The book at my hand is Big Mind: How Collective Intelligence Can Change Our World, written by Geoff Mulgan. I accidentally encountered this book when I was reading a wechat article of CIDEG, a think tank at the School of Public Policy and Management, Tsinghua University. It attracted me for its goal – to understand how individuals operate, cooperate, and learn in scale. The author elaborates this purpose into vivid details. At first, it looks obvious to us that we have big and small organizations, strongly or weakly connected, in our society. Less obvious maybe that some organizations get ever bigger and integrated, and some begin to emphasize decentralization and disconnection. What we see are powerful multinational companies like General Electric, Toyota, and Apple, growing ever bigger while traditional vertically-integrated electricity companies and energy systems being dismantled into more autonomous electricity producers and distributors. Empowered by technologies, effective organizations can not only point to hierarchical structure symbolized by ancient and feudal societies but also loosing, democratic, and modern networks created by infrastructures and protocols such as Wikipedia, Oxford English Dictionary, and Google Map. More crucial is what make some organizations succeed or fail and how the rule of game changes when we get ever more powerful technologies. For instance, artificial intelligence is much more efficient than us when operating according to rules, such as playing chess, identifying patterns, and repeating tasks. There are technologies improving our perception, enlarging our capability, and get us connected in unprecedented ways such as the Internet of Things and augmented reality. So, to go further, the question becomes what make organizations comprised of both individuals and machines succeed or fail. This is an audacious question. The author wets my appetite.
As the book title suggests, the author makes it straightforward that collective intelligence is at the core of organizational/collective success. Elements for a functional collective intelligence are depicted first.
The Elements of Collective Intelligence
A live model of the world: The start point for reasoning/mental models
Observation: Bring in new facts and perspectives without bias/judgement
Focus: Select and prioritize
Memory: Accumulate useful facts and learning
Empathy: Mutual benefits and strategic conversation
Motor coordination: Capability to mobilize the physical world
Creativity: Novel solutions for existing problems
Judgement: Effective decision making in diverse groups
Wisdom: Contextual thinking and subtle understanding
I should say when I first saw this list, I thought it as a complete nonsense. The author only brings in all these fancy words and ideas to make the so-called “collective intelligence” appealing. The book goes on to suggest the five organizing principles of collective intelligence which remind me some content of Principles: Life and Work written by Ray Dalio. The list is shown below:
Five Organizing Principles of Organizations
Autonomous commons: the freedom of knowledge generation and opinion expression; correlates with Ray’s idea about radical truth and transparency.
Balanced use of the capabilities of intelligence: Do not overly emphasize any one element of collective intelligence, a balance of investment should be reached given specific conditions and periods; correlates with Ray’s discussion about start-ups and big corporation.
Focus and the right granularity: Set priorities, observe and learn at the right level for the problem at hand; correlates with Ray’s discussion about business reporting lines for managers.
Reflexivity and learning: Learning by doing and loop-learning; correlates with Ray’s 5-step learning model
Integration for action: Effective decision, mobilization, and execution; correlates with Ray’s believability-weighted decision-making process.
I have to say the arguments made are still more of fancy words instead of critical reasonings or careful case studies. But the following content starts to be interesting.
In Chapter 6 – learning loops, the author categorizes the reflexive learning process of organizations/individuals into three loops. He suggests that these three learning loops are critical ways for organization to maintain sustainable and resilient success. The first-loop learning represents the process we get to learn rules in clearly defined conditions through reasoning. For example, trying to win a chess game through learning new strategies and continuous practices. The second-loop learning involves the process to bring in new information or perspective which have not been envisioned before. In terms of chess game, this can be researching opponents’ preference and observing their recent mental conditions which might affect their strategies during the game. The third-loop learning is about systemic change in which a completely different way of seeing thing or mental model is developed. This can refer to the integration of artificial intelligence and human in competing in chess game. Such a change redefines the rules of game and require new thinking capacity and skillset from human. These three loops of learning are considered as fundamentals for good collective intelligence.
If the book stops here, I would forget everything after one week though good analytical discussion about collective intelligence has been completed. Case study is important and the author does this. The remaining of the book applies the elements and principles of effective collective intelligence to analyze the operation of governments, corporations, democratic politics, the economic system, and global commons such as natural resources and private data. Although structured in a prosaic way, the writing is intentionally completed by analyzing whether the three learning loops are functional and effective, whether autonomous commons are encouraged, whether (triggered) hierarchy is established for different levels of observing, learning, and decision making within organizations. The part is more interesting and help me remember those fancy “elements and principles” in context but, still, I think I am far from implementing these possible insights into my real life.
Throughout the book, the author put special attention on reflexive learning, obtaining of wisdom, and autonomous commons while many other concepts are introduced. Reflexive learning is highlighted as knowledge is not always obtainable through deduction but learn through planned practices. Wisdom is considered as harder to get than abstract knowledge as it suggests an individual or an organization’s ability to operate in specific context and transform abstraction into distinctive reality. Autonomous commons in collective intelligence are represented by widespread observation, free expression, and availability of information, data, and knowledge (to empowering everyone’s autonomous reasoning).
Generally, it is a practical book and more or less offer a new way or model to perceive contemporary organizations which are increasingly ubiquitous, diverse, machine-enhanced, and dealing with unprecedented, big, and common problems, e.g., public health, climate change, poverty and hunger. The author wants also to encourage more research on collective intelligence since few scientists conceptualize their interests in such a grand way. However, it could be well argued that this concept is not new from an academic perspective as it has been already appropriately studied by various subjects such as psychology, business management, public management, and so on.
Jiaxin Zhao 