Decisions and Time in the Information Society
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Decisions and Time in the Information Society

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Policy Paper

Policy Paper
"In Search of Time" Conference
ISIDA, Palermo (IT)
9 May 2003

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Decisions and Time in the Information Society Decisions and Time in the Information Society Presentation Transcript

  • Decisions and Time in the Information Society Jesse Marsh Atelier Studio Associato [email_address]
  • Time and Policy-making
    • Time to read the environment
    • Time to define a strategy
    • Time to build consensus
    • Time to approve
    • Time to finance
    • Time to implement
    • Time to evaluate
  • Time and the Info-society
    • Pace of change
    • Moore’s law
    • Non-linear processes
    • Systemic reflexivity
    • Complexities of globalisation
    • Reversal of the value chain
  • Time and Rules of the game
    • Super Mario vs. chess
    • Vulnerability to shocks
    • Instability of rule-sets
    • Uncertainty of outcomes
    • “ Interactive” policy?
  • Linear to Developmental
    • Effectiveness: change timespan > policy intervention
    • Effectiveness: change timespan = policy intervention
    ENVIRONMENT DECISION ACTION LINEAR POLICY MODEL ENVIRONMENT DECISION ACTION DEVELOPMENTAL POLICY MODEL
  • Time’s blackmail
    • What about: change timespan < policy intervention?
    • Do policy interventions take too long?
    • Are “bottom-up” methods an answer?
    • Who can policy-makers control?
    • What does policy produce anyway?
  • From buying to shaping
    • Example: send a man to the moon; Messina bridge
    • Examples: the Information “Superhighway”; the Euro
    ENVIRONMENT DECISION ACTION BUY READ VERIFY SPHERE OF POLICY INTERVENTION ENVIRONMENT DECISION EVENTS READ VERIFY IMAGE FORMULATE SHAPE
  • Predicting Rule-sets
    • What is an image?
    • Future rule-sets imply changed behaviour of socio-technical systems
    • Collective creativity can be stimulated, but not planned
    • “ Enabling” actions, leverage effects, social amplifiers
    • How to verify that things went right?
  • Predicting Rule-sets
    • Importance of verifying the relation between predicted rule-sets and emerging rule-sets
    READ MONITOR PREDICT SHAPE R t+1 E D R t+1 R t (t+1 -> t) VERIFY t = any given time 1 = time of a single policy-making cycle
  • Fractal Extension
    • Object of image-based policy is not a concrete entity
    • Means of image-based policy is not laws or money
    • Subject of image-based policy is social systems
    • Decision-making (innovation) in social systems is through propagation
    • Everybody “makes policy”
  • Fractal Extension
    • Fractal extension of rule-set images
    READ MONITOR PREDICT SHAPE R t+1 E D R t+1 R t (t+1 -> t) VERIFY READ MONITOR PREDICT SHAPE R t+2 R t (t+2 -> t) VERIFY (t+1 +1 = t+2) R t+2 D
  • Decoding
    • What about t+n?
    • Need to predict other actors’ images of future rule-sets
    • Decoding weak signals
    • t+n must be sound and compelling but also light, quick, flexible
  • Decoding READ MONITOR PREDICT SHAPE E D R t+1 R t (t+1 -> t) VERIFY READ MONITOR PREDICT SHAPE R t+2 R t (t+2 -> t) VERIFY (t+1 +1 = t+2) DECODE R t+1 R t+2 D R t+1
  • Validation
    • Verify
      • Qualitative ex-post evaluation
      • Do what degree do new rule sets correspond to predicted ones?
    • Monitor
      • Process evaluation
      • What new rule sets are emerging?
    • Decode
      • Pre-emptive evaluation
      • What new rule sets are field actors predicting?
  • Open Issues
    • How to promote sustainable lifestyles?
    • Social engineering? Need for democratic control
    • Resistance to dematerialisation of politics
    • Lack of attitude to long-term thinking
    • Time for reflection