Connective intelligence

Connective intelligence tries to come to grips with the fact that when dealing with cities as complex systems, we are confronted with irreducible uncertainty, which we need to embrace.

We cannot solve those problems with past or present methods, so we need to change the paradigm, seek new methods together in networks with others and with technology and explore what is possible.

Principles of Connective Intelligence (CI)

  1. Networked Interactions

    • CI emerges from synergies between people, AI, infrastructure, and ecosystems (e.g., smart cities where traffic data, citizen feedback, and sensors co-regulate flows).

    • Example: Wikipedia’s self-correcting knowledge base relies on human-AI collaboration.

  2. Decentralized Coordination

    • No central "controller"; intelligence arises from local interactions (like neurons in a brain or ants in a colony).

    • Example: Blockchain systems use distributed nodes to reach consensus.

  3. Adaptive Feedback Loops

    • Systems learn and self-correct through real-time feedback (e.g., adaptive traffic lights that respond to congestion patterns).

  4. Human-Technology Symbiosis

    • CI leverages tools like AI, IoT, and crowdsourcing platforms to amplify human capabilities.

    • Example: Disaster response systems that combine satellite data, social media reports, and on-ground volunteers.

Applications of Connective Intelligence

  1. Smart Cities

    • Urban systems (transport, energy grids) that "learn" from citizen behavior and environmental sensors (e.g., Barcelona’s superblocks reduce pollution through data-driven redesign).

  2. Organizational Design

    • Companies like Google use CI principles by fostering open innovation networks across teams and AI tools.

  3. Ecological Resilience

    • CI frameworks help model climate adaptation by linking satellite data, local knowledge, and policy algorithms.

  4. Healthcare

    • Pandemics response systems that integrate genomic sequencing, travel data, and community reporting (e.g., Taiwan’s COVID-19 containment).

Theoretical Roots

  • Complexity Theory, e.g., Juval Portugali’s work on self-organizing cities.

  • Actor-Network Theory, e.g., Bruno Latour’s idea of agency in non-human actors.

  • Swarm Intelligence, e.g., bird flocking, bee colonies.