analytics to address current policy design and decision-making challenges. The induced properties are demand-orientedness, performativity, normative transparency, and data meaningfulness. We show how these properties materialized through these six case studies, and we explain why we consider them key to effective policy analytics applications, particularly in environmental policy design and decision-making on environmental issues. This clarification of the policy analytics concept eventually enables us to highlight research frontiers to further improve the concept.
Subject(s)
Conservation of Natural Resources , Policy Making , Humans , PolicyABSTRACT
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.
