Dr Lorien Pratt of Quantellia argues that decision intelligence can help safeguard organisations from unintended consequences and provide solutions to the complex problems of a hyper-interconnected planet

We make thousands of decisions every day, and modern organisations are considered “decision factories”. Multiply by billions of people, and it’s not hard to imagine that even a small change in how decisions are made could have a monumental impact on everything we do, with the potential for breakthrough results in the environment, conflict, the distribution of wealth, and much more. Even a tiny percentage shift in the quality of our decisions has the potential to effect gigantic global change: humans, well-coordinated, represent a massive untapped resource.

I’m optimistic about the untapped resource of improved human decision-making for three reasons. First, my work and others over the past half-century has demonstrated that, in many situations, we’re atrocious collaborative decision-makers – just witness the collective intelligence of a typical large organisation or government – so there’s lots of room for improvement. Second, we’ve developed a number of tools that are easy to learn (I recently taught the basic approach to some colleagues in a few minutes over lunch) and which provide breakthrough insights into complex decisions. Third, artificial intelligence, along with the data upon which it is based, is a largely untapped resource for assisting us in complex environments.

We have yet to update our decision-making methods for a world that has radically changed

As Ted Danson explains in a recent episode of The Good Place, things have changed in recent years. The simple act of buying a dozen roses for your grandmother on her birthday, explains Danson to his AI assistant Janet, might have had a net positive impact a hundred years ago. In contrast, in today’s globally connected world, the unintended negative consequences can be enormous.

In short, we have yet to update our decision-making methods for a world that has radically changed.

Here’s one way to think of it. Imagine an isolated ecosystem, a pond, for example. It’s a healthy closed system, with a good balance between predators and prey, and between oxygen and CO2. Then, one day, a heavy rainstorm opens a new river between our pond and the ocean. Salmon and briny water flow upstream, and everything is different. It’s going to take a while for things to re-equilibrate. And if we want certain species to survive, we’re going to have to manage the pond in ways that include a much greater understanding of the ecological system than we had before.

Decisions have ripple effects that can lead to unintended outcomes. (Credit: Clayton Suares/Shutterstock)

This is the situation in which we find ourselves today. But our “pond” is what were once local economic, climate and social systems. Now they’re hyper-interconnected, and it’s going to take a new way of thinking to thrive.

Which gets us back to those decisions. Our brains are better at “single-link” than the “multi-link” thinking that’s required to reason about local actions that have global effects in time and space. And yet multi-link thinking is essential, as choices about climate impact poverty, which impacts jobs, which impacts the status of women, which impacts food, and global impacts local in a continuous chain of events.

Yet our current thinking is limited in time and space. This is the role of the new field of decision intelligence (DI): to expand our decision-making horizon in both time and space. Without this understanding, we’re experiencing geometric growth in unintended consequences.

Simply moving from text-based to visual depictions provides a massive improvement in our ability to make good decisions

Simply put, DI is the science and technology of understanding how actions lead to outcomes. For this reason, DI could have just as easily been called “action intelligence”.

An important part of decision intelligence is to make explicit the flows of cause-and-effect that are set in motion by actions. Simply moving from text-based to visual depictions (and, ultimately, to interactive gaming environments) provides a massive improvement in our ability to make good decisions.

Take, by way of example, the commons problem shown below left. Multi-link, long-term thinking is required to recognise that what might appear a cost at first – a decrease in my own savings – has tremendous upside potential if there are multiple contributors.

By drawing pictures like this we can overcome an essential limitation of this kind of prisoner’s dilemma-like problem: where understanding the actions of others helps to find a better solution for many.

More generally, a host of problems suffer from the “lobster claw effect”, shown below. Decision intelligence, simply put, seeks to shift the prediction horizon to the right.

An aside: much of DI is grounded in the original vision of movements like Cybernetics and the thinking of Buckminster Fuller started in the middle of the 20th century: the idea that complex systems understanding should inform how we interact with technology to solve the hardest problems; these concepts are seeing a 21st-century revival as part of DI.

Part of the reason that these problems have not been solved yet is that they resist the primary approach used by science until recently: to “cut up” problems into smaller and smaller pieces, with the belief that simply assembling the solutions together after such specialisation will create a solution. With interconnected problems like these, this approach fails, because, as Nora Bateson points out in her new work on Warm Data, the intelligence is in the interconnections. Wise organisational leaders know this already: they are seeing the limitations of silo-based thinking (whether the silos are data sets or departments) and looking to new approaches to avoid the “whack-a-mole” effect that comes from solving problems in one part of the organiSation, only to find that they create new problems in others.

We might even go so far as to say that the problems shown in the first graphic above must be solved as a single problem, not separately, perhaps we might call it the Complex Holoptic Unified Multi-link Problem (CHUMP).

Imagine if we all understood the ripple effect of the decisions we make, especially the big ones

Which brings us back to decisions. Imagine if we all understood the ripple effect of the decisions we make, especially the big ones such as those about policy, purchases and products, and made them just a few percentage points better for this reason. What if we used AI and data to help us as assistantss?

This is where DI comes in. With roots in many fields, including systems dynamics, complex systems, behavioral economics, and more, DI was recognised recently by Gartner and Forbes. And my book, Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World, is the first (of many) that tracks this expanding global movement.

Emerging, unusually, not from academic departments but from commercial companies like Google (which has trained over 17,000 engineers in DI) and my company, Quantellia, DI boasts dozens of vendors today and a growing list of success stories. And, in an important sign of the times, Alibaba, the world’s largest retailer, also runs a decision Intelligence research lab, a step that is, so far, unparalleled by western-based companies.

Dr. Lorien Pratt is co-founder of Quantellia and author of Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World (Emerald Publishing, HB, £16.99, Sept 2019).


Decision Intelligence  artificial intelligence  Nora Bateson  Behavioral Economics 

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