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Overview of Modern Artificial Intelligence
Published in Mark Chang, Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare, 2020
To deal with complex systems with great uncertainties, probability and statistics are naturally effective tools. However, it cannot be the exact same statistical methods we have used in classical settings. As Leo Breiman (2001) pointed out in his Statistical Modeling: The Two Cultures: “There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.”
Reliability and Human Error in Systems
Published in Robert W. Proctor, Van Zandt Trisha, Human Factors in Simple and Complex Systems, 2018
Robert W. Proctor, Van Zandt Trisha
In complex systems, the risks associated with various system failures are assessed as part of a reliability analysis. Risk refers to events that might cause harm, such as a nuclear power plant releasing radioactive steam into the atmosphere. A risk analysis, therefore, considers not only the reliability of the system but also the risks that accompany specific failures, such as monetary loss and loss of life. Probabilistic risk analysis, the methods of which were developed and applied primarily within the nuclear power industry, involves decomposing the risk of concern into smaller elements for which the probabilities of failure can be quantified (Bedford & Cooke, 2001). These probabilities then are used to estimate the overall risk, with the goal of establishing that the system is safe and to identify the weakest links (Paté-Cornell, 2002).
Towards An Adaptive Urbanism Beyond Hard Control: The Theories Of Johnson And Lefebvre
Published in Manuel Couceiro da Costa, Filipa Roseta, Joana Pestana Lages, Susana Couceiro da Costa, Architectural Research Addressing Societal Challenges, 2017
N. Abbasabadi, M. Ashayeri Jahan Khanemloo
We can now state that cities are as a self-organized system; raised, provoked, and sustained by humanity. This order is an independent phenomenon which manifests without full planning and design and is free from central power. As Johnson makes clear, the mechanism of organic city growth, organization, and operation through an analytical comparison with the interaction of a ‘super organism’ of ants with the city where the neighborhoods of individuals solve their problems through local interactions collectively, which leads to global behavior. In the same way, local interactions and decisions of individuals on an organic city contribute to the creation of the ‘complex order’ of the city. Each ant acts on a system of low-level rules and feedback from its neighbors, which explains how “local information can lead to global wisdom” (Johnson, 2001). The feedback loop between neighbors is critical in city vitality. “Feedback is a way of transforming a complex system into a complex adaptive system” (Johnson, 2001). As Johnson discusses, each ant is not intelligent and only can do simple actions, although an ant colony shows a complex collective behavior. The underlying mechanism of this intelligence emerges from the social interactions among neighboring ants. Therefore, interconnected behaviors support the feedback loops. “All decentralized systems rely extensively on feedback, for both growth and self-regulation” (Johnson, 2001). However, this is a simplification of the image of complexity in the human world that ignores the complicated layers of human society in terms of cultural, social and political aspects.
Assessment of Workforce Systems Preferences/Skills Based on Employment Domain
Published in Engineering Management Journal, 2020
Raed Jaradat, Erin Stirgus, Simon R. Goerger, Randy K. Buchanan, Niamat Ullah Ibne Hossain, Junfeng Ma, Reuben Burch
Because complex systems face increasing emergency, evolutionary development, integration, interdependence, complexity, ambiguity, and uncertainty, specific challenges are posed for engineers who are expected to understand, manage, and solve problems within the complex system domain (see Exhibit 1). These engineers must systematically frame problems to effectively develop optimal solutions for their organization. Framing can become unclear in a rapidly changing environment (Mitrof, 1997). After the engineer has framed the problem, the next challenge is to determine what problem-solving approach to take. Because each problem can be different in this complex environment, there is no standard resolution and therefore no standard approach. The final challenge is for the engineers to understand and eventually make decisions that resolve issues within complex systems based on the culture of the work environment, relevant policies, and human dimensions.
A trade-off balance among urban water infrastructure improvements and financial management to achieve water sustainability
Published in Urban Water Journal, 2022
Sangmin Shin, Danyal Aziz, Uzma Jabeen, Rakhshinda Bano, Steven J. Burian
Many of the existing decision support tools have not represented the full complexity of water infrastructure system management. In particular, the studies generally have not included coverage of the numerous interactions and feedbacks across the physical (water service and infrastructure), social/organizational (water demand and infrastructure management), and financial (expenditure, revenue, and budgets) aspects of an urban water supply system (Ford and Ford 1999; Rehan et al. 2013). A complex system includes one or more feedbacks in which a change in a component causes unexpected changes in other interconnected components and in turn, affects the originating component (Sterman 2000). For example, lack of the water service will increase the infrastructure management activities such as the WDN extension or WTP expansion, which consequently improves the water service – which shows a feedback loop. However, the increase in the infrastructure capacity can result in a rise of operational costs, which can lead to hindering additional management activities due to financial constraints – which is a feedback loop in the conflicting relationship with the previous feedback loop. A detailed description of the feedbacks and their complex interactions for this study is found in the Methodology section. The system structure with the complicated feedback interactions and time delays determines the dynamics and non-linearity of the system behaviors (Ford and Ford 1999; Stave 2003). Lack of consideration of the complex feedback interactions can result in misunderstandings about unintended and counterintuitive consequences derived from water policies and management options (Stave 2003).
Assessing the benefits of serious games to support sustainable decision-making for transboundary watershed governance
Published in Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2018
Alison Furber, Wietske Medema, Jan Adamowski
In theory, if a complex system could be fully understood and mathematically modelled, and all relevant variables measured, there is no reason why it would not be possible to apply a deductive reasoning and rational choice approach to decision-making to identify optimal measures for management of the system. Due to limited time, budget and/or scientific understanding, however, it is usually neither possible nor practical for the decision maker to obtain such a complete system model. For this reason, decision-making for complex system management often goes hand in hand with high levels of uncertainty.