Acting out our dam future: science-based role-play simulations as mechanisms for learning and natural resource planning

Science often does not make its way into decisions, leading to a problematic gap between scientific and societal progress. To tackle this issue, our research tests a novel science-based negotiation simulation that integrates a role-play simulation (RPS) with a system dynamics model (SDM). In RPSs, stakeholders engage in a mock decision-making process (reflecting real-life institutional arrangements and scientific knowledge) for a set period. System dynamics models (SDMs) are visual tools used to simulate the interactions and feedback within a complex system. We test the integration of the two approaches with stakeholders in New England via a series of two consecutive workshops across two states. The workshops engage stakeholders from diverse groups to foster dialogue, learning, and creativity. Participants discuss a hypothetical (yet realistic) decision scenario to consider scientific information and explore dam management options that meet one another\u27s interests. In the first workshop, participants contributed to the design of the fictionalized dam decision scenario and the SDM. In the second workshop, participants assumed another representative\u27s role and discussed dam management options for the fictionalized scenario. This presentation will briefly report on the practical design of this science-based role-play, and particularly emphasize preliminary results of workshop outcomes, which were evaluated using debriefing sessions, surveys, concept mapping exercises, and interviews. Results will determine the extent to which this new knowledge production process leads to learning, use of science, and more collaborative decision-making about dams in New England and beyond

Simulation-based multi-criteria decision making: an interactive method with a case study on infectious disease epidemics

Whenever a system needs to be operated by a central decision making authority in the presence of two or more conflicting goals, methods from multi-criteria decision making can help to resolve the trade-offs between these goals. In this work, we devise an interactive simulation-based methodology for planning and deciding in complex dynamic systems subject to multiple objectives and parameter uncertainty. The outline intermittently employs simulation models and global sensitivity analysis methods in order to facilitate the acquisition of system-related knowledge throughout the iterations. Moreover, the decision maker participates in the decision making process by interactively adjusting control variables and system parameters according to a guiding analysis question posed for each iteration. As a result, the overall decision making process is backed up by sensitivity analysis results providing increased confidence in terms of reliability of considered decision alternatives. Using the efficiency concept of Pareto optimality and the sensitivity analysis method of Sobol’ sensitivity indices, the methodology is then instantiated in a case study on planning and deciding in an infectious disease epidemic situation similar to the 2020 coronavirus pandemic. Results show that the presented simulation-based methodology is capable of successfully addressing issues such as system dynamics, parameter uncertainty, and multi-criteria decision making. Hence, it represents a viable tool for supporting decision makers in situations characterized by time dynamics, uncertainty, and multiple objectives

Innovation in the Knowing Organization: A Case Study of an e-Commerce Initiative

This paper explores the dynamics of information- and knowledge-based activities in one ofthe world’s leading foreign exchange banks and its development of an innovative online trading system.These activities are analyzed using the framework of “the knowing organization,†which postulates thatlearning and innovation in organizations result from managing holistically the activities of sensemaking,knowledge creation, and decision-making (Choo 1998, 2002). In sensemaking, project members at thebank were driven by their shared beliefs about the competition, customers and technology to enact thechallenge of building an online dealing system. Knowledge creation focused on filling perceived gaps,and involved both expanding non-traditional capabilities within the group and acquiring expertise fromoutside the group. Decision making at the enterprise level to approve the project was formal andprocedural, while decision making at the operational level was open and entrepreneurial. As predictedby the model, the interactions between these activities were vital. The outcome of sensemaking providedthe context for knowledge creation and decision making, while the results of knowledge creationprovided expanded resources for decision making. The three sets of activities were integrated throughstrong leadership, group norms of trust and openness, and a set of shared vision and values

Panorámica de las teorías y métodos de investigación en torno a la toma de decisiones en el tenis

In this theoretical and methodological review of decision making in tennis, we describe some of the explanatory theories of the decisional process by using two approaches: cognitive psychology and ecological psychology. Within the former approach, based on models of information processing, we focus on the importance of visual and perceptual behaviour as mediators of anticipation and decision-making skills in tennis, as well as related concepts such as attention and visual acuity. Subsequently, we explain other cognitive theories based on mental representations and the development of different types of knowledge and memory as a central and decisive component in decision making. The latter approach describes decision making on the basis of ecological psychology, using a decision making approach to the ecological dynamics, heightening the importance of the setting and constraints and understanding tennis as a non-linear, dynamic and self-organized system. We discuss various methodological approaches for evaluating decisions in tennis, regardless of the framework that sustains them. We present different ways of evaluating the decision-making process by analysing verbal protocols and questionnaires, observational analysis, kinematic variables analysis and perceptive analysis. Finally, we conclude by presenting the need to overcome several limitations and study decision making in a holistic manner in which decision making is linked directly to game action

An Integrated Decision Support Toolbox (DST) for the Management of Mountain Protected Areas

New tools and methodologies are required in systemic planning and management of mountain protected areas. Among others we propose here a decision support toolbox (DST) conceived as an integrated collection of both soft and hard system methodologies, consisting of participatory and computer-based modules to provide a set of integrated, self-contained tools and approaches to support decision-making processes in the management of mountain protected areas. The Sagarmatha National Park and Buffer Zone (SNPBZ) in Nepal was taken as a pilot case. A number of participatory exercises such as participatory 3-dimensional modeling, scenario planning, and qualitative modeling were carried out to understand social-ecological processes and generate a systemic view over space and time. The qualitative models were then converted into computer-based system dynamics models. The design and development of DST software were carried out with an incremental and modular approach. This process involved stakeholder analysis and decision-making processes through a series of consultations. The software was developed with the main modules including scenario analysis, spatial analysis, and knowledge base. The scenario analysis module runs system dynamics models built in Simile software and provides functions to link them with spatial data for model inputs and outputs. The spatial analysis module provides the basic geographic information system functions to explore, edit, analyze, and visualize spatial information. The knowledge base module was developed as a metadata management system for different categories of information such as spatial data, bibliography, research data, and models. The development of DST software, especially system dynamics modeling and its linkage with spatial components, provided an important methodological approach for spatial and temporal integration. Furthermore, training and interactions with park managers and concerned stakeholders showed that DST is a useful platform for integrating data and information and better understanding ecosystem behavior as a basis for management decisions

Towards an improved understanding of knowledge dynamics in integrated coastal zone management: a knowledge systems framework

Integrated coastal zone management (ICZM) is a complex undertaking that draws on a range of biophysical and social science disciplines, and involves a wide range of stakeholders operating through multiple processes, and crossing various levels  Conceptually, this means that ICZM represents a significant challenge in terms of improving the way in which different disciplinary ‘knowledges’ and different forms of knowledge (scientific, managerial, lay, and indigenous) inform decision making. Depending upon the circumstances, ICZM may be constrained by different knowledge deficits, including: uncertainty; science - policy gaps; and the ‘filtering’ of particular forms of knowledge relative to others. As a means for making sense of these knowledge dynamics, this paper considers the concept of knowledge systems and its potential for improving understanding of coastal management processes. The potential insights that can be gained from four analytical approaches (stakeholder, institutional, network, and discourse analysis) are then discussed, and used to develop an analytical framework for investigating coastal knowledge dynamics, which is based upon a generic coastal knowledge system and associated research questions. Finally, the utility of this framework is illustrated using a case study that examines the knowledge dynamics associated with debates about the establishment of marine protected areas in Victoria, Australia

Can science-informed, consensus-based stakeholder negotiations achieve optimal dam decision outcomes?

Integrating science and decision-making in dam management is needed to address complex tradeoffs among environmental, economic, and social outcomes across varied geographic scales and diverse stakeholder interests. In this study, we introduce an approach that integrates system dynamics modeling (SDM) and role-play simulation (RPS) to facilitate use of the best available knowledge in dam decision-making. Using a hypothetical dam decision context in the New England region of the United States, this research investigates: (1) How do science-informed, negotiated outcomes compare to Pareto-optimal outcomes produced by a scientific model that balance selected system performance tradeoffs?; and (2) How do science-informed, negotiated outcomes compare to the status quo outcome? To our knowledge, this research is the first effort to combine SDM and RPS to support dam decisions and compare science-informed, consensus-based outcomes and optimized system outcomes. Our analyses show Pareto-optimal solutions usually involve a multi-dam management approach with diversified management options. Although all negotiated outcomes produced a net loss compared with at least one of the Pareto-optimal solutions balanced across tradeoffs, some yielded benefits close to or better than specific Pareto-optimal solutions. All negotiated outcomes yielded improvements over the status quo outcome. Our findings highlight the potential for science-informed, stakeholder-engaged approaches to inform decision-making and improve environmental and economic outcomes

Integration of prognostics at a system level: a Petri net approach

This paper presents a mathematical framework for modeling prognostics at a system level, by combining the prognostics principles with the Plausible Petri nets (PPNs) formalism, first developed in M. Chiach´ıo et al. [Proceedings of the Future Technologies Conference, San Francisco, (2016), pp. 165-172]. The main feature of the resulting framework resides in its efficiency to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about the system state like the probability distribution of end-of-life, information from sensors, and information coming from expert knowledge. In addition, the proposed methodology allows us to rigorously model the flow of information through logic operations, thus making it useful for nonlinear control, Bayesian updating, and decision making. A degradation process of an engineering sub-system is analyzed as an example of application using condition-based monitoring from sensors, predicted states from prognostics algorithms, along with information coming from expert knowledge. The numerical results reveal how the information from sensors and prognostics algorithms can be processed, transferred, stored, and integrated with discrete-event maintenance activities for nonlinear control operations at system level