Addressing complex societal challenges, such as improving public health, fostering honesty in workplaces, or encouraging eco-friendly behaviour requires effective nudges to influence human behaviour at scale. Intervention science seeks to design such nudges within complex societal systems. While interventions primarily aim to shift the system toward a desired state, less attention is given to the sustainability of that state, which we define in terms of resilience: the system's ability to retain the desired state even under perturbations. In this work, we offer a more holistic perspective to intervention design by incorporating a nature-inspired postulate i.e., lower energy states tend to exhibit greater resilience, as a regularization mechanism within intervention optimization to ensure that the resulting state is also sustainable. Using a simple agent-based simulation where commuters are nudged to choose eco-friendly options (e.g., cycles) over individually attractive but less eco-friendly ones (e.g., cars), we demonstrate how embedding lower energy postulate into intervention design induces resilience. The system energy is defined in terms of motivators that drive its agent's behaviour. By inherently ensuring that agents are not pushed into actions that contradict their motivators, the energy-based approach helps design effective interventions that contribute to resilient behavioural states.

Discrete choice models are essential for modelling various decision-making processes in human behaviour. However, the specification of these models has depended heavily on domain knowledge from experts, and the fully automated but interpretable modelling of complex human behaviours has been a long-standing challenge. In this paper, we introduce the differentiable discrete choice model (Diff-DCM), a fully data-driven method for the interpretable modelling, learning, prediction, and control of complex human behaviours, which is realised by differentiable programming. Solely from input features and choice outcomes without any prior knowledge, Diff-DCM can estimate interpretable closed-form utility functions that reproduce observed behaviours. Comprehensive experiments with both synthetic and real-world data demonstrate that Diff-DCM can be applied to various types of data and requires only a small amount of computational resources for the estimations, which can be completed within tens of seconds on a laptop without any accelerators. In these experiments, we also demonstrate that, using its differentiability, Diff-DCM can provide useful insights into human behaviours, such as an optimal intervention path for effective behavioural changes. This study provides a strong basis for the fully automated and reliable modelling, prediction, and control of human behaviours.

The detrimental effects of air pollutants on human health have prompted increasing concerns regarding indoor air quality (IAQ). The emergence of digital health interventions and citizen science initiatives has provided new avenues for raising awareness, improving IAQ, and promoting behavioural changes. The Technology Acceptance Model (TAM) offers a theoretical framework to understand user acceptance and adoption of IAQ technology. This paper presents a case study using the COM-B model and Internet of Things (IoT) technology to design a human-centred digital visualisation platform, leading to behavioural changes and improved IAQ. The study also investigates users' acceptance and adoption of the technology, focusing on their experiences, expectations, and the impact on IAQ. Integrating IAQ sensing, digital health-related interventions, citizen science, and the TAM model offers opportunities to address IAQ challenges, enhance public health, and foster sustainable indoor environments. The analytical results show that factors such as human behaviour, indoor activities, and awareness play crucial roles in shaping IAQ.

ChatGPT, the dialogue-based AI chatbot capable of understanding natural human language, has become another icon in the disruptor ecosystem. Gaining over 1 million registered users in just 5 days, it has become the fastest growing tech platform ever. ChatGPT generates impressively detailed human-like written text and thoughtful prose, following a text input prompt. In addition, ChatGPT can write and hack code which is a potential major headache from an infosec point of view and has set the Web3 community on fire. Following the hype around ChatGPT, the race is now on between OpenAI's Chat GPT and Google's LaMDA to be the market leading NLP search tool for users and corporations moving forward.

Artificial intelligence (AI) is not "just around the corner" but here today and proceeding rapidly to change much about how we live and operate in a digital world. Like it or not ... it is here to stay! I got the following guest piece on the impacts of AI to cybersecurity and wanted to share it with you. One thing not mentioned in the piece is how AI will significantly reduce your workforce shortage of cybersecurity technicians. They will be needed, as is pointed out in the summary below, but not in the numbers they are projected to be needed in the coming years. Here's the piece -- which is a summary of an article by the author: Monica Oravcova, COO and co-founder of cybersecurity firm Naoris Protocol, on how AI affects cybersecurity.

Cultivating social emotional learning in the metaverse Nandini Chatterjee Singh and Anantha Duraiappah 19 November 2022 "Can't live this lifeless life anymore. Screens, lectures, messages, mails, marks, deadlines, expectations, this room, that laptop, religion, restrictions, health, family, feelings, theories, equations, numbers ... and me, reasons are many. These were the last words of a young student from a premier institution in India before he took his life. He was a young man in his prime who should have been happy and enjoying life. Was this a one-off incident?

"Can't live this lifeless life anymore. Screens, lectures, messages, mails, marks, deadlines, expectations, this room, that laptop, religion, restrictions, health, family, feelings, theories, equations, numbers…………and me, reasons are many. These were the last words of a young student from a premier institution in India before he took his life. He was a young man in his prime who should have been happy and enjoying life. Was this a one-off incident?

Staring at your phone screen when you should be sleeping can make you depressed over time, new research suggests. Chinese experiments suggest harmful blue light emissions from your device at night trigger a mysterious neural mechanism, leading to behavioural changes. The research team found that mice exposed to blue light for two hours a night over a few weeks started showing depressive-like behaviour. But by blocking brain signals that are triggered by blue light at night, the mice no longer showed behavioural changes. The neural pathway responsible for this phenomenon may provide insight into how exposure to excessive light at night time affects humans.