Using causal inference to avoid fallouts in data-driven parametric analysis: A case study in the architecture, engineering, and construction industry

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. Recognizing the limitations of isolated methodologies - namely, the lack of domain understanding in data-driven models, the subjective nature of empirical knowledge, and the idealized assumptions in first-principles simulations, we explore their synergetic integration. We showed the potential risk of biased results when using data-driven models without causal analysis. Through a case study on energy consumption in building design, we demonstrate how causal analysis significantly enhances the modeling process, mitigating biases and spurious correlations. We concluded that: (a) Sole data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Integrating causal analysis results aid to first-principles simulation design and parameter checking to avoid cognitive biases. We advocate for the routine integration of causal inference within data-driven models in engineering practices, emphasizing its critical role in ensuring the models' reliability and real-world applicability

Balancing fisheries and coastal management across the triple bottom line: objectives and outcomes co-designed with stakeholders

Fisheries and coastal assets are both common pool resources. The management of natural resources has a special focus in sustainability science because of the need to avoid the ‘tragedy of the commons’. Not only do common resources need special attention to ensure future sustainability, there is also a need to ensure management decisions are not made on short timelines, so as to prevent the ‘tragedy of the horizon’. This tragedy occurs when the time to replenish those resources is much longer than the timeframe over which impacts of resource decisions are managed, and often imposes costs on future generations. This thesis focuses on how the management of fisheries and coastal resources can be implemented through a triple bottom line lens to avoid both tragedies. Foremost, the thesis examines how appropriate social objectives can be developed, particularly through stakeholder engagement, and how management options can be assessed to identify options that maximise triple bottom line outcomes. These aspects are demonstrated through a series of case studies. The purpose of the research presented in this thesis is to explore stakeholder participation in fisheries and coastal management decision-making with the triple bottom line approach. The triple bottom line as defined John Elkington (1997) encompasses seven paradigm shifts for sustainability: (i) using markets to improve environmental management and create triple-win situations; (ii) incorporating lifecycle technologies and approaches; (iii) engaging and co-designing with stakeholders so that they become process partners; (iv) transparency throughout assessment and management processes; (v) adopting long time horizons; (vi) uncovering and appreciating social ‘soft’ values and other externalities which will see the need for evolving ways and means to quantify qualitative outcomes; and (vii) establishing governance embedded with corporate social responsibility (CSR)

Adaptive monitoring and control framework in Application Service Management environment

The economics of data centres and cloud computing services have pushed hardware and software requirements to the limits, leaving only very small performance overhead before systems get into saturation. For Application Service Management–ASM, this carries the growing risk of impacting the execution times of various processes. In order to deliver a stable service at times of great demand for computational power, enterprise data centres and cloud providers must implement fast and robust control mechanisms that are capable of adapting to changing operating conditions while satisfying service–level agreements. In ASM practice, there are normally two methods for dealing with increased load, namely increasing computational power or releasing load. The first approach typically involves allocating additional machines, which must be available, waiting idle, to deal with high demand situations. The second approach is implemented by terminating incoming actions that are less important to new activity demand patterns, throttling, or rescheduling jobs. Although most modern cloud platforms, or operating systems, do not allow adaptive/automatic termination of processes, tasks or actions, it is administrators’ common practice to manually end, or stop, tasks or actions at any level of the system, such as at the level of a node, function, or process, or kill a long session that is executing on a database server. In this context, adaptive control of actions termination remains a significantly underutilised subject of Application Service Management and deserves further consideration. For example, this approach may be eminently suitable for systems with harsh execution time Service Level Agreements, such as real–time systems, or systems running under conditions of hard pressure on power supplies, systems running under variable priority, or constraints set up by the green computing paradigm. Along this line of work, the thesis investigates the potential of dimension relevance and metrics signals decomposition as methods that would enable more efficient action termination. These methods are integrated in adaptive control emulators and actuators powered by neural networks that are used to adjust the operation of the system to better conditions in environments with established goals seen from both system performance and economics perspectives. The behaviour of the proposed control framework is evaluated using complex load and service agreements scenarios of systems compatible with the requirements of on–premises, elastic compute cloud deployments, server–less computing, and micro–services architectures