Archetypes of Common Village Pasture Problems in the South Caucasus: Insights from Comparative Case Studies in Georgia and Azerbaijan
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Environmental Systems Analysis Tools for Decision-Making
Environmental systems analysis tools for decision-making LCA and Swedish waste management as an example Åsa Moberg Licentiate thesis Royal Institute of Technology Department of Urban Planning and Environment Environmental Strategies Research Stockholm 2006 Titel: Environmental systems analysis tools for decision‐making LCA and Swedish waste management as an example Author: Åsa Moberg Cover page photo: Marianne Lockner TRITA‐SOM 06‐002 ISSN 1653‐6126 ISRN KTH/SOM/‐‐06/002‐‐SE ISBN 91‐7178‐304‐0 Printed in Sweden by US AB, Stockholm, 2006 2 Abstract Decisions are made based on information of different kinds. Several tools have been developed to facilitate the inclusion of environmental aspects in decision‐making on different levels. Which tool to use in a specific decision‐making situation depends on the decision context. This thesis discusses the choice between different environmental systems analysis (ESA) tools and suggests that key factors influencing the choice of ESA tool are object of study, impacts considered and information type regarding site‐specificity and according to the DPSIR‐framework. Waste management in Sweden is used as an example to illustrate decision‐making situations, but discussions concerning choice of tools are also thought to be of general concern. It is suggested that there is a need for a number of ESA tools in waste management decision‐making. Procedural tools like Environmental Impact Assessment (EIA) and Strategic Environmental Assessment (SEA) should be used e.g. by companies applying for development of waste management facilities and by public authorities preparing plans and programmes. Within these procedural tools analytical tools providing relevant information could be used, e.g. -
A Quantitative Reliability, Maintainability and Supportability Approach for NASA's Second Generation Reusable Launch Vehicle
A Quantitative Reliability, Maintainability and Supportability Approach for NASA's Second Generation Reusable Launch Vehicle Fayssai M. Safie, Ph. D. Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-5278 E-mail: Fayssal.Safie @ msfc.nasa.gov Charles Daniel, Ph.D. Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-5278 E-mail: Charles.Daniel @msfc.nasa.gov Prince Kalia Raytheon ITSS Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-6871 E-mail: Prince.Kalia @ msfc.nasa.gov ABSTRACT The United States National Aeronautics and Space Administration (NASA) is in the midst of a 10-year Second Generation Reusable Launch Vehicle (RLV) program to improve its space transportation capabilities for both cargo and crewed missions. The objectives of the program are to: significantly increase safety and reliability, reduce the cost of accessing low-earth orbit, attempt to leverage commercial launch capabilities, and provide a growth path for manned space exploration. The safety, reliability and life cycle cost of the next generation vehicles are major concerns, and NASA aims to achieve orders of magnitude improvement in these areas. To get these significant improvements, requires a rigorous process that addresses Reliability, Maintainability and Supportability (RMS) and safety through all the phases of the life cycle of the program. This paper discusses the RMS process being implemented for the Second Generation RLV program. 1.0 INTRODUCTION The 2nd Generation RLV program has in place quantitative Level-I RMS, and cost requirements [Ref 1] as shown in Table 1, a paradigm shift from the Space Shuttle program. This paradigm shift is generating a change in how space flight system design is approached. -
What's in a Mental Model of a Dynamic System? Conceptual Structure and Model Comparison
What’s in a mental model of a dynamic system? Conceptual structure and model comparison Martin Schaffernicht Facultad de Ciencias Empresariales Universidad de Talca Avenida Lircay s/n 3460000 Talca, CHILE [email protected] Stefan N. Groesser University of St. Gallen Institute of Management System Dynamics Group Dufourstrasse 40a CH - 9000 St. Gallen, Switzerland Tel: +41 71 224 23 82 / Fax: +41 71 224 23 55 [email protected] Contribution to the International System Dynamics Conference 2009 Albuquerque, NM, U.S.A. Abstract This paper deals with the representation of mental models of dynamic systems (MMDS). Improving ’mental models’ has always been fundamental in the field of system dynamics. Even though a specific definition exists, no conceptual model of the structure of a MMDS has been offered so far. Previous research about the learning effects of system dynamics interventions have used two methods to represent and analyze mental models. To what extend is the result of these methods comparable? Can they be used to account for a MMDS which is suitable for the system dynamics methodology? Two exemplary MMDSs are compared with both methods. We have found that the procedures and results differ significantly . In addition, neither of the methods can account for the concept of feedback loops. Based on this finding, we propose a conceptual model for the structure of a MMDS, a method to compare them, and a revised definition of MMDS. The paper concludes with a call for more substantive research. Keywords : Mental models, dynamic systems, mental model comparison, graph theory, mental model measurement Introduction Mental models have been a key concept in system dynamics from the beginning of the field (Forrester, 1961). -
Guide to the Systems Engineering Body of Knowledge (Sebok) Version 1.3
Guide to the Systems Engineering Body of Knowledge (SEBoK) version 1.3 Released May 30, 2014 Part 2: Systems Please note that this is a PDF extraction of the content from www.sebokwiki.org Copyright and Licensing A compilation copyright to the SEBoK is held by The Trustees of the Stevens Institute of Technology ©2014 (“Stevens”) and copyright to most of the content within the SEBoK is also held by Stevens. Prominently noted throughout the SEBoK are other items of content for which the copyright is held by a third party. These items consist mainly of tables and figures. In each case of third party content, such content is used by Stevens with permission and its use by third parties is limited. Stevens is publishing those portions of the SEBoK to which it holds copyright under a Creative Commons Attribution-NonCommercial ShareAlike 3.0 Unported License. See http://creativecommons.org/licenses/by-nc-sa/3.0/deed.en_US for details about what this license allows. This license does not permit use of third party material but gives rights to the systems engineering community to freely use the remainder of the SEBoK within the terms of the license. Stevens is publishing the SEBoK as a compilation including the third party material under the terms of a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0). See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details about what this license allows. This license will permit very limited noncommercial use of the third party content included within the SEBoK and only as part of the SEBoK compilation. -
System Archetypes As a Learning Aid Within a Tourism Business Planning Software Tool
System Archetypes as a Learning Aid Within a Tourism Business Planning Software Tool G. Michael McGrath Centre for Hospitality and Tourism Research, Victoria University, Melbourne Email: [email protected] ABSTRACT Various studies have identified a need for improved business planning among Small-to-Medium Tourism Enterprise (SMTE) operators. The tourism sector is characterized by complexity, rapid change and interactions between many competing and cooperating sub-systems. System dynamics (SD) has proven itself to be an excellent discipline for capturing and modelling precisely this type of domain. In this paper, we report on an SD-based Tourism Enterprise Planning Simulator (TEPS) and its use as a learning aid within a business planning context. Particular emphasis is focussed on system archetypes. Keywords: tourism, business planning software, system dynamics. 1. Introduction In a recent study conducted for the Australian Sustainable Tourism Cooperative Research Centre (STCRC), improved business planning was identified as one of the most pressing needs of Small-to- Medium Tourism Enterprise (SMTE) operators (McGrath, 2005). A further significant problem confronting these businesses was coping with rapid change: including technological change, major changes in the external business environment, and changes that are having substantial impacts at every point of the tourism supply chain (and at every level – from international to regional and local levels). As a result of these findings, the STCRC provided funding and support for a follow-up research project aimed at producing a Tourism Enterprise Planning Simulator (TEPS). Distinguishing features of TEPS are: • Extensive use is made of system dynamics (SD) modelling technologies and tools (for capturing and simulating key aspects of change). -
Thinking by Ian Bradbury
Downloaded from the Curious Cat Management Improvement Library: http://www.curiouscat.net/library/ So, what's System[s] Thinking by Ian Bradbury System[s] thinking is an area that has attracted quite a lot of attention in recent years. This brief article seeks to introduce the first of two key ideas in system[s] thinking, relate it to popular writings on the subject and illustrate the concept. (the second part, originally published as a second article, is included below) Interdependence Ludwig von Bertalanffy said that in dealing with complexes of elements, three different kinds of distinction may be made: 1) According to their number, 2) According to their species and 3) According to the relations of elements. 1. vs. 2. vs. 3. vs. In cases 1 and 2, the complex may be understood as the sum of elements considered in isolation. In case 3, not only the elements, but also the relationships between them need to be known for the complex to be understood. Characteristics of the first kind are called summative, of the second kind constitutive. Complexes that are constitutive in nature are what is meant by the old phrase "The whole is more than the sum of the parts." An example of cases 1 and 2 might be the value of the change in your pocket. The value of the change taken altogether is obtained by simply summing up the values of the individual coins taken separately. An example of case 3, used frequently by Russell Ackoff to illustrate a constitutive complex, is a car [or truck if preferred]. -
CISM160 Systems Analysis and Design Course Syllabus
ATLANTIC CAPE COMMUNITY COLLEGE ISAS DEPARTMENT COURSE SYLLABUS COURSE TITLE: CISM160 Systems Analysis and Design Instructor: Dr. Otto Hernandez Office: A-142 Phone: 609-343-4978 eMail: [email protected] REQUIRED TEXTBOOK AND MATERIALS: Essentials of Systems Analysis and Design. Author: Valacich; Edition: 6th; ISBN: 9780133546231 COURSE DESCRIPTION: Investigation of information systems with respect to their existence and identification and development of needed informational improvements within an organization. Recommended methods and procedures considering computer involvement are reviewed, designed and implemented using the case-study approach PRE-REQUISITE: one of the following: CISM135, CISM154 or CISM174 ADA STATEMENT: As per the Americans with Disabilities Act (ADA), reasonable accommodations can be provided to students who present current documentation (within five years) of a disability to Atlantic Cape Community College’s Center for Accessibility, located on the first floor of “J” Building in the Counseling and Support Services department (Mays Landing campus). Reasonable accommodations cannot be provided for a course until the student registers with the Center for Accessibility. For more information, please contact the Center for Accessibility via email at [email protected] call 609-343- 5680. LEARNING GOALS & OBJECTIVES: A) Understanding the Systems Development Environment 1. Define information systems analysis and design. 2. Define and discuss the modern approach to systems analysis and design. 3. Illustrate how systems development extends to different types of information systems and not just transaction processing systems. 4. Introduce the traditional information systems development life cycle, which serves as the basis for the organization of the material in this book. 5. Show that the life cycle is a flexible basis for systems analysis and design and that it can support many different tools and techniques, such as prototyping and JAD. -
Success in Acquisition: Using Archetypes to Beat the Odds
Success in Acquisition: Using Archetypes to Beat the Odds William E. Novak Linda Levine September 2010 TECHNICAL REPORT CMU/SEI-2010-TR-016 ESC-TR-2010-016 Acquisition Support Program Unlimited distribution subject to the copyright. http://www.sei.cmu.edu This report was prepared for the SEI Administrative Agent ESC/XPK 5 Eglin Street Hanscom AFB, MA 01731-2100 The ideas and findings in this report should not be construed as an official DoD position. It is published in the interest of scientific and technical information exchange. This work is sponsored by the U.S. Department of Defense. The Software Engineering Institute is a federally funded research and development center sponsored by the U.S. Department of Defense. Copyright 2010 Carnegie Mellon University. NO WARRANTY THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN “AS-IS” BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT. Use of any trademarks in this report is not intended in any way to infringe on the rights of the trademark holder. Internal use. Permission to reproduce this document and to prepare derivative works from this document for inter- nal use is granted, provided the copyright and “No Warranty” statements are included with all reproductions and derivative works. External use. -
Systems Analysis – a New Paradigm and Decision Support Tools for the Water Framework Directive
Hydrol. Earth Syst. Sci., 12, 739–749, 2008 www.hydrol-earth-syst-sci.net/12/739/2008/ Hydrology and © Author(s) 2008. This work is distributed under Earth System the Creative Commons Attribution 3.0 License. Sciences Systems Analysis – a new paradigm and decision support tools for the water framework directive M. Bruen Centre for Water Resources Research, University College Dublin, Newstead, Belfield, Dublin 4, Ireland Received: 23 May 2007 – Published in Hydrol. Earth Syst. Sci. Discuss.: 12 June 2007 Revised: 3 March 2008 – Accepted: 7 April 2008 – Published: 15 May 2008 Abstract. In the early days of Systems Analysis the focus education and outreach influencing behaviour), economic in- was on providing tools for optimisation, modelling and sim- struments and legislation. Regardless of any scientific and ulation for use by experts. Now there is a recognition of the economic justification, it is unlikely that all proposed mea- need to develop and disseminate tools to assist in making de- sures or policies will be acceptable to all stakeholders so cisions, negotiating compromises and communicating pref- that considerable controversy and some planning and legal erences that can easily be used by stakeholders without the challenges can be expected. Consultation, negotiation, com- need for specialist training. The Water Framework Direc- promise and refinement of measures can be expected. Thus tive (WFD) requires public participation and thus provides it is imperative that all decisions on policy and measures a strong incentive for progress in this direction. This pa- be taken not only (i) on the basis of the best available sci- per places the new paradigm in the context of the classical entific and economic information but also (ii) be taken us- one and discusses some of the new approaches which can be ing an unbiased, independent and logical methodology and used in the implementation of the WFD. -
Embedding Learning Aids in System Archetypes
Embedding Learning Aids in System Archetypes Veerendra K Rai * and Dong-Hwan Kim+ * Systems Research Laboratory, Tata Consultancy Services 54B, Hadapsar Industrial Estate, Pune- 411 013, India, [email protected] + Chung –Ang University, School of Public Affairs, Kyunggi-Do, Ansung-Si, Naeri, 456-756, South Korea, [email protected] Abstract: This paper revisits the systems archetypes proposed in The Fifth Discipline. Authors believe there exists a framework, which explains how these archetypes arise. Besides, the framework helps integrate the archetypes and infer principles of organizational learning. It takes the system archetypes as problem archetypes and endeavors to suggest solution by embedding simple learning aids in the archetypes. Authors believe problem in the systems do not arise due to the failure of a single paramount decision-maker. More often than not the problems are manifestations of cumulative and compound failures of all players in the system. Since system dynamics does not account for the behavior of individual actors in the system and it accounts for the individual behavior only by aggregation the only way it can hope to improve the behavior of individual actors is by taking system thinking to their doorsteps. Key words: System archetypes, organizational learning, learning principles, 1. Introduction The old model, “the top thinks and local acts” must now give way to integrating thinking and acting at all levels, thus remarked Peter Senge in the article ‘Leader’s New Work’ (Senge, 1990). Entire universe participates in any given event and thus entire universe is the cause of the event (Maharaj, 1973). However, we must place a boundary to carve out our system in focus in order to understand and solve a ‘problem’ whatever it means. -
ESD.00 Introduction to Systems Engineering, Lecture 3 Notes
Introduction to Engineering Systems, ESD.00 System Dynamics Lecture 3 Dr. Afreen Siddiqi From Last Time: Systems Thinking • “we can’t do just one thing” – things are interconnected and our actions have Decisions numerous effects that we often do not anticipate or realize. Goals • Many times our policies and efforts aimed towards some objective fail to produce the desired outcomes, rather we often make Environment matters worse Image by MIT OpenCourseWare. Ref: Figure 1-4, J. Sterman, Business Dynamics: Systems • Systems Thinking involves holistic Thinking and Modeling for a complex world, McGraw Hill, 2000 consideration of our actions Dynamic Complexity • Dynamic (changing over time) • Governed by feedback (actions feedback on themselves) • Nonlinear (effect is rarely proportional to cause, and what happens locally often doesn’t apply in distant regions) • History‐dependent (taking one road often precludes taking others and determines your destination, you can’t unscramble an egg) • Adaptive (the capabilities and decision rules of agents in complex systems change over time) • Counterintuitive (cause and effect are distant in time and space) • Policy resistant (many seemingly obvious solutions to problems fail or actually worsen the situation) • Char acterized by trade‐offs (h(the l ong run is often differ ent f rom the short‐run response, due to time delays. High leverage policies often cause worse‐before‐better behavior while low leverage policies often generate transitory improvement before the problem grows worse. Modes of Behavior Exponential Growth Goal Seeking S-shaped Growth Time Time Time Oscillation Growth with Overshoot Overshoot and Collapse Time Time Time Image by MIT OpenCourseWare. Ref: Figure 4-1, J. -
A Systems Engineering Framework for Bioeconomic Transitions in a Sustainable Development Goal Context
sustainability Article A Systems Engineering Framework for Bioeconomic Transitions in a Sustainable Development Goal Context Erika Palmer 1,* , Robert Burton 1 and Cecilia Haskins 2 1 Ruralis—Institute for Rural and Regional Research, N-7049 Trondheim, Norway; [email protected] 2 Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway; [email protected] * Correspondence: [email protected] Received: 3 July 2020; Accepted: 13 August 2020; Published: 17 August 2020 Abstract: To address sustainable development goals (SDGs), national and international strategies have been increasingly interested in the bioeconomy. SDGs have been criticized for lacking stakeholder perspectives and agency, and for requiring too little of business. There is also a lack of both systematic and systemic frameworks for the strategic planning of bioeconomy transitions. Using a systems engineering approach, we seek to address this with a process framework to bridge bioeconomy transitions by addressing SDGs. In this methodology paper, we develop a systems archetype mapping framework for sustainable bioeconomy transitions, called MPAST: Mapping Problem Archetypes to Solutions for Transitions. Using this framework with sector-specific stakeholder data facilitates the establishment of the start (problem state) and end (solution state) to understand and analyze sectorial transitions to the bioeconomy. We apply the MPAST framework to the case of a Norwegian agricultural bioeconomy transition, using data from a survey of the Norwegian agricultural sector on transitioning to a bioeconomy. The results of using this framework illustrate how visual mapping methods can be combined as a process, which we then discuss in the context of SDG implementation.