There is a natural human drive towards consolidation over exploration. Inherently we can feel that successes are something to be protected, perhaps to build on, but never to take risks with. This is a easy mindset to develop, but why is it dangerous? At first glance, there are many positive aspects, especially for close-horizon time scales. But across longer periods of time, the negatives significantly outweigh those positives. We find ourselves favouring entrenchment over mobility, and change and innovation become suppressed. So how can we optimise what we have, explore new possibilities, and stay future-focused?

There is a natural human psychology for consolidation over exploration. Inherently we can feel that successes are something to be protected, perhaps to build on, but never to take risks with. This is a easy mindset to develop, but why is it dangerous? At first glance, there are many positive aspects, especially for close-horizon time scales. But across longer periods of time, the negatives significantly outweigh those positives. We find ourselves favouring entrenchment over mobility, and change and innovation become suppressed. So how can we optimise what we have, explore new possibilities, and stay future-focused?

Our technology growth has taken us on a fantastic journey. Inside of the last 25 years we have gone from wired phones in our houses, to powerful computers strapped to our wrists. We can transfer money across to the other side of the world in fractions of a second. Whole autonomous organisations can be contructed from smart contracts that live distributed across the digital world. I can authenticate to my bank through facial recognition. My favourite websites know me better than I know myself, and can act as smart agents, predicting what I would like, where I would want to go, how I might vote… This digital world is one of data and machines, locations, behaviours, machine learning predictions. automated interactions. These abilities give us the power to do great things, but in doing so we have also achieved the power to do immense damage. How do we navigate this new world and maintain integrity? How do we cross ever-more amazing frontiers without losing our ethical direction?

This is the second paper (in review) in the Multi-agent learning in dynamic systems series. This work looks at how agents can learn to allocate restricted resources to handle incoming tasks, adapting to meet the goals of other agents as they gain knowledge on those goals. Multi-agent learning in dynamic systems Focused on applying reinforcement learning techniques to multi-agent systems where the environment is dynamic, and realistic resource constraints exist. This work combines task-allocation optimisation, resource allocation, and self-organising hierarchical agent structures.

This is the first paper (in review) in the Multi-agent learning in dynamic systems series. Developing new algorithms to optimise task allocation in multi-agent systems. Q-learning, historical reward convolution, and dynamically adaptable risk-based system exploration approaches are developed. Multi-agent learning in dynamic systems Focused on applying reinforcement learning techniques to multi-agent systems where the environment is dynamic, and realistic resource constraints exist. This work combines task-allocation optimisation, resource allocation, and self-organising hierarchical agent structures.

As the pace of progress within existing markets increases year on year there are both new opportunities and substantial challenges to the successful and established companies across many industries. One of the key pathways through this modern landscape is often summed up in the all-encompassing term ‘Digital Transformation’. The definition varies depending on who you ask, but broadly speaking is a mix of modernisation of IT infrastructure and movement to more Agile methodologies. In general, trying to establish a modern software house as part of the business. Large organisations however are a myriad of different functionalities, groupings, operational processes, and supplier companies. In the zero-margin world of high automation and digital scaling, where do these supplier companies fit in? And how relevant are their differing business models in providing value to the host business?

This is the introductory poster for the Multi-agent learning in dynamic systems series. In systems with a large number of agents there are fundamental pressures on the centralised coordination techniques used to provide inter-communication, task orchestration, and routing of messages. As the scale of interacting components expands, we reach resource constraint plateaus, where computation, storage, or communication pathways become saturated. At these points we must decompose each agents functionality into a number of specialisms that can then be taken up by other agents, at the cost of even more orchestration communications and synchronisation to provide this distributed functionality. To provide solutions to tackle these issues we focus on distributed agent systems where reinforcement learning behaviours are constrained by resource usage limits and hence by local neighbourhood awareness rather than global system knowledge. Multi-agent learning in dynamic systems Focused on applying reinforcement learning techniques to multi-agent systems where the environment is dynamic, and realistic resource constraints exist. This work combines task-allocation optimisation, resource allocation, and self-organising hierarchical agent structures.

One of the most common challenges as businesses transform more of their traditional capabilities into digital ones is the breadth and depth of the change itself. Core changes to the organisational structure, processes, and culture. The functional components and interactions of these aspects of a large organisation help to define what we mean when we talk about complex systems. But it is also when we look through the lens of complex systems that we can get a different vision of change. Seeing it not as a disruption in so much as a possible instrument of stability and predictability. The rapidity of change not being something that is to be feared but instead something that can be embraced as a stabilising force. Complex systems concepts span a broad horizon as an abstraction of the behaviours of many disparate areas such as biological, computational and societal systems composed of many parts. Here we look at one small technological part and how understanding more of its behaviours as a complex system affects the ways we can view it and work in digital environments. To do this we’ll start by looking at the humble CICD pipeline…

If anything defines the business landscape in the modern world over the last few years it is the increasing sophistication of technology, the ever-quickening pace, complexity, scale of data, and dropping of costs. The power of the tools now available to organisations is incredible. With one click we can add massive data lakes, machine learning, and personal AI assistants, let alone the day-to-day underlying traditional compute uses we are more familiar with. What also seems clear however is that we aren’t able to keep pace with the availability of technologies in the majority of cases. Amazon and Google scale companies, and others whose businesses are essentially reliant on staying at the front-edge of technology, are able to harness the power of new functionality, their survival requires it. But what of the rest of the businesses out there, those who’s primary driver is not necessarily technological?

“I’ve got news for Mr. Santayana: we’re doomed to repeat the past no matter what. That’s what it is to be alive.” - Kurt Vonnegut Jr Whether its passwords to access external service, API keys, or other forms of credentials, we not only know that our applications need them, but we also know that they are in reality, highly likely to be exposed beyond the security boundaries we define for them. Most commonly the exposure will come from a human error. Keys committed to a GitHub repository 1,2, incorrect permissions on an S3 bucket 3,4,5 and so on.