Knowledge complex Systems, decisions, uncertaintly risk

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  • Knowledge complex Systems, decisions, uncertaintly risk

    1. 1. Knowledge, Complex Systems, Decisions, Uncertaintly, Risks. Nora H Sabelli Center for Technology in Learning, SRI International Center on Learning in Informal and Formal Environments
    2. 2. The syllabus indicates the following topics: <ul><li>How to understand, shape and manage unpredictable and accelerating change </li></ul><ul><li>Knowledge production in chaotic systems. </li></ul><ul><li>Knowledge “shelf life” under varying conditions. </li></ul><ul><li>• Tools for analyzing and innovatively solving complex problems. </li></ul>
    3. 3. A goal of this session is to facilitate decision making processes on complex issues. Central to an uncertainty and risk approach when the risks as quite substantial is the concept of different perspectives. risk-seeking, risk-accepting and risk-aversive How to evaluate uncertainty and risk are not always familiar or acceptable to non-scientists in general and to decision-makers in particular.
    4. 4. Rationale We need to understand the nature of solutions optimal efficient effective robust favorable resilient (flexible, adaptable)
    5. 5. We must distinguish between chaos (particularly deterministic chaos) uncertainty unknowability (imposibility of obtaining knowledge) And touch on the nature of innovation expertise
    6. 6. Chaos and Complexity open systems and closed systems Complexity deals with non-linear systems, instead of negative feedback (damping), positive feedback (reinforcement) can occur. Chaos can be deterministic; i.e. may not be fully predictable but may lead to a menu of predicatible behaviors. “ Edge of chaos” systems are referred to as ‘complex adaptive systems’ and are not determined, but and can, in fact, be often modeled probabilistically.
    7. 7. “ Strange attractors” “ Attractors” because their solutions are bounded “ Strange” because the system can jump from one extreme to the other.
    8. 8. Uncertainty can be <ul><li>Technical (inexactness) </li></ul><ul><ul><li>Error analysis </li></ul></ul><ul><li>Methodological (unreliability) </li></ul><ul><ul><li>Triangulation </li></ul></ul><ul><li>Epistemological (ignorance) </li></ul><ul><li>“ unknowability” </li></ul>
    9. 9. Causes of uncertainty <ul><li>Sociopolitical and institutional context </li></ul><ul><li>System boundary & problem framing </li></ul><ul><ul><li>System boundary </li></ul></ul><ul><ul><li>Problem framing </li></ul></ul><ul><ul><li>Scenario framing (storylines) </li></ul></ul><ul><li>Model/instrument </li></ul><ul><ul><li>Indicators </li></ul></ul><ul><ul><li>Conceptual model structure / assumptions </li></ul></ul><ul><ul><li>Technical model structure </li></ul></ul><ul><ul><li>Parameters </li></ul></ul><ul><li>Inputs </li></ul><ul><ul><li>Scenarios </li></ul></ul><ul><ul><li>Data </li></ul></ul>
    10. 10. The certainty trough MacKenzie, D. (1990). Inventing Accuracy: a historical sociology of nuclear missile guidance (Cambridge, Mass.: MIT).
    11. 11. Close relation between expertise and innovation <ul><li>What’s “expertise”? </li></ul><ul><li>disciplinary knowledge (domain base) and </li></ul><ul><li>interdisciplinary knowledge (problem base) </li></ul><ul><li>developed and evidenced in “communities of practice” </li></ul><ul><li>striking a balance of efficiency and innovation </li></ul><ul><li>But Innovation can be innovation </li></ul>
    12. 12. The concept of “Adaptive Expertise” from Hatano & Inagaki offers an initial framework. The LIFE Center considers it as a balance between efficiency and innovation, and including the need to abandon prior ideas and procedures.. Innovation Efficiency Adaptive Expert Routine Expert Frustrated Novice Novice Optimal adaptation corridor
    13. 13. Can innovation and efficiency coexist? <ul><li>Innovation and efficiency are compatible </li></ul><ul><li>The goal is to achieve a </li></ul><ul><li>balance between them </li></ul><ul><li>Research shows that one can </li></ul><ul><li>achieve both. </li></ul>
    14. 14. Adaptive Expertise Adaptation over Time Fault Productivity Dip Efficiency Plateau Transfer the Idea of Innovating Past an Impasse (S) (D) Learning to handle steep “faults” in adaptiveness (from Dan Schwartz)
    15. 15. Definitions from NSF Innovation and Discovery Workshop: The Scientific Basis of Individual and Team Innovation and Discovery (2006) Innovation does not necessarily imply a fundamental change in some aspect of the general environment or of the process. It can refer to changes in ways of working and thinking that are new to the individual, his or her local environment, or that coordinate in new ways the interaction between a person and his or her resources. I nnovation o i nnovation Both imply processes that are reproducible, social, cognitive and/or physical, situated simultaneously in the individual and his or her team and organization.
    16. 16. <ul><li>What experts develop are competencies and dispositions for acting adaptively in problem domains: </li></ul><ul><ul><li>Knowledge and skills (e.g., conceptual, procedural, strategic, tactical, and analogical capabilities </li></ul></ul><ul><ul><li>Metacognition (e.g., knowing when and how to use resources if you have them, and how to recruit them if you do not - in terms of people, tools, information) </li></ul></ul><ul><ul><li>Sense of self (e.g., identity development, interests, engagement, persistence, orientation to error and failure) </li></ul></ul><ul><ul><li>Social network relationships with others (and their resources of all these kinds, possible divisions of labor if they can help) </li></ul></ul><ul><ul><li>Uses of and innovations with technologies and material resources (e.g., representational and computational tools for problem solving, physical stuff that can be leveraged in the situation at hand) </li></ul></ul><ul><ul><li>Values (e.g., the dimensions that influence whether something is viewed as a problem or not, strategies considered culturally appropriate in addressing it, consideration of acceptable tradeoffs when values conflict) </li></ul></ul>
    17. 17. <ul><li>A range of “innovation conditions” in the context promote </li></ul><ul><li>innovation rather than routine action for the learner. </li></ul><ul><li>Valued models : Other persons spark a vision for attaining greater expertise </li></ul><ul><li>Social guidance: Supports of different types from parents or other people </li></ul><ul><li>Playful frames leading to exploration and interest development </li></ul><ul><li>Innovations as means: Where the learner has a goal </li></ul><ul><li>to create what they envision, but requires new </li></ul><ul><li>learning for creation to become possible </li></ul><ul><li>Responding to a “chronic snag” or a crisis </li></ul>
    18. 18. Cognitive Criteria (based on affective effects) <ul><li>Characteristics of adaptive expertise </li></ul><ul><ul><li>Curiosity </li></ul></ul><ul><ul><li>Risk acceptance </li></ul></ul><ul><ul><li>Experiment with the new </li></ul></ul><ul><ul><li>Interaction with others </li></ul></ul><ul><li>Characteristics of efficiency </li></ul><ul><ul><li>Avoid distractions </li></ul></ul><ul><ul><li>Restriction to familiar tasks </li></ul></ul><ul><ul><li>Minimize errors </li></ul></ul><ul><ul><li>Immediate proof of success </li></ul></ul>
    19. 19. Affective criteria (based on their learning effects) <ul><li>“ Positivity offset” </li></ul><ul><ul><li>In neutral environments, more positive than negative </li></ul></ul><ul><ul><li>“ leave the nest to explore” </li></ul></ul><ul><ul><li>Start at a relatively high level </li></ul></ul><ul><ul><li>Grows slowly in the presence of external input </li></ul></ul><ul><li>“ Negativity bias” </li></ul><ul><ul><li>In neutral environments, more negative than positive </li></ul></ul><ul><ul><li>“ leave the situation immediately” </li></ul></ul><ul><ul><li>Start at a relatively low level </li></ul></ul><ul><ul><li>Grows rapidly to avoid harm </li></ul></ul>
    20. 20. Additional reading materials: System Dynamics and Uncertainty, Risk, Robustness, Resilience and Flexibility. Erik Pruyt, Delft University of Technology www.systemdynamics.org/cgi-bin/sdsweb?P386 Fundamental uncertainty and ambiguity. David Dequech Texto para Discussão. IE/UNICAMP no. 93, mar. 2000. A complex systems approach to learning in adaptive systems. Peter Allen. International Journal of Innovation Management. Vol 5, June 2001. No. 2 pp, 149-180.

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