July 18, 2007

Knowledge,

Complex Systems,

Decisions,

Uncertaintly,

Risks.

Nora H Sabelli

Center for Technology in Learning, SRI International

Center on Learning in Informal and Formal Environments

July 18, 2007

The syllabus indicates the following topics:

How to understand, shape and manage unpredictable and accelerating change

Knowledge production in chaotic systems.

Knowledge “shelf life” under varying conditions.

• Tools for analyzing and innovatively solving complex problems.

July 18, 2007

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.

July 18, 2007

Rationale

We need to understand the nature of solutions

optimal

efficient

effective

robust

favorable

resilient (flexible, adaptable)

July 18, 2007

We must distinguish between

chaos (particularly deterministic chaos)

uncertainty

unknowability (imposibility of obtainingknowledge)

And touch on the nature of

innovation

expertise

July 18, 2007

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.

July 18, 2007

“Strange attractors”

“Attractors” because their solutions are bounded

“Strange” because the system can jump from one extreme to the other.

July 18, 2007

Uncertainty can be

• Technical (inexactness)Error analysis

• Methodological (unreliability)Triangulation

• Epistemological (ignorance)“unknowability”

July 18, 2007

Causes of uncertainty

Sociopolitical and institutional context System boundary & problem framing

– System boundary– Problem framing– Scenario framing (storylines)

Model/instrument– Indicators– Conceptual model structure / assumptions– Technical model structure– Parameters

Inputs– Scenarios– Data

July 18, 2007

The certainty trough

MacKenzie, D. (1990). Inventing Accuracy: a historical sociology of nuclear missile guidance (Cambridge, Mass.: MIT).

Close relation between expertise and innovation

What’s “expertise”?

• disciplinary knowledge (domain base) and

• interdisciplinary knowledge (problem base)

• developed and evidenced in “communities of practice”

• striking a balance of efficiency and innovation

But Innovation can be innovation

July 18, 2007

Innovation

Efficiency

AdaptiveExpert

Routine Expert

Frustrated Novice

Novice

Optimal

adap

tatio

n corri

dor

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..

July 18, 2007

Can innovation and efficiency coexist?

Innovation and efficiency are compatible

The goal is to achieve a

balance between them

Research shows that one can

achieve both.

July 18, 2007

AdaptiveExpertise

Adaptation over Time

Fault

ProductivityDip

Efficiency Plateau

Transfer the Idea of

Innovating Past an Impasse

(S)

(D)

Learning to handle steep “faults” in adaptiveness (from Dan Schwartz)

July 18, 2007

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.

Innovation o innovation

Both imply processes that are reproducible, social,

cognitive and/or physical, situated simultaneously in the

individual and his or her team and organization.

July 18, 2007

What experts develop are competencies and dispositions for acting adaptively in problem domains:

•Knowledge and skills (e.g., conceptual, procedural, strategic, tactical, and analogical capabilities

•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)

•Sense of self (e.g., identity development, interests, engagement, persistence, orientation to error and failure)

•Social network relationships with others (and their resources of all these kinds, possible divisions of labor if they can help)

•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)

•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)

July 18, 2007

A range of “innovation conditions” in the context promote

innovation rather than routine action for the learner.

• Valued models: Other persons spark a vision for attaining greater expertise

• Social guidance: Supports of different types from parents or other people

• Playful frames leading to exploration and interest development

• Innovations as means: Where the learner has a goal to create what they envision, but requires new learning for creation to become possible

• Responding to a “chronic snag” or a crisis

July 18, 2007

Cognitive Criteria (based on affective effects)

Characteristics of adaptive expertise– Curiosity– Risk acceptance– Experiment with the new– Interaction with others

Characteristics of efficiency– Avoid distractions– Restriction to familiar tasks– Minimize errors– Immediate proof of success

July 18, 2007

Affective criteria(based on their learning effects)

“Positivity offset”– In neutral environments, more positive than negative– “leave the nest to explore”– Start at a relatively high level– Grows slowly in the presence of external input

“Negativity bias”– In neutral environments, more negative than positive– “leave the situation immediately”– Start at a relatively low level– Grows rapidly to avoid harm

July 18, 2007

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 DequechTexto 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.