When it comes to survival, nature tends to find a way. If done correctly, organisations can, like bees, harness swarm intelligence to mitigate risk.

When a swarm of bees wants to set up a new colony it comes to a collective decision about where to build it. This is a life or death decision for the colony - they need to get it right. 

Bees are very discriminating house hunters as their new home will need to be the right size, have proven good access to pollen, have adequate shelter from weather and water supplies, etc. It’s a complex problem and research biologists report that bees find the optimum solution over 80% of the time.

A human mind on its own would be hard pressed to find the right answer for the bees’ new home location. And yet together, as a swarm, using their collective intelligence, wisdom and intuition, the bees seem to almost always find the right answer. 

What is swarm intelligence?

Biologists call this behaviour by bees a ‘waggle dance’ (because it looks like they’re dancing) to communicate information about what they’ve found in the swarm. They’re conveying signals to one another that represent support for the various home sites under consideration. By combining these signals, the bees engage in a multi-directional tug of war, pushing and pulling on the decision, until they converge on the one solution that they can best agree upon. They find the solution that’s best for the group as a whole.

Unlike many other social species, humans have not evolved the natural ability to form closed-loop swarms that converge in synchrony on an optimised solution. That’s because we lack the subtle connections that other organisms use to establish high speed feedback loops among members. Schooling fish detect vibrations in the water around them and flocking birds detect subtle motions propagating through the population. 

However, to enable swarming by human groups, specialised technology is required in lieu of these natural abilities like the 'waggle dance'.

Artificial Swarm Intelligence (ASI) has been shown to substantially amplify the collective insights and significantly increase predictive accuracy of human groups. It works by connecting teams of networked users into real-time systems moderated by AI algorithms.

Swarm intelligence in humans

Sometimes referred to as “human swarms” or “hive minds,” these systems function very differently than traditional methods for harnessing the wisdom of human groups. Unlike votes, polls or surveys, which treat each participant as a passive source of data for statistical aggregation, “swarming” treats each person as an active member of a real-time control system, enabling the full population to think together and converge on optimised solutions as a unified amplified intelligence.

A brain is a system of neurons so deeply connected, an intelligence forms. A swarm is a system of brains so deeply connected that it forms a brain of brains, a super intelligence, which is smarter than any individual member. And by harnessing this power, with the assistance of AI technology, better decisions are made.

Because “human swarms” are interactive systems in which the participants act and react, continually adjusting their conviction as the group converges on solutions, much smaller populations are required to achieve statistically significant results than polls, surveys or votes.

As participants weigh into a swarm, AI algorithms monitor the behaviour of each participant, inferring how strongly each felt about their choice, based on the relative motions of their icon over time. Someone who holds out longer on one choice, for example, may be expressing a stronger sentiment than someone who switches opinion quickly or several times.

To really find the optimal solution, it’s not enough to just know what people’s opinions are, you really need to know their varying levels of confidence, and that’s what the AI does in the background.

The algorithms combine those levels of confidence and preferences and turns them into a specific choice.

Amplifying group intelligence

Participants within real-time swarms serve a very different function than the “respondents” within votes, polls and surveys. In traditional crowd-based instruments, respondents are simply that - a source of discrete responses that are captured as isolated data points and combined statistically with data from other respondents. While such methods are often said to tap into the wisdom of crowds, the “crowd” is a statistical metaphor for data aggregation. (And there’s nothing wrong with this method, especially for the purposes of data aggregation. In fact, it can be incredibly useful!)

When using swarm AI, on the other hand, human participants are not treated as passive data points, but as active data processors, empowered to act, react and interact with the full population of others. 

By thinking together in real-time systems, swarming groups interactively explore the decision space and converge on solutions that maximise their combined knowledge, wisdom, insights and opinions.

So, while a “crowd” might be a statistical metaphor, “a swarm” is a true emergent system, powered by AI to amplify group intelligence. This enables any team, from small groups to large teams, to answer questions, make predictions, reach decisions, prioritise options and generate insights quickly and accurately.

In a swarm, the input from each user is not a discrete vote, but a stream of vectors that varies freely over time. Because the full population of users can adjust their intent continuously in real-time, the swarm moves, not based on the input of any individual member, but based on the dynamics of the full system. This enables a complex physical negotiation among all members at once, empowering the group to explore the decision space together and converge on the most agreeable solution in synchrony.

The algorithms that moderate the swarm platform enable similar amplification effects achieved by biological swarms. Specifically, AI algorithms track the behaviour of networked participants in real-time, monitoring how users modulate their sentiment every 250 milliseconds in response to others. In this way, swarms don’t ask participants to merely “report” their views, as polls, surveys and focus groups do. Instead, swarms inspire participants to “behave” as part of an interactive system, tracking their changing sentiments. This difference between “reporting” and “behaving” is significant to the power of swarm-based systems.

A real-life example

One of the most famous examples relates to the Kentucky Derby, a famous horse racing event in the US. CBS challenged the swarm scientists to predict the first four horses in the race, in order. In horseracing it’s called a superfecta and odds were 540-1. The scientists formed a swarm of 20 horse racing enthusiasts, not experts. They then had them think together to predict the winners of the race and the reporter who issued the challenge published the results, went to the race and placed a $20 bet tweeting her ticket based on the predictions. 

That ticket won her $11k. Of the 20 people in the group, not a single person got all four horses right, on their own. In fact, in a vote, they only got one horse right out of four. But by thinking together as a swarm, their output was accurate.

Is this a fluke? Well, it seems not. This technology has also been used to predict Oscar winners, football results and politicians’ popularity scores. The medical profession has used it to test whether swarming diagnostic predictions is better than practitioners working alone. But, up until now, no one has been using this technology to make business decisions.

Application for business decision making

Boards and senior management committees are often populated with strong personalities who may be resistant to their decisions being questioned. They may be subject to overconfidence bias that leads them to believe that their decision-making abilities are always correct. There is also the risk of confirmation bias and groupthink in this environment. 

A board may act as a collective (or herd) and seek confirmation from one another to reinforce the position that their view is correct. While consciously, individuals who are members of a board, executive or risk committee may reject this concept. But there is a strong subconscious need to avoid rejection by a group and this may be altering their behaviour without their knowledge.

So, from a risk management perspective, this equates to “board blindness” and runs counter to the foundations of a resilient organisation. There are many examples of organisational failures that have been caused by a lack of challenge or behavioural awareness in boards.

The UK Corporate Governance Code 2018, which applies to all companies with a premium listing, whether incorporated in the UK or elsewhere, sets out principles to ensure that (amongst other things) the organisational culture is reflected in the composition of the board.

It includes the principles that appointments to boards “should promote diversity of gender, social and ethnic backgrounds, cognitive and personal strengths”, and board membership is externally reviewed to ensure that no one director, or small group of directors, are exerting undue influence on decision making.

By ensuring diversity, independence, and review of boards, the firm’s culture will move towards a more de-biased position as led by the “tone from the top”. This may well be a challenge for directors who have been in place for many years and are used to maintaining the status quo. 

However, for businesses to survive and flourish, securing the right skills and cognitive diversity at every level of an organisation will be essential.

Using a system like swarming decisions could help. After all, it doesn’t make the decision binding as the ultimate decision may still rest elsewhere. However, the swarmed decision will provide a strong indication of the collective wisdom of the group.

If you would like to talk about BW Swarm AI, visit our webpage for more details. You can also get in touch with your usual Barnett Waddingham contact. Alternatively, please contact me below.

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