Behavioural science is being increasingly prioritised in businesses and scientific organisations. And with good reason: by establishing evidence-based models that can explain and predict decisions that people will make in a given situation, we can develop practices and applications with far-reaching and powerful benefits to both internal and external audiences.
Forbes recently identified the rise of behavioural science at an enterprise level as one of its key emerging trend in 2018, indicating that “savvy companies [will embrace] these economic and social science principles to increase worker productivity and nudge consumers towards desirable outcomes.” But behavioural science, in and of itself, is not a new phenomenon. Richard Thaler, the University of Chicago professor who recently won the Nobel Memorial Prize for his work in Economic Sciences and the man widely known as the father of behavioural economics, posited that certain anomalies in people’s behavior could not be explained by standard economic theory in the early 1990s. Behavioural science teams (or ‘nudge units’) have been common fixtures of larger organisations for over a decade.
What is new, however, is the way we’re building on these assertions and adapting business practices in today’s data-intensive environment. Previously, behavioural science manifested itself at a relatively niche or tactical level - think: the development of UX strategies in web and app design, or the implementation of cookie-based behavioural targeting in performance marketing. In other words, relatively straightforward A/B tests with easily accessible, short-term outcomes.
Over time, and in conjunction with the explosion of generated and captured data, the scope, impact, and pace of these applications have changed dramatically. While behavioural science is still being used to optimise individual elements within organisations, it is being incorporated much more powerfully at a fundamental level in modern business. Look at AirBNB and Uber: their respective business models are defined by behavioural principles, with their core services successfully ‘nudging’ consumers away from entrenched habits to a very lucrative tune (AirBNB’s revenue jumped 50% to roughly $1 billion in Q3 2017). More and more businesses are shifting away from selling products and services to promising outcomes and experiences to their customers. In this context, the predicted rise of behavioural science consultancies and incorporation of behavioral scientists in HR, strategy, and marketing at an enterprise level makes complete sense. What company wouldn’t want to port the success of an AirBNB or an Uber into their own business?
At the same time, the power of behavioural science has to be tempered with a consideration for its potential ramifications. While AirBNB and Uber were built around a disruption of the hospitality and transport industries, respectively, it’s fair to assume that their founders were not able to visualise all of the widespread effects that their businesses have ultimately had - those with both significant effects on business metrics, as well as a broader, equally tangible socio-economic factors.
A recent study by the McGill Urban Planning department has driven up longer-term rental prices in New York by 1.4% (or $384 per year) for the median renter as more and more properties are being snapped up by ghost renters to list on AirBNB. The same study indicates that between 7,000 and 13,500 long-term properties have been removed from an already competitive market.This has projected impact on livability, immigration, urban design, population planning, and legislation in every city that AirBNB operates - an incredibly complex web of knock-on effects.
Similarly, Uber’s service has produced significant, unexpected side effects. While it predictably displaced a large portion of the existing driver workforce, Uber also unexpectedly created debilitating social effects amongst this population. Drivers who borrowed money to pay for the right to operate a taxi are now saddled with a depreciating asset and deeply in debt - in some cases, battling bankruptcy, homelessness, and/or paralysing depression. Although these effects sounds hyperbolic, the recent tragedy in New York is illustrative of their significance.
Accordingly, there’s an urgent need for tools and technologies that can help us predict, account for, and intelligently operate around a growing set of complex and dynamic variables. Traditional (or ‘static’) data modelling is unable to reveal these kind of nuances. It tends to exist in a vacuum that’s not often realistic or accurate, failing to incorporate the chaos or randomness that exists in real life and therefore unable to project scenarios and phenomena that haven’t been previously encountered.
Simulation is a much more powerful solution for this type of scenario planning. Whereas modelling is concerned with building a system of interest, simulations use models to manipulate variables that cannot be controlled in a real system. As the variables in a simulation constantly change, they are much more dynamic than their counterparts and able to produce a greater range of unexpected results. Crucially, this dynamism creates emergent behaviour - that is, behaviour that organically develops from the interaction of different agents and that aren’t possessed by the individual agents themselves, but the group as a whole. Think, for example, of the way that a traffic jam moves as its own entity, whilst emerging from the individual behaviour of the drivers in each car. Emergence is the key that allows us to intelligently predict outcomes from a constantly shifting pool of complex variables. It is something that would have enabled AirBNB and Uber to foresee the complex dynamics that are at play now ten years ago and implement appropriate strategies to pre-empt or counter them.
At Hadean, we believe that uncertainty comes with the opportunity for positive change. That understanding, foresight, and creativity can help us create tools that not only allow insight into these forces at play, but help shape positive and rewarding futures. Simulation is a critical enabler of experimentation in organisational and product design - one that allows us to create more powerful business outcomes whilst retaining organisational agility.
We’ve recently created a simulation engine that runs atop our distributed real-time compute platform, Hadean OS - one that, unlike existing technologies, is capable of supporting simulations at unbounded scales and levels of complexity. This means that a single developer can easily create a simulation, run multiple scenarios with different variables, and quickly amass a comprehensive list of potential outcomes. In a business environment where the stakes are higher than ever and the rate of change is constantly accelerating, this isn’t just a key advantage - it’s a critical necessity.
HadeanOS is a cloud-first operating system that has been engineered and optimized for performance across massively distributed computing infrastructures. HadeanOS natively understands the dynamic scale and real-time demands of modern applications in the cloud and removes the need for complex operations and engineering.
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