Sustainability and transparency in computational cognitive neuroscience

In this talk, I will discuss open science practices that aim to foster sustainability and transparency in computational cognitive neuroscience. First, I will review recent community efforts that aim to ease data sharing and analytical reproducibility, such as the reports of the OHBM Committees on Best Practice in Data Analysis and Sharing (COBIDAS) and the Brain Imaging Data Structures (BIDS). Second, I will discuss neuroimaging data sharing strategies in the light of ethical and legal constraints, such as the European General Data Protection Regulation (GDPR). Finally, I will discuss some common-sense guidelines for day-to-day research practice that aim to maximize the societal impact of computational cognitive neuroscience

Modelling and visualizing sustainability assessment in urban environments

Major urban development projects extend over prolonged timescales (up to 25 years in the case of major regeneration projects), involve a large number of stakeholders, and necessitate complex decision making. Comprehensive assessment of critical information will involve a number of domains, such as social, economic and environmental, and input from a wide a range of stakeholders. This makes rigorous and holistic decision making, with respect to sustainability, exceptionally difficult without access to appropriate decision support tools. Assessing and communicating the key aspects of sustainability and often conflicting information remains a major hurdle to be overcome if sustainable development is to be achieved. We investigate the use of an integrated simulation and visualization engine and will test if it is effective in: 1) presenting a physical representation of the urban environment, 2) modelling sustainability of the urban development using a subset of indicators, here the modelling and the visualization need to be integrated seamlessly in order to achieve real time updates of the sustainability models in the 3D urban representation, 3) conveying the sustainability information to a range of stakeholders making the assessment of sustainability more accessible. In this paper we explore the first two objectives. The prototype interactive simulation and visualization platform (S-City VT) integrates and communicates complex multivariate information to diverse stakeholder groups. This platform uses the latest 3D graphical rendering techniques to generate a realistic urban development and novel visualization techniques to present sustainability data that emerge from the underlying computational model. The underlying computational model consists of two parts: traditional multicriteria evaluation methods and indicator models that represent the temporal changes of indicators. These models are informed from collected data and/or existing literature. The platform is interactive and allows real time movements of buildings and/or material properties and the sustainability assessment is updated immediately. This allows relative comparisons of contrasting planning and urban layouts. Preliminary usability results show that the tool provides a realistic representation of a real development and is effective at conveying the sustainability assessment information to a range of stakeholders. S-City VT is a novel tool for calculating and communicating sustainability assessment. It therefore begins to open up the decision making process to more stakeholders, reducing the reliance on expert decision makers

Safety first portfolio choice based on financial and sustainability returns

This paper lays the mathematical foundations of the notion of an investment's sustainability return and investigates three different models of portfolio selection with probabilistic constraints for safety first investors caring about the financial and the sustainability consequences of their investments. The discussion of these chance-constrained programming problems for stochastic and deterministic sustainability returns includes theoretical results especially on the existence of a unique solution under certain conditions, an illustrating example, and a computational time analysis. Furthermore, we conclude that a simple convex combination of financial and sustainability returns - yielding a new univariate decision variable - is not sufficiently general.Finance; Socially Responsible Investing; Sustainability Value; Safety First Investor

Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics

Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies

Curriculum Design of Artificial Intelligence and Sustainability in Secondary School

Artificial Intelligence is revolutionizing numerous sectors with its transformative power, while at the same time, there is an increasing sense of urgency to address sustainability challenges. Despite the significance of both areas, secondary school curriculums still lack comprehensive integration of AI and sustainability education. This paper presents a curriculum designed to bridge this gap. The curriculum integrates progressive objectives, computational thinking competencies and system thinking components across five modules—awareness, knowledge, interaction, empowerment and ethics—to cater to varying learner levels. System thinking components help students understand sustainability in a holistic manner. Computational thinking competencies aim to cultivate computational thinkers to guide the design of curriculum activities

LOGIC AND CONSTRAINT PROGRAMMING FOR COMPUTATIONAL SUSTAINABILITY

Computational Sustainability is an interdisciplinary field that aims to develop computational and mathematical models and methods for decision making concerning the management and allocation of resources in order to help solve environmental problems. This thesis deals with a broad spectrum of such problems (energy efficiency, water management, limiting greenhouse gas emissions and fuel consumption) giving a contribution towards their solution by means of Logic Programming (LP) and Constraint Programming (CP), declarative paradigms from Artificial Intelligence of proven solidity. The problems described in this thesis were proposed by experts of the respective domains and tested on the real data instances they provided. The results are encouraging and show the aptness of the chosen methodologies and approaches. The overall aim of this work is twofold: both to address real world problems in order to achieve practical results and to get, from the application of LP and CP technologies to complex scenarios, feedback and directions useful for their improvement

Dynamic Resource Allocation in Conservation Planning

Consider the problem of protecting endangered species by selecting patches of land to be used for conservation purposes. Typically, the availability of patches changes over time, and recommendations must be made dynamically. This is a challenging prototypical example of a sequential optimization problem under uncertainty in computational sustainability. Existing techniques do not scale to problems of realistic size. In this paper, we develop an efficient algorithm for adaptively making recommendations for dynamic conservation planning, and prove that it obtains near-optimal performance. We further evaluate our approach on a detailed reserve design case study of conservation planning for three rare species in the Pacific Northwest of the United States