This is our skunkworks where Optomatica experts work on the coal-face of our industry on internally-driven and externally focused collaborations with academic, industry, and governmental partners. Our analytics team continuously deepens its understanding of the “behaviour of behavioural data” and the challenges and opportunities to understand it and improve performance. It boils down to getting every greater handle on what is a ‘signal’ and what is ‘noise’ generated by the different kinds of information through which we view the world through.
In human systems, behaviour can take many forms: the behaviour of individuals, the behaviour of individuals acting as part of a collective system and the behaviour of populations of diverse actors to create complex emergent systems. At Optomatica, we specialise in making sense of behavioural information across this breadth of human endeavour to understand the critical nuances that differentiate what “data” means in these contexts and what it can and cannot be used for, and when and how to improve it.
Our skunkworks team always have the same ultimate goal – understanding the complexity of cause and effect and how to help individuals understand and improve their performance through better intelligence gathering and information processing.
Our analytics experts focus on three particular areas of behaviour and insights from where these areas feed into the design of prototypes, production of digital products, and the services our engineers help create.
Our human behaviours team have great experience in sports and health. Information about how we behave physically when engaging in sports and our day-to-day lives are central to our health and improving it. While the market for ‘trackers’ and ‘wearables’ may seem mature, it is not. This is because the quality of information gathered is still dependent upon mass consumer-grade hardware that is far behind the gold standards needed to generate reliable measurements. This is also because normally, one only look at behaviour through the filter of one kind of measurement.
This is where AI and the love of analytics are critical in achieving progress. AI is not only about solutions. It is also about problem-solving and intelligence creating behind the scenes. Nowhere is this more clear than in the importance of how you use AI to distinguishing ‘signals’ from ‘noise’ as well as in helping differentiate what the signals actually signify.
Behavioural data differs depending on how you look at that behaviour. AI is not a substitute for deep insights and expertise in how behavioural data behaves. It is part of the tools needed to generate that expertise in the first place!
Markets determine much of the economics of modern life. Moreover, markets impact us whether or not we as individuals consciously choose to interact with them or not. Consequently, we have a primary choice. We can choose to be left to their mercy and the mercy of others who interact with markets on our behalf (e.g., your pension fund and their managers for instance). Or we can choose the difficult path of better understanding how markets behave and how we manage the opportunities and risks they pose. At Optomatica, we choose the latter and aim to help provide the systems to help you and others manage that learning curve.
Markets represent a particular form of behaviour that presents unique challenges and opportunities for understanding what information means. As values at stake in markets depend upon how others perceive and behave, markets are usually filled with a large amount of noise from analysts, pundits and companies, all trying to persuade the participants and observers how the wind will blow. One can take many positions in the marketplace, which assets one chooses to invest in, how much, and how long. Deciding how one interacts with the market poses a massive informational challenge.
At Optomatica, we believe in levelling the playing field by tackling the problem head-on. The majority of individuals in the markets face what is technically referred to as ‘information asymmetry’— the reality that some people know more about the things that matter more than you. As market knowledge that matters have a short shelf life, having that knowledge quicker than your competitors becomes the key to winning and losing in markets.
Hedge funds are not made of individuals with all-knowing, all-seeing powers of divination. They are made up of people who use intensive information systems that give them better odds than those who try to pick ‘winners’ using a simpler yet error-prone set of heuristics and intuitions.
Building blocks of systematisation strategies are well-known computational techniques. They revolve around the truth that fates of any given asset are more or less correlated with the fates of other assets competing for your custom. What matters is knowing how ‘loose’ or ‘tightly’ these correlations are and detecting when they are formed, how long they persist, when they are broken, and what new relationships are created in their wake. By giving you the ability to turn fundamental uncertainties about the future into a measurable and manageable view of risk and reward, you can make better choices.
Why these systems work lies in the behaviour of supply and demand. These boil down to a continuous flow where people who buy and sell move on to buying and selling something else. While monitoring the movement of every member of these herds is not possible, looking for the effect of these changing relationships formed in the movement of prices is observable. As with all information, the trick is distinguishing signal from noise at ever greater levels of precision.
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The key to levelling the playing field is recognising that the techniques used by the professionals are mostly know-how and high-performance systems that are increasingly accessible in the public domain. The techniques hedge funds use are very similar to those used by computer scientists and AI specialists working in other domains of endeavour. Indeed, the underlying methodology looks very familiar to any number of researchers in biology and ecology who study natural interactions not through direct observation but through the identification of underlying correlations between the measurements taken to describe an ecosystem.
Our ‘markets’ team offers ways in which tools, techniques and information publicly accessible can be made readily available, deployable, and user-friendly. Our goal is to help individuals and organisations with the deep academic and technical skills needed to build such systems. Keep your eyes open for our first iteration!
The nature of corporations data is an ideal fit for AI techniques to work through the complexity of information to help users distinguish signals from noise. Like markets, the behaviour of corporations is central to the economics of modern life. What markets people are often familiar with, aside from the supermarket, is the market for stocks in corporations. Yet, most people cannot understand those corporations besides what they tell the world and what other ‘analysts’ say in their news feeds based on the same limited information.
Most wealth in society is made legitimately or otherwise by how people use corporate entities to manage various activities to cover up any number of sins. Unlike real people who can never escape the confines of their own body, corporate individuals can possess many of the same rights and privileges that a real person has. Yet, they are not restricted to a single body or a pre-ordained lifespan. Indeed, most corporations everyday people are aware of, such as those listed in stock markets, are corporations made up of hundreds or thousands of sub-parts, each with roles to play in group performance, but never talked about in the public disclosures and annual reports when managers account for their activities and performance through a simpler form of storytelling. These hidden behaviours matter, and getting a handle on them is another information challenge we at Optomatica are leading the charge on.
Technological developments are breaking open this ‘corporate veil’, and AI is at the centre of it. One fundamental reason is that the nature of the data is an ideal fit for AI techniques to work through the complexity of information to help users distinguish signals from noise. Behind the veil of a single corporate personality is a reality of a veritable jig-saw puzzle of entities, each with its individual existence and its own rights to acts and behave. But there is also its own responsibilities and requirements to report to the world information about who they are and what they do.
As is true of all behaviour, the behaviour we see does not exist in isolation. It responds to the complex set of relationships “inside” the collective it is part of and the relationships with those “outside” of that group. Due diligence experts and investigators use this information to detect signs of strategic shifts in behaviour, better understand financial management and reporting, identify strategic relationships, offshore activities, and detect areas of repetitional risks for the group. Investigative journalists and public regulatory bodies also use the same information to understand who they are dealing with and if regulations or taxes are circumvented. Tracking this behaviour systematically and in real-time will be the domain of AI working alongside expert investigators.
Our strong relationship with each partner has a unique story that reflects the essence of our profound expertise, deep commitment and investment. Contact us today for your custom fit collaboration!
Our ‘corporate’ team is actively working with partners to understand better the deep issues that come with this data and working out the logistics of turning early research into the real-time corporate “CAT scan” technology of the future. Keep your eyes open as we will have more to talk about in the coming months!
