Modelling collective learning in design

In this paper, a model of collective learning in design is developed in the context of team design. It explains that a team design activity uses input knowledge, environmental information, and design goals to produce output knowledge. A collective learning activity uses input knowledge from different agents and produces learned knowledge with the process of knowledge acquisition and transformation between different agents, which may be triggered by learning goals and rationale triggers. Different forms of collective learning were observed with respect to agent interactions, goal(s) of learning, and involvement of an agent. Three types of links between team design and collective learning were identified, namely teleological, rationale, and epistemic. Hypotheses of collective learning are made based upon existing theories and models in design and learning, which were tested using a protocol analysis approach. The model of collective learning in design is derived from the test results. The proposed model can be used as a basis to develop agent-based learning systems in design. In the future, collective learning between design teams, the links between collective learning and creativity, and computational support for collective learning can be investigated

Developing an agent-based model on how different individuals solve complex problems

Purpose: Research that focuses on the emotional, mental, behavioral and cognitive capabilities of individuals has been abundant within disciplines such as psychology, sociology, and anthropology, among others. However, when facing complex problems, a new perspective to understand individuals is necessary. The main purpose of this paper is to develop an agentbased model and simulation to gain understanding on the decision-making and problem-solving abilities of individuals. Design/methodology/approach: The micro-level analysis modeling and simulation paradigm Agent-Based Modeling Through the use of Agent-Based Modeling, insight is gained on how different individuals with different profiles deal with complex problems. Using previous literature from different bodies of knowledge, established theories and certain assumptions as input parameters, a model is built and executed through a computer simulation. Findings: The results indicate that individuals with certain profiles have better capabilities to deal with complex problems. Moderate profiles could solve the entire complex problem, whereas profiles within extreme conditions could not. This indicates that having a strong predisposition is not the ideal way when approaching complex problems, and there should always be a component from the other perspective. The probability that an individual may use these capabilities provided by the opposite predisposition provides to be a useful option.Originality/value: The originality of the present research stems from how individuals are profiled, and the model and simulation that is built to understand how they solve complex problems. The development of the agent-based model adds value to the existing body of knowledge within both social sciences, and modeling and simulation.Peer Reviewe

Developing an agent-based model on how different individuals solve complex problems

Purpose: Research that focuses on the emotional, mental, behavioral and cognitive capabilities of individuals has been abundant within disciplines such as psychology, sociology, and anthropology, among others. However, when facing complex problems, a new perspective to understand individuals is necessary. The main purpose of this paper is to develop an agentbased model and simulation to gain understanding on the decision-making and problem-solving abilities of individuals. Design/methodology/approach: The micro-level analysis modeling and simulation paradigm Agent-Based Modeling Through the use of Agent-Based Modeling, insight is gained on how different individuals with different profiles deal with complex problems. Using previous literature from different bodies of knowledge, established theories and certain assumptions as input parameters, a model is built and executed through a computer simulation. Findings: The results indicate that individuals with certain profiles have better capabilities to deal with complex problems. Moderate profiles could solve the entire complex problem, whereas profiles within extreme conditions could not. This indicates that having a strong predisposition is not the ideal way when approaching complex problems, and there should always be a component from the other perspective. The probability that an individual may use these capabilities provided by the opposite predisposition provides to be a useful option.Originality/value: The originality of the present research stems from how individuals are profiled, and the model and simulation that is built to understand how they solve complex problems. The development of the agent-based model adds value to the existing body of knowledge within both social sciences, and modeling and simulation.Peer Reviewe

Redesign Support Framework for Complex Technical Processes

Els processos industrials requereixen avaluacions periòdiques per a verificar la seva correcta operació en termes tècnics i econòmics. Aquestes avaluacions són necessàries a causa de els canvis en els mercats i en la legislació ambiental i de seguretat. Per a satisfer aquestes demandes és necessari investigar les alternatives dels processos que permetin l'ús òptim dels recursos existents amb la mínima inversió econòmica possible. Aquesta tasca es coneix com redisseny, que és un procediment per a determinar possibles canvis en un procés existent per a millorar-lo pel en alguna mètrica, tal com econòmica, ambiental, de seguretat, etc. En aquesta tesi es proposa un marc d'ajuda al redisseny per a processos tècnics. Aquest marc fa ús d'una representació jeràrquica de models múltiples del procés que es re dissenyarà en conjunció amb un motor que raonament basat en casos per a ajudar a decidir quins elements del procés han de ser modificats. El marc consisteix en quatre etapes principals: adquisició de la descripció del disseny, identificació de candidats, generació d'alternatives, i adaptació i avaluació d'alternatives.El procés original es modela jeràrquicament emprant conceptes de mitjans-fins i parts-tot. Així el coneixement sobre el comportament, l'estructura, la funció i l'objectiu de cadascuna de les parts del procés es genera i s'emmagatzema automàticament. Donat les noves especificacions o requisits que el procés ha de satisfer, el sistema troba les parts del procés que ha de ser redissenyades. S'utilitza una llibreria de casos per a obtenir seccions alternatives del procés que es puguin adaptar per a substituir parts del procés original. Per tant, el marc proposat permet modelar el procés, identificar els components de procés viables a redissenyar, obtenir components alternatius i finalment adaptar aquests components alternatius en el procés original. Aquest procediment es pot veure com activitat d'enginyeria inversa on es generen models abstractes en diversos nivells a partir d'una descripció detallada d'un procés existent per a reduir la seva complexitat. El marc ha estat implementat i provat en el domini d'Enginyeria Química.Los procesos industriales requieren evaluaciones periódicas para verificar su correcta operación en términos técnicos y económicos. Estas evaluaciones son necesarias debido a los cambios en los mercados y en la legislación ambiental y de seguridad. Para satisfacer estas demandas es necesario investigar las alternativas de los procesos que permitan el uso óptimo de los recursos existentes con la mínima inversión económica posible. Esta tarea se conoce como rediseño, que es un procedimiento para determinar posibles cambios en un proceso existente para mejorarlo con respecto a alguna métrica, tal como económica, ambiental, de seguridad, etc.En esta tesis se propone un marco de ayuda al rediseño para procesos técnicos. Este marco emplea una representación jerárquica de modelos múltiples del proceso que se rediseñará en conjunción con un motor que razonamiento basado en casos para ayudar a decidir qué elementos del proceso deben ser modificados. El marco consiste en cuatro etapas principales: adquisición de la descripción del diseño, identificación de candidatos, generación de alternativas, y adaptación y evaluación de alternativas. El proceso original se modela jerárquicamente empleando conceptos de medios-fines y partes-todo. Así el conocimiento sobre el comportamiento, la estructura, la función y el objetivo de cada una de las parte del proceso se genera y se almacena automáticamente. Dado las nuevas especificaciones o requisitos que el proceso debe satisfacer, el sistema encuentra las partes del proceso que debe ser rediseñadas. Se utiliza una librería de casos para obtener secciones alternativas del proceso que se puedan adaptar para sustituir partes del proceso original. Por lo tanto, el marco propuesto permite modelar el proceso, identificar los componentes de proceso viables a rediseñar, obtener componentes alternativos y finalmente adaptar estos componentes alternativos en el proceso original. Este procedimiento se puede ver como actividad de ingeniería inversa donde se generan modelos abstractos en diversos niveles a partir de una descripción detallada de un proceso existente para reducir su complejidad. El marco ha sido implementado y probado en el dominio de Ingeniería Química.Industrial processes require periodic evaluations to verify their correct operation, both in technical and economical terms. These evaluations are necessary due to changes in the markets, and in safety and environmental legislation. In order to satisfy these demands it is necessary to investigate process alternatives that allow the optimal use of existing resources with the minimum possible investment. This task is known as redesign, which is a procedure to determine possible changes to an existing process in order to improve it with respect to some metric, such as economical, environmental, safety, etc.A redesign support framework for technical processes is proposed in this thesis. This framework employs a multiple-model hierarchical representation of the process to be redesigned together with a case-based reasoning engine that helps to decide which elements of the process should be modified. The framework consists of four main stages: acquisition of the design description, identification of candidates, generation of alternatives, and adaptation and evaluation of alternatives.The original process is modelled hierarchically exploiting means-end and part-whole concepts, and thus knowledge about the behaviour, structure, function and intention of each part of the process is automatically generated and stored. Given the new specifications or requirements that the process must fulfil, the system finds the parts of the process which must be redesigned and a case library is used to obtain alternative process sections which can be adapted to substitute parts of the original process. Therefore, the proposed framework allows to model the process, to identify process components suitable for redesign, to obtain alternative components, and finally, to adapt these components into the original process. This procedure can be seen as a reverse engineering activity where abstract models at different levels are generated from a detailed description of an existing process to reduce its complexity. The framework has been implemented and tested on the Chemical Engineering domain.Postprint (published version

A Theoretical Framework for Simulating Organizations

This work proposes a theoretical framework using a systemic modeling paradigm to implement computational agents in the simulation of organizations. The potential of its use is demonstrated in the modeling of supply chains. Finally, research tending to develop an organizational modeling system in real-time is proposed.Comment: 6 pages, 2 figures, Chilean Conference Operational Research - OPTIMA 200

Theory of deferred action: Agent-based simulation model for designing complex adaptive systems

Deferred action is the axiom that agents act in emergent organisation to achieve predetermined goals. Enabling deferred action in designed artificial complex adaptive systems like business organisations and IS is problematical. Emergence is an intractable problem for designers because it cannot be predicted. We develop proof-of-concept, conceptual proto-agent model, of emergent organisation and emergent IS to understand better design principles to enable deferred action as a mechanism for coping with emergence in artefacts. We focus on understanding the effect of emergence when designing artificial complex adaptive systems by developing an exploratory proto-agent model and evaluate its suitability for implementation as agent-based simulation

Modeling and Analyzing Systemic Risk in Complex Sociotechnical Systems The Role of Teleology, Feedback, and Emergence

Recent systemic failures such as the BP Deepwater Horizon Oil Spill, Global Financial Crisis, and Northeast Blackout have reminded us, once again, of the fragility of complex sociotechnical systems. Although the failures occurred in very different domains and were triggered by different events, there are, however, certain common underlying mechanisms of abnormalities driving these systemic failures. Understanding these mechanisms is essential to avoid such disasters in the future. Moreover, these disasters happened in sociotechnical systems, where both social and technical elements can interact with each other and with the environment. The nonlinear interactions among these components can lead to an “emergent” behavior – i.e., the behavior of the whole is more than the sum of its parts – that can be difficult to anticipate and control. Abnormalities can propagate through the systems to cause systemic failures. To ensure the safe operation and production of such complex systems, we need to understand and model the associated systemic risk. Traditional emphasis of chemical engineering risk modeling is on the technical components of a chemical plant, such as equipment and processes. However, a chemical plant is more than a set of equipment and processes, with the human elements playing a critical role in decision-making. Industrial statistics show that about 70% of the accidents are caused by human errors. So, new modeling techniques that go beyond the classical equipment/process-oriented approaches to include the human elements (i.e., the “socio” part of the sociotechnical systems) are needed for analyzing systemic risk of complex sociotechnical systems. This thesis presents such an approach. This thesis presents a new knowledge modeling paradigm for systemic risk analysis that goes beyond chemical plants by unifying different perspectives. First, we develop a unifying teleological, control theoretic framework to model decision-making knowledge in a complex system. The framework allows us to identify systematically the common failure mechanisms behind systemic failures in different domains. We show how cause-and-effect knowledge can be incorporated into this framework by using signed directed graphs. We also develop an ontology-driven knowledge modeling component and show how this can support decision-making by using a case study in public health emergency. This is the first such attempt to develop an ontology for public health documents. Lastly, from a control-theoretic perspective, we address the question, “how do simple individual components of a system interact to produce a system behavior that cannot be explained by the behavior of just the individual components alone?” Through this effort, we attempt to bridge the knowledge gap between control theory and complexity science