From evolutionary ecosystem simulations to computational models of human behavior

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence

Holistic Flood Risk Assessment In Coastal Areas - The PEARL Approach

Coastal floods are one of the most dangerous and harmful natural hazards affecting urban areas adjacent to shorelines. The present paper discusses the FP7-ENV-2013 EU funded PEARL (Preparing for Extreme And Rare events in coastaL regions) project which brings together world leading expertise in both the domain of hydro-engineering and risk reduction and management services to pool knowledge and practical experience in order to develop more sustainable risk management solutions for coastal communities focusing on present and projected extreme hydro-meteorological events. The PEARL approach draws upon the complexity theory and the use of complex adaptive system (CAS) models as tools to identify root causes of vulnerabilities and their multi-stressors and to analyze risk and the behavior of key actors

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant

Kill chaos with kindness: agreeableness improves team performance under uncertainty

Teams are central to human accomplishment. Over the past half-century, psychologists have identified the Big-Five cross-culturally valid personality variables: Neuroticism, Extraversion, Openness, Conscientiousness, and Agreeableness. The first four have shown consistent relationships with team performance. Agreeableness (being harmonious, altruistic, humble, and cooperative), however, has demonstrated a non-significant and highly variable relationship with team performance. We resolve this inconsistency through computational modelling. An agent-based model (ABM) is used to predict the effects of personality traits on teamwork, and a genetic algorithm is then used to explore the limits of the ABM in order to discover which traits correlate with best and worst performing teams for a problem with different levels of uncertainty (noise). New dependencies revealed by the exploration are corroborated by analyzing previously unseen data from one of the largest datasets on team performance to date comprising 3698 individuals in 593 teams working on more than 5000 group tasks with and without uncertainty, collected over a 10-year period. Our finding is that the dependency between team performance and Agreeableness is moderated by task uncertainty. Combining evolutionary computation with ABMs in this way provides a new methodology for the scientific investigation of teamwork, making new predictions, and improving our understanding of human behaviors. Our results confirm the potential usefulness of computer modelling for developing theory, as well as shedding light on the future of teams as work environments are becoming increasingly fluid and uncertain

Exploratory Assessment of Roadway Infrastructure Adaptation to the Impacts of Sea-level Rise

Transportation agencies in coastal urban areas face a significant challenge to enhance the long-term resilience of their networks to flooding and storm surge events exacerbated by sea level rise. The problem of sea-level rise adaptation is characterized by deep uncertainty that makes it complex to assess the value of adaptation investments. To enable informed adaptation decisions, the present study created a dynamic stochastic modeling framework based on the theoretical underpinnings of complex adaptive systems that integrates: (i) stochastic simulation of sea-level rise stressors based on the data obtained from downscaled climate studies pertaining to future projections of sea-level and precipitation; (ii) dynamic modeling of roadway conditions by considering regular decay of roadways, as well as structural damages caused by storm surge events; and (iii) a decision-theoretic modeling of agency infrastructure management and adaptation processes based on cognitive psychology, bounded rationality, and regret theories. In this framework, resilience is examined based on trend changes in the network performance measures (e.g., life cycle costs and performance). The created framework and model were tested in a case study related to the road network of the city of Miami-Beach, which global assessments rank first iv among the world\u27s urban areas most exposed to sea-level rise risks. The results indicated that: (i) SLR Adaptation investment and life cycle costs of roadway infrastructure are negatively correlated. In addition, it was shown that the sensitivity of network’s life cycle cost to actual sea-level rise scenario decreases when adaptation investment increases. These finding emphasize the importance of proactive improvement of the network resilience to alleviate the long-term costs of sea-level rise. (ii) When funding is sufficient for all required adaptation actions, mid-term adaptation planning yields lower life cycle cost. When funding is insufficient, aggregated investment in long-term adaptation planning intervals yields lower network LCC. These findings imply that different adaptation planning approaches should be taken for different levels of adaptation investment. (iii) The agency’s perception of SLR and risk attitude do not have significant effect on life cycle cost of roadway networks. Hence, implementation of adaptation action based on any perception of sea-level rise and risk attitude can significantly reduce the life cycle costs of roadway networks under the impacts of SLR. (iv) The devised performance target has negative correlation with life cycle cost of a roadway network affected by SLR impacts. Therefore, compromising the network performance condition will never result in lower life cycle costs

Prise de décision en gestion des actifs industriels en tenant compte des risques

Les entreprises modernes sont des organisations complexes par leur structure organisationnelle, opérationnelle et le type de gestion. Elles évoluent dans un environnement opérationnel et d’affaires complexe confronté à des incertitudes significatives liées à des facteurs naturels, techniques, technologiques, commerciaux, organisationnels, économiques, financiers, politiques, etc. affectant leur gestion et leurs opérations. L'environnement opérationnel et d’affaires complexe génère également de nouveaux types de risques relativement inconnus il y a quelques décennies (par exemple,la cyber sécurité). Un tel environnement crée aussi des conditions favorables à l'émergence d'événements extrêmes et rares susceptibles de perturber sérieusement la performance des entreprises à court et à long terme. Les pratiques et analyses actuelles négligent généralement de prendre en compte ces types de risques. Les intrants des experts techniques, des planificateurs stratégiques ou des gestionnaires pourraient s’avérer insuffisants ou trop circonscrits pour tenir compte adéquatement de la complexité dans un environnement complexe en constante évolution et à peine prévisible. Cette situation est généralement causée par un manque de connaissances concernant le type et l’envergure des incertitudes, la nature des interconnexions entre les facteurs d’influence, le niveau de complexité, ainsi que notre faible capacité à prédire les événements futurs. La mondialisation et la forte concurrence font partie de l'environnement opérationnel et d’affaires contemporain typique. La capacité des entreprises à créer et à mettre en oeuvre des concepts innovants est déterminante pour répondre aux exigences en matière de compétitivité et pour assurer leur fonctionnement durable et leur développement futur. Au cours des deux dernières décennies, la gestion des actifs (GDA) est devenue une approche répandue parmi les organisations à succès en tant que concept efficace permettant de générer de la valeur à partir des actifs et d'assurer la durabilité de l'entreprise et de ses opérations. La prise de décision est essentielle dans la GDA. Elle est influencée par différents facteurs (stratégiques, techniques/technologiques, économiques, organisationnels, réglementaires/juridiques, sécurité, marchés, concurrence, etc.). La prise de décision adéquate doit tenir compte de la complexité et des facteurs d’influence pertinents pour équilibrer les risques, les opportunités, la performance, les coûts et les bénéfices. Malgré les progrès récents afin de mieux comprendre les défis et développer de nouvelles approches de modélisation, les programmes de gestion d'actifs n'ont pas toujours réussi à éviter des pertes coûteuses ou même des faillites d'organisations causées par divers facteurs économiques ou non techniques discutés ci-dessus qui n’ont pas été compris ou pris en compte adéquatement dans le processus de prise de décision. La pratique montre également qu'une définition inadéquate des rôles et des responsabilités et le manque de communication contribuent également à l'inefficacité de la GDA et de son processus de prise de décision. Le but du présent travail de recherche est de développer une méthodologie de prise de décision en gestion des actifs en tenant compte de la complexité de l’environnement d’affaires et opérationnel. Dans la présente recherche, une méthodologie intégrale de prise de décision en GDA en tenant compte des risques (Risk-Informed Decision-Making – RIDM) en trois étapes a été développée. La GDA est considérée comme un système de systèmes complexes adaptatifs. La recherche a également développé la méthode de caractérisation et d'intégration des risques d'événements extrêmes et rares dans le processus décisionnel par l'application de la science de la complexité et de la théorie des valeurs extrêmes. La méthodologie est appliquée et validée avec succès dans le cas de trois industries : minière, nucléaire et une utilité électrique. Elle démontre le potentiel d'une application répandue dans diverses industries lors d’un développement futur. Modern companies are complex organizations as per their organizational, management and operational structure. They also operate in a complex business and operational environment facing significant uncertainties related to natural, technical, technological, market, organizational, economic, financial, political, etc. influential factors affecting their business, management and operations. The complex business and operational environment also generates new types of risks that were relatively unknown just a few decades ago (e.g. cyber security) and creates favorable conditions for the emerging of extreme and rare events that may seriously disrupt the short and long-term performance of enterprises. Current practices and analyses generally neglect taking into account those risks. Advice and input from technical experts, strategic planners or knowledgeable managers may be insufficient or too narrowly focused to adequately manage the complexity of the systems and structures in a constantly changing and barely predictable environment. It is generally due to a lack of knowledge regarding the type and range of uncertainties, the nature of interconnections, the level of complexity, as well as our low ability to predict future events. Globalization and strong competition are part of a typical contemporary operational and business environment. The ability of enterprises to create and implement innovative concepts is decisive to meet the demands regarding competitiveness, and to ensure their sustainable operations and further development. During the last two decades, Asset Management (AsM) has become prevalent approach among successful organizations as an effective concept allowing delivering value from assets and ensuring the sustainability of the business and its operations. The decision-making is essential in AsM. It is influenced by various factors (strategic, technical/technological, economic, organizational, regulatory/legal, safety, markets, competition, etc.). A sound decision-making ought to take into account relevant factors for balancing risks, opportunities, performance, costs, and benefits. Despite recent progress in better understanding challenges and developing new modeling approaches, asset management programs have not always been successful in avoiding costly losses or even bankruptcies of organizations caused by various economic or non-technical factors discussed above that have not been eitherunderstood or adequately considered and addressed in the decision-making process. Practice also shows that inadequate definition of roles and responsibilities and lack of communication also contribute to the inefficiency of the AsM and its decisionmaking process.The goal of this thesis is to develop an integral asset management decision-making methodology taking into account the complexity of the business and operational environment. A holistic three-step Risk-Informed Decision-Making (RIDM) methodology tailored for AsM considering it as a Complex Adaptive System of Systems (CASoS) has been developed in this research work. The research has also developed the method regarding the integration of risks of extreme and rare events into the RIDM through the application of the complexity science and the extreme value theory. The methodology is successfully applied and validated in the case of three industries: mining, nuclear and electrical utilities. It demonstrates its potential of a large application across various industries through a further development

On the development of Agent-Based Models for infrastructure evolution

Infrastructure systems for energy, water, transport, information etc. are large scale socio-technical systems that are critical for achieving a sustainable world. They were not created at the current global scale at once, but have slowly evolved from simple local systems, through many social and technical decisions. If we are to understand them and manage them sustainably, we need to capture their full diversity and adaptivity in models that respect Ashby's law of requisite variety. Models of evolving complex systems must themselves be evolving complex systems that can not be created from scratch but must be grown from simple to complex. This paper presents a socio-technical evolutionary modeling process for creating evolving, complex agent based models for understanding the evolution of large scale socio-technical systems such as infrastructures. It involves the continuous co-evolution and improvement of a social process for model specification, the technical design of a modular simulation engine, the encoding of formalized knowledge and collection of relevant facts. In the paper we introduce the process design, the requirements for guiding the evolution of the modeling process and illustrate the process for Agent Based Model development by showing a series of ever more complex models.Section Energy and IndustryTechnology, Policy and Managemen