🙋🏻‍♂️ Collective Intelligence for Collective Action

27 Sep

Summary:

An ACI Framework emerges from the interaction of people, technology, and the physical environment, providing communities with tools for how to integrate and interact with artificial intelligence to deal with community-based challenges.

The Problem

Given the ecological crisis is happening at the intersection of natural and human systems at multiple scales, it can be challenging to predict or understand the effects of this interdependence.

Due to the non-linear behaviour of these complex systems, attempted interventions can result in unintended outcomes.

Yet the ecological crisis requires community-coordinated decision-making and action involving multiple stakeholders.

Already difficult for experts, it is near-impossible for communities to account for and understand the potential risks and uncertainties involved with systemic interventions.

The ACI Framework promises to help communities coordinate their response to the impending problems of climate change. To offer a more intelligent framework for mobilising and organising responses by coordinating different decision-makers.

The Scape

Imagine in the not-so-distant future, communities are not organised by a centralised top-down form of local government, but instead, make collective decisions relating to civic and environmental assets with the help of Artificial Intelligence.

The framework used for this is the Augmented Collective Intelligence Framework, pioneered by Lucidminds and Dark Matter Labs. Throughout the country, communities have designed and implemented their own model for tackling the changing problems facing communities, and their wider ecology.

The framework emerges as a hybrid system combining human and artificial intelligence, each serving as a subsystem within the framework and consisting of varying individual actors. People and civic assets (like libraries, microgrids, etc.) represent the physical context, and are connected through data streams to digital assets; ones that range from AI models to databases and digital simulations.

This system is dynamically evolving. It will not involve a one-size-fits-all solution. Communities (physical context) can test and study the potential effects of actions by using this digital infrastructure to simulate potential outcomes. AI models can democratise this deliberation process with each decision-maker getting access to previously inaccessible information.

Communities can understand the potential trade-offs between stakeholders, and how different decisions may affect one another. Previously unheard voices can contribute to the deliberation process, with even non-human stakeholders able to be represented by their digital twins, like AI agents representing an ancient woodland, in the ACI infrastructure.

Even future generations can be incorporated within this framework ‘with future generations as something to value’ being something communities have chosen to design within the AI’s goals.

One example is a community who have chosen to use this ACI framework as a way to respond to the climate crisis and its effects on their local neighbourhood.

In particular, they use AI to make decisions relating to their proposed rewilding project, offering insights into the potential benefits of natural carbon sequestration and increased biodiversity through certain practices.

Information about the local environment has been mapped onto the digital infrastructure, meaning the community can best understand which rewilding efforts may be most effective, along with how they align to more national climate and biodiversity targets. The potential solutions proposed come with the trade-offs between different stakeholders visualised; these include human, non-human, and system-wide entities.

Alongside this, the community’s ability to prepare and predict the effects of ecological change over the next 5/10 years partially informs their decisions relating to civic assets.

What emerges is a complex adaptive system between humans, machines and the environment. A collective intelligence that requires all parts to operate. Rather than fearing our own replacement from AI, our ability to make sense and give value becomes a central feature in this intelligence system.

Downstream Value Creation

The ACI framework offers a multitude of potential benefits for community sense-making and coordinated action. The potential value creation down the line includes:

Coordinated Community Responses to Crises
- Instead of a disjointed system of different decision-makers unable to take action, the ACI framework radically reforms how communities respond and prepare for the ecological crisis.
Greater Value Placed on Environment
- Creating AI agents to speak on behalf of non-human species and systems, communities are encouraged to value our wider environment.
Improved Impact Modelling
- Enhanced ability to predict and understand impact of ecological change on the community, and vice-versa.
De-centralisation and Localisation of Governance
- This technology empowers communities to make informed decisions through a pluralistic approach.